parinfluence {influence.SEM} | R Documentation |
Case influence on model parameters.
Description
Computes direction of change in parameter estimates with
where and
are the parameter estimates obtained from original and deleted
samples.
Usage
parinfluence(parm, model, data, cook = FALSE, ...)
Arguments
parm |
Single parameter or vector of parameters. |
model |
A description of the user-specified model using the lavaan model syntax. See |
data |
A data frame containing the observed variables used in the model. If any variables are declared as ordered factors, this function will treat them as ordinal variables. |
cook |
Logical, if |
... |
Additional parameters for |
Value
Returns a list:
gCD |
Generalized Cook's Distance, if |
Dparm |
Direction of change in parameter estimates. |
Note
If for observation model does not converge or yelds a solution with negative estimated variances or NA parameter values, the associated values of
are set to
NA
.
Author(s)
Massimiliano Pastore
References
Pek, J., MacCallum, R.C. (2011). Sensitivity Analysis in Structural Equation Models: Cases and Their Influence. Multivariate Behavioral Research, 46, 202-228.
Examples
## not run: this example take several minutes
data("PDII")
model <- "
F1 =~ y1+y2+y3+y4
"
# fit0 <- sem(model, data=PDII)
# PAR <- c("F1=~y2","F1=~y3","F1=~y4")
# LY <- parinfluence(PAR,model,PDII)
# str(LY)
# explore.influence(LY$Dparm[,1])
## not run: this example take several minutes
## an example in which the deletion of a case yelds a solution
## with negative estimated variances
model <- "
F1 =~ x1+x2+x3
F2 =~ y1+y2+y3+y4
F3 =~ y5+y6+y7+y8
"
# fit0 <- sem(model, data=PDII)
# PAR <- c("F2=~y2","F2=~y3","F2=~y4")
# LY <- parinfluence(PAR,model,PDII)
## not run: this example take several minutes
## dealing with ordinal data
data(Q)
model <- "
F1 =~ it1+it2+it3+it4+it5+it6+it7+it8+it9+it10
"
# fit0 <- sem(model, data=Q, ordered=colnames(Q))
# LY <- parinfluence("F1=~it4",model,Q,ordered=colnames(Q))
# explore.influence(LY$Dparm[,1])