Package 'tidySEM'

Title: Tidy Structural Equation Modeling
Description: A tidy workflow for generating, estimating, reporting, and plotting structural equation models using 'lavaan', 'OpenMx', or 'Mplus'. Throughout this workflow, elements of syntax, results, and graphs are represented as 'tidy' data, making them easy to customize. Includes functionality to estimate latent class analyses, and to plot 'dagitty' and 'igraph' objects.
Authors: Caspar J. van Lissa [aut, cre] (ORCID: <https://orcid.org/0000-0002-0808-5024>), Mauricio Garnier-Villarreal [ctb] (ORCID: <https://orcid.org/0000-0002-2951-6647>), Frank C Gootjes [ctb] (ORCID: <https://orcid.org/0000-0002-0639-1001>)
Maintainer: Caspar J. van Lissa <[email protected]>
License: GPL (>= 3)
Version: 0.2.11
Built: 2026-05-12 10:19:47 UTC
Source: https://github.com/cjvanlissa/tidySEM

Help Index

Add paths to an object of class 'tidy_sem'

Description

Add paths to an object of class tidy_sem, or replace existing paths. The paths must be specified as lavaan::model.syntax, and separated by commas.

Usage

add_paths(model, ...)

Arguments

model

An object of class tidy_sem.
...

Paths to add or substitute, specified in lavaan::model.syntax, and separated by commas.

Details

Currently, only the lavaan commands ~, ~~, =~, and ~1 are parsed.

This function relies on lavaan::model.syntax to convert syntax strings to lavaan parameter tables. By default, is uses the arguments int.ov.free = TRUE, int.lv.free = FALSE, auto.fix.first = TRUE, auto.fix.single = TRUE, auto.var = TRUE, auto.cov.lv.x = TRUE, auto.efa = TRUE, auto.th = TRUE, auto.delta = TRUE, auto.cov.y = TRUE, meanstructure = TRUE, in a similar way to lavaan::sem() and lavaan::cfa().

Value

An object of class tidy_sem.

See Also

lavaan::model.syntax

Examples

library(lavaan)
df <- iris[, 1:4]
names(df) <- paste0("x_", 1:4)
model <- tidy_sem(df)
model <- measurement(model)
model <- add_paths(model, x =~ a*x_1 + b*x_2 + a*x_3 + b*x_4)
res <- estimate_lavaan(model)
summary(res)

Ocean Microplastics Data

Description

These data were collected by Alkema during a cruise from 04/2018 to 06/2018 traversing the Atlantic Ocean from South Africa to Norway. A 500 micrometer meshed Manta Trawl was towed outside the wake of the ship for 1 h each day. Length, width, height and polymer type of 6.942 particles were measured using infrared spectroscopy and image analysis.

Usage

data(alkema_microplastics)

Format

A data frame with 6942 rows and 11 variables.

Details

current factor Which ocean current the sample was taken from
sample integer Sample ID
length numeric Particle length in mm
width numeric Particle width in mm
height_est numeric Estimated particle height in mm
height_obs numeric Observed particle height in mm. Height was only measured for large particles
category factor Particle category based on visual inspection
poly_type factor Polymer type as determined by near infrared spectroscopy (NIR)
two_dim logical Whether or not the particle can be treated as two-dimensional
film logical Whether or not the particle appears to be a film
line logical Whether or not the particle appears to be a line

References

Alkema, L. M., Van Lissa, C. J., Kooi, M., & Koelmans, A. A. (2022). Maximizing Realism: Mapping Plastic Particles at the Ocean Surface Using Mixtures of Normal Distributions. Environmental Science & Technology, 56(22), 15552-15562. doi:10.1021/acs.est.2c03559

Append Pseudo-class Draws

Description

Generates m datasets with random draws of a variable named class, with probability for these draws based on each case's probability of belonging to that class according to the model in x.

Usage

append_class_draws(x, data = NULL, m = 20)

Arguments

x

An object for which a method exists, usually a mx_mixture model.
data

A data.frame which the class variable is appended to. Note that the row order must be identical to that of the data used to fit x, as these data will be augmented with a pseudo-class draw for that specific individual.
m

Integer. Number of datasets to generate. Default is 10.

Value

A data.frame of class class_draws.

Examples

if(requireNamespace("OpenMx", quietly = TRUE)){
library(OpenMx)
dat <- iris[c(1:5, 50:55, 100:105),1:3]
colnames(dat) <- letters[1:3]
fit <- mx_profiles(data = dat, classes = 2)

append_class_draws(fit, data = iris[c(1:5, 50:55, 100:105), 4, drop = FALSE])
}

Convert tidy_sem to 'lavaan' syntax

Description

Final stage in the 'tidySEM' workflow for syntax generation: Convert the tidy_sem object to lavaan syntax in tabular format (see model.syntax).

Usage

as_lavaan(x, ...)

Arguments

x

An object of class tidy_sem
...

Additional parameters to be passed to and from functions.

Value

Character vector.

Examples

mod <- list(syntax = structure(list(lhs = "x", op = "~", rhs = "y",
                                    free = TRUE, value = "", label = "",
                                    category = "", aspect = ""),
            class = "data.frame", row.names = c(NA, -1L)))
class(mod) <- "tidy_sem"
as_lavaan(mod)

Convert tidy_sem to 'Mplus' syntax

Description

Final stage in the 'tidySEM' workflow for syntax generation: Convert the tidy_sem object to 'Mplus' syntax.

Usage

as_mplus(x, ...)

Arguments

x

An object of class tidy_sem.
...

Additional parameters to be passed to and from functions.

Value

Character vector.

Examples

mod <- list(syntax = structure(list(lhs = "x", op = "~", rhs = "y",
                                    free = TRUE, value = "", label = "",
                                    category = "", aspect = ""),
            class = "data.frame", row.names = c(NA, -1L)))
class(mod) <- "tidy_sem"
as_mplus(mod)

Convert lavaan syntax to RAM specification

Description

Converts SEM models to RAM models for OpenMx.

Usage

as_ram(x, ...)

## S3 method for class 'data.frame'
as_ram(x, groups = NULL, data = NULL, threshold_method = "mxThreshold", ...)

Arguments

x

An object for which a method exists, such as a tidy_sem object, or character vector describing the user-specified model using the lavaan model syntax.
...

Parameters passed on to other functions.
groups

Character, either corresponding to the name of a column in data, or else a vector of group names.
data

Optional, a data.frame which is added to the model.
threshold_method

Character, one of c("mxThreshold", "mx_deviances"). Method used to specify thresholds for ordinal indicators. See the mxThreshold function in the OpenMx package, and mx_deviances, respectively.

Details

For models specified using lavaan syntax, the procedure is as follows:

  1. Apply lavaanify to the model. The default arguments to lavaanify correspond to those of the sem function.

  2. Apply the method for data.frame to the resulting lavaan parameter table. This uses mxPath for means, regression coefficients, and (co)variances, and the specified threshold_method for thresholds.

  3. Apply mxModel to the resulting list of mxPath and mxMatrix elements to create an mxModel using RAM specification.

Value

Returns an mxModel.

Examples

as_ram("y ~ x")

Estimate an Auxiliary Model using the BCH Method

Description

Estimate an auxiliary model based on a latent classification by means of mixture modeling (see mx_mixture).

The auxiliary model is treated as a multi-group model. All cases are used in all groups, but they are weighted by group-specific BCH weights as described in Bolck, Croon, & Hagenaars, 2004.

Usage

BCH(x, model, data, ...)

Arguments

x

An object for which a method exists.
model

An object that can be converted to an OpenMx model using as_ram.
data

A data.frame on which the auxiliary model can be evaluated.
...

further arguments to be passed to or from other methods.

Value

An MxModel.

References

Bolck, A., Croon, M., & Hagenaars, J. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis, 12(1), 3-27. doi:10.1093/pan/mph001

Examples

if(requireNamespace("OpenMx", quietly = TRUE)){
library(OpenMx)
dat <- data.frame(x = iris$Petal.Length)
mixmod <- mx_profiles(dat,
                      classes = 2)
res <- BCH(mixmod, "y ~ 1", data = data.frame(y = iris$Sepal.Length))
}

Conduct Bootstrapped Likelihood Ratio Test

Description

Conduct Bootstrapped Likelihood Ratio Test to compare two mixture models.

Usage

BLRT(x, replications = 100, ...)

Arguments

x

An object for which a method exists.
replications

Integer reflecting the number of bootstrapped replications, defaults to 100.
...

further arguments to be passed to or from other methods.

Value

A data.frame.

Examples

## Not run: 
df <- iris[, 1, drop = FALSE]
names(df) <- "x"
res <- mx_mixture(model = "x ~ m{C}*1
                           x ~~ v{C}*x", classes = 1:2, data = df)
BLRT(res, replications = 4)

## End(Not run)

Calculate Chi Square Statistic

Description

Given two datasets with ordinal variables, computes the chi squared statistic. To obtain the lambda statistic as used in pmc(), subtract the degrees of freedom.

Usage

chi_sq(x, y)

Arguments

x

An object for which a method exists (e.g, data.frame).
y

An object for which a method exists (e.g, data.frame).

Value

numeric

Examples

## Not run: 
if(interactive()){
 chi_sq(iris[1:2], iris[3:4])
}

## End(Not run)

Obtain latent class probabilities

Description

Obtain latent class probabilities for an object for which a method exists. See Details.

Usage

class_prob(
  x,
  type = c("sum.posterior", "sum.mostlikely", "mostlikely.class", "avg.mostlikely",
    "individual"),
  ...
)

Arguments

x

An object for which a method exists.
type

Character vector, indicating which types of probabilities to extract. See Details.
...

Further arguments to be passed to or from other methods.

Details

The following types are available:

"sum.posterior"

A summary table of the posterior class probabilities; this indicates what proportion of your data contributes to each class.

"sum.mostlikely"

A summary table of the most likely class membership, based on the highest posterior class probability. Note that this is subject to measurement error.

"mostlikely.class"

If C is the true class of an observation, and N is the most likely class based on the model, then this table shows the probability P(N==i|C==j). The diagonal represents the probability that observations in each class will be correctly classified.

"avg.mostlikely"

Average posterior probabilities for each class, for the subset of observations with most likely class of 1:k, where k is the number of classes.

"individual"

The posterior probability matrix, with dimensions n (number of cases in the data) x k (number of classes).

Value

A data.frame.

Examples

## Not run: 
df <- iris[, 1, drop = FALSE]
names(df) <- "x"
res <- mx_mixture(model = "x ~ m{C}*1
                           x ~~ v{C}*x", classes = 1, data = df)
class_prob(res)

## End(Not run)

Format confidence intervals

Description

Creates 'APA'-formatted confidence intervals, either from an object for which a method exists, or from the arguments lb and ub. When argument x is a numeric vector, it is also possible to construct a confidence interval using the standard error (se) and a percentile interval (ci).

Usage

conf_int(x, digits = 2, se = NULL, lb = NULL, ub = NULL, ci = 95)

Arguments

x

Optional. An object for which a method exists.
digits

Integer. The number of digits to round the confidence boundaries to.
se

Optional, numeric. Standard error of the parameters.
lb

Optional, numeric. Lower boundary of confidence intervals.
ub

Optional, numeric. Upper boundary of confidence intervals.
ci

Optional, numeric. What percentage CI to use (only used when computing CI from a numeric vector x, and the standard error se, based on a normal distribution).

Value

A character vector of formatted confidence intervals.

Author(s)

Caspar J. van Lissa

See Also

table_results est_sig

Other Reporting tools: est_sig(), table_fit(), table_prob(), table_results()

Examples

conf_int(x = c(1.325, 2.432), se = c(.05336, .00325))

Generate syntax for correlations

Description

Generate syntax for correlations between variables.

Usage

cors(x, ...)

Arguments

x

Object for which a method exists. If x is an object of class tidy_sem, then correlations between all observed and latent variables in the data dictionary of that object are computed, by default. If x is a character vector, all elements of the vector are used.
...

Optional additional character vectors of variables to be correlated. If x is an object of class tidy_sem, then up to two vectors can be provided. If x is a vector, then one more optional vector can be provided. When no additional vectors of variable names are provided, only the correlations among the elements of x are returned.

Value

An object of class tidy_sem.

Examples

dict <- tidy_sem(c("bfi_1", "bfi_2", "bfi_3", "bfi_4", "bfi_5"))
cors(dict, c("bfi_1", "bfi_2"))

Create scale scores from observed variables

Description

This function calculates mean or sum scores from a data.frame and a named list describing the items in each scale. It returns the scores, a scale descriptive table, and a scale correlation table. It relies on several functions from the psych package.

Usage

create_scales(
  x,
  keys.list,
  missing = TRUE,
  impute = "none",
  omega = NULL,
  digits = 2,
  ...
)

## S3 method for class 'tidy_sem'
create_scales(
  x,
  keys.list,
  missing = TRUE,
  impute = "none",
  omega = NULL,
  digits = 2,
  ...
)

Arguments

x

A data.frame containing all variables referenced in the keys.list, or an object of class tidy_sem.
keys.list

A named list, indicating which variables belong to which scale.
missing

Whether to use rows with partially missing values. Default: TRUE.
impute

Method for handling missing values, Default: 'none'. This default method uses all available data to calculate scale scores, which is acceptable for mean scales, but not for sum scales.
omega

Which of McDonald's omega coefficients to report. Default: NULL; valid options include: "omega_h", "omega.lim", "alpha", "omega.tot", "G6".
digits

Number of digits for rounding, Default: 2
...

Additional parameters to pass to and from functions.

Details

For scales with less than 3 items, Cronbach's alpha might not be suitable as an estimate of reliability. For such scales, the Spearman-Brown reliability coefficient for two-item scales is computed, as described in Eisinga, R., Grotenhuis, M. te, & Pelzer, B. (2012). The reliability of a two-item scale: Pearson, Cronbach, or Spearman-Brown? International Journal of Public Health, 58(4), 637-642. doi:10.1007/s00038-012-0416-3. These coefficients are marked with "(sb)".

Value

List with elements: $descriptives, $correlations, and $scores.

Examples

out <- create_scales(iris, keys.list = list(scalename =
            c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")))
out$descriptives
dict <- tidy_sem(iris, split = "\\.")
create_scales(dict)

Simulated MAC data

Description

This simulated dataset, based on Curry et al., 2019, contains data on moral relevance and judgment across the seven domains of the Morality As Cooperation scale.

Usage

data(curry_mac)

Format

A data.frame with 1392 rows and 42 variables.

Details

sex factor Self-identified sex of participants, Male, Female, or Transgendered.
age_years numeric Participants' age in years.
KinshipR numeric Mean score of moral relevance, kinship subscale.
MutualismR numeric Mean score of moral relevance, mutualism subscale.
ExchangeR numeric Mean score of moral relevance, exchange subscale.
HawkR numeric Mean score of moral relevance, hawk subscale.
DoveR numeric Mean score of moral relevance, dove subscale.
DivisionR numeric Mean score of moral relevance, division subscale.
PossessionR numeric Mean score of moral relevance, possession subscale.
KinshipJ numeric Mean score of moral judgment, kinship subscale.
MutualismJ numeric Mean score of moral judgment, mutualism subscale.
ExchangeJ numeric Mean score of moral judgment, exchange subscale.
HawkJ numeric Mean score of moral judgment, hawk subscale.
DoveJ numeric Mean score of moral judgment, dove subscale.
DivisionJ numeric Mean score of moral judgment, division subscale.
PossessionJ numeric Mean score of moral judgment, possession subscale.

References

Curry, O. S., Jones Chesters, M., & Van Lissa, C. J. (2019). Mapping morality with a compass: Testing the theory of 'morality-as-cooperation' with a new questionnaire. Journal of Research in Personality, 78, 106-124. doi:10.1016/j.jrp.2018.10.008

Simulated data for mixture model with ordinal indicators

Description

This simulated dataset, based on the 'Mplus' User's Guide example 7.6, contains four columns of integer data that should be treated as ordinal.

