compareLavaan: Prepare a table to compare fit measures of confirmatory...
In semTable: Structural Equation Modeling Tables
Description
Usage
Arguments
Details
Value
Author(s)
Examples
View source: R/modelcomparison.R
Description
If the parameter nesting is not specified, then this
function attempts to discern which models are nested and they are
ordered accordingly. The user can override that by specifing a
nesting structure. This uses a new notation to represent nesting
of models. See the parameter nesting. The syntax uses the
symbols ">" and "+" in an obvious way to indicate that one model
is the superset or on the same level as another. If the
Usage
1
2
3
Arguments
models
list of lavaan cfa or sem models. Model names can be
supplied. See examples.
fitmeas
A vector of fit measures. One or more of these
c("chisq", "df", "pvalue", "rmsea", "cfi", "tli",
"srmr", "aic", "bic", "srmr", "aic", "bic",
"srmr_mplus"). Other fit measures present in the lavaan
objects will be allowed; fit measures that are requested but
not found are ignored.
nesting
character string indicating the nesting structure
of the models. Must only contain model names, ">", and "+"
separated by spaces. The model to the left of a ">" is the
parent model for all models to the right of the same ">", up
until another ">" is reached. When multiple models are nested
in the same parent, they are separated by a "+".
scaled
should scaled versions of the fit measures requested
be used if available? The scaled statistic is determined by
the model estimation method. The defaul value is TRUE.
chidif
should the nested models be compared by using the
anova function? The anova function may pass the model
comparison on to another lavaan function. The results are
added to the last three columns of the comparison table. The
default value is TRUE.
digits
The digits argument that will be passed to xtable.
...
Arguments that will be passed to print.xtable. These
arguments can be used to control table caption, label, and so
forth. See ?print.xtable. If type = "latex" or
"html", this function sets additional default values
for print.xtable that can be overridden by specifying
arguments here. Default type is an R data.frame, which is printed on
screen. Note the print.xtable parameter print.results determines
whether the markup is displayed before it is returned. The file
parameter can specify a file into which the markup is to be saved.
Details
In May 2018, the output approach was changed. The functions
xtable and print.xtable are used to render the final
result and any of the arguments allowed by print.xtable can
be used here (via the ... argument). We have some default
settings for some print.xtable, such as type = NULL,
file = NULL, print.results = TRUE, and
math.style.exponents = TRUE. There are some other specific
defaults for type = "latex" and type = "html", but they can all be
overridden by the user. We include a model legend at the bottom of
the table, indicating which models are compared by the Chi-squared
statistic.
If the type argument is not specified, then the output will be a
simple text display of the model table. If type is either "latex"
or "html", then a marked up table will be displayed and the file
argument can be used to ask for a saved version. If the user wants
to simply save the result in a file, and not display on screen,
insert the argument print.results = FALSE.
Value
If type = NULL, a data.frame object includes an attribute
called "noteinfo". If type = "tex", return is a character
vector created by xtable. If type = "html", a vector of HTML
markup created by xtable.
