R: Extract Residual Errors

sigma.hat {arm} | R Documentation

## Extract Residual Errors

### Description

This generic function extracts residual errors from a fitted model.

### Usage

    sigma.hat(object,...)

    ## S3 method for class 'lm'
    sigma.hat(object,...)
    ## S3 method for class 'glm'
    sigma.hat(object,...)
    ## S3 method for class 'merMod'
    sigma.hat(object,...)
    ## S3 method for class 'sim'
    sigma.hat(object,...)
    ## S3 method for class 'sim.merMod'
    sigma.hat(object,...)

### Arguments

`object` | any fitted model object of `lm`, `glm` and `merMod` class
`...` | other arguments

### Author(s)

Andrew Gelman gelman@stat.columbia.edu; Yu-Sung Su suyusung@tsinghua.edu.cn

### See Also

`display`, `summary`, `lm`, `glm`, `lmer`

### Examples

       group <- rep(1:10, rep(10,10))
       mu.a <- 0
       sigma.a <- 2
       mu.b <- 3
       sigma.b <- 4
       rho <- 0
       Sigma.ab <- array (c(sigma.a^2, rho*sigma.a*sigma.b,
                        rho*sigma.a*sigma.b, sigma.b^2), c(2,2))
       sigma.y <- 1
       ab <- mvrnorm (10, c(mu.a,mu.b), Sigma.ab)
       a <- ab[,1]
       b <- ab[,2]

       x <- rnorm (100)
       y1 <- rnorm (100, a[group] + b[group]*x, sigma.y)
       y2 <- rbinom(100, 1, prob=invlogit(a[group] + b*x))

       M1 <- lm (y1 ~ x)
       sigma.hat(M1)

       M2 <- bayesglm (y1 ~ x, prior.scale=Inf, prior.df=Inf)
       sigma.hat(M2) # should be same to sigma.hat(M1)

       M3 <- glm (y2 ~ x, family=binomial(link="logit"))
       sigma.hat(M3)

       M4 <- lmer (y1 ~ (1+x|group))
       sigma.hat(M4)

       M5 <- glmer (y2 ~ (1+x|group), family=binomial(link="logit"))
       sigma.hat(M5)

[Package arm version 1.9-3 Index]