Deltachi | R Documentation |
Quantifies case influence on overall model fit by change in the test statistic
Δ_{χ^2_i}=χ^2-χ^2_{(i)}
where χ^2 and χ^2_{(i)} are the test statistics obtained from original and deleted i samples.
Deltachi(model, data, ..., scaled = FALSE)
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. |
... |
Additional parameters for |
scaled |
Logical, if |
Returns a vector of Δ_{χ^2_i}.
If for observation i model does not converge or yelds a solution with negative estimated variances, the associated value of Δ_{χ^2_i} is set to NA
.
This function is a particular case of fitinfluence
, see example below.
Massimiliano Pastore
Pek, J., MacCallum, R.C. (2011). Sensitivity Analysis in Structural Equation Models: Cases and Their Influence. Multivariate Behavioral Research, 46, 202-228.
Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48, 1-36.
Rosseel, Y. (2022). The lavaan
tutorial. URL: https://lavaan.ugent.be/tutorial/.
## not run: this example take several minutes data("PDII") model <- " F1 =~ y1+y2+y3+y4 " # fit0 <- sem(model, data=PDII) # Dchi <- Deltachi(model,data=PDII) # plot(Dchi,pch=19,xlab="observations",ylab="Delta chisquare") ## 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) # Dchi <- Deltachi(model,data=PDII) # plot(Dchi,pch=19,xlab="observations",ylab="Delta chisquare",main="Deltachi function") ## the case that produces negative estimated variances # sem(model,data=PDII[-which(is.na(Dchi)),]) ## same results # Dchi <- fitinfluence("chisq",model,data=PDII)$Dind$chisq # plot(Dchi,pch=19,xlab="observations",ylab="Delta chisquare",main="fitinfluence function")
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