Author(s)
Ben Kite <bakite@ku.edu> and Paul Johnson
<pauljohn@ku.edu>
Examples
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library(lavaan)
library(xtable)
set.seed(123)
genmodel <- "f1 =~ .7*v1 + .7*v2 + .7*v3 + .7*v4 + .7*v5 + .7*v6
f1 ~~ 1*f1"
genmodel2 <- "f1 =~ .7*v1 + .7*v2 + .7*v3 + .7*v4 + .7*v5 + .2*v6
f1 ~~ 1*f1"
##'
dat1 <- simulateData(genmodel, sample.nobs = 300)
dat2 <- simulateData(genmodel2, sample.nobs = 300)
dat1$group <- 0
dat2$group <- 1
dat <- rbind(dat1, dat2)
## In order from less constrained to restricted
## Model cc1 configural model
cc1.model <- "
f1 =~ 1*v1 + v2 + v3 + v4 + v5 + v6
f1 ~~ f1
f1 ~0*1
"
## Model2: cc2 partial weak model (AKA partial metric)
cc2.model <- "
f1 =~ 1*v1 + c(L2,L2)*v2 + c(L3,L3)*v3 + c(L4,L4)*v4 + c(L5,L5)*v5 + v6
f1 ~~ f1
f1 ~0*1
"
## Model 3: weak model (AKA metric)
cc3.model <- "
f1 =~ 1*v1 + c(L2,L2)*v2 + c(L3,L3)*v3 + c(L4,L4)*v4 + c(L5,L5)*v5 + c(L6,L6)*v6
f1 ~~ f1
f1 ~0*1
"
## Model 4: scalar model (AKA strong)
cc4.model <- "
f1 =~ 1*v1 + c(L2,L2)*v2 + c(L3,L3)*v3 + c(L4,L4)*v4 + c(L5,L5)*v5 + v6
f1 ~~ f1
f1 ~ c(0,NA)*1
v1 ~ c(I1,I1)*1
v2 ~ c(I2,I2)*1
v3 ~ c(I3,I3)*1
v4 ~ c(I4,I4)*1
v5 ~ c(I5,I5)*1
v6 ~ c(I6,I6)*1
"
## Reload saved models if available: avoids slow re-estimation that bothers CRAN
cc1 <- tryCatch(readRDS(system.file("cfa/cc1.rds", package = "semTable")),
error = function(e) cfa(cc1.model, data=dat, group="group",
meanstructure=TRUE, estimator = "MLR"))
cc2 <- tryCatch(readRDS(system.file("cfa/cc2.rds", package = "semTable")),
error = function(e) cfa(cc2.model, data=dat, group="group",
meanstructure=TRUE, estimator = "MLR"))
cc3 <- tryCatch(readRDS(system.file("cfa/cc3.rds", package = "semTable")),
error = function(e) cfa(cc3.model, data=dat, group="group",
meanstructure=TRUE, estimator = "MLR"))
cc4 <- tryCatch(readRDS(system.file("cfa/cc4.rds", package = "semTable")),
error = function(e) cfa(cc4.model, data=dat, group="group",
meanstructure=TRUE, estimator = "MLR"))
models <- list(cc1, cc2, cc3, cc4)
## Note, nesting is not specified, so built-in nesting detection applies
compareLavaan(models)
compareLavaan(models, type = "latex")
compareLavaan(models, type = "html")
##'
## Now we specify model labels in the list
models <- list("Configural" = cc1, "PartialMetric" = cc2, "Metric" = cc3, "Scalar" = cc4)
## The model labels are used in the nesting parameter
compareLavaan(models, nesting = "Configural > PartialMetric > Metric > Scalar")
##' Previous incorrect, treat cc2 and cc3 as children of cc1 instead:
compareLavaan(models, nesting = "Configural > PartialMetric + Metric > Scalar")
##'
compareLavaan(models, fitmeas = c("chisq", "df", "cfi", "rmsea", "tli"),
nesting = "Configural > Metric + PartialMetric > Scalar")
compareLavaan(models, fitmeas = c("chisq", "df", "cfi", "rmsea", "tli"),
nesting = "Configural > PartialMetric + Metric > Scalar")
##'
## Creates output file
## compareLavaan(models, fitmeas = c("chisq", "df", "cfi", "rmsea", "tli"),
## nesting = "Configural > Metric + PartialMetric > Scalar", type = "tex",
## file = "/tmp/table.tex")
Example output
This is lavaan 0.6-7
lavaan is BETA software! Please report any bugs.
chisq.scaled df.scaled pvalue.scaled rmsea.scaled cfi.scaled tli.scaled
Model1 14.230 18 0.714 0.000 1.000 1.013
Model2 19.670 22 0.604 0.000 1.000 1.006
Model3 39.065 23 0.020 0.048 0.967 0.958
Model4 22.476 27 0.713 0.000 1.000 1.010
srmr aic bic dchi ddf npval
Model1 0.022 11011.18 11169.47 - - -
Model2 0.033 11008.58 11149.28 5.509a 4 0.239
Model3 0.061 11026.07 11162.37 20.409b 1 0
Model4 0.035 11001.36 11120.08 -16.582c 4 1
a = Model2 vs Model1 b = Model3 vs Model2 c = Model4 vs Model3
Warning message:
In lavTestLRT(object = new("lavaan", version = "0.6.3", call = lavaan::lavaan(model = cc4.model, :
lavaan WARNING: some restricted models fit better than less
restricted models; either these models are not nested, or
the less restricted model failed to reach a global optimum.
