prcomp {stats} | R Documentation

## Principal Components Analysis

### Description

Performs a principal components analysis on the given data matrix and returns the results as an object of class `prcomp`.

### Usage

    prcomp(x, ...)

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

    ## Default S3 method:
    prcomp(x, retx = TRUE, center = TRUE, scale. = FALSE,
           tol = NULL, rank. = NULL, ...)

    ## S3 method for class 'prcomp'
    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`.
`...` | arguments passed to or from other methods. If `x` is a formula one might specify `scale.` or `tol`.
`x` | a numeric or complex matrix (or data frame) which provides the data for the principal components analysis.
`retx` | a logical value indicating whether the rotated variables should be returned.
`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`.
`scale.` | a logical value indicating whether the variables should be scaled to have unit variance before the analysis takes place. The default is `FALSE` for consistency with S, but in general scaling is advisable. Alternatively, a vector of length equal the number of columns of `x` can be supplied. The value is passed to `scale`.
`tol` | 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 (unless `rank.` is specified less than `min(dim(x))`.). Other settings for tol could be `tol = 0` or `tol = sqrt(.Machine$double.eps)`, which would omit essentially constant components.
`rank.` | optionally, a number specifying the maximal rank, i.e., maximal number of principal components to be used. Can be set as alternative or in addition to `tol`, useful notably when the desired rank is considerably smaller than the dimensions of the matrix.
`object` | object of class inheriting from `"prcomp"`
`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

The calculation is done by a singular value decomposition of the (centered and possibly scaled) data matrix, not by using `eigen` on the covariance matrix. This is generally the preferred method for numerical accuracy. The `print` method for these objects prints the results in a nice format and the `plot` method produces a scree plot.

Unlike `princomp`, variances are computed with the usual divisor N - 1.

Note that `scale = TRUE` cannot be used if there are zero or constant (for `center = TRUE`) variables.

### Value

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

`sdev` | the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix).
`rotation` | the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors). The function `princomp` returns this in the element `loadings`.
`x` | if `retx` is true the value of the rotated data (the centred (and scaled if requested) data multiplied by the `rotation` matrix) is returned. Hence, `cov(x)` is the diagonal matrix `diag(sdev^2)`. For the formula method, `napredict()` is applied to handle the treatment of values omitted by the `na.action`.
`center, scale` | the centering and scaling used, or `FALSE`.

### Note

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

### References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

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

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

### Examples

    C <- chol(S <- toeplitz(.9 ^ (0:31))) # Cov.matrix and its root
    all.equal(S, crossprod(C))
    set.seed(17)
    X <- matrix(rnorm(32000), 1000, 32)
    Z <- X %*% C  ## ==>  cov(Z) ~=  C'C = S
    all.equal(cov(Z), S, tol = 0.08)
    pZ <- prcomp(Z, tol = 0.1)
    summary(pZ) # only ~14 PCs (out of 32)
    ## or choose only 3 PCs more directly:
    pz3 <- prcomp(Z, rank. = 3)
    summary(pz3) # same numbers as the first 3 above
    stopifnot(ncol(pZ$rotation) == 14, ncol(pz3$rotation) == 3,
              all.equal(pz3$sdev, pZ$sdev, tol = 1e-15)) # exactly equal typically

    ## signs are random
    require(graphics)
    ## the variances of the variables in the
    ## USArrests data vary by orders of magnitude, so scaling is appropriate
    prcomp(USArrests)  # inappropriate
    prcomp(USArrests, scale = TRUE)
    prcomp(~ Murder + Assault + Rape, data = USArrests, scale = TRUE)
    plot(prcomp(USArrests))
    summary(prcomp(USArrests, scale = TRUE))
    biplot(prcomp(USArrests, scale = TRUE))

[Package stats version 3.5.1 Index]