SciViews (version 0.9-13.1)

pcomp: Principal Components Analysis.

Description

Perform a principal components analysis on a matrix or data frame and return a pcomp object.

Usage

pcomp(x, ...)
# S3 method for formula
pcomp(formula, data = NULL, subset, na.action,
  method = c("svd", "eigen"), ...)
# S3 method for default
pcomp(x, method = c("svd", "eigen"), scores = TRUE,
  center = TRUE, scale = TRUE, tol = NULL, covmat = NULL,
  subset = rep(TRUE, nrow(as.matrix(x))), ...)
# S3 method for pcomp
print(x, ...)
# S3 method for pcomp
summary(object, loadings = TRUE, cutoff = 0.1, ...)
# S3 method for summary.pcomp
print(x, digits = 3, loadings = x$print.loadings,
  cutoff = x$cutoff, ...)
# S3 method for pcomp
plot(x, which = c("screeplot", "loadings", "correlations",
  "scores"), choices = 1L:2L, col = par("col"), bar.col = "gray",
  circle.col = "gray", ar.length = 0.1, pos = NULL, labels = NULL,
  cex = par("cex"), main = paste(deparse(substitute(x)), which, sep =
  " - "), xlab, ylab, ...)
# S3 method for pcomp
screeplot(x, npcs = min(10, length(x$sdev)),
  type = c("barplot", "lines"), col = "cornsilk",
  main = deparse(substitute(x)), ...)
# S3 method for pcomp
points(x, choices = 1L:2L, type = "p", pch = par("pch"),
  col = par("col"), bg = par("bg"), cex = par("cex"), ...)
# S3 method for pcomp
lines(x, choices = 1L:2L, groups, type = c("p", "e"),
  col = par("col"), border = par("fg"), level = 0.9, ...)
# S3 method for pcomp
text(x, choices = 1L:2L, labels = NULL, col = par("col"),
  cex = par("cex"), pos = NULL, ...)
# S3 method for pcomp
biplot(x, choices = 1L:2L, scale = 1, pc.biplot = FALSE,
  ...)
# S3 method for pcomp
pairs(x, choices = 1L:3L, type = c("loadings",
  "correlations"), col = par("col"), circle.col = "gray",
  ar.col = par("col"), ar.length = 0.05, pos = NULL,
  ar.cex = par("cex"), cex = par("cex"), ...)
# S3 method for pcomp
predict(object, newdata, dim = length(object$sdev), ...)
# S3 method for pcomp
correlation(x, newvars, dim = length(x$sdev), ...)
scores(x, ...)
# S3 method for pcomp
scores(x, labels = NULL, dim = length(x$sdev), ...)

Arguments

x

A matrix or data frame with numeric data.

...

Arguments passed to or from other methods. If `xis a formula one might specifyscale =,tol =orcovmat =`.

formula

A formula with no response variable, referring only to numeric variables.

data

An optional data frame (or similar: see model.frame()) containing the variables in the formula formula =. By default the variables are taken from environment(formula).

subset

An optional vector used to select rows (observations) of the data matrix x.

na.action

A function which indicates what should happen when the data contain NAs. The default is set by the na.action = setting of options(), and is na.fail() if that is not set. The 'factory-fresh' default is na.omit().

method

Either "svd" (using prcomp()), "eigen" (using princomp()), or an abbreviation.

scores

A logical value indicating whether the score on each principal component should be calculated.

center

A logical value indicating whether the variables should be shifted to be zero centered. Alternately, a vector of length equal the number of columns of x can be supplied. The value is passed to scale =. Note that this argument is ignored for method = "eigen" and the dataset is always centered in this case.

scale

A logical value indicating whether the variables should be scaled to have unit variance before the analysis takes place. The default is TRUE, which in general, is advisable. Alternatively, a vector of length equal the number of columns of x can be supplied. The value is passed to scale().