Usage

data(data_mix_ordinal)

Format

A data frame with 5000 rows and 4 variables.

Details

u1 integer Indicator 1, should be treated as ordinal.
u2 integer Indicator 2, should be treated as ordinal.
u3 integer Indicator 3, should be treated as ordinal.
u4 integer Indicator 4, should be treated as ordinal.

References

Muthén, L.K. and Muthén, B.O. (1998-2017). Mplus User's Guide. Eighth Edition. Los Angeles, CA: Muthén & Muthén

Describe a dataset

Description

Provide descriptive statistics for a dataset.

Usage

descriptives(x, ...)

Arguments

x

An object for which a method exists.
...

Additional arguments.

Value

A data.frame with descriptive statistics for x. Its elements are:

name Character Variable name
type character Data type in R, as obtained by class(x)[1]
n Integer Number of valid observations
missing Numeric Proportion missing
unique Integer Number of unique values
mean numeric Mean value of non-missing entries, only defined for variables that can be coerced to numeric
median numeric Median value of non-missing entries, only defined for numeric variables
mode Integer For numeric variables: The mode value. For factors: The frequency of the mode value
mode_value Character For factors: value of the mode
sd numeric Standard deviation of non-missing entries, only defined for variables that can be coerced to numeric
v numeric Variability coefficient V for factor variables (Agresti, 1990). V is the probability that two independent observations fall in different categories
min numeric Minimum value for numeric variables
max numeric Maximum value for numeric variables
range numeric Range (distance between min and max) for numeric variables
skew numeric Skewness. The normalized third central moment of a numeric variable, which reflects its skewness. A symmetric distribution has a skewness of zero
skew_2se numeric Skewness, divided by two times its standard error. Values greater than one can be considered "significant" according to a Z-test with significance level of .05
kurt numeric Kurtosis. The normalized fourth central moment of a numeric variable, which reflects its peakedness. A heavy-tailed distribution has high kurtosis, a light-tailed distribution has low kurtosis (sometimes called platykurtic).
kurt_2se numeric Kurtosis, divided by two times its standard error. Values greater than one can be considered "significant" according to a Z-test with significance level of .05

References

Agresti, A. (2012). Categorical data analysis (Vol. 792). John Wiley & Sons.

Examples

descriptives(iris)

Extract dictionary from tidy_sem

Description

Provides access to the dictionary element of a tidy_sem object. This can be used to return or assign to the dictionary element.

Usage

dictionary(x)

dictionary(x) <- value

Arguments

x

Object of class tidy_sem.
value

A valid value for dictionary(x).

Value

data.frame

Examples

dict <- tidy_sem(iris, split = "\\.")
dictionary(dict)

Extract edges from sem_graph

Description

Provides access to the edges element of a sem_graph object. This can be used to return or assign to the edges element.

Usage

edges(x)

edges(x) <- value

Arguments

x

Object of class sem_graph.
value

A valid value for edges(x).

Value

data.frame

Examples

edg <- data.frame(from = "x", to = "y")
p <- prepare_graph(edges = edg, layout = get_layout("x", "y", rows = 1))
edges(p)

Edit graph elements

Description

Evaluate an R expression within the environment of the elements of a sem_graph object, and return the modified sem_graph.

Usage

edit_graph(x, expr, element = c("edges", "nodes"), ...)

edit_nodes(x, expr, ...)

edit_edges(x, expr, ...)

Arguments

x

An object of class sem_graph.
expr

expression to evaluate.
element

Character. The element of the sem_graph to edit, defaults to c("edges", "nodes").
...

Arguments passed on to within.

Value

An object of class sem_graph.

Examples

p <- prepare_graph(layout = get_layout("x", rows = 1))
p <- edit_graph(p, {colour = "blue"}, element = "nodes")
plot(p)

Simulated empathy data

Description

This simulated dataset, based on Van Lissa et al., 2014, contains six annual assessments of adolescents' mean scores on the empathic concern and perspective taking subscales of the Interpersonal Reactivity Index (Davis, 1983). The first measurement wave occurred when adolescents were, on average, 13 years old, and the last one when they were 18 years old.

Usage

data(empathy)

Format

A data frame with 467 rows and 13 variables.

Details

ec1 numeric Mean score of empathic concern in wave 1
ec2 numeric Mean score of empathic concern in wave 2
ec3 numeric Mean score of empathic concern in wave 3
ec4 numeric Mean score of empathic concern in wave 4
ec5 numeric Mean score of empathic concern in wave 5
ec6 numeric Mean score of empathic concern in wave 6
pt1 numeric Mean score of perspective taking in wave 1
pt2 numeric Mean score of perspective taking in wave 2
pt3 numeric Mean score of perspective taking in wave 3
pt4 numeric Mean score of perspective taking in wave 4
pt5 numeric Mean score of perspective taking in wave 5
pt6 numeric Mean score of perspective taking in wave 6
sex factor Adolescent sex; M = male, F = female.

References

Van Lissa, C. J., Hawk, S. T., Branje, S. J., Koot, H. M., Van Lier, P. A., & Meeus, W. H. (2014). Divergence Between Adolescent and Parental Perceptions of Conflict in Relationship to Adolescent Empathy Development. Journal of Youth and Adolescence, (Journal Article), 1-14. doi:10.1007/s10964-014-0152-5

Add significance asterisks to object

Description

Takes an object, and adds significance asterisks.

Usage

est_sig(x, digits = 2, sig = NULL)

Arguments

x

An object for which a method exists. This will be treated as numeric by the default method.
digits

Integer. The number of digits to round the estimate column to.
sig

Optional, a vector of p-values for the default method.

Value

A character vector of formatted estimates.

Author(s)

Caspar J. van Lissa

See Also

table_results

Other Reporting tools: conf_int(), table_fit(), table_prob(), table_results()

Examples

est_sig(c(.222, .3333), sig = c(.054, .045))

Estimate tidy_sem using 'lavaan'

Description

This function is a wrapper for the lavaan estimating functions. By default, the wrapper uses sem, but users can also specify lavaan, cfa, or growth.

Usage

estimate_lavaan(x, func = "sem", ...)

Arguments

x

An object of class tidy_sem.
func

The lavaan modeling function to invoke, Default: 'sem'.
...

Additional parameters passed to the estimating function.

Value

An object of class lavaan.

Examples

library(lavaan)
model <- tidy_sem(iris, "\\.")
model <- measurement(model)
res <- estimate_lavaan(model)
summary(res)

Estimate tidy_sem using 'Mplus'

Description

This function is a wrapper for the functions mplusObject and mplusModeler. Using this function requires 'Mplus' to be installed.

Usage

estimate_mplus(x, ...)

Arguments

x

An object of class tidy_sem.
...

Additional parameters passed to mplusObject and mplusModeler. These arguments are matched to the correct function by name. The arguments rdata, and MODEL cannot be edited, as they are determined from the tidy_sem object.

Details

The arguments dataout, modelout, and run are optional. If these are not specified, the model will be run in tempdir.

Value

An object of class mplusObject.

Examples

library(MplusAutomation)
model <- tidy_sem(iris, "\\.")
model <- measurement(model)
## Not run: 
  estimate_mplus(model, run = 0L)

## End(Not run)

Estimate tidy_sem using 'OpenMx'

Description

This function is a wrapper for the as_ram and run_mx functions.

Usage

estimate_mx(x, ...)

Arguments

x

An object of class tidy_sem.
...

Additional parameters passed to the estimating function.

Value

An object of class MxModel.

Examples

df <- iris[1:4]
names(df) <- paste0("x_", 1:4)
model <- tidy_sem(df)
model <- measurement(model)
res <- estimate_mx(model)
summary(res)

Extract data from tidy_sem

Description

Provides access to the data element of a tidy_sem object. This can be used to return or assign to the data element.

Usage

get_data(x)

get_data(x) <- value

Arguments

x

Object of class tidy_sem.
value

A valid value for get_data(x).

Value

data.frame

Examples

dict <- tidy_sem(iris, split = "\\.")
get_data(dict)

Extract edges from a SEM model object

Description

Attempts to extract edges from a SEM model object, where edges are defined as regression paths and covariances between variables (nodes).

Usage

get_edges(x, label = "est_sig", ...)

Arguments

x

A model object of class mplusObject or lavaan.
label

Either a character, indicating which column to use for edge labels, or an expression. See Details. Defaults to "est_sig", which labels edges with the estimated value with significance asterisks, as obtained from table_results. See Details and examples for more information.
...

Additional parameters passed to table_results. For example, users can pass the digits argument to control the number of digits in the edge label, or pass the columns argument to retain auxiliary columns in the tidy_edges data.frame for further processing (see Examples).

Details

The function get_edges identifies all regression paths, latent variable definitions, and covariances in the model as edges. The output of table_results for those paths is used to label the edges.

Custom labels

One way to create custom edge labels is by passing an expression to label. When an expression is passed to label, it is evaluated in the context of a data.frame containing the results of a call to table_results on the x argument.

Another way to create custom labels is by requesting auxiliary variables using the columns argument (which is passed to table_results), and then using these columns to construct a new label. See examples.

Value

An object of class 'tidy_edges'

Examples

# Standard use
library(lavaan)
res <- sem("dist ~ speed", cars)
get_edges(res)

# Pass an expression to the 'label' argument for custom labels
get_edges(res, label = paste(est_sig, confint))

# Pass the argument 'columns' to table_results through '...' to retain
# auxiliary columns for further processing
edg <- get_edges(res, columns = c("est_sig", "confint"))
edg
edg <- within(edg, {label <- paste(est_sig, confint)})
edg

Get fit indices from objects

Description

Get fit indices from objects for which a method exists.

Usage

get_fit(x, ...)

Arguments

x

An object for which a method exists.
...

further arguments to be passed to or from other methods.

Value

A data.frame.

Examples

## Not run: 
df <- iris[, 1, drop = FALSE]
names(df) <- "x"
res <- mx_mixture(model = "x ~ m{C}*1
                           x ~~ v{C}*x", classes = 1, data = df)
table_fit(res)

## End(Not run)

Generate graph layout

Description

Generate a tidy_layout for a SEM graph.

Usage

## S3 method for class 'lavaan'
get_layout(x, ..., layout_algorithm = "layout_as_tree")

get_layout(x, ...)

## Default S3 method:
get_layout(x, ..., rows = NULL)

Arguments

x

An object for which a method exists; currently, methods exist for character, lavaan, and mplus.model objects.
...

Character arguments corresponding to layout elements. Use node names, empty strings (""), or NA values.
layout_algorithm

Optional argument for fit model objects. Character string, indicating which igraph layout algorithm to apply to position the nodes. Defaults to "layout_as_tree"; see details for more options.
rows

Numeric, indicating the number of rows of the graph.

Details

There are three ways to generate a layout:

  1. Specify the layout in the call to get_layout() by providing node names and the number of rows to create a layout matrix. Empty strings ("") or NA can be used for empty cells. See Example 1.

  2. Call get_layout() on a model object or tidy_results object. It will use the function layout_as_tree, or any other layout function from the igraph package, to generate a rudimentary layout. See Example 2.

  3. Instead of using get_layout(), just use a matrix or data.frame with your layout. For example, specify the layout in a spreadsheet program, and load it into R (see Example 3). Or, copy the layout to the clipboard from your spreadsheet program, and load it from the clipboard (see Example 4)

The layout algorithms imported from igraph are: c("layout_as_star", "layout_as_tree", "layout_in_circle", "layout_nicely", "layout_on_grid", "layout_randomly", "layout_with_dh", "layout_with_fr", "layout_with_gem", "layout_with_graphopt", "layout_with_kk", "layout_with_lgl", "layout_with_mds"). These can be used by specifying the optional argument layout_algorithm = "".

Value

Object of class 'tidy_layout'

Examples

# Example 1
get_layout("c", NA,  "d",
           NA,  "e", NA, rows = 2)

# Example 2
library(lavaan)
fit <- cfa(' visual  =~ x1 + x2 + x3 ',
           data = HolzingerSwineford1939[1:50, ])
get_layout(fit)

## Not run: 
# Example 3
# Here, we first write the layout to .csv, but you could create it in a
# spreadsheet program, and save the spreadsheet to .csv:
write.csv(matrix(c("c", "",  "d", "",  "e", ""), nrow = 2, byrow = TRUE),
          file = file.path(tempdir(), "example3.csv"), row.names = FALSE)
# Now, we load the .csv:
read.csv(file.path(tempdir(), "example3.csv"))

# Example 4
# For this example, make your layout in a spreadsheet program, select it, and
# copy to clipboard. Reading from the clipboard works differently in Windows
# and Mac. For this example, I used Microsoft Excel.
# On Windows, run:
read.table("clipboard", sep = "\t")
# On Mac, run:
read.table(pipe("pbpaste"), sep="\t")

## End(Not run)

Extract nodes from a SEM model object

Description

Attempts to extract nodes from a SEM model object, where nodes are defined as observed or latent variables.

Usage

get_nodes(x, label = paste2(name, est_sig, sep = "\n"), ...)

Arguments

x

A model object of class mplusObject or lavaan.
label

Either a character, indicating which column to use for node labels, or an expression. See Details. Defaults to paste(name, est_sig, sep = "\n", which gives the node name followed by the estimated value with significance asterisks.
...

Additional parameters passed to table_results. For example, users can pass the digits argument to control the number of digits in the node label, or pass the columns argument to retain auxiliary columns in the tidy_nodes data.frame for further processing (see Examples).

Details

The function get_nodes identifies all dependent and independent variables in the model as nodes. If a mean structure / intercepts are included in the model, the output of table_results for those means / intercepts is used to label the nodes.

Custom labels

One way to create custom node labels is by passing an expression to label, as in the default value of the argument. When an expression is passed to label, it is evaluated in the context of a data.frame containing the results of a call to table_results on the x argument, with an additional column labeled name, which contains the node names.

Another way to create custom labels is by requesting auxiliary variables using the columns argument (which is passed to table_results), and then using these columns to construct a new label. See examples.

Value

An object of class 'tidy_nodes'

Examples

# Standard use extracts node names and shape
# (rect for observed, oval for latent)
library(lavaan)
res <- sem("dist ~ speed", cars)
get_nodes(res)

# To label nodes with mean values, include meanstructure in the model
# Note that it is possible to pass the argument 'digits' to table_results
# through '...'
res <- sem("dist ~ speed", cars, meanstructure = TRUE)
get_nodes(res, digits = 3)

# Pass an expression to the 'label' argument for custom labels
get_nodes(res, label = paste0(name, " ", est_sig, "\n", confint))

# Pass the argument 'columns' to table_results through '...' to retain
# auxiliary columns for further processing
nod <- get_nodes(res, columns = c("est_sig", "confint"))
nod
nod <- within(nod, {label <- paste0(name, " ", est_sig, "\n", confint)})
nod

Render a graph

Description

Render a graph based on a layout, and either nodes and edges, or a model object.

Usage

## S3 method for class 'dagitty'
graph_sem(model, ...)

graph_sem(...)

## Default S3 method:
graph_sem(
  edges = NULL,
  layout = NULL,
  nodes = NULL,
  rect_width = 1.2,
  rect_height = 0.8,
  ellipses_width = 1,
  ellipses_height = 1,
  variance_diameter = 0.8,
  spacing_x = 2,
  spacing_y = 2,
  text_size = 4,
  curvature = 60,
  angle = NULL,
  fix_coord = FALSE,
  ...
)

## S3 method for class 'lavaan'
graph_sem(model, edges = NULL, layout = NULL, nodes = NULL, ...)

## S3 method for class 'MxModel'
graph_sem(model, edges = NULL, layout = NULL, nodes = NULL, ...)

## S3 method for class 'mplus.model'
graph_sem(model, edges = NULL, layout = NULL, nodes = NULL, ...)

## S3 method for class 'igraph'
graph_sem(model, edges = NULL, layout = NULL, nodes = NULL, ...)

## S3 method for class 'character'
graph_sem(...)

## S3 method for class 'mplusObject'
graph_sem(model, edges = NULL, layout = NULL, nodes = NULL, ...)

Arguments

model

Instead of the edges argument, it is also possible to use the model argument and pass an object for which a method exists (e.g., mplus.model or lavaan).
...