% latex table generated in R 4.0.3 by xtable 1.8-4 package
% Thu Mar 4 10:39:04 2021
\begin{table}[ht]
\centering
\begin{tabular}{rrrrrrrrrrlll}
\hline
& $\chi^{2}$ & df & \textit{p}-value & rmsea & cfi & tli & srmr & aic & bic & $\Delta\chi^{2}$ & $\Delta df$ & \textit{p} \\
\hline
Model1 & 14.23 & 18.00 & 0.71 & 0.00 & 1.00 & 1.01 & 0.02 & 11011.18 & 11169.47 & - & - & - \\
Model2 & 19.67 & 22.00 & 0.60 & 0.00 & 1.00 & 1.01 & 0.03 & 11008.58 & 11149.28 & 5.509a & 4 & 0.239 \\
Model3 & 39.06 & 23.00 & 0.02 & 0.05 & 0.97 & 0.96 & 0.06 & 11026.07 & 11162.37 & 20.409b & 1 & 0 \\
Model4 & 22.48 & 27.00 & 0.71 & 0.00 & 1.00 & 1.01 & 0.04 & 11001.36 & 11120.08 & -16.582c & 4 & 1 \\
\hline
\multicolumn{4}{l}{a = Model2 vs Model1} \\
\multicolumn{4}{l}{b = Model3 vs Model2} \\
\multicolumn{4}{l}{c = Model4 vs Model3} \\
\hline
\end{tabular}
\end{table}
Warning message:
In lavTestLRT(object = new("lavaan", version = "0.6.3", call = lavaan::lavaan(model = cc4.model, :
lavaan WARNING: some restricted models fit better than less
restricted models; either these models are not nested, or
the less restricted model failed to reach a global optimum.
<!-- html table generated in R 4.0.3 by xtable 1.8-4 package -->
<!-- Thu Mar 4 10:39:05 2021 -->
<table 0>
<tr> <th> </th> <th> chisq </th> <th> df </th> <th> pvalue </th> <th> rmsea </th> <th> cfi </th> <th> tli </th> <th> srmr </th> <th> aic </th> <th> bic </th> <th> dchi </th> <th> ddf </th> <th> npval </th> </tr>
<tr> <td align="right"> Model1 </td> <td align="right"> 14.23 </td> <td align="right"> 18.00 </td> <td align="right"> 0.71 </td> <td align="right"> 0.00 </td> <td align="right"> 1.00 </td> <td align="right"> 1.01 </td> <td align="right"> 0.02 </td> <td align="right"> 11011.18 </td> <td align="right"> 11169.47 </td> <td> - </td> <td> - </td> <td> - </td> </tr>
<tr> <td align="right"> Model2 </td> <td align="right"> 19.67 </td> <td align="right"> 22.00 </td> <td align="right"> 0.60 </td> <td align="right"> 0.00 </td> <td align="right"> 1.00 </td> <td align="right"> 1.01 </td> <td align="right"> 0.03 </td> <td align="right"> 11008.58 </td> <td align="right"> 11149.28 </td> <td> 5.509a </td> <td> 4 </td> <td> 0.239 </td> </tr>
<tr> <td align="right"> Model3 </td> <td align="right"> 39.06 </td> <td align="right"> 23.00 </td> <td align="right"> 0.02 </td> <td align="right"> 0.05 </td> <td align="right"> 0.97 </td> <td align="right"> 0.96 </td> <td align="right"> 0.06 </td> <td align="right"> 11026.07 </td> <td align="right"> 11162.37 </td> <td> 20.409b </td> <td> 1 </td> <td> 0 </td> </tr>
<tr> <td align="right"> Model4 </td> <td align="right"> 22.48 </td> <td align="right"> 27.00 </td> <td align="right"> 0.71 </td> <td align="right"> 0.00 </td> <td align="right"> 1.00 </td> <td align="right"> 1.01 </td> <td align="right"> 0.04 </td> <td align="right"> 11001.36 </td> <td align="right"> 11120.08 </td> <td> -16.582c </td> <td> 4 </td> <td> 1 </td> </tr>
<tr><td colspan="4">a = Model2 vs Model1</td></tr>
<tr><td colspan="4">b = Model3 vs Model2</td></tr>
<tr><td colspan="4">c = Model4 vs Model3</td></tr>
</table>
Warning message:
In lavTestLRT(object = new("lavaan", version = "0.6.3", call = lavaan::lavaan(model = cc4.model, :
lavaan WARNING: some restricted models fit better than less
restricted models; either these models are not nested, or
the less restricted model failed to reach a global optimum.