tol

Only when method = "svd". A value indicating the magnitude below which components should be omitted. (Components are omitted if their standard deviations are less than or equal to tol times the standard deviation of the first component.) With the default null setting, no components are omitted. Other settings for tol = could be tol = 0 or tol = sqrt(.Machine$double.eps), which would omit essentially constant components.

covmat

A covariance matrix, or a covariance list as returned by cov.wt() (and cov.mve() or cov.mcd() from package MASS). If supplied, this is used rather than the covariance matrix of x.

object

A 'pcomp' object.

loadings

Do we also summarize the loadings?

cutoff

The cutoff value below which loadings are replaced by white spaces in the table. That way, larger values are easier to spot and to read in large tables.

digits

The number of digits to print.

which

The graph to plot.

choices

Which principal axes to plot. For 2D graphs, specify two integers.

col

The color to use in graphs.

bar.col

The color of bars in the screeplot.

circle.col

The color for the circle in the loadings or correlations plots.

ar.length

The length of the arrows in the loadings and correlations plots.

pos

The position of text relative to arrows in loadings and correlation plots.

labels

The labels to write. If NULL default values are computed.

cex

The factor of expansion for text (labels) in the graphs.

main

The title of the graph.

xlab

The label of the x-axis.

ylab

The label of the y-axis.

npcs

The number of principal components to represent in the screeplot.

type

The type of screeplot ("barplot" or "lines") or pairs plot ("loadings" or "correlations").

pch

The type of symbol to use.

bg

The background color for symbols.

groups

A grouping factor.

border

The color of the border.

level

The probability level to use to draw the ellipse.

pc.biplot

Do we create a Gabriel's biplot (see biplot())?

ar.col

Color of arrows.

ar.cex

Expansion factor for terxt on arrows.

newdata

New individuals with observations for the same variables as those used for calculating the PCA. You can then plot these additional individuals in the scores plot.

dim

The number of principal components to keep.

newvars

New variables with observations for same individuals as those used for mcalculating the PCA. Correlation with PCs is calculated. You can then plot these additional variables in the correlation plot.

Value

A c("pcomp", "pca", "princomp") object.

Details

pcomp() is a generic function with "formula" and "default" methods. It is essentially a wrapper around prcomp() and princomp() to provide a coherent interface and object for both methods.

A 'pcomp' object is created. It inherits from 'pca' (as in labdsv package, but not compatible with the 'pca' object of package ade4) and of 'princomp'.

For more information on calculation done, refer to prcomp() for method = "svd" or princomp() for method = "eigen".

See Also

vectorplot(), prcomp(), princomp(), loadings(), Correlation()

Examples

# NOT RUN {
# We will analyze mtcars without the Mercedes data (rows 8:14)
data(mtcars)
cars.pca <- pcomp(~ mpg + cyl + disp + hp + drat + wt + qsec, data = mtcars,
  subset = -(8:14))
cars.pca
summary(cars.pca)
screeplot(cars.pca)

# Loadings are extracted and plotted like this
(cars.ldg <- loadings(cars.pca))
plot(cars.pca, which = "loadings") # Equivalent to vectorplot(cars.ldg)

# Similarly, correlations of variables with PCs are extracted and plotted
(cars.cor <- Correlation(cars.pca))
plot(cars.pca, which = "correlations") # Equivalent to vectorplot(cars.cor)
# One can add supplementary variables on this graph
lines(Correlation(cars.pca,
  newvars = mtcars[-(8:14), c("vs", "am", "gear", "carb")]))

# Plot the scores
plot(cars.pca, which = "scores", cex = 0.8) # Similar to plot(scores(x)[, 1:2])
# Add supplementary individuals to this plot (labels), also points() or lines()
text(predict(cars.pca, newdata = mtcars[8:14, ]), col = "gray", cex = 0.8)

# Pairs plot for 3 PCs
iris.pca <- pcomp(iris[, -5])
pairs(iris.pca, col = (2:4)[iris$Species])
# }