View source: R/multilevel.cfa.R
multilevel.cfa | R Documentation |
This function is a wrapper function for conducting multilevel confirmatory factor
analysis to investigate four types of constructs, i.e., within-cluster constructs,
shared cluster-level constructs, configural cluster constructs, and simultaneous
shared and configural cluster constructs by calling the cfa
function in
the R package lavaan.
multilevel.cfa(x, cluster, model = NULL, rescov = NULL,
model.w = NULL, model.b = NULL, rescov.w = NULL, rescov.b = NULL,
const = c("within", "shared", "config", "shareconf"),
fix.resid = NULL, ident = c("marker", "var", "effect"),
ls.fit = TRUE, estimator = c("ML", "MLR"),
optim.method = c("nlminb", "em"), missing = c("listwise", "fiml"),
print = c("all", "summary", "coverage", "descript", "fit", "est",
"modind", "resid"),
mod.minval = 6.63, resid.minval = 0.1, digits = 3, p.digits = 3,
as.na = NULL, write = NULL, check = TRUE, output = TRUE)
x |
a matrix or data frame. If |
cluster |
either a character string indicating the variable name of the cluster variable in 'x' or a vector representing the nested grouping structure (i.e., group or cluster variable). |
model |
a character vector for specifying the same factor structure
with one factor at the Within and Between Level, or a list
of character vectors for specifying the same measurement
model with more than one factor at the Within and Between
Level, e.g., |
rescov |
a character vector or a list of character vectors for specifying
residual covariances at the Within level, e.g. |
model.w |
a character vector specifying a measurement model with one factor at the Within level, or a list of character vectors for specifying a measurement model with more than one factor at the Within level. |
model.b |
a character vector specifying a measurement model with one factor at the Between level, or a list of character vectors for specifying a measurement model with more than one factor at the Between level. |
rescov.w |
a character vector or a list of character vectors for specifying residual covariances at the Within level. |
rescov.b |
a character vector or a list of character vectors for specifying residual covariances at the Between level. |
const |
a character string indicating the type of construct(s), i.e.,
|
fix.resid |
a character vector for specifying residual variances to be
fixed at 0 at the Between level, e.g., |
ident |
a character string indicating the method used for identifying
and scaling latent variables, i.e., |
ls.fit |
logical: if |
estimator |
a character string indicating the estimator to be used:
|
optim.method |
a character string indicating the optimizer, i.e., |
missing |
a character string indicating how to deal with missing data,
i.e., |
print |
a character string or character vector indicating which
results to show on the console, i.e. |
mod.minval |
numeric value to filter modification indices and only
show modifications with a modification index value equal
or higher than this minimum value. By default, modification
indices equal or higher 6.63 are printed. Note that a
modification index value of 6.63 is equivalent to a
significance level of |
resid.minval |
numeric value indicating the minimum absolute residual correlation coefficients and standardized means to highlight in boldface. By default, absolute residual correlation coefficients and standardized means equal or higher 0.1 are highlighted. Note that highlighting can be disabled by setting the minimum value to 1. |
digits |
an integer value indicating the number of decimal places
to be used for displaying results. Note that loglikelihood,
information criteria and chi-square test statistic is
printed with |
p.digits |
an integer value indicating the number of decimal places to be used for displaying the p-value. |
as.na |
a numeric vector indicating user-defined missing values,
i.e. these values are converted to |
write |
a character string for writing the results into a Excel
file naming a file with or without file extension '.xlsx',
e.g., |
check |
logical: if |
output |
logical: if |
Returns an object of class misty.object
, which is a list with following
entries:
call |
function call |
type |
type of analysis |
data |
matrix or data frame specified in |
args |
specification of function arguments |
model |
specified model |
model.fit |
fitted lavaan object ( |
check |
results of the convergence and model identification check |
result |
list with result tables, i.e., |
The function uses the functions cfa
, lavInspect
, lavTech
,
modindices
, parameterEstimates
, and standardizedsolution
provided in the R package lavaan by Yves Rosseel (2012).
Takuya Yanagida takuya.yanagida@univie.ac.at
Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48, 1-36. https://doi.org/10.18637/jss.v048.i02
item.cfa
, multilevel.fit
, multilevel.invar
,
multilevel.omega
, multilevel.cor
, multilevel.descript
## Not run:
# Load data set "Demo.twolevel" in the lavaan package
data("Demo.twolevel", package = "lavaan")
#---------------------------
# Model specification using 'x' for a one-factor model
# with the same factor structure with one factor at the Within and Between Level
#..........
