rlm {limma} | R Documentation |
Fit linear models for each gene to a series of microarrays. Fit is by robust M-estimation.
mrlm(M,design=NULL,ndups=1,spacing=1,weights=NULL,...) rlm.series(x,...)
M |
numeric matrix containing log-ratio or log-expression values for a series of microarrays, rows correspond to genes and columns to arrays. |
x |
same as M |
design |
the design matrix of the microarray experiment, with rows corresponding to arrays and columns to comparisons to be estimated. The number of rows must match the number of columns of M . Defaults to the unit vector meaning that the arrays are treated as replicates. |
ndups |
a positive integer giving the number of times each gene is printed on an array. nrow(M) must be divisible by ndups . |
spacing |
the spacing between the rows of M corresponding to duplicate spots, spacing=1 for consecutive spots. |
weights |
numeric matrix of the same dimension as M containing weights. If it is of different dimension to M , it will be filled out to the same size. NULL is equivalent to equal weights. |
... |
any other arguments are passed to rlm.default . |
The linear model is fit for each gene by calling the function rlm
from the MASS library.
Warning: don't use weights with this function unless you understand how rlm
treats weights.
The treatment of weights is somewhat different from that of lm.series
and gls.series
.
The function rlm.series
is equivalent to mrlm
but is deprecated and will be removed at some time in the future.
A list with components
coefficients |
numeric matrix containing the estimated coefficients for each linear model. Same number of rows as M , same number of columns as design . |
stdev.unscaled |
numeric matrix conformal with coef containing the unscaled standard deviations for the coefficient estimators. The standard errors are given by stdev.unscaled * sigma . |
sigma |
numeric vector containing the residual standard deviation for each gene. |
df.residual |
numeric vector giving the degrees of freedom corresponding to sigma . |
qr |
QR decomposition of design . |
Gordon Smyth
rlm
.
An overview of linear model functions in limma is given by 06.LinearModels.
# Simulate gene expression data, # 6 microarrays and 100 genes with one gene differentially expressed in first 3 arrays M <- matrix(rnorm(100*6,sd=0.3),100,6) M[1,1:3] <- M[1,1:3] + 2 # Design matrix includes two treatments, one for first 3 and one for last 3 arrays design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1)) fit <- mrlm(M,design=design) eb <- ebayes(fit) # Large values of eb$t indicate differential expression qqt(eb$t[,1],df=fit$df+eb$df.prior) abline(0,1)