Additional arguments passed to and from functions.
edges

Object of class 'tidy_edges', or a data.frame with (at least) the columns c("from", "to"), and optionally, c("arrow", "label", "connect_from", "connect_to", "curvature").
layout

A matrix (or data.frame) that describes the layout; see get_layout.
nodes

Optional, object of class 'tidy_nodes', created with the get_nodes function, or a data.frame with (at least) the column c("name"), and optionally, c("shape", "label"). If set to NULL (the default), nodes are inferred from the layout and edges arguments.
rect_width

Width of rectangles (used to display observed variables), Default: 1.2
rect_height

Height of rectangles (used to display observed variables), Default: 0.8
ellipses_width

Width of ellipses (used to display latent variables), Default: 1
ellipses_height

Height of ellipses (used to display latent variables), Default: 1
variance_diameter

Diameter of variance circles, Default: .8
spacing_x

Spacing between columns of the graph, Default: 1
spacing_y

Spacing between rows of the graph, Default: 1
text_size

Point size of text, Default: 4
curvature

Curvature of curved edges. The curve is a circle segment originating in a point that forms a triangle with the two connected points, with angles at the two connected points equal to curvature. To flip a curved edge, use a negative value for curvature. Default: 60
angle

Angle used to connect nodes by the top and bottom. Defaults to NULL, which means Euclidean distance is used to determine the shortest distance between node sides. A numeric value between 0-180 can be provided, where 0 means that only nodes with the same x-coordinates are connected top-to-bottom, and 180 means that all nodes are connected top-to-bottom.
fix_coord

Whether or not to fix the aspect ratio of the graph. Does not work with multi-group or multilevel models. Default: FALSE.

Details

The default interface simply Runs the functions prepare_graph and plot. The alternative interface first runs get_nodes and get_edges on the model argument.

Value

Object of class 'sem_graph'

Examples

library(lavaan)
res <- sem("dist ~ speed", cars)
graph_sem(res)

Compare Information Criteria

Description

IC weights quantify the evidence in favor of different models in a set. This function normalizes the IC values to obtain IC weights, which sum to 1. The model with the highest weight is most supported by the data. The ratio of different weights gives the relative support in favor of one model over another.

Usage

ic_weights(x, ...)

Arguments

x

An object for which a method exists.
...

Additional arguments.

Value

A list of class ic_weights with elements ⁠$weights⁠, which contains the model weights, and ⁠$comparison⁠, which contains the relative support in favor of each model over the others.

References

Wagenmakers, E. J., & Farrell, S. (2004). AIC model selection using Akaike weights. Psychonomic bulletin & review, 11(1), 192-196. doi:10.3758/BF03206482

Examples

ics <- c(100, 200, 102, 300)
ic_weights(ics)

Conditionally edit a sem_graph object

Description

This function allows users to conditionally manipulate the edges and nodes of a sem_graph object. The generic function if_edit applies the expression expr to all rows of the nodes and edges data.frames for which condition is TRUE.

The wrapper functions documented in the Usage section have a hard-coded expr and condition; for example, color_sig(color = "green") colors all nodes and edges with pval < .05 green. If no column exists for the assigned aesthetic (e.g., color), the wrappers assign the default argument (in this case, color = "black") to all other nodes and edges.

Usage

if_edit(data, condition, expr, ...)

if_edges(data, condition, expr, ...)

if_nodes(data, condition, expr, ...)

## S3 method for class 'sem_graph'
if_edit(data, condition, expr, element = c("edges", "nodes"), ...)

all_sig(data, expr, ...)

hide_sig(data, ...)

show_sig(data, ...)

colour_sig(data, colour = "black", ...)

color_sig(data, color = "black", ...)

linetype_sig(data, linetype = 1, ...)

size_sig(data, size = 1, ...)

alpha_sig(data, alpha = 1, ...)

fill_sig(data, fill = "white", ...)

label_colour_sig(data, label_colour = "black", ...)

label_color_sig(data, label_color = "black", ...)

label_fill_sig(data, label_fill = "white", ...)

label_size_sig(data, label_size = 4, ...)

label_alpha_sig(data, label_alpha = 1, ...)

label_family_sig(data, label_family = "sans", ...)

label_fontface_sig(data, label_fontface = "plain", ...)

label_hjust_sig(data, label_hjust = "center", ...)

label_vjust_sig(data, label_vjust = "middle", ...)

label_lineheight_sig(data, label_lineheight = 1, ...)

label_location_sig(data, label_location = 0.5, ...)

all_nonsig(data, expr, ...)

hide_nonsig(data, ...)

show_nonsig(data, ...)

colour_nonsig(data, colour = "black", ...)

color_nonsig(data, color = "black", ...)

linetype_nonsig(data, linetype = 1, ...)

size_nonsig(data, size = 1, ...)

alpha_nonsig(data, alpha = 1, ...)

fill_nonsig(data, fill = "white", ...)

label_colour_nonsig(data, label_colour = "black", ...)

label_color_nonsig(data, label_color = "black", ...)

label_fill_nonsig(data, label_fill = "white", ...)

label_size_nonsig(data, label_size = 4, ...)

label_alpha_nonsig(data, label_alpha = 1, ...)

label_family_nonsig(data, label_family = "sans", ...)

label_fontface_nonsig(data, label_fontface = "plain", ...)

label_hjust_nonsig(data, label_hjust = "center", ...)

label_vjust_nonsig(data, label_vjust = "middle", ...)

label_lineheight_nonsig(data, label_lineheight = 1, ...)

label_location_nonsig(data, label_location = 0.5, ...)

all_fixed(data, expr, ...)

hide_fixed(data, ...)

show_fixed(data, ...)

colour_fixed(data, colour = "black", ...)

color_fixed(data, color = "black", ...)

linetype_fixed(data, linetype = 1, ...)

size_fixed(data, size = 1, ...)

alpha_fixed(data, alpha = 1, ...)

fill_fixed(data, fill = "white", ...)

label_colour_fixed(data, label_colour = "black", ...)

label_color_fixed(data, label_color = "black", ...)

label_fill_fixed(data, label_fill = "white", ...)

label_size_fixed(data, label_size = 4, ...)

label_alpha_fixed(data, label_alpha = 1, ...)

label_family_fixed(data, label_family = "sans", ...)

label_fontface_fixed(data, label_fontface = "plain", ...)

label_hjust_fixed(data, label_hjust = "center", ...)

label_vjust_fixed(data, label_vjust = "middle", ...)

label_lineheight_fixed(data, label_lineheight = 1, ...)

label_location_fixed(data, label_location = 0.5, ...)

all_pos(data, expr, ...)

hide_pos(data, ...)

show_pos(data, ...)

colour_pos(data, colour = "black", ...)

color_pos(data, color = "black", ...)

linetype_pos(data, linetype = 1, ...)

size_pos(data, size = 1, ...)

alpha_pos(data, alpha = 1, ...)

fill_pos(data, fill = "white", ...)

label_colour_pos(data, label_colour = "black", ...)

label_color_pos(data, label_color = "black", ...)

label_fill_pos(data, label_fill = "white", ...)

label_size_pos(data, label_size = 4, ...)

label_alpha_pos(data, label_alpha = 1, ...)

label_family_pos(data, label_family = "sans", ...)

label_fontface_pos(data, label_fontface = "plain", ...)

label_hjust_pos(data, label_hjust = "center", ...)

label_vjust_pos(data, label_vjust = "middle", ...)

label_lineheight_pos(data, label_lineheight = 1, ...)

label_location_pos(data, label_location = 0.5, ...)

all_neg(data, expr, ...)

hide_neg(data, ...)

show_neg(data, ...)

colour_neg(data, colour = "black", ...)

color_neg(data, color = "black", ...)

linetype_neg(data, linetype = 1, ...)

size_neg(data, size = 1, ...)

alpha_neg(data, alpha = 1, ...)

fill_neg(data, fill = "white", ...)

label_colour_neg(data, label_colour = "black", ...)

label_color_neg(data, label_color = "black", ...)

label_fill_neg(data, label_fill = "white", ...)

label_size_neg(data, label_size = 4, ...)

label_alpha_neg(data, label_alpha = 1, ...)

label_family_neg(data, label_family = "sans", ...)

label_fontface_neg(data, label_fontface = "plain", ...)

label_hjust_neg(data, label_hjust = "center", ...)

label_vjust_neg(data, label_vjust = "middle", ...)

label_lineheight_neg(data, label_lineheight = 1, ...)

label_location_neg(data, label_location = 0.5, ...)

all_var(data, expr, ...)

hide_var(data, ...)

show_var(data, ...)

colour_var(data, colour = "black", ...)

color_var(data, color = "black", ...)

linetype_var(data, linetype = 1, ...)

size_var(data, size = 1, ...)

alpha_var(data, alpha = 1, ...)

label_colour_var(data, label_colour = "black", ...)

label_color_var(data, label_color = "black", ...)

label_fill_var(data, label_fill = "white", ...)

label_size_var(data, label_size = 4, ...)

label_alpha_var(data, label_alpha = 1, ...)

label_family_var(data, label_family = "sans", ...)

label_fontface_var(data, label_fontface = "plain", ...)

label_hjust_var(data, label_hjust = "center", ...)

label_vjust_var(data, label_vjust = "middle", ...)

label_lineheight_var(data, label_lineheight = 1, ...)

all_cov(data, expr, ...)

hide_cov(data, ...)

show_cov(data, ...)

colour_cov(data, colour = "black", ...)

color_cov(data, color = "black", ...)

linetype_cov(data, linetype = 1, ...)

size_cov(data, size = 1, ...)

alpha_cov(data, alpha = 1, ...)

label_colour_cov(data, label_colour = "black", ...)

label_color_cov(data, label_color = "black", ...)

label_fill_cov(data, label_fill = "white", ...)

label_size_cov(data, label_size = 4, ...)

label_alpha_cov(data, label_alpha = 1, ...)

label_family_cov(data, label_family = "sans", ...)

label_fontface_cov(data, label_fontface = "plain", ...)

label_hjust_cov(data, label_hjust = "center", ...)

label_vjust_cov(data, label_vjust = "middle", ...)

label_lineheight_cov(data, label_lineheight = 1, ...)

label_location_cov(data, label_location = 0.5, ...)

all_reg(data, expr, ...)

hide_reg(data, ...)

show_reg(data, ...)

colour_reg(data, colour = "black", ...)

color_reg(data, color = "black", ...)

linetype_reg(data, linetype = 1, ...)

size_reg(data, size = 1, ...)

alpha_reg(data, alpha = 1, ...)

label_colour_reg(data, label_colour = "black", ...)

label_color_reg(data, label_color = "black", ...)

label_fill_reg(data, label_fill = "white", ...)

label_size_reg(data, label_size = 4, ...)

label_alpha_reg(data, label_alpha = 1, ...)

label_family_reg(data, label_family = "sans", ...)

label_fontface_reg(data, label_fontface = "plain", ...)

label_hjust_reg(data, label_hjust = "center", ...)

label_vjust_reg(data, label_vjust = "middle", ...)

label_lineheight_reg(data, label_lineheight = 1, ...)

label_location_reg(data, label_location = 0.5, ...)

all_load(data, expr, ...)

hide_load(data, ...)

show_load(data, ...)

colour_load(data, colour = "black", ...)

color_load(data, color = "black", ...)

linetype_load(data, linetype = 1, ...)

size_load(data, size = 1, ...)

alpha_load(data, alpha = 1, ...)

label_colour_load(data, label_colour = "black", ...)

label_color_load(data, label_color = "black", ...)

label_fill_load(data, label_fill = "white", ...)

label_size_load(data, label_size = 4, ...)

label_alpha_load(data, label_alpha = 1, ...)

label_family_load(data, label_family = "sans", ...)

label_fontface_load(data, label_fontface = "plain", ...)

label_hjust_load(data, label_hjust = "center", ...)

label_vjust_load(data, label_vjust = "middle", ...)

label_lineheight_load(data, label_lineheight = 1, ...)

label_location_load(data, label_location = 0.5, ...)

all_obs(data, expr, ...)

hide_obs(data, ...)

show_obs(data, ...)

colour_obs(data, colour = "black", ...)

color_obs(data, color = "black", ...)

linetype_obs(data, linetype = 1, ...)

size_obs(data, size = 1, ...)

alpha_obs(data, alpha = 1, ...)

fill_obs(data, fill = "white", ...)

label_colour_obs(data, label_colour = "black", ...)

label_color_obs(data, label_color = "black", ...)

label_fill_obs(data, label_fill = "white", ...)

label_size_obs(data, label_size = 4, ...)

label_alpha_obs(data, label_alpha = 1, ...)

label_family_obs(data, label_family = "sans", ...)

label_fontface_obs(data, label_fontface = "plain", ...)

label_hjust_obs(data, label_hjust = "center", ...)

label_vjust_obs(data, label_vjust = "middle", ...)

label_lineheight_obs(data, label_lineheight = 1, ...)

all_latent(data, expr, ...)

hide_latent(data, ...)

show_latent(data, ...)

colour_latent(data, colour = "black", ...)

color_latent(data, color = "black", ...)

linetype_latent(data, linetype = 1, ...)

size_latent(data, size = 1, ...)

alpha_latent(data, alpha = 1, ...)

fill_latent(data, fill = "white", ...)

label_colour_latent(data, label_colour = "black", ...)

label_color_latent(data, label_color = "black", ...)

label_fill_latent(data, label_fill = "white", ...)

label_size_latent(data, label_size = 4, ...)

label_alpha_latent(data, label_alpha = 1, ...)

label_family_latent(data, label_family = "sans", ...)

label_fontface_latent(data, label_fontface = "plain", ...)

label_hjust_latent(data, label_hjust = "center", ...)

label_vjust_latent(data, label_vjust = "middle", ...)

label_lineheight_latent(data, label_lineheight = 1, ...)

all_sig_nodes(data, expr, ...)

hide_sig_nodes(data, ...)

show_sig_nodes(data, ...)

colour_sig_nodes(data, colour = "black", ...)

color_sig_nodes(data, color = "black", ...)

linetype_sig_nodes(data, linetype = 1, ...)

size_sig_nodes(data, size = 1, ...)

alpha_sig_nodes(data, alpha = 1, ...)

label_colour_sig_nodes(data, label_colour = "black", ...)

label_color_sig_nodes(data, label_color = "black", ...)

label_fill_sig_nodes(data, label_fill = "white", ...)

label_size_sig_nodes(data, label_size = 4, ...)

label_alpha_sig_nodes(data, label_alpha = 1, ...)

label_family_sig_nodes(data, label_family = "sans", ...)

label_fontface_sig_nodes(data, label_fontface = "plain", ...)

label_hjust_sig_nodes(data, label_hjust = "center", ...)

label_vjust_sig_nodes(data, label_vjust = "middle", ...)

label_lineheight_sig_nodes(data, label_lineheight = 1, ...)

all_nonsig_nodes(data, expr, ...)

hide_nonsig_nodes(data, ...)

show_nonsig_nodes(data, ...)

colour_nonsig_nodes(data, colour = "black", ...)

color_nonsig_nodes(data, color = "black", ...)

linetype_nonsig_nodes(data, linetype = 1, ...)

size_nonsig_nodes(data, size = 1, ...)

alpha_nonsig_nodes(data, alpha = 1, ...)

label_colour_nonsig_nodes(data, label_colour = "black", ...)

label_color_nonsig_nodes(data, label_color = "black", ...)

label_fill_nonsig_nodes(data, label_fill = "white", ...)

label_size_nonsig_nodes(data, label_size = 4, ...)

label_alpha_nonsig_nodes(data, label_alpha = 1, ...)

label_family_nonsig_nodes(data, label_family = "sans", ...)

label_fontface_nonsig_nodes(data, label_fontface = "plain", ...)

label_hjust_nonsig_nodes(data, label_hjust = "center", ...)