# Cluster variable specification
# Cluster variable 'cluster' in 'x'
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4", "cluster")], cluster = "cluster")
# Cluster variable 'cluster' not in 'x'
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster)
#..........
# Type of construct
# Within-cluster constructs
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
const = "within")
# Shared cluster-level construct
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
const = "shared")
# Configural cluster construct (default)
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
const = "config")
# Simultaneous shared and configural cluster construct
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
const = "shareconf")
#..........
# Residual covariances at the Within level
# Residual covariance between 'y1' and 'y3'
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
rescov = c("y1", "y3"))
# Residual covariance between 'y1' and 'y3', and 'y2' and 'y4'
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
rescov = list(c("y1", "y3"), c("y2", "y4")))
#..........
# Residual variances at the Between level fixed at 0
# All residual variances fixed at 0
# i.e., strong factorial invariance across clusters
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
fix.resid = "all")
# Fesidual variances of 'y1', 'y2', and 'y4' fixed at 0
# i.e., partial strong factorial invariance across clusters
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
fix.resid = c("y1", "y2", "y4"))
#..........
# Print all results
# Set minimum value for modification indices at 1
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
print = "all", min.value = 1)
#..........
# lavaan model and summary of the estimated model
mod <- multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
output = FALSE)
# lavaan model syntax
cat(mod$model)
# Fitted lavaan object
lavaan::summary(mod$model.fit, standardized = TRUE, fit.measures = TRUE)
#..........
# Write results
# Assign results into an object and write results into an Excel file
mod <- multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
print = "all", output = FALSE)
# Write results into an Excel file
write.result(mod, "Multilevel_CFA.xlsx")
# Estimate model and write results into an Excel file
multilevel.cfa(Demo.twolevel[, c("y1", "y2", "y3", "y4")], cluster = Demo.twolevel$cluster,
print = "all", write = "Multilevel_CFA.xlsx")
#---------------------------
# Model specification using 'model' for one or multiple factor model
# with the same factor structure at the Within and Between Level
# One-factor model
multilevel.cfa(Demo.twolevel, cluster = "cluster", model = c("y1", "y2", "y3", "y4"))
# Two-factor model
multilevel.cfa(Demo.twolevel, cluster = "cluster",
model = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")))
# Two-factor model with user-specified labels for the factors
multilevel.cfa(Demo.twolevel, cluster = "cluster",
model = list(factor1 = c("y1", "y2", "y3"), factor2 = c("y4", "y5", "y6")))
#..........
# Type of construct
# Within-cluster constructs
multilevel.cfa(Demo.twolevel, cluster = "cluster", const = "within",
model = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")))
# Shared cluster-level construct
multilevel.cfa(Demo.twolevel, cluster = "cluster", const = "shared",
model = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")))
# Configural cluster construct (default)
multilevel.cfa(Demo.twolevel, cluster = "cluster", const = "config",
model = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")))
# Simultaneous shared and configural cluster construct
multilevel.cfa(Demo.twolevel, cluster = "cluster", const = "shareconf",
model = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")))
#..........
# Residual covariances at the Within level
# Residual covariance between 'y1' and 'y4' at the Within level
multilevel.cfa(Demo.twolevel, cluster = "cluster",
model = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")),
rescov = c("y1", "y4"))
# Fix all residual variances at 0
# i.e., strong factorial invariance across clusters
multilevel.cfa(Demo.twolevel, cluster = "cluster",
model = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")),
fix.resid = "all")
#---------------------------
# Model specification using 'model.w' and 'model.b' for one or multiple factor model
# with different factor structure at the Within and Between Level
# Two-factor model at the Within level and one-factor model at the Between level
multilevel.cfa(Demo.twolevel, cluster = "cluster",
model.w = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")),
model.b = c("y1", "y2", "y3", "y4", "y5", "y6"))
# Residual covariance between 'y1' and 'y4' at the Within level
# Residual covariance between 'y5' and 'y6' at the Between level
multilevel.cfa(Demo.twolevel, cluster = "cluster",
model.w = list(c("y1", "y2", "y3"), c("y4", "y5", "y6")),
model.b = c("y1", "y2", "y3", "y4", "y5", "y6"),
rescov.w = c("y1", "y4"),
rescov.b = c("y5", "y6"))
## End(Not run)
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