chisq.scaled df.scaled pvalue.scaled rmsea.scaled cfi.scaled
Configural 14.230 18 0.714 0.000 1.000
PartialMetric 19.670 22 0.604 0.000 1.000
Metric 39.065 23 0.020 0.048 0.967
Scalar 22.476 27 0.713 0.000 1.000
tli.scaled srmr aic bic dchi ddf npval
Configural 1.013 0.022 11011.18 11169.47 - - -
PartialMetric 1.006 0.033 11008.58 11149.28 5.509a 4 0.239
Metric 0.958 0.061 11026.07 11162.37 20.409b 1 0
Scalar 1.010 0.035 11001.36 11120.08 -16.582c 4 1
a = PartialMetric vs Configural b = Metric vs PartialMetric
c = Scalar vs Metric
Warning message:
In lavTestLRT(object = new("lavaan", version = "0.6.3", call = lavaan::lavaan(model = cc4.model, :
lavaan WARNING: some restricted models fit better than less
restricted models; either these models are not nested, or
the less restricted model failed to reach a global optimum.
chisq.scaled df.scaled pvalue.scaled rmsea.scaled cfi.scaled
Configural 14.230 18 0.714 0.000 1.000
PartialMetric 19.670 22 0.604 0.000 1.000
Metric 39.065 23 0.020 0.048 0.967
Scalar 22.476 27 0.713 0.000 1.000
tli.scaled srmr aic bic dchi ddf npval
Configural 1.013 0.022 11011.18 11169.47 - - -
PartialMetric 1.006 0.033 11008.58 11149.28 5.509a 4 0.239
Metric 0.958 0.061 11026.07 11162.37 25.527b 5 0
Scalar 1.010 0.035 11001.36 11120.08 -16.582c 4 1
a = PartialMetric vs Configural b = Metric vs Configural c = Scalar vs Metric
Warning message:
In lavTestLRT(object = new("lavaan", version = "0.6.3", call = lavaan::lavaan(model = cc4.model, :
lavaan WARNING: some restricted models fit better than less
restricted models; either these models are not nested, or
the less restricted model failed to reach a global optimum.
chisq.scaled df.scaled cfi.scaled rmsea.scaled tli.scaled dchi
Configural 14.230 18 1.000 0.000 1.013 -
Metric 39.065 23 0.967 0.048 0.958 25.527a
PartialMetric 19.670 22 1.000 0.000 1.006 5.509b
Scalar 22.476 27 1.000 0.000 1.010 2.789c
ddf npval
Configural - -
Metric 5 0
PartialMetric 4 0.239
Scalar 5 0.733
a = Metric vs Configural b = PartialMetric vs Configural
c = Scalar vs PartialMetric
chisq.scaled df.scaled cfi.scaled rmsea.scaled tli.scaled
Configural 14.230 18 1.000 0.000 1.013
PartialMetric 19.670 22 1.000 0.000 1.006
Metric 39.065 23 0.967 0.048 0.958
Scalar 22.476 27 1.000 0.000 1.010
dchi ddf npval
Configural - - -
PartialMetric 5.509a 4 0.239
Metric 25.527b 5 0
Scalar -16.582c 4 1
a = PartialMetric vs Configural b = Metric vs Configural c = Scalar vs Metric
Warning message:
In lavTestLRT(object = new("lavaan", version = "0.6.3", call = lavaan::lavaan(model = cc4.model, :
lavaan WARNING: some restricted models fit better than less
restricted models; either these models are not nested, or
the less restricted model failed to reach a global optimum.
semTable documentation built on April 30, 2020, 1:05 a.m.
