20241002 - update

Author: Petersen

15-Factor-Analysis-PCA.Rmd

@@ -22,3 +22,32 @@ library("tinytex")
 library("knitr")
 library("rmarkdown")
 ```
+
+### Load Data {#loadData-factorAnalysis}
+
+### Prepare Data {#prepareData-factorAnalysis}
+
+#### Add Missing Data {#addMissingData-factorAnalysis}
+
+Adding missing data to dataframes helps make examples more realistic to real-life data and helps you get in the habit of programming to account for missing data.
+For reproducibility, I set the seed below.
+Using the same seed will yield the same answer every time.
+There is nothing special about this particular seed.
+
+`HolzingerSwineford1939` is a data set from the `lavaan` package [@R-lavaan] that contains mental ability test scores (`x1`–`x9`) for seventh- and eighth-grade children.
+
+```{r}
+set.seed(52242)
+
+varNames <- names(HolzingerSwineford1939)
+dimensionsDf <- dim(HolzingerSwineford1939)
+unlistedDf <- unlist(HolzingerSwineford1939)
+unlistedDf[sample(
+  1:length(unlistedDf),
+  size = .15 * length(unlistedDf))] <- NA
+HolzingerSwineford1939 <- as.data.frame(matrix(
+  unlistedDf,
+  ncol = dimensionsDf[2]))
+names(HolzingerSwineford1939) <- varNames
+vars <- c("x1","x2","x3","x4","x5","x6","x7","x8","x9")
+```