sjPlot (version 1.7)

sjp.pca: Plot PCA results

Description

Performes a principle component analysis on a data frame or matrix and plots the factor solution as ellipses or tiles. In case a data frame is used as parameter, the cronbach's alpha value for each factor scale will be calculated, i.e. all variables with the highest loading for a factor are taken for the reliability test. The result is an alpha value for each factor dimension.

Usage

sjp.pca(data, numberOfFactors = NULL, factorLoadingTolerance = 0.1,
  plotEigenvalues = FALSE, digits = 2, title = NULL,
  axisLabels.y = NULL, type = "tile", geom.size = 10,
  geom.colors = "RdBu", breakTitleAt = 50, breakLabelsAt = 20,
  showValueLabels = TRUE, showCronbachsAlpha = TRUE, printPlot = TRUE)

Value

(Invisibly) returns a structure with

• the varimax-rotated factor loading matrix (varim)
• the column indices of removed variables (for more details see next list item) (removed.colindex)
• an updated data frame containing all factors that have a clear loading on a specific scale in casedatawas a data frame (See parameterfactorLoadingTolerancefor more details) (removed.df)
• the ggplot-object (plot),
• the data frame that was used for setting up the ggplot-object (df).

See Also

• http://www.strengejacke.de/sjPlot/sjp.pca/{sjPlot manual: sjp.pca}
• sjt.pca
• reliab_test
• sjt.itemanalysis
• cronb

Examples

# randomly create data frame with 7 items, each consisting of 4 categories
likert_4 <- data.frame(sample(1:4, 500, replace = TRUE, prob = c(0.2,0.3,0.1,0.4)),
                       sample(1:4, 500, replace = TRUE, prob = c(0.5,0.25,0.15,0.1)),
                       sample(1:4, 500, replace = TRUE, prob = c(0.4,0.15,0.25,0.2)),
                       sample(1:4, 500, replace = TRUE, prob = c(0.25,0.1,0.4,0.25)),
                       sample(1:4, 500, replace = TRUE, prob = c(0.1,0.4,0.4,0.1)),
                       sample(1:4, 500, replace = TRUE),
                       sample(1:4, 500, replace = TRUE, prob = c(0.35,0.25,0.15,0.25)))

# Create variable labels
colnames(likert_4) <- c("V1", "V2", "V3", "V4", "V5", "V6", "V7")

# plot results from PCA as square-tiled "heatmap"
sjp.pca(likert_4)

# manually compute PCA
pca <- prcomp(na.omit(likert_4), retx = TRUE, center = TRUE, scale. = TRUE)
# plot results from PCA as circles, including Eigenvalue-diagnostic.
# note that this plot does not compute the Cronbach's Alpha
sjp.pca(pca, plotEigenvalues = TRUE, type = "circle")


# -------------------------------
# Data from the EUROFAMCARE sample dataset
# -------------------------------
data(efc)

# retrieve variable and value labels
varlabs <- get_var_labels(efc)

# recveive first item of COPE-index scale
start <- which(colnames(efc) == "c82cop1")
# recveive last item of COPE-index scale
end <- which(colnames(efc) == "c90cop9")

# create data frame with COPE-index scale
df <- as.data.frame(efc[,c(start:end)])
colnames(df) <- varlabs[c(start:end)]

sjp.pca(df)

# -------------------------------
# auto-detection of labels
# -------------------------------
efc <- set_var_labels(efc, varlabs)
sjp.pca(efc[, c(start:end)])