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Description Usage Arguments Details Value Author(s) Examples
View source: R/modelcomparison.R
Description
If the parameter nesting is not specified, then this
function attempts to discern which models are nested and they are
ordered accordingly. The user can override that by specifing a
nesting structure. This uses a new notation to represent nesting
of models. See the parameter nesting. The syntax uses the
symbols ">" and "+" in an obvious way to indicate that one model
is the superset or on the same level as another. If the
Usage
1 2 3 |
Arguments
models |
list of lavaan cfa or sem models. Model names can be supplied. See examples. |
fitmeas |
A vector of fit measures. One or more of these
|
nesting |
character string indicating the nesting structure of the models. Must only contain model names, ">", and "+" separated by spaces. The model to the left of a ">" is the parent model for all models to the right of the same ">", up until another ">" is reached. When multiple models are nested in the same parent, they are separated by a "+". |
scaled |
should scaled versions of the fit measures requested be used if available? The scaled statistic is determined by the model estimation method. The defaul value is TRUE. |
chidif |
should the nested models be compared by using the anova function? The anova function may pass the model comparison on to another lavaan function. The results are added to the last three columns of the comparison table. The default value is TRUE. |
digits |
The digits argument that will be passed to xtable. |
... |
Arguments that will be passed to print.xtable. These
arguments can be used to control table caption, label, and so
forth. See |
Details
In May 2018, the output approach was changed. The functions
xtable and print.xtable are used to render the final
result and any of the arguments allowed by print.xtable can
be used here (via the ... argument). We have some default
settings for some print.xtable, such as type = NULL,
file = NULL, print.results = TRUE, and
math.style.exponents = TRUE. There are some other specific
defaults for type = "latex" and type = "html", but they can all be
overridden by the user. We include a model legend at the bottom of
the table, indicating which models are compared by the Chi-squared
statistic.
If the type argument is not specified, then the output will be a
simple text display of the model table. If type is either "latex"
or "html", then a marked up table will be displayed and the file
argument can be used to ask for a saved version. If the user wants
to simply save the result in a file, and not display on screen,
insert the argument print.results = FALSE.
Value
If type = NULL, a data.frame object includes an attribute called "noteinfo". If type = "tex", return is a character vector created by xtable. If type = "html", a vector of HTML markup created by xtable.
Author(s)
Ben Kite <bakite@ku.edu> and Paul Johnson <pauljohn@ku.edu>
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | library(lavaan)
library(xtable)
set.seed(123)
genmodel <- "f1 =~ .7*v1 + .7*v2 + .7*v3 + .7*v4 + .7*v5 + .7*v6
f1 ~~ 1*f1"
genmodel2 <- "f1 =~ .7*v1 + .7*v2 + .7*v3 + .7*v4 + .7*v5 + .2*v6
f1 ~~ 1*f1"
##'
dat1 <- simulateData(genmodel, sample.nobs = 300)
dat2 <- simulateData(genmodel2, sample.nobs = 300)
dat1$group <- 0
dat2$group <- 1
dat <- rbind(dat1, dat2)