label_vjust_nonsig_nodes(data, label_vjust = "middle", ...)

label_lineheight_nonsig_nodes(data, label_lineheight = 1, ...)

all_fixed_nodes(data, expr, ...)

hide_fixed_nodes(data, ...)

show_fixed_nodes(data, ...)

colour_fixed_nodes(data, colour = "black", ...)

color_fixed_nodes(data, color = "black", ...)

linetype_fixed_nodes(data, linetype = 1, ...)

size_fixed_nodes(data, size = 1, ...)

alpha_fixed_nodes(data, alpha = 1, ...)

label_colour_fixed_nodes(data, label_colour = "black", ...)

label_color_fixed_nodes(data, label_color = "black", ...)

label_fill_fixed_nodes(data, label_fill = "white", ...)

label_size_fixed_nodes(data, label_size = 4, ...)

label_alpha_fixed_nodes(data, label_alpha = 1, ...)

label_family_fixed_nodes(data, label_family = "sans", ...)

label_fontface_fixed_nodes(data, label_fontface = "plain", ...)

label_hjust_fixed_nodes(data, label_hjust = "center", ...)

label_vjust_fixed_nodes(data, label_vjust = "middle", ...)

label_lineheight_fixed_nodes(data, label_lineheight = 1, ...)

all_pos_nodes(data, expr, ...)

hide_pos_nodes(data, ...)

show_pos_nodes(data, ...)

colour_pos_nodes(data, colour = "black", ...)

color_pos_nodes(data, color = "black", ...)

linetype_pos_nodes(data, linetype = 1, ...)

size_pos_nodes(data, size = 1, ...)

alpha_pos_nodes(data, alpha = 1, ...)

label_colour_pos_nodes(data, label_colour = "black", ...)

label_color_pos_nodes(data, label_color = "black", ...)

label_fill_pos_nodes(data, label_fill = "white", ...)

label_size_pos_nodes(data, label_size = 4, ...)

label_alpha_pos_nodes(data, label_alpha = 1, ...)

label_family_pos_nodes(data, label_family = "sans", ...)

label_fontface_pos_nodes(data, label_fontface = "plain", ...)

label_hjust_pos_nodes(data, label_hjust = "center", ...)

label_vjust_pos_nodes(data, label_vjust = "middle", ...)

label_lineheight_pos_nodes(data, label_lineheight = 1, ...)

all_neg_nodes(data, expr, ...)

hide_neg_nodes(data, ...)

show_neg_nodes(data, ...)

colour_neg_nodes(data, colour = "black", ...)

color_neg_nodes(data, color = "black", ...)

linetype_neg_nodes(data, linetype = 1, ...)

size_neg_nodes(data, size = 1, ...)

alpha_neg_nodes(data, alpha = 1, ...)

label_colour_neg_nodes(data, label_colour = "black", ...)

label_color_neg_nodes(data, label_color = "black", ...)

label_fill_neg_nodes(data, label_fill = "white", ...)

label_size_neg_nodes(data, label_size = 4, ...)

label_alpha_neg_nodes(data, label_alpha = 1, ...)

label_family_neg_nodes(data, label_family = "sans", ...)

label_fontface_neg_nodes(data, label_fontface = "plain", ...)

label_hjust_neg_nodes(data, label_hjust = "center", ...)

label_vjust_neg_nodes(data, label_vjust = "middle", ...)

label_lineheight_neg_nodes(data, label_lineheight = 1, ...)

all_sig_edges(data, expr, ...)

hide_sig_edges(data, ...)

show_sig_edges(data, ...)

colour_sig_edges(data, colour = "black", ...)

color_sig_edges(data, color = "black", ...)

linetype_sig_edges(data, linetype = 1, ...)

size_sig_edges(data, size = 1, ...)

alpha_sig_edges(data, alpha = 1, ...)

label_colour_sig_edges(data, label_colour = "black", ...)

label_color_sig_edges(data, label_color = "black", ...)

label_fill_sig_edges(data, label_fill = "white", ...)

label_size_sig_edges(data, label_size = 4, ...)

label_alpha_sig_edges(data, label_alpha = 1, ...)

label_family_sig_edges(data, label_family = "sans", ...)

label_fontface_sig_edges(data, label_fontface = "plain", ...)

label_hjust_sig_edges(data, label_hjust = "center", ...)

label_vjust_sig_edges(data, label_vjust = "middle", ...)

label_lineheight_sig_edges(data, label_lineheight = 1, ...)

all_nonsig_edges(data, expr, ...)

hide_nonsig_edges(data, ...)

show_nonsig_edges(data, ...)

colour_nonsig_edges(data, colour = "black", ...)

color_nonsig_edges(data, color = "black", ...)

linetype_nonsig_edges(data, linetype = 1, ...)

size_nonsig_edges(data, size = 1, ...)

alpha_nonsig_edges(data, alpha = 1, ...)

label_colour_nonsig_edges(data, label_colour = "black", ...)

label_color_nonsig_edges(data, label_color = "black", ...)

label_fill_nonsig_edges(data, label_fill = "white", ...)

label_size_nonsig_edges(data, label_size = 4, ...)

label_alpha_nonsig_edges(data, label_alpha = 1, ...)

label_family_nonsig_edges(data, label_family = "sans", ...)

label_fontface_nonsig_edges(data, label_fontface = "plain", ...)

label_hjust_nonsig_edges(data, label_hjust = "center", ...)

label_vjust_nonsig_edges(data, label_vjust = "middle", ...)

label_lineheight_nonsig_edges(data, label_lineheight = 1, ...)

all_fixed_edges(data, expr, ...)

hide_fixed_edges(data, ...)

show_fixed_edges(data, ...)

colour_fixed_edges(data, colour = "black", ...)

color_fixed_edges(data, color = "black", ...)

linetype_fixed_edges(data, linetype = 1, ...)

size_fixed_edges(data, size = 1, ...)

alpha_fixed_edges(data, alpha = 1, ...)

label_colour_fixed_edges(data, label_colour = "black", ...)

label_color_fixed_edges(data, label_color = "black", ...)

label_fill_fixed_edges(data, label_fill = "white", ...)

label_size_fixed_edges(data, label_size = 4, ...)

label_alpha_fixed_edges(data, label_alpha = 1, ...)

label_family_fixed_edges(data, label_family = "sans", ...)

label_fontface_fixed_edges(data, label_fontface = "plain", ...)

label_hjust_fixed_edges(data, label_hjust = "center", ...)

label_vjust_fixed_edges(data, label_vjust = "middle", ...)

label_lineheight_fixed_edges(data, label_lineheight = 1, ...)

all_pos_edges(data, expr, ...)

hide_pos_edges(data, ...)

show_pos_edges(data, ...)

colour_pos_edges(data, colour = "black", ...)

color_pos_edges(data, color = "black", ...)

linetype_pos_edges(data, linetype = 1, ...)

size_pos_edges(data, size = 1, ...)

alpha_pos_edges(data, alpha = 1, ...)

label_colour_pos_edges(data, label_colour = "black", ...)

label_color_pos_edges(data, label_color = "black", ...)

label_fill_pos_edges(data, label_fill = "white", ...)

label_size_pos_edges(data, label_size = 4, ...)

label_alpha_pos_edges(data, label_alpha = 1, ...)

label_family_pos_edges(data, label_family = "sans", ...)

label_fontface_pos_edges(data, label_fontface = "plain", ...)

label_hjust_pos_edges(data, label_hjust = "center", ...)

label_vjust_pos_edges(data, label_vjust = "middle", ...)

label_lineheight_pos_edges(data, label_lineheight = 1, ...)

all_neg_edges(data, expr, ...)

hide_neg_edges(data, ...)

show_neg_edges(data, ...)

colour_neg_edges(data, colour = "black", ...)

color_neg_edges(data, color = "black", ...)

linetype_neg_edges(data, linetype = 1, ...)

size_neg_edges(data, size = 1, ...)

alpha_neg_edges(data, alpha = 1, ...)

label_colour_neg_edges(data, label_colour = "black", ...)

label_color_neg_edges(data, label_color = "black", ...)

label_fill_neg_edges(data, label_fill = "white", ...)

label_size_neg_edges(data, label_size = 4, ...)

label_alpha_neg_edges(data, label_alpha = 1, ...)

label_family_neg_edges(data, label_family = "sans", ...)

label_fontface_neg_edges(data, label_fontface = "plain", ...)

label_hjust_neg_edges(data, label_hjust = "center", ...)

label_vjust_neg_edges(data, label_vjust = "middle", ...)

label_lineheight_neg_edges(data, label_lineheight = 1, ...)

Arguments

data

Object to manipulate.
condition

Expression that returns a logical vector when evaluated in the environment of data.
expr

Expression to perform on elements of data for which condition == TRUE.
...

Additional arguments passed to and from functions.
element

Character vector. The elements of the sem_graph to edit, defaults to c("edges", "nodes").
colour

Atomic character vector, indicating which colour to assign to the selected elements.
color

Atomic character vector, indicating which color to assign to the selected elements.
linetype

Atomic character vector, indicating which linetype to assign to the selected elements.
size

Atomic character vector, indicating which size to assign to the selected elements.
alpha

Atomic character vector, indicating which alpha to assign to the selected elements.
fill

Atomic character vector, indicating which fill to assign to the selected elements.
label_colour

Atomic character vector, indicating which label_colour to assign to the selected elements.
label_color

Atomic character vector, indicating which label_color to assign to the selected elements.
label_fill

Atomic character vector, indicating which label_fill to assign to the selected elements.
label_size

Atomic character vector, indicating which label_size to assign to the selected elements.
label_alpha

Atomic character vector, indicating which label_alpha to assign to the selected elements.
label_family

Atomic character vector, indicating which label_family to assign to the selected elements.
label_fontface

Atomic character vector, indicating which label_fontface to assign to the selected elements.
label_hjust

Atomic character vector, indicating which label_hjust to assign to the selected elements.
label_vjust

Atomic character vector, indicating which label_vjust to assign to the selected elements.
label_lineheight

Atomic character vector, indicating which label_lineheight to assign to the selected elements.
label_location

Atomic character vector, indicating which label_location to assign to the selected elements.

Value

Object of the same class as data.

