R: Residuals of Robust Generalized Linear Model Fits

residuals.glmrob {robustbase} | R Documentation

## Residuals of Robust Generalized Linear Model Fits

### Description

Compute residuals of a fitted `glmrob` model, i.e., robust generalized linear model fit.

### Usage

    ## S3 method for class 'glmrob'
    residuals(object,
              type = c("deviance", "pearson", "working",
                       "response", "partial"),
              ...)

### Arguments

`object` | an object of class `glmrob`, typically the result of a call to `glmrob`.
`type` | the type of residuals which should be returned. The alternatives are: `"deviance"` (default), `"pearson"`, `"working"`, `"response"`, and `"partial"`.
`...` | further arguments passed to or from other methods.

### Details

The references in `glm` define the types of residuals: Davison & Snell is a good reference for the usages of each.

The partial residuals are a matrix of working residuals, with each column formed by omitting a term from the model.

The `residuals` (S3) method (see `methods`) for `glmrob` models has been modeled to follow closely the method for classical (non-robust) `glm` fitted models. Possibly, see its documentation, i.e., residuals.glm, for further details.

### References

See those for the classical GLM's, `glm`.

### See Also

`glmrob` for computing `object`, `anova.glmrob`; the corresponding generic functions, `summary.glmrob`, `coef`, `fitted`, `residuals`.

### Examples

    ### -------- Gamma family -- data from example(glm) ---
    clotting <- data.frame(
                u = c(5,10,15,20,30,40,60,80,100),
             lot1 = c(118,58,42,35,27,25,21,19,18),
             lot2 = c(69,35,26,21,18,16,13,12,12))
    summary(cl <- glm   (lot1 ~ log(u), data=clotting, family=Gamma))
    summary(ro <- glmrob(lot1 ~ log(u), data=clotting, family=Gamma))
    clotM5.high <- within(clotting, { lot1[5] <- 60 })
    cl5.high <- glm   (lot1 ~ log(u), data=clotM5.high, family=Gamma)
    ro5.high <- glmrob(lot1 ~ log(u), data=clotM5.high, family=Gamma)
    rr <- range(residuals(ro), residuals(cl), residuals(ro5.high))
    plot(residuals(ro5.high) ~ residuals(cl5.high), xlim = rr, ylim = rr, asp = 1)
    abline(0,1, col=2, lty=3)
    points(residuals(ro) ~ residuals(cl), col = "gray", pch=3)

    ## Show all kinds of residuals:
    r.types <- c("deviance", "pearson", "working", "response")
    sapply(r.types, residuals, object = ro5.high)

[Package robustbase version 0.92-7 Index]