princomp {stats} | R Documentation

## Principal Components Analysis

### Description

`princomp` performs a principal components analysis on the given numeric data matrix and returns the results as an object of class `princomp`.

### Usage

    princomp(x, ...)

    ## S3 method for class 'formula'
    princomp(formula, data = NULL, subset, na.action, ...)

    ## Default S3 method:
    princomp(x, cor = FALSE, scores = TRUE, covmat = NULL,
             subset = rep_len(TRUE, nrow(as.matrix(x))), ...)

    ## S3 method for class 'princomp'
    predict(object, newdata, ...)

### Arguments

`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 `NA`s. The default is set by the `na.action` setting of `options`, and is `na.fail` if that is unset. The ‘factory-fresh’ default is `na.omit`.
`x` | a numeric matrix or data frame which provides the data for the principal components analysis.
`cor` | a logical value indicating whether the calculation should use the correlation matrix or the covariance matrix. (The correlation matrix can only be used if there are no constant variables.)
`scores` | a logical value indicating whether the score on each principal component should be calculated.
`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`.
`...` | arguments passed to or from other methods. If `x` is a formula one might specify `cor` or `scores`.
`object` | Object of class inheriting from `"princomp"`
`newdata` | An optional data frame or matrix in which to look for variables with which to predict. If omitted, the scores are used. If the original fit used a formula or a data frame or a matrix with column names, `newdata` must contain columns with the same names. Otherwise it must contain the same number of columns, to be used in the same order.

### Details

`princomp` is a generic function with `"formula"` and `"default"` methods.

The calculation is done using `eigen` on the correlation or covariance matrix, as determined by `cor`. This is done for compatibility with the S-PLUS result. A preferred method of calculation is to use `svd` on `x`, as is done in `prcomp`.

Note that the default calculation uses divisor `N` for the covariance matrix.

The `print` method for these objects prints the results in a nice format and the `plot` method produces a scree plot (`screeplot`). There is also a `biplot` method.

If `x` is a formula then the standard NA-handling is applied to the scores (if requested): see `napredict`.

`princomp` only handles so-called R-mode PCA, that is feature extraction of variables. If a data matrix is supplied (possibly via a formula) it is required that there are at least as many units as variables. For Q-mode PCA use `prcomp`.

### Value

`princomp` returns a list with class `"princomp"` containing the following components:

`sdev` | the standard deviations of the principal components.
`loadings` | the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors). This is of class `"loadings"`: see `loadings` for its `print` method.
`center` | the means that were subtracted.
`scale` | the scalings applied to each variable.
`n.obs` | the number of observations.
`scores` | if `scores = TRUE`, the scores of the supplied data on the principal components. These are non-null only if `x` was supplied, and if `covmat` was also supplied if it was a covariance list. For the formula method, `napredict()` is applied to handle the treatment of values omitted by the `na.action`.
`call` | the matched call.
`na.action` | If relevant.

### Note

The signs of the columns of the loadings and scores are arbitrary, and so may differ between different programs for PCA, and even between different builds of R.

### References

Mardia, K. V., J. T. Kent and J. M. Bibby (1979). Multivariate Analysis, London: Academic Press.

Venables, W. N. and B. D. Ripley (2002). Modern Applied Statistics with S, Springer-Verlag.

### See Also

`summary.princomp`, `screeplot`, `biplot.princomp`, `prcomp`, `cor`, `cov`, `eigen`.

### Examples

    require(graphics)

    ## The variances of the variables in the
    ## USArrests data vary by orders of magnitude, so scaling is appropriate
    (pc.cr <- princomp(USArrests))  # inappropriate
    princomp(USArrests, cor = TRUE) # =^= prcomp(USArrests, scale=TRUE)
    ## Similar, but different:
    ## The standard deviations differ by a factor of sqrt(49/50)

    summary(pc.cr <- princomp(USArrests, cor = TRUE))
    loadings(pc.cr)  # note that blank entries are small but not zero
    ## The signs of the columns are arbitrary
    plot(pc.cr) # shows a screeplot.
    biplot(pc.cr)

    ## Formula interface
    princomp(~ ., data = USArrests, cor = TRUE)

    ## NA-handling
    USArrests[1, 2] <- NA
    pc.cr <- princomp(~ Murder + Assault + UrbanPop,
                      data = USArrests, na.action = na.exclude, cor = TRUE)
    pc.cr$scores[1:5, ]

    ## (Simple) Robust PCA:
    ## Classical:
    (pc.cl  <- princomp(stackloss))
    ## Robust:
    (pc.rob <- princomp(stackloss, covmat = MASS::cov.rob(stackloss)))

[Package stats version 3.3.2 Index]