Examples

library(lavaan)
res <- sem("dist ~ speed", cars, meanstructure = TRUE)
p <- prepare_graph(res)
out <- if_edit(p, condition = {pval < .05}, expr = {label = "sig"})
out <- if_edges(p, condition = {pval < .05}, expr = {label = "sig"})
out <- if_nodes(p, condition = {pval < .05}, expr = {label = "sig"})
out <- all_sig(p, expr = {label = "sig"})
out <- hide_sig(p)
out <- show_sig(p)
out <- colour_sig(p, { colour = "black" })
out <- color_sig(p, { color = "black" })
out <- linetype_sig(p, { linetype = 1 })
out <- size_sig(p, { size = 1 })
out <- alpha_sig(p, { alpha = 1 })
out <- fill_sig(p, { fill = "white" })
out <- label_colour_sig(p, { label_colour = "black" })
out <- label_color_sig(p, { label_color = "black" })
out <- label_fill_sig(p, { label_fill = "white" })
out <- label_size_sig(p, { label_size = 4 })
out <- label_alpha_sig(p, { label_alpha = 1 })
out <- label_family_sig(p, { label_family = "sans" })
out <- label_fontface_sig(p, { label_fontface = "plain" })
out <- label_hjust_sig(p, { label_hjust = "center" })
out <- label_vjust_sig(p, { label_vjust = "middle" })
out <- label_lineheight_sig(p, { label_lineheight = 1 })
out <- label_location_sig(p, { label_location = .5 })
out <- all_nonsig(p, expr = {label = "sig"})
out <- hide_nonsig(p)
out <- show_nonsig(p)
out <- colour_nonsig(p, { colour = "black" })
out <- color_nonsig(p, { color = "black" })
out <- linetype_nonsig(p, { linetype = 1 })
out <- size_nonsig(p, { size = 1 })
out <- alpha_nonsig(p, { alpha = 1 })
out <- fill_nonsig(p, { fill = "white" })
out <- label_colour_nonsig(p, { label_colour = "black" })
out <- label_color_nonsig(p, { label_color = "black" })
out <- label_fill_nonsig(p, { label_fill = "white" })
out <- label_size_nonsig(p, { label_size = 4 })
out <- label_alpha_nonsig(p, { label_alpha = 1 })
out <- label_family_nonsig(p, { label_family = "sans" })
out <- label_fontface_nonsig(p, { label_fontface = "plain" })
out <- label_hjust_nonsig(p, { label_hjust = "center" })
out <- label_vjust_nonsig(p, { label_vjust = "middle" })
out <- label_lineheight_nonsig(p, { label_lineheight = 1 })
out <- label_location_nonsig(p, { label_location = .5 })
out <- all_fixed(p, expr = {label = "sig"})
out <- hide_fixed(p)
out <- show_fixed(p)
out <- colour_fixed(p, { colour = "black" })
out <- color_fixed(p, { color = "black" })
out <- linetype_fixed(p, { linetype = 1 })
out <- size_fixed(p, { size = 1 })
out <- alpha_fixed(p, { alpha = 1 })
out <- fill_fixed(p, { fill = "white" })
out <- label_colour_fixed(p, { label_colour = "black" })
out <- label_color_fixed(p, { label_color = "black" })
out <- label_fill_fixed(p, { label_fill = "white" })
out <- label_size_fixed(p, { label_size = 4 })
out <- label_alpha_fixed(p, { label_alpha = 1 })
out <- label_family_fixed(p, { label_family = "sans" })
out <- label_fontface_fixed(p, { label_fontface = "plain" })
out <- label_hjust_fixed(p, { label_hjust = "center" })
out <- label_vjust_fixed(p, { label_vjust = "middle" })
out <- label_lineheight_fixed(p, { label_lineheight = 1 })
out <- label_location_fixed(p, { label_location = .5 })
out <- all_pos(p, expr = {label = "sig"})
out <- hide_pos(p)
out <- show_pos(p)
out <- colour_pos(p, { colour = "black" })
out <- color_pos(p, { color = "black" })
out <- linetype_pos(p, { linetype = 1 })
out <- size_pos(p, { size = 1 })
out <- alpha_pos(p, { alpha = 1 })
out <- fill_pos(p, { fill = "white" })
out <- label_colour_pos(p, { label_colour = "black" })
out <- label_color_pos(p, { label_color = "black" })
out <- label_fill_pos(p, { label_fill = "white" })
out <- label_size_pos(p, { label_size = 4 })
out <- label_alpha_pos(p, { label_alpha = 1 })
out <- label_family_pos(p, { label_family = "sans" })
out <- label_fontface_pos(p, { label_fontface = "plain" })
out <- label_hjust_pos(p, { label_hjust = "center" })
out <- label_vjust_pos(p, { label_vjust = "middle" })
out <- label_lineheight_pos(p, { label_lineheight = 1 })
out <- label_location_pos(p, { label_location = .5 })
out <- all_neg(p, expr = {label = "sig"})
out <- hide_neg(p)
out <- show_neg(p)
out <- colour_neg(p, { colour = "black" })
out <- color_neg(p, { color = "black" })
out <- linetype_neg(p, { linetype = 1 })
out <- size_neg(p, { size = 1 })
out <- alpha_neg(p, { alpha = 1 })
out <- fill_neg(p, { fill = "white" })
out <- label_colour_neg(p, { label_colour = "black" })
out <- label_color_neg(p, { label_color = "black" })
out <- label_fill_neg(p, { label_fill = "white" })
out <- label_size_neg(p, { label_size = 4 })
out <- label_alpha_neg(p, { label_alpha = 1 })
out <- label_family_neg(p, { label_family = "sans" })
out <- label_fontface_neg(p, { label_fontface = "plain" })
out <- label_hjust_neg(p, { label_hjust = "center" })
out <- label_vjust_neg(p, { label_vjust = "middle" })
out <- label_lineheight_neg(p, { label_lineheight = 1 })
out <- label_location_neg(p, { label_location = .5 })
out <- all_var(p, expr = {label = "sig"})
out <- hide_var(p)
out <- show_var(p)
out <- colour_var(p, { colour = "black" })
out <- color_var(p, { color = "black" })
out <- linetype_var(p, { linetype = 1 })
out <- size_var(p, { size = 1 })
out <- alpha_var(p, { alpha = 1 })
out <- label_colour_var(p, { label_colour = "black" })
out <- label_color_var(p, { label_color = "black" })
out <- label_fill_var(p, { label_fill = "white" })
out <- label_size_var(p, { label_size = 4 })
out <- label_alpha_var(p, { label_alpha = 1 })
out <- label_family_var(p, { label_family = "sans" })
out <- label_fontface_var(p, { label_fontface = "plain" })
out <- label_hjust_var(p, { label_hjust = "center" })
out <- label_vjust_var(p, { label_vjust = "middle" })
out <- label_lineheight_var(p, { label_lineheight = 1 })
out <- all_cov(p, expr = {label = "sig"})
out <- hide_cov(p)
out <- show_cov(p)
out <- colour_cov(p, { colour = "black" })
out <- color_cov(p, { color = "black" })
out <- linetype_cov(p, { linetype = 1 })
out <- size_cov(p, { size = 1 })
out <- alpha_cov(p, { alpha = 1 })
out <- label_colour_cov(p, { label_colour = "black" })
out <- label_color_cov(p, { label_color = "black" })
out <- label_fill_cov(p, { label_fill = "white" })
out <- label_size_cov(p, { label_size = 4 })
out <- label_alpha_cov(p, { label_alpha = 1 })
out <- label_family_cov(p, { label_family = "sans" })
out <- label_fontface_cov(p, { label_fontface = "plain" })
out <- label_hjust_cov(p, { label_hjust = "center" })
out <- label_vjust_cov(p, { label_vjust = "middle" })
out <- label_lineheight_cov(p, { label_lineheight = 1 })
out <- label_location_cov(p, { label_location = .5 })
out <- all_reg(p, expr = {label = "sig"})
out <- hide_reg(p)
out <- show_reg(p)
out <- colour_reg(p, { colour = "black" })
out <- color_reg(p, { color = "black" })
out <- linetype_reg(p, { linetype = 1 })
out <- size_reg(p, { size = 1 })
out <- alpha_reg(p, { alpha = 1 })
out <- label_colour_reg(p, { label_colour = "black" })
out <- label_color_reg(p, { label_color = "black" })
out <- label_fill_reg(p, { label_fill = "white" })
out <- label_size_reg(p, { label_size = 4 })
out <- label_alpha_reg(p, { label_alpha = 1 })
out <- label_family_reg(p, { label_family = "sans" })
out <- label_fontface_reg(p, { label_fontface = "plain" })
out <- label_hjust_reg(p, { label_hjust = "center" })
out <- label_vjust_reg(p, { label_vjust = "middle" })
out <- label_lineheight_reg(p, { label_lineheight = 1 })
out <- label_location_reg(p, { label_location = .5 })
out <- all_load(p, expr = {label = "sig"})
out <- hide_load(p)
out <- show_load(p)
out <- colour_load(p, { colour = "black" })
out <- color_load(p, { color = "black" })
out <- linetype_load(p, { linetype = 1 })
out <- size_load(p, { size = 1 })
out <- alpha_load(p, { alpha = 1 })
out <- label_colour_load(p, { label_colour = "black" })
out <- label_color_load(p, { label_color = "black" })
out <- label_fill_load(p, { label_fill = "white" })
out <- label_size_load(p, { label_size = 4 })
out <- label_alpha_load(p, { label_alpha = 1 })
out <- label_family_load(p, { label_family = "sans" })
out <- label_fontface_load(p, { label_fontface = "plain" })
out <- label_hjust_load(p, { label_hjust = "center" })
out <- label_vjust_load(p, { label_vjust = "middle" })
out <- label_lineheight_load(p, { label_lineheight = 1 })
out <- label_location_load(p, { label_location = .5 })
out <- all_obs(p, expr = {label = "sig"})
out <- hide_obs(p)
out <- show_obs(p)
out <- colour_obs(p, { colour = "black" })
out <- color_obs(p, { color = "black" })
out <- linetype_obs(p, { linetype = 1 })
out <- size_obs(p, { size = 1 })
out <- alpha_obs(p, { alpha = 1 })
out <- fill_obs(p, { fill = "white" })
out <- label_colour_obs(p, { label_colour = "black" })
out <- label_color_obs(p, { label_color = "black" })
out <- label_fill_obs(p, { label_fill = "white" })
out <- label_size_obs(p, { label_size = 4 })
out <- label_alpha_obs(p, { label_alpha = 1 })
out <- label_family_obs(p, { label_family = "sans" })
out <- label_fontface_obs(p, { label_fontface = "plain" })
out <- label_hjust_obs(p, { label_hjust = "center" })
out <- label_vjust_obs(p, { label_vjust = "middle" })
out <- label_lineheight_obs(p, { label_lineheight = 1 })
out <- all_latent(p, expr = {label = "sig"})
out <- hide_latent(p)
out <- show_latent(p)
out <- colour_latent(p, { colour = "black" })
out <- color_latent(p, { color = "black" })
out <- linetype_latent(p, { linetype = 1 })
out <- size_latent(p, { size = 1 })
out <- alpha_latent(p, { alpha = 1 })
out <- fill_latent(p, { fill = "white" })
out <- label_colour_latent(p, { label_colour = "black" })
out <- label_color_latent(p, { label_color = "black" })
out <- label_fill_latent(p, { label_fill = "white" })
out <- label_size_latent(p, { label_size = 4 })
out <- label_alpha_latent(p, { label_alpha = 1 })
out <- label_family_latent(p, { label_family = "sans" })
out <- label_fontface_latent(p, { label_fontface = "plain" })
out <- label_hjust_latent(p, { label_hjust = "center" })
out <- label_vjust_latent(p, { label_vjust = "middle" })
out <- label_lineheight_latent(p, { label_lineheight = 1 })
out <- all_sig(p, expr = {label = "sig"})
out <- hide_sig(p)
out <- show_sig(p)
out <- colour_sig(p, { colour = "black" })
out <- color_sig(p, { color = "black" })
out <- linetype_sig(p, { linetype = 1 })
out <- size_sig(p, { size = 1 })
out <- alpha_sig(p, { alpha = 1 })
out <- label_colour_sig(p, { label_colour = "black" })
out <- label_color_sig(p, { label_color = "black" })
out <- label_fill_sig(p, { label_fill = "white" })
out <- label_size_sig(p, { label_size = 4 })
out <- label_alpha_sig(p, { label_alpha = 1 })
out <- label_family_sig(p, { label_family = "sans" })
out <- label_fontface_sig(p, { label_fontface = "plain" })
out <- label_hjust_sig(p, { label_hjust = "center" })
out <- label_vjust_sig(p, { label_vjust = "middle" })
out <- label_lineheight_sig(p, { label_lineheight = 1 })
out <- all_nonsig(p, expr = {label = "sig"})
out <- hide_nonsig(p)
out <- show_nonsig(p)
out <- colour_nonsig(p, { colour = "black" })
out <- color_nonsig(p, { color = "black" })
out <- linetype_nonsig(p, { linetype = 1 })
out <- size_nonsig(p, { size = 1 })
out <- alpha_nonsig(p, { alpha = 1 })
out <- label_colour_nonsig(p, { label_colour = "black" })
out <- label_color_nonsig(p, { label_color = "black" })
out <- label_fill_nonsig(p, { label_fill = "white" })
out <- label_size_nonsig(p, { label_size = 4 })
out <- label_alpha_nonsig(p, { label_alpha = 1 })
out <- label_family_nonsig(p, { label_family = "sans" })
out <- label_fontface_nonsig(p, { label_fontface = "plain" })
out <- label_hjust_nonsig(p, { label_hjust = "center" })
out <- label_vjust_nonsig(p, { label_vjust = "middle" })
out <- label_lineheight_nonsig(p, { label_lineheight = 1 })
out <- all_fixed(p, expr = {label = "sig"})
out <- hide_fixed(p)
out <- show_fixed(p)
out <- colour_fixed(p, { colour = "black" })
out <- color_fixed(p, { color = "black" })
out <- linetype_fixed(p, { linetype = 1 })
out <- size_fixed(p, { size = 1 })
out <- alpha_fixed(p, { alpha = 1 })
out <- label_colour_fixed(p, { label_colour = "black" })
out <- label_color_fixed(p, { label_color = "black" })
out <- label_fill_fixed(p, { label_fill = "white" })
out <- label_size_fixed(p, { label_size = 4 })
out <- label_alpha_fixed(p, { label_alpha = 1 })
out <- label_family_fixed(p, { label_family = "sans" })
out <- label_fontface_fixed(p, { label_fontface = "plain" })
out <- label_hjust_fixed(p, { label_hjust = "center" })
out <- label_vjust_fixed(p, { label_vjust = "middle" })
out <- label_lineheight_fixed(p, { label_lineheight = 1 })
out <- all_pos(p, expr = {label = "sig"})
out <- hide_pos(p)
out <- show_pos(p)
out <- colour_pos(p, { colour = "black" })
out <- color_pos(p, { color = "black" })
out <- linetype_pos(p, { linetype = 1 })
out <- size_pos(p, { size = 1 })
out <- alpha_pos(p, { alpha = 1 })
out <- label_colour_pos(p, { label_colour = "black" })
out <- label_color_pos(p, { label_color = "black" })
out <- label_fill_pos(p, { label_fill = "white" })
out <- label_size_pos(p, { label_size = 4 })
out <- label_alpha_pos(p, { label_alpha = 1 })
out <- label_family_pos(p, { label_family = "sans" })
out <- label_fontface_pos(p, { label_fontface = "plain" })
out <- label_hjust_pos(p, { label_hjust = "center" })
out <- label_vjust_pos(p, { label_vjust = "middle" })
out <- label_lineheight_pos(p, { label_lineheight = 1 })
out <- all_neg(p, expr = {label = "sig"})
out <- hide_neg(p)
out <- show_neg(p)
out <- colour_neg(p, { colour = "black" })
out <- color_neg(p, { color = "black" })
out <- linetype_neg(p, { linetype = 1 })
out <- size_neg(p, { size = 1 })
out <- alpha_neg(p, { alpha = 1 })
out <- label_colour_neg(p, { label_colour = "black" })
out <- label_color_neg(p, { label_color = "black" })
out <- label_fill_neg(p, { label_fill = "white" })
out <- label_size_neg(p, { label_size = 4 })
out <- label_alpha_neg(p, { label_alpha = 1 })
out <- label_family_neg(p, { label_family = "sans" })
out <- label_fontface_neg(p, { label_fontface = "plain" })
out <- label_hjust_neg(p, { label_hjust = "center" })
out <- label_vjust_neg(p, { label_vjust = "middle" })
out <- label_lineheight_neg(p, { label_lineheight = 1 })
out <- all_sig(p, expr = {label = "sig"})
out <- hide_sig(p)
out <- show_sig(p)
out <- colour_sig(p, { colour = "black" })
out <- color_sig(p, { color = "black" })
out <- linetype_sig(p, { linetype = 1 })
out <- size_sig(p, { size = 1 })
out <- alpha_sig(p, { alpha = 1 })
out <- label_colour_sig(p, { label_colour = "black" })
out <- label_color_sig(p, { label_color = "black" })
out <- label_fill_sig(p, { label_fill = "white" })
out <- label_size_sig(p, { label_size = 4 })
out <- label_alpha_sig(p, { label_alpha = 1 })
out <- label_family_sig(p, { label_family = "sans" })
out <- label_fontface_sig(p, { label_fontface = "plain" })
out <- label_hjust_sig(p, { label_hjust = "center" })
out <- label_vjust_sig(p, { label_vjust = "middle" })
out <- label_lineheight_sig(p, { label_lineheight = 1 })
out <- all_nonsig(p, expr = {label = "sig"})
out <- hide_nonsig(p)
out <- show_nonsig(p)
out <- colour_nonsig(p, { colour = "black" })
out <- color_nonsig(p, { color = "black" })
out <- linetype_nonsig(p, { linetype = 1 })
out <- size_nonsig(p, { size = 1 })
out <- alpha_nonsig(p, { alpha = 1 })
out <- label_colour_nonsig(p, { label_colour = "black" })
out <- label_color_nonsig(p, { label_color = "black" })
out <- label_fill_nonsig(p, { label_fill = "white" })
out <- label_size_nonsig(p, { label_size = 4 })
out <- label_alpha_nonsig(p, { label_alpha = 1 })
out <- label_family_nonsig(p, { label_family = "sans" })
out <- label_fontface_nonsig(p, { label_fontface = "plain" })
out <- label_hjust_nonsig(p, { label_hjust = "center" })
out <- label_vjust_nonsig(p, { label_vjust = "middle" })
out <- label_lineheight_nonsig(p, { label_lineheight = 1 })
out <- all_fixed(p, expr = {label = "sig"})
out <- hide_fixed(p)
out <- show_fixed(p)
out <- colour_fixed(p, { colour = "black" })
out <- color_fixed(p, { color = "black" })
out <- linetype_fixed(p, { linetype = 1 })
out <- size_fixed(p, { size = 1 })
out <- alpha_fixed(p, { alpha = 1 })
out <- label_colour_fixed(p, { label_colour = "black" })
out <- label_color_fixed(p, { label_color = "black" })
out <- label_fill_fixed(p, { label_fill = "white" })
out <- label_size_fixed(p, { label_size = 4 })
out <- label_alpha_fixed(p, { label_alpha = 1 })
out <- label_family_fixed(p, { label_family = "sans" })
out <- label_fontface_fixed(p, { label_fontface = "plain" })
out <- label_hjust_fixed(p, { label_hjust = "center" })
out <- label_vjust_fixed(p, { label_vjust = "middle" })
out <- label_lineheight_fixed(p, { label_lineheight = 1 })
out <- all_pos(p, expr = {label = "sig"})
out <- hide_pos(p)
out <- show_pos(p)
out <- colour_pos(p, { colour = "black" })
out <- color_pos(p, { color = "black" })
out <- linetype_pos(p, { linetype = 1 })
out <- size_pos(p, { size = 1 })
out <- alpha_pos(p, { alpha = 1 })
out <- label_colour_pos(p, { label_colour = "black" })
out <- label_color_pos(p, { label_color = "black" })
out <- label_fill_pos(p, { label_fill = "white" })
out <- label_size_pos(p, { label_size = 4 })
out <- label_alpha_pos(p, { label_alpha = 1 })
out <- label_family_pos(p, { label_family = "sans" })
out <- label_fontface_pos(p, { label_fontface = "plain" })
out <- label_hjust_pos(p, { label_hjust = "center" })
out <- label_vjust_pos(p, { label_vjust = "middle" })
out <- label_lineheight_pos(p, { label_lineheight = 1 })
out <- all_neg(p, expr = {label = "sig"})
out <- hide_neg(p)
out <- show_neg(p)
out <- colour_neg(p, { colour = "black" })
out <- color_neg(p, { color = "black" })
out <- linetype_neg(p, { linetype = 1 })
out <- size_neg(p, { size = 1 })
out <- alpha_neg(p, { alpha = 1 })
out <- label_colour_neg(p, { label_colour = "black" })
out <- label_color_neg(p, { label_color = "black" })
out <- label_fill_neg(p, { label_fill = "white" })
out <- label_size_neg(p, { label_size = 4 })
out <- label_alpha_neg(p, { label_alpha = 1 })
out <- label_family_neg(p, { label_family = "sans" })
out <- label_fontface_neg(p, { label_fontface = "plain" })
out <- label_hjust_neg(p, { label_hjust = "center" })
out <- label_vjust_neg(p, { label_vjust = "middle" })
out <- label_lineheight_neg(p, { label_lineheight = 1 })

Lo-Mendell-Rubin Likelihood Ratio Test

Description

A likelihood ratio test for class enumeration in latent class analysis, proposed by Lo, Mendell, & Rubin (2001) based on work by Vuong (1989). See Details for important clarification.

