Nothing
Confirmatory Factor Analysis"
In Statsomat: Shiny Apps for Automated Data Analysis and Automated Interpretation
\normalsize
```{whites, eval=FALSE, echo = eval_code}
Model Definition (please remark the single quotation marks enclosing the equations)
model <-
'
```r
model <- params$model
modelsplit <- unlist(strsplit(model, split="\n"))
modelsplit <- modelsplit[modelsplit != ""]
modelsplit <- gsub(" ", "", modelsplit, fixed = TRUE)
for (i in 1:length(modelsplit)){
cat(modelsplit[i],fill=TRUE)
}
```{whites, eval=FALSE, echo = eval_code}
'
```{whites, eval=FALSE, echo = indic_missings}
# Model Fit
fit <- cfa(model, data=df, missing="fiml", estimator="ML")
# Parameter Estimates
pe <- parameterEstimates(fit, rsquare = TRUE)
```{whites, eval=FALSE, echo = !indic_missings}
Model Fit Unstandardized
fit <- cfa(model, data=df, estimator="ML")
Parameter Estimates
pe <- parameterEstimates(fit, rsquare = TRUE)
```{whites, eval=FALSE, echo = eval_code}
# Check Model Equations
info <- inspect(fit, what="list")
info <- info[,c(2,3,4,5,8)]
```{whites, eval=FALSE, echo = indic_missings}
Model Fit Standardized Observed Variables
fit2 <- cfa(model, data=df, missing="fiml", estimator="ML", std.ov=TRUE)
Parameter Estimates
pe_stand <- standardizedsolution(fit2, type="std.all")
```{whites, eval=FALSE, echo = !indic_missings}
# Model Fit Standardized Observed Variables
fit2 <- cfa(model, data=df, estimator="ML", std.ov=TRUE)
## Parameter Estimates
pe_stand <- standardizedsolution(fit2, type="std.all")
```{whites, eval=FALSE, echo = eval_code}
Outputs
Summary Unstandardized Parameter Estimates
summary(fit, fit.measures=TRUE)
```{whites, eval=FALSE, echo = eval_code}
## Summary Completely Standardized Parameter Estimates
standardizedSolution(fit2, type="std.all", output="text")
```{whites, eval=FALSE, echo = eval_code}
Communality
if ("label" %in% colnames(pe)) pe$label <- NULL
r2 <- pe[pe$op=="r2",c(1,4)]
r2 <- r2[order(r2$lhs),]
colnames(r2) <- c("Variable","Communality")
rownames(r2) <- NULL
r2
```{whites, eval=FALSE, echo = nfactors_indic}
## Factor Discriminant Validity
discr <- suppressWarnings(discriminantValidity(fit, cutoff = 0.85))
discr <- discr[,c(1:4,11:13)]
discr <- discr[!is.na(discr$`Pr(>Chisq)`),]
kable(discr,caption="Factor Discriminant Validity Test at Cutoff 0.85",digits=3,
row.names = NA, linesep = '', col.names =
c("","","","Factor Correlation","Chisq diff","Df diff","P-Value"), format="simple")
```{whites, eval=FALSE, echo = eval_code}
Factor Reliability
rel <- reliability(fit, return.total = TRUE, dropSingle = FALSE,
omit.imps = c("no.conv", "no.se", "no.npd"))
rel <- rel[3:5,]
rel <- as.data.frame(rel)
rownames(rel) <- c("Omega (Bentler)","Omega (McDonald)","AVE")
Colname for single factor case
if (length(fit@pta$vnames$lv.x[[1]])==1) colnames(rel) <- fit@pta$vnames$lv.x[[1]]
rel_transpose <- as.data.frame(t(rel))
rel_transpose <- rel_transpose[1:(nrow(rel_transpose)-1),]
Output table
opts <- options(knitr.kable.NA = "Not provided in case of cross-loadings")
kable(rel, caption="Factor Reliability",digits=2, linesep = '', longtable=T, format="simple")
options(opts)
```{whites, eval=FALSE, echo = eval_code}
## Observed Covariance Matrix
obscov <- inspect(fit, "sampstat")$cov
corrplot::corrplot(obscov, is.corr = FALSE,
type = 'lower',
order = "original",
col='black', method="number", cl.pos = "n", tl.cex=.50, number.cex=0.50)
```{whites, eval=FALSE, echo = eval_code}
Model-Implied Covariance Matrix
fittedcov <- inspect(fit, what="cov.ov") # Fitted cov
corrplot::corrplot(fittedcov, is.corr = FALSE,
type = 'lower',
order = "original",
