residuals.ols {rms} | R Documentation |
Residuals for ols
Description
Computes various residuals and measures of influence for a
fit from ols
.
Usage
## S3 method for class 'ols'
residuals(object,
type=c("ordinary", "score", "dfbeta", "dfbetas",
"dffit", "dffits", "hat", "hscore", "influence.measures",
"studentized"), ...)
Arguments
object |
object created by ols . Depending on type , you may have had to
specify x=TRUE to ols .
|
type |
type of residual desired. "ordinary" refers to the usual residual.
"score" is the matrix of score residuals (contributions to first
derivative of log likelihood).
dfbeta and dfbetas mean respectively the raw and normalized matrix of changes in regression coefficients after
deleting in turn each observation. The coefficients are normalized by their
standard errors. hat contains the leverages — diagonals of the “hat” matrix.
dffit and dffits contain respectively the difference and normalized
difference in predicted values when each observation is omitted.
The S lm.influence function is used. When type="hscore" , the
ordinary residuals are divided by one minus the corresponding hat
matrix diagonal element to make residuals have equal variance. When
type="influence.measures" the model is converted to an
lm model and influence.measures(object)$infmat is
returned. This is a matrix with dfbetas for all predictors, dffit,
cov.r, Cook's d, and hat. For type="studentized" studentized leave-out-one residuals are computed.
See the help file for influence.measures for more details.
|
... |
ignored
|
Value
a matrix or vector, with places for observations that were originally
deleted by ols
held by NA
s
Author(s)
Frank Harrell
Department of Biostatistics
Vanderbilt University
fh@fharrell.com
See Also
lm.influence
, ols
,
which.influence
Examples
set.seed(1)
x1 <- rnorm(100)
x2 <- rnorm(100)
x1[1] <- 100
y <- x1 + x2 + rnorm(100)
f <- ols(y ~ x1 + x2, x=TRUE, y=TRUE)
resid(f, "dfbetas")
which.influence(f)
i <- resid(f, 'influence.measures') # dfbeta, dffit, etc.
[Package
rms version 6.8-1
Index]