## In order from less constrained to restricted
## Model cc1 configural model
cc1.model <- "
f1 =~ 1*v1 + v2 + v3 + v4 + v5 + v6
f1 ~~ f1
f1 ~0*1
"
## Model2: cc2 partial weak model (AKA partial metric)
cc2.model <- "
f1 =~ 1*v1 + c(L2,L2)*v2 + c(L3,L3)*v3 + c(L4,L4)*v4 + c(L5,L5)*v5 + v6
f1 ~~ f1
f1 ~0*1
"
## Model 3: weak model (AKA metric)
cc3.model <- "
f1 =~ 1*v1 + c(L2,L2)*v2 + c(L3,L3)*v3 + c(L4,L4)*v4 + c(L5,L5)*v5 + c(L6,L6)*v6
f1 ~~ f1
f1 ~0*1
"
## Model 4: scalar model (AKA strong)
cc4.model <- "
f1 =~ 1*v1 + c(L2,L2)*v2 + c(L3,L3)*v3 + c(L4,L4)*v4 + c(L5,L5)*v5 + v6
f1 ~~ f1
f1 ~ c(0,NA)*1
v1 ~ c(I1,I1)*1
v2 ~ c(I2,I2)*1
v3 ~ c(I3,I3)*1
v4 ~ c(I4,I4)*1
v5 ~ c(I5,I5)*1
v6 ~ c(I6,I6)*1
"
## Reload saved models if available: avoids slow re-estimation that bothers CRAN
cc1 <- tryCatch(readRDS(system.file("cfa/cc1.rds", package = "semTable")),
error = function(e) cfa(cc1.model, data=dat, group="group",
meanstructure=TRUE, estimator = "MLR"))
cc2 <- tryCatch(readRDS(system.file("cfa/cc2.rds", package = "semTable")),
error = function(e) cfa(cc2.model, data=dat, group="group",
meanstructure=TRUE, estimator = "MLR"))
cc3 <- tryCatch(readRDS(system.file("cfa/cc3.rds", package = "semTable")),
error = function(e) cfa(cc3.model, data=dat, group="group",
meanstructure=TRUE, estimator = "MLR"))
cc4 <- tryCatch(readRDS(system.file("cfa/cc4.rds", package = "semTable")),
error = function(e) cfa(cc4.model, data=dat, group="group",
meanstructure=TRUE, estimator = "MLR"))
models <- list(cc1, cc2, cc3, cc4)
## Note, nesting is not specified, so built-in nesting detection applies
compareLavaan(models)
compareLavaan(models, type = "latex")
compareLavaan(models, type = "html")
##'
## Now we specify model labels in the list
models <- list("Configural" = cc1, "PartialMetric" = cc2, "Metric" = cc3, "Scalar" = cc4)
## The model labels are used in the nesting parameter
compareLavaan(models, nesting = "Configural > PartialMetric > Metric > Scalar")
##' Previous incorrect, treat cc2 and cc3 as children of cc1 instead:
compareLavaan(models, nesting = "Configural > PartialMetric + Metric > Scalar")
##'
compareLavaan(models, fitmeas = c("chisq", "df", "cfi", "rmsea", "tli"),
nesting = "Configural > Metric + PartialMetric > Scalar")
compareLavaan(models, fitmeas = c("chisq", "df", "cfi", "rmsea", "tli"),
nesting = "Configural > PartialMetric + Metric > Scalar")
##'
## Creates output file
## compareLavaan(models, fitmeas = c("chisq", "df", "cfi", "rmsea", "tli"),
## nesting = "Configural > Metric + PartialMetric > Scalar", type = "tex",
## file = "/tmp/table.tex")
|
Example output
This is lavaan 0.6-7
lavaan is BETA software! Please report any bugs.
chisq.scaled df.scaled pvalue.scaled rmsea.scaled cfi.scaled tli.scaled
Model1 14.230 18 0.714 0.000 1.000 1.013
Model2 19.670 22 0.604 0.000 1.000 1.006
Model3 39.065 23 0.020 0.048 0.967 0.958
Model4 22.476 27 0.713 0.000 1.000 1.010
srmr aic bic dchi ddf npval
Model1 0.022 11011.18 11169.47 - - -
Model2 0.033 11008.58 11149.28 5.509a 4 0.239
Model3 0.061 11026.07 11162.37 20.409b 1 0
Model4 0.035 11001.36 11120.08 -16.582c 4 1
a = Model2 vs Model1 b = Model3 vs Model2 c = Model4 vs Model3
Warning message:
In lavTestLRT(object = new("lavaan", version = "0.6.3", call = lavaan::lavaan(model = cc4.model, :
lavaan WARNING: some restricted models fit better than less
restricted models; either these models are not nested, or
the less restricted model failed to reach a global optimum.
