princompDAS {DASplusR} | R Documentation |
Performs principal component analysis on the given variables of a data
set. The results are returned as an object of class "princomp"
.
princompDAS(data, vars, logVars = character(), subset, cor = FALSE, robust = NULL, robargs = list(), covmat = NULL, scores = TRUE)
data |
A data object of class "DASData" or
"data.frame" . |
vars |
A vector for selecting columns (variables) of data .
It can be either a character vector containing the variable names, a
numeric vector containing the column numbers or a logical vector. If
missing, all columns of data are used. |
logVars |
A subset of vars , specifying the columns (variables)
of data to be log-transformed. Again, either a character vector
containing the variable names, a numeric vector containing the column
numbers or a logical vector can be supplied. |
subset |
A vector for selecting the rows (observations) of
data . It can be either a character, numeric or logical vector,
too. |
cor |
A logical value indicating whether the correlation matrix or the covariance matrix should be used for computing the principal components. The correlation matrix can only be used if there are no constant variables. |
robust |
A character string containing the name of a function for
computing a robust estimate of the covariance matrix, or the function
itself. Examples are "covMcd" for MCD estimation and
"covOGK" for OGK estimation. The function must take a matrix
or a data frame as its first argument and should return a matrix,
or a list that contains elements named center (an estimate
for the central location) and cov (an estimate for the covariance
matrix). Scores can only be computed in the latter case. If covmat
is supplied as well, this argument is ignored. |
robargs |
A list of additional arguments for the function specified
by robust . The argument names should be used to name the
list elements. |
covmat |
A covariance matrix, or a covariance list as returned by
cov.wt (or covMcd from package
robustbase). If supplied, logVars and robust are
ignored. Make sure that covmat has been calculated using the same
data as defined by data , vars and subset . Scores
can only be computed if covmat is a covariance list that also
contains an estimate of the central location of the data. |
scores |
A logical value indicating whether the scores on the
principal components should be calculated. Supplying data and
vars is necessary for calculating the scores. |
The principal components are computed by using eigen
on the
correlation or covariance matrix, as determined by cor
.
Note that for S-PLUS
compatibility, the divisor N
is used
for non-robust calculation of the covariance matrix.
Observations that contain missing values are removed before computing the components. It is required that there are at least as many observations left after the removal as there are variables.
princompDAS
returns an object of class "princomp"
that contains the following elements:
sdev |
The standard deviations of the principal components. |
loadings |
The variable loadings (i.e., the matrix
whose columns contain the eigenvectors of the correlation or covariance
matrix). 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 |
The scores of the supplied data on the principal components, if requested. |
call |
The matched function call. |
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.
Andreas Alfons <andreas.alfons@student.tuwien.ac.at>
Mardia, K.V., Kent, J.T. and Bibby, J.M. (1979) Multivariate Analysis. Academic Press.
Johnson, R.A. and Wichern, D.W. (2002) Applied Multivariate Statistical Analysis. Prentice Hall. 5th edition.
Reimann, C., Filzmoser, P., Garrett, R.G. and Dutter, R. (2008) Statistical Data Analysis Explained. Wiley & Sons.
DAS+R functions: PrincompGUI
, screeplotDAS
,
biplotDAS
, factanalDAS
.
R functions: princomp
, eigen
,
covMcd
, covOGK
.
## KOLA95_Moss data data(KOLA95_MOSS) princompDAS(data = KOLA95_MOSS, vars = c("Cu","Mg","Mn","Ni","Rb","Th"), logVars = c("Cu","Mg","Mn","Ni","Rb","Th"), cor = TRUE, robust = "covMcd")