Usage

lr_lmr(x, ...)

Arguments

x

An object for which a method exists.
...

Additional arguments.

Details

The likelihood ratio test for non-nested models, based on work by Vuong (1989), is often used for class enumeration in latent class analysis (see Lo, Mendell, & Rubin, 2001). Following work by Merkle, You, & Preacher (2016), the models to be compared must first be tested for distinguishability in the population, using the w2 test. The null hypothesis is that the models are indistinguishable. If this null hypothesis is not rejected, there is no point in statistical model comparison, either using the LMR LRT or other statistics. If the null hypothesis is rejected, the LMR LRT can be evaluated using a Z-test. This function wraps ⁠\link[nonnest2]{vuongtest}⁠ to perform that test.

Value

A data.frame containing the Z-value for the likelihood ratio test, its p-value, df (which indicates the difference in number of parameters, not true degrees of freedom, which may be zero), w2 (omega squared) statistic for the test of distinguishability, an its p-value.

References

Lo Y, Mendell NR, Rubin DB. Testing the number of components in a normal mixture. Biometrika. 2001;88(3):767-778. doi:10.1093/biomet/88.3.767

Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 57, 307-333. doi:10.2307/1912557

Merkle, E. C., You, D., & Preacher, K. (2016). Testing non-nested structural equation models. Psychological Methods, 21, 151-163. doi:10.1037/met0000038

Examples

if(requireNamespace("OpenMx", quietly = TRUE)){
library(OpenMx)
df <- iris[c(1:5, 100:105), 1:3]
names(df) <- letters[1:3]
res <- mx_profiles(df, classes = 1:2)
lr_lmr(res)
}

Conduct Likelihood Ratio tests

Description

For a multigroup model of class MxModel, conduct overall and pairwise likelihood ratio tests. All submodels must be identical.

Usage

lr_test(x, compare = c("All", "A", "S", "F", "M", "Thresholds"), ...)

Arguments

x

An object for which a method exists.
compare

Character vector, indicating which matrices to constrain to be equal in pairwise comparisons.
...

Additional arguments passed to other functions.

Value

An object of class lr_test and list.

Examples

if(requireNamespace("OpenMx", quietly = TRUE)){
library(OpenMx)
df <- iris[c(1:10, 140:150), c(1, 5)]
names(df) <- c("x", "group")
mod <- as_ram("x~1", data = df, group = "group")
mod <- run_mx(mod)
lr_test(mod)
}

Apply pattern replacement over a vector

Description

lsub returns a list of the same length as replacement, each element of which is the result of applying gsub to x using lapply.

Usage

lsub(x, replacement = NULL, pattern = "{C}", fixed = TRUE, ...)

Arguments

x

A character vector where matches are sought.
replacement

a character vector of length 1 or more. Each element is applied to x in turn. Default: NULL
pattern

A character string containing a regular expression (or character string when fixed = TRUE). Default: '{C}'.
fixed

logical. If TRUE, pattern is a string to be matched as is. Default: TRUE
...

Parameters passed on to gsub.

Value

A list of results returned by gsub.

Examples

lsub("a{C}", 1:3)

National Identity, Discrimination and Depression

Description

These synthetic data are based on a study by Maene and colleagues, which conducted an LCA with ordinal indicators on National, Regional, and Heritage Identities in Flemish (Belgian) high-school students with a migration background, and examined between class differences in perceived discrimination by teachers and depressive symptoms.

Usage

data(maene_identity)

Format

A data frame with 439 rows and 13 variables.

Details

Ethnic_1 ordered when I introduce myself, I would definitely say I belong to this group, answered on a 5-point Likert scale
Ethnic_2 ordered I have a strong sense of belonging to this group, answered on a 5-point Likert scale
Ethnic_3 ordered I see myself as a member of this group, answered on a 5-point Likert scale
Belgian ordered Do you feel a member of the Belgian group, answered on a 10-point Likert scale
Flemish ordered Do you feel a member of the Flemish group, answered on a 10-point Likert scale
age numeric Participant age
sex factor Participant sex
ses numeric Socio-economic status, measured using the International Socio-Economic Index of Occupational Status (ISEI)
belgianborn factor Whether or not the participant was born in Belgium
age_belgium numeric Age at which the participant migrated to Belgium
vict_bully factor Whether or not the participant has ever been the victim of peer bullying for any reason
vict_teacher factor Whether or not the participant has ever been insulted, threatened, pushed, treated unfairly or excluded by teachers because of their foreign descent, language use, and skin colour
depression numeric Scale scores of self-reported depressive feelings, assessed using the a ten-item scale with 5-point Likert-type response options

References

Maene, C., D'hondt, F., Van Lissa, C. J., Thijs, J., & Stevens, P. A. (2022). Perceived teacher discrimination and depressive feelings in adolescents: the role of national, regional, and heritage identities in Flemish schools. Journal of youth and adolescence, 51(12), 2281-2293. doi:10.1007/s10964-022-01665-7

Generate syntax for a measurement model

Description

Generate syntax for a measurement model for latent variables. This function relies on add_paths to generate syntax.

Usage

measurement(x, ...)

Arguments

x

An object for which a method exists, including tidy_sem (generated using dictionary, or data.frame (for which dictionary will be run first).
...

Additional parameters passed to add_paths.

Value

An object of class tidy_sem.

Examples

dict <- tidy_sem(c("bfi_1", "bfi_2", "bfi_3", "bfi_4", "bfi_5"))
measurement(dict)

Automatically set starting values for an OpenMx mixture model

Description

Automatically set starting values for an OpenMx mixture model. This function was designed to work with mixture models created using tidySEM functions like mx_mixture, and may not work with other mxModels.

Usage

mixture_starts(model, splits, ...)

Arguments

model

A mixture model of class mxModel.
splits

Optional. A numeric vector of length equal to the number of rows in the OpenMx::mxData() used in the model object. The data will be split by this vector. See Details for the default setting and possible alternatives.
...

Additional arguments, passed to functions.

Details

Starting values are derived by the following procedure:

  1. The mixture model is converted to a multi-group model.

  2. The data are split along splits, and assigned to the corresponding groups of the multi-group model.

  3. The multi-group model is run, and the final values of each group are assigned to the corresponding mixture component as starting values.

  4. The mixture model is returned with these starting values.

If the argument splits is not provided, the function will call stats::kmeans(x = data, centers = classes)$cluster, where data is extracted from the model argument.

Sensible ways to split the data include:

  • Using Hierarchical clustering: cutree(hclust(dist(data)), k = classes))

  • Using K-means clustering: stats::kmeans(x = data, centers = classes)$cluster

  • Using agglomerative hierarchical clustering: mclust::hclass(data = data), G = classes)[, 1]

  • Using a random split: sample.int(n = classes, size = nrow(data), replace = TRUE)

Value

Returns an OpenMx::mxModel() with starting values.

References

Shireman, E., Steinley, D. & Brusco, M.J. Examining the effect of initialization strategies on the performance of Gaussian mixture modeling. Behav Res 49, 282-293 (2017). doi:10.3758/s13428-015-0697-6

Van Lissa, C. J., Garnier-Villarreal, M., & Anadria, D. (2023). Recommended Practices in Latent Class Analysis using the Open-Source R-Package tidySEM. Structural Equation Modeling. doi:10.1080/10705511.2023.2250920

Examples

## Not run: 
df <- iris[, 1, drop = FALSE]
names(df) <- "x"
mod <- mx_mixture(model = "x ~ m{C}*1
                           x ~~ v{C}*x",
                           classes = 2,
                           data = df,
                           run = FALSE)
mod <- mixture_starts(mod)

## End(Not run)

Expand abbreviated Mplus variable names

Description

Expand the Mplus syntax for abbreviating lists of variable names.

Usage

mplus_expand_names(x)

Arguments

x

Atomic character string containing the variable names section of an Mplus syntax file.

Value

Character vector of names.

Examples

mplus_expand_names("test1-test12")
mplus_expand_names("testa-testb")

Data Quantiles

Description

Get quantiles based on empirical normal distribution of data.

Usage

mx_data_quantiles(df)

Arguments

df

A data.frame with only columns of class ordered.

Value

A matrix with the appropriate dimensions for the threshold matrix for df, see OpenMx::mxThreshold().

Examples

set.seed(1)
df <- data.frame(X = ordered(sample(c(1:4), size = 100, replace = TRUE,
prob = c(.1, .2, .5, .2))))
mx_data_quantiles(df)

Create Thresholds Based on Deviances

Description

This function creates a list of mxMatrices to specify thresholds for ordinal variables as a function of strictly positive deviances, see Details.

Usage

mx_deviances(
  vars,
  nThresh = NA,
  free = NULL,
  values = NULL,
  labels = NA,
  lbound = NULL,
  ubound = NULL
)

Arguments

vars

Character vector. Names of variables for which thresholds should be specified.
nThresh

Numeric vector. Number of thresholds for each of variable in vars.
free

Logical vector, used to fill the free matrix which indicates whether the deviance will be freely estimated or fixed.
values

Numeric vector of starting values. Default uses mxNormalQuantiles().
labels

Character vector. Note that constraints specified using these labels are applied using mxConstraint(), because the thresholds are derived quantities, not parameters of the model (the deviances are).
lbound

PARAM_DESCRIPTION, Default: NULL
ubound

PARAM_DESCRIPTION, Default: NULL

Details

Specifying thresholds as a function of strictly positive deviances is an efficient way to get models with ordinal indicators to converge, if they tend to arrive at impossible solutions where the thresholds are not monotonously increasing (e.g., in mixture models). The parameters of a model specified this way are deviances (i.e., the differences between threshold values), which have a lower bound of a small positive number to ensure that thresholds are always increasing. The thresholds are computed by adding subsequent deviances.

Value

A list of mxMatrix objects, to be included in an mxModel.

Dummy Code Factor Variables

Description

For each variable v that inherits factor, create a number of new variables equal to levels(v) to indicate group membership (1) or non-membership (0) of that level. The resulting dummies have class mxFactor.

Usage

mx_dummies(x, classes = c("factor", "character"), ...)

Arguments

x

An object for which a method exists.
classes

Character vector, indicating which classes to dummy code. Defaults to c("factor", "character").
...

Arguments

Value

A data.frame.

Examples

if(requireNamespace("OpenMx", quietly = TRUE)){
mx_dummies(iris[1:5,])
}

Estimate growth mixture models using OpenMx

Description

This function is a wrapper around mx_mixture, adding the default arguments of lavaan::growth() to simplify the specification of growth mixture models. This function is only useful if all the latent variables in the model are growth factors.

Usage

mx_growth_mixture(model, classes = 1L, data = NULL, run = TRUE, ...)

Arguments

model

Syntax for the model; either a character string, or a list of character strings, or a list of mxModel objects. See Details.
classes

A vector of integers, indicating which class solutions to generate. Defaults to 1L. E.g., classes = 1:6, classes = c(1:4, 6:8).
data

The data.frame to be used for model fitting.
run

Logical, whether or not to run the model. If run = TRUE, the function calls mixture_starts and run_mx.
...

Additional arguments, passed to functions.

Value

Returns an OpenMx::mxModel().

References

Van Lissa, C. J., Garnier-Villarreal, M., & Anadria, D. (2023). Recommended Practices in Latent Class Analysis using the Open-Source R-Package tidySEM. Structural Equation Modeling. doi:10.1080/10705511.2023.2250920

Examples

## Not run: 
data("empathy")
df <- empathy[1:6]
mx_growth_mixture(model = "i =~ 1*ec1 + 1*ec2 + 1*ec3 +1*ec4 +1*ec5 +1*ec6
                           s =~ 0*ec1 + 1*ec2 + 2*ec3 +3*ec4 +4*ec5 +5*ec6
                           ec1 ~~ vec1*ec1
                           ec2 ~~ vec2*ec2
                           ec3 ~~ vec3*ec3
                           ec4 ~~ vec4*ec4
                           ec5 ~~ vec5*ec5
                           ec6 ~~ vec6*ec6
                           i ~~ 0*i
                           s ~~ 0*s
                           i ~~ 0*s",
                  classes = 2,
                  data = df) -> res

## End(Not run)

Estimate latent class analyses using OpenMx

Description

This function simplifies the specification of latent class models: models that estimate membership of a categorical latent variable based on binary or ordinal indicators.

Usage

mx_lca(data = NULL, classes = 1L, run = TRUE, ...)

Arguments

data

The data.frame to be used for model fitting.
classes

A vector of integers, indicating which class solutions to generate. Defaults to 1L. E.g., classes = 1:6,
run

Logical, whether or not to run the model. If run = TRUE, the function calls OpenMx::mxTryHardOrdinal().
...

Additional arguments, passed to functions.

Value

Returns an OpenMx::mxModel().

References

Van Lissa, C. J., Garnier-Villarreal, M., & Anadria, D. (2023). Recommended Practices in Latent Class Analysis using the Open-Source R-Package tidySEM. Structural Equation Modeling. doi:10.1080/10705511.2023.2250920

Examples

## Not run: 
df <- data_mix_ordinal
df[1:4] <- lapply(df, ordered)
mx_lca(data = df,
       classes = 2) -> res

## End(Not run)

Estimate mixed data latent class analysis using OpenMx

Description

This function simplifies the specification of latent class models with mixed data types: models that estimate membership of a categorical latent variable based on binary/ordinal and continuous indicators. See Details for more information.

Usage

mx_mixed_lca(
  data = NULL,
  classes = 1L,
  variances = "equal",
  covariances = "zero",
  run = TRUE,
  expand_grid = FALSE,
  ...
)

Arguments

data

The data.frame to be used for model fitting.
classes

A vector of integers, indicating which class solutions to generate. Defaults to 1L. E.g., classes = 1:6,
variances

Character vector. Specifies which variance components to estimate. Defaults to "equal" (constrain variances across classes); the other option is "varying" (estimate variances freely across classes). Each element of this vector refers to one of the models you wish to run.
covariances

Character vector. Specifies which covariance components to estimate. Defaults to "zero" (covariances constrained to zero; this corresponds to an assumption of conditional independence of the indicators); other options are "equal" (covariances between items constrained to be equal across classes), and "varying" (free covariances across classes).
run

Logical, whether or not to run the model. If run = TRUE, the function calls mixture_starts and run_mx.
expand_grid

Logical, whether or not to estimate all possible combinations of the variances and covariances arguments. Defaults to FALSE.
...

Additional arguments, passed to functions.

Details

The procedure is as follows:

  1. Construct a latent profile model for the continuous indicators using mx_profiles().

  2. Construct a latent class model for the categorical indicators using mx_lca().

  3. Combine the models from steps 1. and 2. into one joint model.

If run = TRUE, simulated annealing is used to estimate the mixture model, as explained in Van Lissa, Garnier-Villareal, & Anadria (2023). However, the inclusion of categorical indicators often leads to a large ordinal error, which automatically initiates a final optimization step using OpenMx::mxTryHardOrdinal().

Value

A list of class mixture_list.

References

Van Lissa, C. J., Garnier-Villarreal, M., & Anadria, D. (2023). Recommended Practices in Latent Class Analysis using the Open-Source R-Package tidySEM. Structural Equation Modeling. doi:10.1080/10705511.2023.2250920

Examples

## Not run: 
if(isTRUE(requireNamespace("OpenMx", quietly = TRUE))) {
library(tidySEM)
library(OpenMx)
# Construct dataset with ordinal and categorical indicators
set.seed(1)
n = 200
mns <- c(rep(0, floor(.3*n)), rep(2, ceiling(.7*n)))
df <- rnorm(4*n, mean = rep(mns, 4))
df <- matrix(df, nrow = n)
df <- t(t(df) * c(1, 2, .5, 1))
df <- data.frame(df)
df$X4 <- cut(df$X4, 3, labels = FALSE)
df$X4 <- OpenMx::mxFactor(df$X4, levels = c(1:3))
# Estimate the model
set.seed(1)
res <- mx_mixed_lca(data = df, classes = 2)
}

## End(Not run)

Estimate mixture models using OpenMx

Description

Dynamically creates a batch of mixture models, with intelligent defaults. See Details for more information.