% latex table generated in R 4.0.3 by xtable 1.8-4 package
% Thu Mar 4 10:39:04 2021
\begin{table}[ht]
\centering
\begin{tabular}{rrrrrrrrrrlll}
\hline
& $\chi^{2}$ & df & \textit{p}-value & rmsea & cfi & tli & srmr & aic & bic & $\Delta\chi^{2}$ & $\Delta df$ & \textit{p} \\
\hline
Model1 & 14.23 & 18.00 & 0.71 & 0.00 & 1.00 & 1.01 & 0.02 & 11011.18 & 11169.47 & - & - & - \\
Model2 & 19.67 & 22.00 & 0.60 & 0.00 & 1.00 & 1.01 & 0.03 & 11008.58 & 11149.28 & 5.509a & 4 & 0.239 \\
Model3 & 39.06 & 23.00 & 0.02 & 0.05 & 0.97 & 0.96 & 0.06 & 11026.07 & 11162.37 & 20.409b & 1 & 0 \\
Model4 & 22.48 & 27.00 & 0.71 & 0.00 & 1.00 & 1.01 & 0.04 & 11001.36 & 11120.08 & -16.582c & 4 & 1 \\
\hline
\multicolumn{4}{l}{a = Model2 vs Model1} \\
\multicolumn{4}{l}{b = Model3 vs Model2} \\
\multicolumn{4}{l}{c = Model4 vs Model3} \\
\hline
\end{tabular}
\end{table}
Warning message:
In lavTestLRT(object = new("lavaan", version = "0.6.3", call = lavaan::lavaan(model = cc4.model, :
lavaan WARNING: some restricted models fit better than less
restricted models; either these models are not nested, or
the less restricted model failed to reach a global optimum.
<!-- html table generated in R 4.0.3 by xtable 1.8-4 package -->
<!-- Thu Mar 4 10:39:05 2021 -->
<table 0>
<tr> <th> </th> <th> chisq </th> <th> df </th> <th> pvalue </th> <th> rmsea </th> <th> cfi </th> <th> tli </th> <th> srmr </th> <th> aic </th> <th> bic </th> <th> dchi </th> <th> ddf </th> <th> npval </th> </tr>
<tr> <td align="right"> Model1 </td> <td align="right"> 14.23 </td> <td align="right"> 18.00 </td> <td align="right"> 0.71 </td> <td align="right"> 0.00 </td> <td align="right"> 1.00 </td> <td align="right"> 1.01 </td> <td align="right"> 0.02 </td> <td align="right"> 11011.18 </td> <td align="right"> 11169.47 </td> <td> - </td> <td> - </td> <td> - </td> </tr>
<tr> <td align="right"> Model2 </td> <td align="right"> 19.67 </td> <td align="right"> 22.00 </td> <td align="right"> 0.60 </td> <td align="right"> 0.00 </td> <td align="right"> 1.00 </td> <td align="right"> 1.01 </td> <td align="right"> 0.03 </td> <td align="right"> 11008.58 </td> <td align="right"> 11149.28 </td> <td> 5.509a </td> <td> 4 </td> <td> 0.239 </td> </tr>
<tr> <td align="right"> Model3 </td> <td align="right"> 39.06 </td> <td align="right"> 23.00 </td> <td align="right"> 0.02 </td> <td align="right"> 0.05 </td> <td align="right"> 0.97 </td> <td align="right"> 0.96 </td> <td align="right"> 0.06 </td> <td align="right"> 11026.07 </td> <td align="right"> 11162.37 </td> <td> 20.409b </td> <td> 1 </td> <td> 0 </td> </tr>
<tr> <td align="right"> Model4 </td> <td align="right"> 22.48 </td> <td align="right"> 27.00 </td> <td align="right"> 0.71 </td> <td align="right"> 0.00 </td> <td align="right"> 1.00 </td> <td align="right"> 1.01 </td> <td align="right"> 0.04 </td> <td align="right"> 11001.36 </td> <td align="right"> 11120.08 </td> <td> -16.582c </td> <td> 4 </td> <td> 1 </td> </tr>
<tr><td colspan="4">a = Model2 vs Model1</td></tr>
<tr><td colspan="4">b = Model3 vs Model2</td></tr>
<tr><td colspan="4">c = Model4 vs Model3</td></tr>
</table>
Warning message:
In lavTestLRT(object = new("lavaan", version = "0.6.3", call = lavaan::lavaan(model = cc4.model, :
lavaan WARNING: some restricted models fit better than less
restricted models; either these models are not nested, or
the less restricted model failed to reach a global optimum.
