ggscreeplot {ggbiplot}

Screeplot for Principal Components

Description

Produces scree plots (Cattell, 1966) of the variance proportions explained by each dimension against dimension number from various PCA-like dimension reduction techniques.

Usage

ggscreeplot(
  pcobj,
  type = c("pev", "cev"),
  size = 4,
  shape = 19,
  color = "black",
  linetype = 1,
  linewidth = 1
)

Arguments

pcobj

an object returned by prcomp, princomp, PCA, dudi.pca, or lda

type

the type of scree plot, one of c('pev', 'cev'). 'pev' plots the proportion of explained variance, i.e. the eigenvalues divided by the trace. 'cev' plots the cumulative proportion of explained variance, i.e. the partial sum of the first k eigenvalues divided by the trace.

size

point size

shape

shape of the points. Default: 19, a filled circle.

color

color for points and line. Default: "black".

linetype

type of line

linewidth

width of line

Value

A ggplot2 object with the aesthetics x = PC, y = yvar

References

Cattell, R. B. (1966). The Scree Test For The Number Of Factors. Multivariate Behavioral Research, 1, 245–276.

Examples

data(wine)
wine.pca <- prcomp(wine, scale. = TRUE)
ggscreeplot(wine.pca)

# show horizontal lines for 80, 90% of cumulative variance
ggscreeplot(wine.pca, type = "cev") +
  geom_hline(yintercept = c(0.8, 0.9), color = "blue") 

# Make a fancy screeplot, higlighting the scree starting at component 4
data(crime)
crime.pca <- 
  crime |> 
  dplyr::select(where(is.numeric)) |>
  prcomp(scale. = TRUE)
  
(crime.eig <- crime.pca |> 
   broom::tidy(matrix = "eigenvalues"))

ggscreeplot(crime.pca) +
  stat_smooth(data = crime.eig |> dplyr::filter(PC>=4), 
              aes(x=PC, y=percent), method = "lm", 
              se = FALSE,
              fullrange = TRUE)