Usage

mx_mixture(model, classes = 1L, data = NULL, run = TRUE, ...)

Arguments

model

Syntax for the model; either a character string, or a list of character strings, or a list of mxModel objects. See Details.
classes

A vector of integers, indicating which class solutions to generate. Defaults to 1L. E.g., classes = 1:6, classes = c(1:4, 6:8).
data

The data.frame to be used for model fitting.
run

Logical, whether or not to run the model. If run = TRUE, the function calls mixture_starts and run_mx.
...

Additional arguments, passed to functions.

Details

Model syntax can be specified in three ways, for ease of use and flexibility:

  1. An atomic character string with lavaan syntax. Within this syntax, the character string {C} is dynamically substituted with the correct class number using lsub, for example to set unique parameter labels for each class, or to specify equality constraints. E.g., x ~ m{C}*1 will be expanded to x ~ m1*1 and x ~ m2*1 when classes = 2. The resulting syntax for each class will be converted to an mxModel using as_ram.

  2. A list of character strings with lavaan syntax. Each item of the list will be converted to a class-specific mxModel using as_ram.

  3. A list of mxModel objects, specified by the user.

Value

Returns an OpenMx::mxModel().

References

Van Lissa, C. J., Garnier-Villarreal, M., & Anadria, D. (2023). Recommended Practices in Latent Class Analysis using the Open-Source R-Package tidySEM. Structural Equation Modeling. doi:10.1080/10705511.2023.2250920

Examples

## Not run: 
# Example 1: Dynamic model generation using {C}
df <- iris[, 1, drop = FALSE]
names(df) <- "x"
mx_mixture(model = "x ~ m{C}*1
                    x ~~ v{C}*x", classes = 1, data = df)
# Example 2: Manually specified class-specific models
df <- iris[1:2]
names(df) <- c("x", "y")
mx_mixture(model = list("y ~ a*x",
                        "y ~ b*x"),
                        meanstructure = TRUE,
                        data = df) -> res

# Example 3: Latent growth model
df <- empathy[1:6]
mx_mixture(model = "i =~ 1*ec1 + 1*ec2 + 1*ec3 +1*ec4 +1*ec5 +1*ec6
                    s =~ 0*ec1 + 1*ec2 + 2*ec3 +3*ec4 +4*ec5 +5*ec6",
                    classes = 2,
                    data = df) -> res

## End(Not run)

Estimate latent profile analyses using OpenMx

Description

This function is a wrapper around mx_mixture to simplify the specification of latent profile models, also known as finite mixture models. By default, the function estimates free means for all observed variables across classes.

Usage

mx_profiles(
  data = NULL,
  classes = 1L,
  variances = "equal",
  covariances = "zero",
  run = TRUE,
  expand_grid = FALSE,
  ...
)

Arguments

data

The data.frame to be used for model fitting.
classes

A vector of integers, indicating which class solutions to generate. Defaults to 1L. E.g., classes = 1:6,
variances

Character vector. Specifies which variance components to estimate. Defaults to "equal" (constrain variances across classes); the other option is "varying" (estimate variances freely across classes). Each element of this vector refers to one of the models you wish to run.
covariances

Character vector. Specifies which covariance components to estimate. Defaults to "zero" (covariances constrained to zero; this corresponds to an assumption of conditional independence of the indicators); other options are "equal" (covariances between items constrained to be equal across classes), and "varying" (free covariances across classes).
run

Logical, whether or not to run the model. If run = TRUE, the function calls mixture_starts and run_mx.
expand_grid

Logical, whether or not to estimate all possible combinations of the variances and covariances arguments. Defaults to FALSE.
...

Additional arguments, passed to functions.

Value

Returns an OpenMx::mxModel().

References

Van Lissa, C. J., Garnier-Villarreal, M., & Anadria, D. (2023). Recommended Practices in Latent Class Analysis using the Open-Source R-Package tidySEM. Structural Equation Modeling. doi:10.1080/10705511.2023.2250920

Examples

## Not run: 
data("empathy")
df <- empathy[1:6]
mx_profiles(data = df,
            classes = 2) -> res

## End(Not run)

Switch LCA Class Labels

Description

The order of class labels in LCA is arbitrary. This can result in a phenomenon called 'label switching', where classes change places between replications of an analysis. This function attempts to re-order classes in a substantively meaningful way.

Usage

mx_switch_labels(x, param = "weights", decreasing = TRUE, order = NULL)

Arguments

x

An MxModel estimated by mx_mixture or one of its wrappers.
param

The parameter by which to order the classes, defaults to 'weights', which orders classes based on their sample size.
decreasing

logical. Should the classes be sorted in increasing or decreasing order? Default: TRUE
order

Integer, indicating the ordering of classes. Ignored when NULL (default).

Details

The argument param can accept either:

  1. The default string "weights", in which classes are sorted by size.

  2. The OpenMx matrix indicator for a specific model parameter; e.g., the first mean is indicated by "M[1,1]". These indicators can be viewed by running table_results(x, columns = NULL).

  3. The letter indicating an OpenMx model matrix, e.g., "M" refers to the matrix of means. To account for all elements of the matrix, Euclidean distance to the origin is used.

Value

An MxModel with "tidySEM" attribute: "mixture"

Examples

## Not run: 
df <- iris[1:4]
names(df) <- letters[1:4]
res1 <- mx_profiles(data = df, classes = 2)
mx_switch_labels(res1, decreasing = FALSE)

## End(Not run)

Extract nodes from sem_graph

Description

Provides access to the nodes element of a sem_graph object. This can be used to return or assign to the nodes element.

Usage

nodes(x)

nodes(x) <- value

Arguments

x

Object of class sem_graph.
value

A valid value for nodes(x).

Value

data.frame

Examples

edg <- data.frame(from = "x", to = "y")
p <- prepare_graph(edges = edg, layout = get_layout("x", "y", rows = 1))
nodes(p)

Concatenate Strings while omitting NA

Description

Concatenate vectors after converting to character and removing NA values. See paste.

Usage

paste2(..., sep = " ", collapse = NULL, na.rm = TRUE)

Arguments

...

one or more R objects, to be converted to character vectors.
sep

a character string to separate the terms. Not NA_character_.
collapse

an optional character string to separate the results. Not NA_character_.
na.rm

logical, indicating whether NA values should be stripped before concatenation. Not NA_character_.

Value

A character vector of the concatenated values.

Examples

paste2("word", NA)

Simulated depression data

Description

This simulated dataset, based on work in progress by Plas and colleagues, contains six repeated measurements of the Depression subscale of the Symptom Checklist-90 (SCL-90).

Usage

data(plas_depression)

Format

A data frame with 978 rows and 6 variables.

Details

These data are inspired by the Prospection in Stress-related Military Research (PRISMO) study, which examined of psychological problems after deployment in more than 1,000 Dutch military personnel who were deployed to Afghanistan, from 2005-2019.

scl.1 integer Sum score of SCL90 depression pre-deployment
scl.2 integer Sum score of SCL90 depression 1 month post-deployment
scl.3 integer Sum score of SCL90 depression 6 months post-deployment
scl.4 integer Sum score of SCL90 depression 1 year post-deployment
scl.5 integer Sum score of SCL90 depression 2 years post-deployment
scl.6 integer Sum score of SCL90 depression 10 years post-deployment

References

van der Wal, S. J., Gorter, R., Reijnen, A., Geuze, E., & Vermetten, E. (2019). Cohort profile: The Prospective Research In Stress-Related Military Operations (PRISMO) study in the Dutch Armed Forces. BMJ Open, 9(3), e026670. doi:10.1136/bmjopen-2018-026670

Create correlation plots for a mixture model

Description

Creates a faceted plot of two-dimensional correlation plots and unidimensional density plots for a single mixture model.

Usage

plot_bivariate(
  x,
  variables = NULL,
  sd = TRUE,
  cors = TRUE,
  rawdata = TRUE,
  bw = FALSE,
  alpha_range = c(0, 0.1),
  return_list = FALSE,
  ...
)

Arguments

x

An object for which a method exists.
variables

Which variables to plot. If NULL, plots all variables that are present in the model.
sd

Logical. Whether to show the estimated standard deviations as lines emanating from the cluster centroid.
cors

Logical. Whether to show the estimated correlation (standardized covariance) as ellipses surrounding the cluster centroid.
rawdata

Logical. Whether to plot raw data, weighted by posterior class probability.
bw

Logical. Whether to make a black and white plot (for print) or a color plot. Defaults to FALSE, because these density plots are hard to read in black and white.
alpha_range

Numeric vector (0-1). Sets the transparency of geom_density and geom_point.
return_list

Logical. Whether to return a list of ggplot objects, or just the final plot. Defaults to FALSE.
...

Additional arguments.

Value

An object of class 'ggplot'.

Author(s)

Caspar J. van Lissa

Examples

if(requireNamespace("OpenMx", quietly = TRUE)){
library(OpenMx)
iris_sample <- iris[c(1:5, 145:150), c("Sepal.Length", "Sepal.Width")]
names(iris_sample) <- c("x", "y")
res <- mx_profiles(iris_sample, classes = 2)
plot_bivariate(res, rawdata = FALSE)
}

Create density plots for mixture models

Description

Creates mixture density plots. For each variable, a Total density plot will be shown, along with separate density plots for each latent class, where cases are weighted by the posterior probability of being assigned to that class.

Usage

plot_density(
  x,
  variables = NULL,
  bw = FALSE,
  conditional = FALSE,
  alpha = 0.2,
  facet_labels = NULL
)

Arguments

x

Object for which a method exists.
variables

Which variables to plot. If NULL, plots all variables that are present in all models.
bw

Logical. Whether to make a black and white plot (for print) or a color plot. Defaults to FALSE, because these density plots are hard to read in black and white.
conditional

Logical. Whether to show a conditional density plot (surface area is divided among the latent classes), or a classic density plot (surface area of the total density plot is equal to one, and is divided among the classes).
alpha

Numeric (0-1). Only used when bw and conditional are FALSE. Sets the transparency of geom_density, so that classes with a small number of cases remain visible.
facet_labels

Named character vector, the names of which should correspond to the facet labels one wishes to rename, and the values of which provide new names for these facets. For example, to rename variables, in the example with the 'iris' data below, one could specify: facet_labels = c("Pet_leng" = "Petal length").

Value

An object of class 'ggplot'.

Author(s)

Caspar J. van Lissa

Examples

## Not run: 
dat <-
  iris[, c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")]
names(dat) <- paste0("x", 1:4)
res <- mx_profiles(dat, 1:3)
plot_density(res)

## End(Not run)

Plot categorical variable probabilities

Description

Creates a bar chart of categorical variable probabilities with bars reflecting the probability of category membership for each category of the observed variable.

Usage

plot_prob(
  x,
  variables = NULL,
  bars = c("Variable", "group", "class"),
  facet = c("group", "class", "Variable"),
  bw = FALSE,
  ...
)

Arguments

x

An object for which a method exists
variables

A character vectors with the names of the variables to be plotted (optional).
bars

Atomic character, indicating what separate bars represent. One of c("Variable", "group", "class").
facet

Atomic character, indicating what separate facets represent. One of c("group", "class", "Variable").
bw

Logical. Should the plot be black and white (for print), or color?
...

Arguments passed to and from other functions.

Value

An object of class 'ggplot'.

Author(s)

Caspar J. van Lissa

Examples

df_plot <- data.frame(Variable = rep(c("u1", "u2"), each = 3),
Category = rep(1:3, 2),
Probability = c(0.3381302605812, 0.148395173612088, 0.513474565806711,
0.472337708760608, 0.118484201496432, 0.40917808974296))
plot_prob(df_plot)

Create latent profile plots

Description

Creates a profile plot (ribbon plot) according to best practices, focusing on the visualization of classification uncertainty by showing:

  1. Bars reflecting a confidence interval for the class centroids

  2. Boxes reflecting the standard deviations within each class; a box encompasses +/- 64 percent of the observations in a normal distribution

  3. Raw data, whose transparency is weighted by the posterior class probability, such that each observation is most clearly visible for the class it is most likely to be a member of.

Usage

plot_profiles(
  x,
  variables = NULL,
  ci = 0.95,
  sd = TRUE,
  add_line = FALSE,
  rawdata = TRUE,
  bw = FALSE,
  alpha_range = c(0, 0.1),
  ...
)

## Default S3 method:
plot_profiles(
  x,
  variables = NULL,
  ci = 0.95,
  sd = TRUE,
  add_line = FALSE,
  rawdata = TRUE,
  bw = FALSE,
  alpha_range = c(0, 0.1),
  ...
)

Arguments

x

An object containing the results of a mixture model analysis.
variables

A character vectors with the names of the variables to be plotted (optional).
ci

Numeric. What confidence interval should the error bars span? Defaults to a 95 percent confidence interval. Set to NULL to remove error bars.
sd

Logical. Whether to display a box encompassing +/- 1SD Defaults to TRUE.
add_line

Logical. Whether to display a line, connecting cluster centroids belonging to the same latent class. Defaults to FALSE, as it is not recommended to imply connectivity between the different variables on the X-axis.
rawdata

Should raw data be plotted in the background? Setting this to TRUE might result in long plotting times.
bw

Logical. Should the plot be black and white (for print), or color?
alpha_range

The minimum and maximum values of alpha (transparency) for the raw data. Minimum should be 0; lower maximum values of alpha can help reduce overplotting.
...

Arguments passed to and from other functions.

Value

An object of class 'ggplot'.

Author(s)

Caspar J. van Lissa

Examples

df_plot <- data.frame(Variable = "x1",
Class = "class1",
Classes = 1,
Model = "equal var 1",
Value = 3.48571428571429,
se = 0.426092805342181,
Value.Variances = 3.81265306156537,
se.Variances = 1.17660769119959)
plot_profiles(list(df_plot = df_plot, df_raw = NULL),
ci = NULL, sd = FALSE, add_line = FALSE,
rawdata = FALSE, bw = FALSE)

Predictive Model Comparison

Description

Compare (non-nested) models estimated on the same data using model-simulated data.

Usage

pmc(x, ..., reps = 20, ci = 0.95, FUN = NULL)

pmc_srmr(x, ..., reps = 20, ci = 0.95)

Arguments

x

An object for which a method exists.
...

Additional arguments.
reps

Number of data sets to simulate, Default: 100.
ci

Confidence interval coverage, Default: 0.95.
FUN

Function used to compare the real data (referred to as x) to the model-implied data (referred to as y). Defaults to NULL, which uses chi_sq() for models with all ordinal variables, and srmr() otherwise, treating all variables as continuous.

Value

A data.frame.

Predict Class Membership

Description

Predict class membership for latent class analyses conducted with tidySEM in OpenMx.

Usage

predict_class(object, newdata, ...)

Arguments

object

An MxModel object with attribute tidySEM = "mixture".
newdata

A data.frame with the same column names and data types as the one used to estimate the model.
...

Other arguments passed to predict.MxModel().

Value

A data.frame.

Prepare graph data

Description

Prepare an object of class sem_graph, containing data objects that can be rendered into a SEM graph. Using this function allows users to manually change the default graph specification before plotting it. Input consists of (at least) a layout, and either nodes and edges, or a model object.

Usage

## S3 method for class 'dagitty'
prepare_graph(model, rect_height = 0.5, rect_width = 0.5, ...)

prepare_graph(...)

