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Last data update: 2014.03.03

R: Case influence on model parameters.

parinfluence

R Documentation

Case influence on model parameters.

Description

Computes direction of change in parameter estimates with

Δ hat{θ}_{ji}=frac{hat{θ}_j-hat{θ}_{j(i)}}{[VAR(hat{θ}_{j(i)})]^{1/2}}

where hat{θ}_j and hat{θ}_{j(i)} are the parameter estimates obtained from original and deleted i 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 `lavaan` for more information.
`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 `TRUE` returns generalized Cook's Distance computed as [Δ hat{θ}_{ji}]^2.
`...`	Additional parameters for `sem` function.

Value

Return a list:

`gCD`	Generalized Cook's Distance, if `cook=TRUE`.
`Dparm`	Direction of change in parameter estimates.
`THi`	Parameter estimates.

Note

If for observation i model does not converge or yelds a solution with negative estimated variances or NA parameter values, the associated values of Δ hat{θ}_{ji} 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])