# Generalized Cook's Distance Compute generalize Cook's distances (gCD's) for exploratory and confirmatory FA. Can return DFBETA matrix if requested. If mirt is used, then the values will be associated with the unique response patterns instead. ```r gCD(data, model, progress = TRUE, ...) ## S3 method for class 'gCD' print(x, ncases = 10, DFBETAS = FALSE, ...) ## S3 method for class 'gCD' plot( x, y = NULL, main = "Generalized Cook Distance", type = c("p", "h"), ylab = "gCD", ... ) ``` ## Arguments - `data`: matrix or data.frame - `model`: if a single numeric number declares number of factors to extract in exploratory factor analysis (requires complete dataset, i.e., no missing). If `class(model)` is a sem (semmod), or lavaan (character), then a confirmatory approach is performed instead - `progress`: logical; display the progress of the computations in the console? - `...`: additional parameters to be passed - `x`: an object of class `gCD` - `ncases`: number of extreme cases to display - `DFBETAS`: logical; return DFBETA matrix in addition to gCD? If TRUE, a list is returned - `y`: a `NULL` value ignored by the plotting function - `main`: the main title of the plot - `type`: type of plot to use, default displays points and lines - `ylab`: the y label of the plot ## Details Note that `gCD` is not limited to confirmatory factor analysis and can apply to nearly any model being studied where detection of influential observations is important. ## Examples ```r ## Not run: #run all gCD functions using multiple cores setCluster() #Exploratory nfact <- 3 (gCDresult <- gCD(holzinger, nfact)) (gCDresult.outlier <- gCD(holzinger.outlier, nfact)) plot(gCDresult) plot(gCDresult.outlier) #------------------------------------------------------------------- #Confirmatory with sem model <- sem::specifyModel() F1 -> Remndrs, lam11 F1 -> SntComp, lam21 F1 -> WrdMean, lam31 F2 -> MissNum, lam41 F2 -> MxdArit, lam52 F2 -> OddWrds, lam62 F3 -> Boots, lam73 F3 -> Gloves, lam83 F3 -> Hatchts, lam93 F1 <-> F1, NA, 1 F2 <-> F2, NA, 1 F3 <-> F3, NA, 1 (gCDresult2 <- gCD(holzinger, model)) (gCDresult2.outlier <- gCD(holzinger.outlier, model)) plot(gCDresult2) plot(gCDresult2.outlier) #------------------------------------------------------------------- #Confirmatory with lavaan model <- 'F1 =~ Remndrs + SntComp + WrdMean F2 =~ MissNum + MxdArit + OddWrds F3 =~ Boots + Gloves + Hatchts' (gCDresult2 <- gCD(holzinger, model, orthogonal=TRUE)) (gCDresult2.outlier <- gCD(holzinger.outlier, model, orthogonal=TRUE)) plot(gCDresult2) plot(gCDresult2.outlier) # categorical data with mirt library(mirt) data(LSAT7) dat <- expand.table(LSAT7) model <- mirt.model('F = 1-5') result <- gCD(dat, model) plot(result) mod <- mirt(dat, model) res <- residuals(mod, type = 'exp') cbind(res, gCD=round(result$gCD, 3)) ## End(Not run) ``` ## References Chalmers, R. P. & Flora, D. B. (2015). faoutlier: An R Package for Detecting Influential Cases in Exploratory and Confirmatory Factor Analysis. **Applied Psychological Measurement, 39**, 573-574. tools:::Rd_expr_doi("10.1177/0146621615597894") Flora, D. B., LaBrish, C. & Chalmers, R. P. (2012). Old and new ideas for data screening and assumption testing for exploratory and confirmatory factor analysis. **Frontiers in Psychology, 3**, 1-21. tools:::Rd_expr_doi("10.3389/fpsyg.2012.00055") ## See Also `LD`, `obs.resid`, `robustMD`, `setCluster` ## Author(s) Phil Chalmers rphilip.chalmers@gmail.com