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rms

residuals.ols

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"), ...)

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.
`...`	ignored

Value

a matrix or vector, with places for observations that were originally deleted by ols held by NAs

Author(s)

Frank Harrell
Department of Biostatistics
Vanderbilt University
fh@fharrell.com

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)

rms

Regression Modeling Strategies

v6.2-0

GPL (>= 2)

Authors

Frank E Harrell Jr <fh@fharrell.com>

Initial release

2021-03-17