R: Average Residuals within Factor Levels

meanResiduals {gnm} | R Documentation

## Average Residuals within Factor Levels

### Description

Computes the mean working residuals from a model fitted using Iterative Weighted Least Squares for each level of a factor or interaction of factors.

### Usage

    meanResiduals(object, by, standardized=TRUE, as.table=TRUE, ...)

### Arguments

`object` | model object for which `object$residuals` gives the working residuals and `object$weights` gives the working weights.
`by` | either a formula specifying a factor or interaction of factors (recommended), or a list of factors (the elements of which must correspond exactly to observations in the model frame). When a list of factors is specified, their interaction is used to specify the grouping factor.
`standardized` | logical: if `TRUE`, the mean residuals are standardized to be approximately standard normal.
`as.table` | logical: logical: if `TRUE` and `by` specifies an interaction of factors, the result is returned as a table cross-classified by these factors.
`...` | currently ignored

### Details

For level i of the grouping factor A the mean working residual is defined as

(r_ij * w_ij)/(sum_(j = 1)^(n_i) w_ij)

where r_ij is the j'th residual for level i, w_ij is the corresponding working weight and n_i is the number of observations for level i. The denominator gives the weight corresponding to mean residual.

For non-aggregated residuals, i.e. when the factor has one level per observation, the residuals are the same as Pearson residuals.

### Value

An object of class `"meanResiduals"`, for which `print` and `summary` methods are provided. A `"meanResiduals"` object is a list containing the following elements:

`call` | the call used to create the model object from which the mean residuals are derived.
`by` | a label for the grouping factor.
`residuals` | the mean residuals.
`df` | the degrees of freedom associated with the mean residuals.
`standardized` | the `standardized` argument.
`weights` | the weights corresponding to the mean residuals.

### Author(s)

Heather Turner

### Examples

    ## Fit a conditional independence model, leaving out
    ## the uninformative subtable for dest == 7:
    CImodel <- gnm(Freq ~ educ*orig + educ*dest, family = poisson,
                   data = yaish, subset = (dest != 7))

    ## compute mean residuals over origin and destination
    meanRes <- meanResiduals(CImodel, ~ orig:dest)
    meanRes
    summary(meanRes)

    ## Not run:
    ## requires vcdExtra package
    ## display mean residuals for origin and destination
    library(vcdExtra)
    mosaic(CImodel, ~orig+dest)

    ## End(Not run)

    ## non-aggregated residuals
    res1 <- meanResiduals(CImodel, ~ educ:orig:dest)
    res2 <- residuals(CImodel, type = "pearson")
    all.equal(as.numeric(res1), as.numeric(res2))

[Package gnm version 1.1-2 Index]