col='black', method="number", cl.pos = "n", tl.cex=.50, number.cex=0.50)
```{whites, eval=FALSE, echo = eval_code}
## Residual Covariance Matrix
covraw <- lavResiduals(fit,type="raw")$cov
corrplot::corrplot(covraw, is.corr = FALSE,
type = 'lower',
order = "original",
tl.col='black', tl.cex=.50, number.cex=0.50, method="number", cl.cex = 0.5)
```{whites, eval=FALSE, echo = eval_code}
Standardized Residual Matrix
covstd <- lavResiduals(fit,type="raw")$cov.z
corrplot::corrplot(covstd, is.corr = FALSE,
type = 'lower',
order = "original",
tl.col='black', tl.cex=.50, number.cex=0.50, method="number", cl.cex = 0.5)
```{whites, eval=FALSE, echo = eval_code}
## Residual Correlation Matrix
r <- resid(fit, "cor")$cov # Residuals on cor
corrplot::corrplot(r, is.corr = FALSE,
type = 'lower',
order = "original",
tl.col='black', tl.cex=.50, number.cex=0.50, method="number", cl.cex = 0.5)
# Indicator for Modification Indices
# MI for Error Covariances
mi_corr <- modindices(fit, sort.=TRUE, power=TRUE, high.power = 0.75, na.remove=TRUE, op="~~", maximum.number=10)
mi_corr <- mi_corr[which(mi_corr$lhs %in% fit@pta$vnames$ov[[1]]),]
if (nrow(mi_corr)>0) mi_corr_indic <- TRUE else mi_corr_indic <- FALSE
# MI for Factor Loadings
mi_fl <- modindices(fit, sort.=TRUE, power=TRUE, high.power = 0.75, na.remove=TRUE, op="=~", delta=0.4, maximum.number=10)
if (nrow(mi_fl)>0) mi_fl_indic <- TRUE else mi_fl_indic <- FALSE
# MI for Factor Covariances
mi_fcov <- modindices(fit, sort.=TRUE, power=TRUE, high.power = 0.75, na.remove=TRUE, op="~~", maximum.number=10)
mi_fcov <- mi_fcov[which(mi_fcov$lhs %in% fit@pta$vnames$lv[[1]]),]
if (nrow(mi_fcov)>0) mi_fcov_indic <- TRUE else mi_fcov_indic <- FALSE
```{whites, eval=FALSE, echo = mi_corr_indic}
Modification Indices for Correlation
mi_corr <- modindices(fit, sort.=TRUE, power=TRUE, high.power = 0.75,
na.remove=TRUE, op="~~", maximum.number=10)
mi_corr <- mi_corr[which(mi_corr$lhs %in% fit@pta$vnames$ov[[1]]),]
mi_corr <- mi_corr[,c(-6,-8)]
rownames(mi_corr) <- NULL
kable(mi_corr,col.names=c("Left","Operator","Right","Modification Index",
"Expected Parameter Change", "Delta", "Power", "Decision"),
digits=3, caption="Modification Indices With Respect To Error Covariances",
linesep = '', longtable=T, format="simple")
```{whites, eval=FALSE, echo = mi_fl_indic}
## Modification Indices for Factor Loadings
mi_fl <- modindices(fit, sort.=TRUE, power=TRUE, high.power = 0.75,
na.remove=TRUE, op="=~", delta=0.4, maximum.number=10)
mi_fl <- mi_fl[,c(-6,-8)]
rownames(mi_fl) <- NULL
kable(mi_fl,col.names=c("Left","Operator","Right","Modification Index",
"Expected Parameter Change", "Delta", "Power", "Decision"),
digits=3, caption="Modification Indices With Respect To Factor Loadings",
linesep = '', longtable=T, format="simple")
```{whites, eval=FALSE, echo = mi_fcov_indic}
Modification Indices for Factor Covariances
mi_fcov <- modindices(fit, sort.=TRUE, power=TRUE, high.power = 0.75,
na.remove=TRUE, op="~~", maximum.number=10)
mi_fcov <- mi_fcov[which(mi_fcov$lhs %in% fit@pta$vnames$lv[[1]]),]
mi_fcov <- mi_fcov[,c(-6,-8)]
rownames(mi_fcov) <- NULL
kable(mi_fcov,col.names=c("Left","Operator","Right","Modification Index",
"Expected Parameter Change", "Delta", "Power", "Decision"),
digits=3, caption="Modification Indices With Respect To Factor Covariances",
linesep = '', longtable=T, format="simple")
```{whites, eval=FALSE, echo = eval_code}
## Path Diagram
semPaths(fit, what="std", intercepts = FALSE)
cat("\n# References", fill=TRUE)
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Statsomat documentation built on Nov. 17, 2021, 5:17 p.m.