chisq.scaled df.scaled pvalue.scaled rmsea.scaled cfi.scaled
Configural 14.230 18 0.714 0.000 1.000
PartialMetric 19.670 22 0.604 0.000 1.000
Metric 39.065 23 0.020 0.048 0.967
Scalar 22.476 27 0.713 0.000 1.000
tli.scaled srmr aic bic dchi ddf npval
Configural 1.013 0.022 11011.18 11169.47 - - -
PartialMetric 1.006 0.033 11008.58 11149.28 5.509a 4 0.239
Metric 0.958 0.061 11026.07 11162.37 20.409b 1 0
Scalar 1.010 0.035 11001.36 11120.08 -16.582c 4 1
a = PartialMetric vs Configural b = Metric vs PartialMetric
c = Scalar vs Metric
Warning message:
In lavTestLRT(object = new("lavaan", version = "0.6.3", call = lavaan::lavaan(model = cc4.model, :
lavaan WARNING: some restricted models fit better than less
restricted models; either these models are not nested, or
the less restricted model failed to reach a global optimum.
chisq.scaled df.scaled pvalue.scaled rmsea.scaled cfi.scaled
Configural 14.230 18 0.714 0.000 1.000
PartialMetric 19.670 22 0.604 0.000 1.000
Metric 39.065 23 0.020 0.048 0.967
Scalar 22.476 27 0.713 0.000 1.000
tli.scaled srmr aic bic dchi ddf npval
Configural 1.013 0.022 11011.18 11169.47 - - -
PartialMetric 1.006 0.033 11008.58 11149.28 5.509a 4 0.239
Metric 0.958 0.061 11026.07 11162.37 25.527b 5 0
Scalar 1.010 0.035 11001.36 11120.08 -16.582c 4 1
a = PartialMetric vs Configural b = Metric vs Configural c = Scalar vs Metric
Warning message:
In lavTestLRT(object = new("lavaan", version = "0.6.3", call = lavaan::lavaan(model = cc4.model, :
lavaan WARNING: some restricted models fit better than less
restricted models; either these models are not nested, or
the less restricted model failed to reach a global optimum.
chisq.scaled df.scaled cfi.scaled rmsea.scaled tli.scaled dchi
Configural 14.230 18 1.000 0.000 1.013 -
Metric 39.065 23 0.967 0.048 0.958 25.527a
PartialMetric 19.670 22 1.000 0.000 1.006 5.509b
Scalar 22.476 27 1.000 0.000 1.010 2.789c
ddf npval
Configural - -
Metric 5 0
PartialMetric 4 0.239
Scalar 5 0.733
a = Metric vs Configural b = PartialMetric vs Configural
c = Scalar vs PartialMetric
chisq.scaled df.scaled cfi.scaled rmsea.scaled tli.scaled
Configural 14.230 18 1.000 0.000 1.013
PartialMetric 19.670 22 1.000 0.000 1.006
Metric 39.065 23 0.967 0.048 0.958
Scalar 22.476 27 1.000 0.000 1.010
dchi ddf npval
Configural - - -
PartialMetric 5.509a 4 0.239
Metric 25.527b 5 0
Scalar -16.582c 4 1
a = PartialMetric vs Configural b = Metric vs Configural c = Scalar vs Metric
Warning message:
In lavTestLRT(object = new("lavaan", version = "0.6.3", call = lavaan::lavaan(model = cc4.model, :
lavaan WARNING: some restricted models fit better than less
restricted models; either these models are not nested, or
the less restricted model failed to reach a global optimum.
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