sjPlot (version 2.0.0)

sjp.pca: Plot PCA results

Description

Performes a principle component analysis on a data frame or matrix (with varimax rotation) and plots the factor solution as ellipses or tiles. In case a data frame is used as argument, 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, nmbr.fctr = NULL, fctr.load.tlrn = 0.1, plot.eigen = FALSE,
  digits = 2, title = NULL, axis.labels = NULL, type = c("bar",
  "circle", "tile"), geom.size = 0.6, geom.colors = "RdBu",
  wrap.title = 50, wrap.labels = 30, show.values = TRUE,
  show.cronb = TRUE, prnt.plot = 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 argumentfctr.load.tlrnfor more details) (removed.df)
• thefactor.index, i.e. the column index of each variable with the highest factor loading for each factor,
• 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

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, type = "tile")

# plot results from PCA as bars
sjp.pca(likert_4, type = "bar")

# 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, plot.eigen = TRUE, type = "circle", geom.size = 10)

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

# retrieve variable and value labels
varlabs <- get_label(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
mydf <- data.frame(efc[, c(start:end)])
colnames(mydf) <- varlabs[c(start:end)]

sjp.pca(mydf)
sjp.pca(mydf, type = "tile")

# -------------------------------
# auto-detection of labels
# -------------------------------
sjp.pca(efc[, c(start:end)], type = "circle", geom.size = 10)