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\normalsize
```{whites, eval=FALSE, echo = eval_code}
Model Definition (please remark the single quotation marks enclosing the equations)
model <- '
```r model <- params$model modelsplit <- unlist(strsplit(model, split="\n")) modelsplit <- modelsplit[modelsplit != ""] modelsplit <- gsub(" ", "", modelsplit, fixed = TRUE) for (i in 1:length(modelsplit)){ cat(modelsplit[i],fill=TRUE) }
```{whites, eval=FALSE, echo = eval_code} '
```{whites, eval=FALSE, echo = indic_missings}
# Model Fit
fit <- cfa(model, data=df, missing="fiml", estimator="ML")
# Parameter Estimates
pe <- parameterEstimates(fit, rsquare = TRUE)
```{whites, eval=FALSE, echo = !indic_missings}
Model Fit Unstandardized
fit <- cfa(model, data=df, estimator="ML")
Parameter Estimates
pe <- parameterEstimates(fit, rsquare = TRUE)
```{whites, eval=FALSE, echo = eval_code}
# Check Model Equations
info <- inspect(fit, what="list")
info <- info[,c(2,3,4,5,8)]
```{whites, eval=FALSE, echo = indic_missings}
Model Fit Standardized Observed Variables
fit2 <- cfa(model, data=df, missing="fiml", estimator="ML", std.ov=TRUE)
Parameter Estimates
pe_stand <- standardizedsolution(fit2, type="std.all")
```{whites, eval=FALSE, echo = !indic_missings}
# Model Fit Standardized Observed Variables
fit2 <- cfa(model, data=df, estimator="ML", std.ov=TRUE)
## Parameter Estimates
pe_stand <- standardizedsolution(fit2, type="std.all")
```{whites, eval=FALSE, echo = eval_code}
Outputs
Summary Unstandardized Parameter Estimates
summary(fit, fit.measures=TRUE)
```{whites, eval=FALSE, echo = eval_code}
## Summary Completely Standardized Parameter Estimates
standardizedSolution(fit2, type="std.all", output="text")
```{whites, eval=FALSE, echo = eval_code}
Communality
if ("label" %in% colnames(pe)) pe$label <- NULL r2 <- pe[pe$op=="r2",c(1,4)] r2 <- r2[order(r2$lhs),] colnames(r2) <- c("Variable","Communality") rownames(r2) <- NULL r2
```{whites, eval=FALSE, echo = nfactors_indic}
## Factor Discriminant Validity
discr <- suppressWarnings(discriminantValidity(fit, cutoff = 0.85))
discr <- discr[,c(1:4,11:13)]
discr <- discr[!is.na(discr$`Pr(>Chisq)`),]
kable(discr,caption="Factor Discriminant Validity Test at Cutoff 0.85",digits=3,
row.names = NA, linesep = '', col.names =
c("","","","Factor Correlation","Chisq diff","Df diff","P-Value"), format="simple")
```{whites, eval=FALSE, echo = eval_code}
Factor Reliability
rel <- reliability(fit, return.total = TRUE, dropSingle = FALSE, omit.imps = c("no.conv", "no.se", "no.npd")) rel <- rel[3:5,] rel <- as.data.frame(rel) rownames(rel) <- c("Omega (Bentler)","Omega (McDonald)","AVE")
Colname for single factor case
if (length(fit@pta$vnames$lv.x[[1]])==1) colnames(rel) <- fit@pta$vnames$lv.x[[1]] rel_transpose <- as.data.frame(t(rel)) rel_transpose <- rel_transpose[1:(nrow(rel_transpose)-1),]
Output table
opts <- options(knitr.kable.NA = "Not provided in case of cross-loadings") kable(rel, caption="Factor Reliability",digits=2, linesep = '', longtable=T, format="simple") options(opts)
```{whites, eval=FALSE, echo = eval_code}
## Observed Covariance Matrix
obscov <- inspect(fit, "sampstat")$cov
corrplot::corrplot(obscov, is.corr = FALSE,
type = 'lower',
order = "original",
col='black', method="number", cl.pos = "n", tl.cex=.50, number.cex=0.50)
```{whites, eval=FALSE, echo = eval_code}
Model-Implied Covariance Matrix
fittedcov <- inspect(fit, what="cov.ov") # Fitted cov corrplot::corrplot(fittedcov, is.corr = FALSE, type = 'lower', order = "original", col='black', method="number", cl.pos = "n", tl.cex=.50, number.cex=0.50)
