R: Principal Components Analysis. (2024)

pcomp {SciViews}

R Documentation

Description

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

Usage

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

Arguments

`x`	A matrix or data frame with numeric data.
`...`	Arguments passed to or from other methods. If 'x`is a formula one might specify`scale =`,`tol =`or`covmat ='.
`formula`	A formula with no response variable, referring only to numericvariables.
`data`	An optional data frame (or similar: see `model.frame()`)containing the variables in the formula `formula =`. By default the variablesare taken from `environment(formula)`.
`subset`	An optional vector used to select rows (observations) of thedata matrix `x`.
`na.action`	A function which indicates what should happen when the datacontain `NA`s. 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 principalcomponent should be calculated.
`center`	A logical value indicating whether the variables should beshifted to be zero centered. Alternately, a vector of length equal thenumber 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 isalways centered in this case.
`scale`	A logical value indicating whether the variables should bescaled to have unit variance before the analysis takes place. The default is`TRUE`, which in general, is advisable. Alternatively, a vector of lengthequal the number of columns of `x` can be supplied. The value is passed to`scale()`.
`tol`	Only when `method = "svd"`. A value indicating the magnitudebelow which components should be omitted. (Components are omitted if theirstandard deviations are less than or equal to `tol` times the standarddeviation of the first component.) With the default null setting, nocomponents are omitted. Other settings for `tol =` could be `tol = 0` or`tol = sqrt(.Machine$double.eps)`, which would omit essentially constantcomponents.
`covmat`	A covariance matrix, or a covariance list as returned by`cov.wt()` (and `cov.mve()` or `cov.mcd()` from package MASS). Ifsupplied, 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 whitespaces in the table. That way, larger values are easier to spot and to readin large tables.
`digits`	The number of digits to print.
`which`	The graph to plot.
`choices`	Which principal axes to plot. For 2D graphs, specify twointegers.
`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 correlationsplots.
`ar.length`	The length of the arrows in the loadings and correlationsplots.
`pos`	The position of text relative to arrows in loadings andcorrelation 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 asthose used for calculating the PCA. You can then plot these additionalindividuals in the scores plot.
`dim`	The number of principal components to keep.
`newvars`	New variables with observations for same individuals as thoseused for mcalculating the PCA. Correlation with PCs is calculated. You canthen plot these additional variables in the correlation plot.

Details

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

Value

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

Note

The signs of the columns of the loadings and scores are arbitrary, andso may differ between functions for PCA, and even between different builds ofR.

Author(s)

Philippe Grosjean phgrosjean@sciviews.org, but the core code isindeed in package stats.

Examples

# 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.pcasummary(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 graphlines(Correlation(cars.pca, newvars = mtcars[-(8:14), c("vs", "am", "gear", "carb")]))# Plot the scoresplot(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 PCsiris.pca <- pcomp(iris[, -5])pairs(iris.pca, col = (2:4)[iris$Species])

[Package SciViews version 0.9-13.1 Index]