## Default S3 method:
prepare_graph(
  edges = NULL,
  layout = NULL,
  nodes = NULL,
  rect_width = 1.2,
  rect_height = 0.8,
  ellipses_width = 1,
  ellipses_height = 1,
  variance_diameter = 0.8,
  spacing_x = 2,
  spacing_y = 2,
  text_size = 4,
  curvature = 60,
  angle = NULL,
  fix_coord = FALSE,
  ...
)

## S3 method for class 'lavaan'
prepare_graph(model, edges = NULL, layout = NULL, nodes = NULL, ...)

## S3 method for class 'MxModel'
prepare_graph(model, ...)

## S3 method for class 'character'
prepare_graph(...)

## S3 method for class 'mplus.model'
prepare_graph(model, edges = NULL, layout = NULL, nodes = NULL, ...)

## S3 method for class 'mplusObject'
prepare_graph(model, edges = NULL, layout = NULL, nodes = NULL, ...)

Arguments

model

Instead of the edges argument, it is also possible to use the model argument and pass an object for which a method exists (e.g., mplus.model or lavaan).
rect_height

Height of rectangles (used to display observed variables), Default: 0.8
rect_width

Width of rectangles (used to display observed variables), Default: 1.2
...

Additional arguments passed to and from functions.
edges

Object of class 'tidy_edges', or a data.frame with (at least) the columns c("from", "to"), and optionally, c("arrow", "label", "connect_from", "connect_to", "curvature").
layout

A matrix (or data.frame) that describes the layout; see get_layout.
nodes

Optional, object of class 'tidy_nodes', created with the get_nodes function, or a data.frame with (at least) the column c("name"), and optionally, c("shape", "label"). If set to NULL (the default), nodes are inferred from the layout and edges arguments.
ellipses_width

Width of ellipses (used to display latent variables), Default: 1
ellipses_height

Height of ellipses (used to display latent variables), Default: 1
variance_diameter

Diameter of variance circles, Default: .8
spacing_x

Spacing between columns of the graph, Default: 1
spacing_y

Spacing between rows of the graph, Default: 1
text_size

Point size of text, Default: 4
curvature

Curvature of curved edges. The curve is a circle segment originating in a point that forms a triangle with the two connected points, with angles at the two connected points equal to curvature. To flip a curved edge, use a negative value for curvature. Default: 60
angle

Angle used to connect nodes by the top and bottom. Defaults to NULL, which means Euclidean distance is used to determine the shortest distance between node sides. A numeric value between 0-180 can be provided, where 0 means that only nodes with the same x-coordinates are connected top-to-bottom, and 180 means that all nodes are connected top-to-bottom.
fix_coord

Whether or not to fix the aspect ratio of the graph. Does not work with multi-group or multilevel models. Default: FALSE.

Value

Object of class 'sem_graph'

Examples

library(lavaan)
res <- sem("dist ~ speed", cars)
prepare_graph(res)

Estimate an Auxiliary Model using the Pseudo-Class Method

Description

Estimate an auxiliary model based on multiple datasets, randomly drawing latent class values based on the estimated probability of belonging to each class. The pseudo class variable is treated as an observed variable within each dataset, and results are pooled across datasets to account for classification uncertainty.

Usage

pseudo_class(x, model, df_complete = NULL, ...)

## S3 method for class 'MxModel'
pseudo_class(x, model, df_complete = NULL, data = NULL, m = 20, ...)

Arguments

x

An object for which a method exists, typically either a fitted mx_mixture model or class_draws object.
model

Either an expression to execute on every generated dataset, or a function that performs the analysis on every generated dataset, or a character that can be interpreted as a structural equation model using as_ram. This model can explicitly refer to data.
df_complete

Integer. Degrees of freedom of the complete-data analysis.
...

Additional arguments passed to other functions.
data

A data.frame on which the auxiliary model can be evaluated. Note that the row order must be identical to that of the data used to fit x, as these data will be augmented with a pseudo-class draw for that specific individual.
m

Integer. Number of datasets to generate. Default is 20.

Value

An object of class data.frame containing pooled estimates.

References

Pseudo-class technique: Wang C-P, Brown CH, Bandeen-Roche K (2005). Residual Diagnostics for Growth Mixture Models: Examining the Impact of a Preventive Intervention on Multiple Trajectories of Aggressive Behavior. Journal of the American Statistical Association 100(3):1054-1076. doi:10.1198/016214505000000501

Pooling results across samples: Van Buuren, S. 2018. Flexible Imputation of Missing Data. Second Edition. Boca Raton, FL: Chapman & Hall/CRC. doi:10.1201/9780429492259

Examples

if(requireNamespace("OpenMx", quietly = TRUE)){
library(OpenMx)
set.seed(2)
dat <- iris[c(1:5, 50:55, 100:105), 1:4]
colnames(dat) <- c("SL", "SW", "PL", "PW")
fit <- suppressWarnings(mx_profiles(data = dat, classes = 3))

pct_mx <- pseudo_class(x = fit,
                       model = "SL ~ class",
                       data = dat,
                       m = 2)

pct_lm <- pseudo_class(x = fit,
             model = lm( SL ~ class, data = data),
             data = dat,
             m = 2)


pcte <- pseudo_class(x = fit,
                     model = lm(SL ~ class, data = data),
                     data = dat,
                     m = 2)

pct_func <- pseudo_class(x = fit,
                         model = function(data){lm(SL ~ class, data = data)},
                         data = dat,
                         m = 2)


}

Run as lavaan model

Description

This convenience function runs objects for which a method exists using lavaan. It is intended for use with tidySEM, and passes the $syntax and $data elements of a tidy_sem object on to lavaan.

Usage

run_lavaan(x, ...)

Arguments

x

An object for which a method exists.
...

Parameters passed on to other functions.

Value

Returns a lavaan object.

Examples

df <- iris[1:3]
names(df) <- paste0("X_", 1:3)
run_lavaan(measurement(tidy_sem(df), meanstructure = TRUE))

Run as OpenMx model with sensible defaults

Description

This convenience function runs objects for which a method exists using OpenMx, with sensible defaults. It is intended for use with tidySEM. For instance, it will convert a tidySEM object to a mxModel and run it, and it will try to ensure convergence for mixture models created using mx_mixture. Knowledgeable users may want to run models manually.

Usage

run_mx(x, ...)

Arguments

x

An object for which a method exists.
...

Parameters passed on to other functions.

Value

Returns an mxModel with free parameters updated to their final values.

Examples

df <- iris[1:3]
names(df) <- paste0("X_", 1:3)
run_mx(measurement(tidy_sem(df), meanstructure = TRUE))

Calculate skew and kurtosis

Description

Calculate skew and kurtosis, standard errors for both, and the estimates divided by two times the standard error. If this latter quantity exceeds an absolute value of 1, the skew/kurtosis is significant. With very large sample sizes, significant skew/kurtosis is common.

Usage

skew_kurtosis(x, verbose = FALSE, se = FALSE, ...)

Arguments

x

An object for which a method exists.
verbose

Logical. Whether or not to print messages to the console, Default: FALSE
se

Whether or not to return the standard errors, Default: FALSE
...

Additional arguments to pass to and from functions.

Value

A matrix of skew and kurtosis statistics for x.

Examples

skew_kurtosis(datasets::anscombe)

Calculate Standardized Root Mean Residual

Description

Given two datasets, computes the correlation matrix for both, and then calculates the standardized root mean residual difference between these two correlation matrices.

Usage

srmr(x, y)

Arguments

x

An object for which a method of stats::cor() exists (e.g, data.frame).
y

An object for which a method of stats::cor() exists (e.g, data.frame).

Value

numeric

Examples

## Not run: 
if(interactive()){
 srmr(iris[1:2], iris[3:4])
}

## End(Not run)

Extract syntax from tidy_sem

Description

Provides access to the syntax element of a tidy_sem object. This can be used to return or assign to the syntax element.

Usage

syntax(x)

syntax(x) <- value

Arguments

x

Object of class tidy_sem.
value

A valid value for syntax(x).

Value

data.frame

Examples

dict <- tidy_sem(iris, split = "\\.")
dict <- add_paths(dict, Sepal.Width ~~ Sepal.Length)
syntax(dict)

Extract correlation tables

Description

Extracts a publication-ready covariance or correlation matrix from an object for which a method exists.

Usage

table_cors(x, value_column = "est_sig_std", digits = 2, ...)

Arguments

x

An object for which a method exists.
value_column

Character. Name of the column to use to propagate the matrix. Defaults to "est_sig_std", the standardized estimate with significance asterisks.
digits

Number of digits to round to when formatting values.
...

Additional arguments passed to and from methods.

Value

A Matrix or a list of matrices (in case there are between/within correlation matrices).

Author(s)

Caspar J. van Lissa

Examples

library(lavaan)
HS.model <- '  visual =~ x1 + x2 + x3
               textual =~ x4 + x5 + x6
               speed   =~ x7 + x8 + x9 '
fit <- cfa(HS.model,
           data = HolzingerSwineford1939,
           group = "school")
table_cors(fit)

Print model fit table formatted for publication

Description

Takes a model object, extracts model fit information, and formats it as a publication-ready table.

Usage

table_fit(x, ...)

Arguments

x

A model object for which a method exists.
...

Arguments passed to other functions.

Value

A data.frame of formatted results.

Author(s)

Caspar J. van Lissa

See Also

Other Reporting tools: conf_int(), est_sig(), table_prob(), table_results()

Examples

library(lavaan)
HS.model <- '  visual =~ x1 + x2 + x3
               textual =~ x4 + x5 + x6
               speed   =~ x7 + x8 + x9 '
fit <- cfa(HS.model,
           data = HolzingerSwineford1939,
           group = "school")
table_fit(fit)

Results table in probability scale

Description

Returns thresholds for ordinal dependent variables in probability scale.

Usage

table_prob(x, ...)

Arguments

x

An object for which a method exists.
...

Arguments passed to other functions.

Value

A data.frame with results in probability scale.

See Also

Other Reporting tools: conf_int(), est_sig(), table_fit(), table_results()

Examples

## Not run: 
df <- data_mix_ordinal
df[1:4] <- lapply(df, ordered)
mx_lca(data = df,
       classes = 2) -> res

## End(Not run)

Print results table formatted for publication

Description

Takes a model object, and formats it as a publication-ready table.

Usage

table_results(
  x,
  columns = c("label", "est_sig", "se", "pval", "confint", "group", "level"),
  digits = 2,
  format_numeric = TRUE,
  ...
)

Arguments

x

A model object for which a method exists.
columns

A character vector of columns to retain from the results section. If this is set to NULL, all available columns are returned. Defaults to c("label", "est_sig", "se", "pval", "confint", "group", "level"). These correspond to 1) the parameter label, 2) estimate column with significance asterisks appended (* <.05, ** < .01, *** < .001); 3) standard error, 4) p-value, 5) a formatted confidence interval, 6) grouping variable (if available), 7) level variable for multilevel models, if available.
digits

Number of digits to round to when formatting numeric columns.
format_numeric

Logical, indicating whether or not to format numeric columns. Defaults to TRUE.
...

Logical expressions used to filter the rows of results returned.

Value

A data.frame of formatted results.

Author(s)

Caspar J. van Lissa

See Also

Other Reporting tools: conf_int(), est_sig(), table_fit(), table_prob()

Examples

library(lavaan)
HS.model <- '  visual =~ x1 + x2 + x3
               textual =~ x4 + x5 + x6
               speed   =~ x7 + x8 + x9 '
fit <- cfa(HS.model,
           data = HolzingerSwineford1939,
           group = "school")
table_results(fit)

Create a tidy_sem object

Description

Create an object of class tidy_sem, which has the following elements:

  • dictionary An overview of the variables in the tidy_sem object, and their assignment to scale/latent variables.

  • data Optionally, the data.frame containing the data referenced in $dictionary.

  • syntax Optionally, syntax defining a SEM-model by reference to the variables contained in $data.

Usage

tidy_sem(x, split = "_")

Arguments

x

An object for which a method exists, e.g., a vector of variable names, or a data.frame.
split

Character. Defining the regular expression used by strsplit to separate variable names into 1) the name of the scale/construct and 2) the number (or name) of the item.

Details

When tidy_sem is called on a character string or data.frame, it attempts to assign variables to superordinate scale/latent variables based on the variable name and the splitting character defined in the split argument. Thus, the function will assign the variable "scale_01" to a scale/latent variable called "scale" when split = "_". Alternatively, if the variable name is "construct.1", the split character "\." separates the "construct" name from item number "1". The character "." is escaped with a double backslash, because it is a special character in regular expressions.

Value

An object of class "tidy_sem"

Author(s)

Caspar J. van Lissa

Examples

tidy_sem(c("bfi_1", "bfi_2", "bfi_3", "bfi_4", "bfi_5",
"macqj_1", "macqj_2", "macqj_3", "macqj_4", "macqj_5", "macqj_6",
"macqj_7", "macqj_8", "macqj_9", "macqj_10", "macqj_11",
"macqj_12", "macqj_13", "macqj_14", "macqj_15", "macqj_16",
"macqj_17", "macqj_18", "macqj_19", "macqj_20", "macqj_21",
"macqr_1", "macqr_2", "macqr_3", "macqr_4", "macqr_5", "macqr_6",
"macqr_7", "macqr_8", "macqr_9", "macqr_10", "macqr_11",
"macqr_12", "macqr_13", "macqr_14", "macqr_15", "macqr_16",
"macqr_17", "macqr_18", "macqr_19", "macqr_20", "macqr_21", "sex"))
suppressMessages(tidy_sem(c("bfi_1", "bfi_2", "bfi_3", "bfi_4", "bfi_5",
"mac_q_j_1", "mac_q_j_2", "mac_q_j_3", "mac_q_j_4", "mac_q_j_5", "mac_q_j_6",
"mac_q_j_7", "mac_q_j_8", "mac_q_j_9", "mac_q_j_10", "mac_q_j_11",
"mac_q_j_12", "mac_q_j_13", "mac_q_j_14", "mac_q_j_15", "mac_q_j_16",
"mac_q_j_17", "mac_q_j_18", "mac_q_j_19", "mac_q_j_20", "mac_q_j_21",
"mac_q_r_1", "mac_q_r_2", "mac_q_r_3", "mac_q_r_4", "mac_q_r_5", "mac_q_r_6",
"mac_q_r_7", "mac_q_r_8", "mac_q_r_9", "mac_q_r_10", "mac_q_r_11",
"mac_q_r_12", "mac_q_r_13", "mac_q_r_14", "mac_q_r_15", "mac_q_r_16",
"mac_q_r_17", "mac_q_r_18", "mac_q_r_19", "mac_q_r_20", "mac_q_r_21")))

Wald Test for Linear Hypotheses

Description

This function is a wrapper for the function car::linearHypothesis(), but which uses the bain::bain() syntax to parse equality constrained hypotheses.

Usage

wald_test(x, hypothesis, ...)

Arguments

x

An object for which a method exists.
hypothesis

A character string with equality constrained hypotheses, specified according to the bain::bain() syntax.
...

Additional arguments passed to car::linearHypothesis().

Value

A data.frame of class wald_test.

See Also

linearHypothesis

Examples

mod <- lm(Sepal.Length ~ Sepal.Width, data = iris)
coef(mod)
wald_test(mod, "Sepal.Width = 0")

Caregiver Compass Data

Description

These simulated data are based on a study by Dijenborgh, Swildens, and Zegwaard on different types of caregivers among those providing informal care to outpatients receiving mental healthcare.

Usage

data(zegwaard_carecompass)

Format

A data frame with 513 rows and 10 variables.

Details

burdened numeric How strongly is the caregiver's life affected by their responsibilities? Scale score, based on 15 items with Likert-type response options. Example: "I never feel free of responsibilities"
trapped numeric Caregiver's cognitions regarding freedom of choice. Scale score, based on 3 items with Likert-type response options. Example: "I feel trapped by the affliction of my charge"
negaffect numeric Different types of negative emotions experienced by the caregiver. Scale score, based on 9 items with Likert-type response options. Example: "I feel angry in the relationship with my charge"
loneliness numeric Caregiver's perceived loneliness. Scale score, based on 11 items with Likert-type response options. Example: "I miss having people around"
sex factor Caregiver sex
sexpatient factor Sex of the patient
cohabiting factor Whether or not the caregiver cohabits with the patient
distance numeric Travel time in minutes for the caregiver to reach the patient
freqvisit ordered Ordinal variable, indicating frequency of visits
relationship factor Type of relationship of patient with caregiver