```{whites, eval=FALSE, echo = eval_code}
## Residual Covariance Matrix
covraw <- lavResiduals(fit,type="raw")$cov
corrplot::corrplot(covraw, is.corr = FALSE,
type = 'lower',
order = "original",
tl.col='black', tl.cex=.50, number.cex=0.50, method="number", cl.cex = 0.5)
```{whites, eval=FALSE, echo = eval_code}
Standardized Residual Matrix
covstd <- lavResiduals(fit,type="raw")$cov.z corrplot::corrplot(covstd, is.corr = FALSE, type = 'lower', order = "original", tl.col='black', tl.cex=.50, number.cex=0.50, method="number", cl.cex = 0.5)
```{whites, eval=FALSE, echo = eval_code}
## Residual Correlation Matrix
r <- resid(fit, "cor")$cov # Residuals on cor
corrplot::corrplot(r, is.corr = FALSE,
type = 'lower',
order = "original",
tl.col='black', tl.cex=.50, number.cex=0.50, method="number", cl.cex = 0.5)
# Indicator for Modification Indices # MI for Error Covariances mi_corr <- modindices(fit, sort.=TRUE, power=TRUE, high.power = 0.75, na.remove=TRUE, op="~~", maximum.number=10) mi_corr <- mi_corr[which(mi_corr$lhs %in% fit@pta$vnames$ov[[1]]),] if (nrow(mi_corr)>0) mi_corr_indic <- TRUE else mi_corr_indic <- FALSE # MI for Factor Loadings mi_fl <- modindices(fit, sort.=TRUE, power=TRUE, high.power = 0.75, na.remove=TRUE, op="=~", delta=0.4, maximum.number=10) if (nrow(mi_fl)>0) mi_fl_indic <- TRUE else mi_fl_indic <- FALSE # MI for Factor Covariances mi_fcov <- modindices(fit, sort.=TRUE, power=TRUE, high.power = 0.75, na.remove=TRUE, op="~~", maximum.number=10) mi_fcov <- mi_fcov[which(mi_fcov$lhs %in% fit@pta$vnames$lv[[1]]),] if (nrow(mi_fcov)>0) mi_fcov_indic <- TRUE else mi_fcov_indic <- FALSE
```{whites, eval=FALSE, echo = mi_corr_indic}
Modification Indices for Correlation
mi_corr <- modindices(fit, sort.=TRUE, power=TRUE, high.power = 0.75, na.remove=TRUE, op="~~", maximum.number=10) mi_corr <- mi_corr[which(mi_corr$lhs %in% fit@pta$vnames$ov[[1]]),] mi_corr <- mi_corr[,c(-6,-8)] rownames(mi_corr) <- NULL kable(mi_corr,col.names=c("Left","Operator","Right","Modification Index", "Expected Parameter Change", "Delta", "Power", "Decision"), digits=3, caption="Modification Indices With Respect To Error Covariances", linesep = '', longtable=T, format="simple")
```{whites, eval=FALSE, echo = mi_fl_indic}
## Modification Indices for Factor Loadings
mi_fl <- modindices(fit, sort.=TRUE, power=TRUE, high.power = 0.75,
na.remove=TRUE, op="=~", delta=0.4, maximum.number=10)
mi_fl <- mi_fl[,c(-6,-8)]
rownames(mi_fl) <- NULL
kable(mi_fl,col.names=c("Left","Operator","Right","Modification Index",
"Expected Parameter Change", "Delta", "Power", "Decision"),
digits=3, caption="Modification Indices With Respect To Factor Loadings",
linesep = '', longtable=T, format="simple")
```{whites, eval=FALSE, echo = mi_fcov_indic}
Modification Indices for Factor Covariances
mi_fcov <- modindices(fit, sort.=TRUE, power=TRUE, high.power = 0.75, na.remove=TRUE, op="~~", maximum.number=10) mi_fcov <- mi_fcov[which(mi_fcov$lhs %in% fit@pta$vnames$lv[[1]]),] mi_fcov <- mi_fcov[,c(-6,-8)] rownames(mi_fcov) <- NULL kable(mi_fcov,col.names=c("Left","Operator","Right","Modification Index", "Expected Parameter Change", "Delta", "Power", "Decision"), digits=3, caption="Modification Indices With Respect To Factor Covariances", linesep = '', longtable=T, format="simple")
```{whites, eval=FALSE, echo = eval_code}
## Path Diagram
semPaths(fit, what="std", intercepts = FALSE)
cat("\n# References", fill=TRUE)
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