glmD {Design} | R Documentation

## Design Version of glm

### Description

This function saves `Design` attributes with the fit object so that `anova.Design`, `plot.Design`, etc. can be used just as with `ols` and other fits. No `validate` or `calibrate` methods exist for `glmD` though.

### Usage

    glmD(formula, family = gaussian, data = list(), weights = NULL, subset =
    NULL, na.action = na.fail, start = NULL, offset = NULL, control =
    glm.control(...), model = TRUE, method = "glm.fit", x = FALSE, y = TRUE,
    contrasts = NULL, ...)

    ## S3 method for class 'glmD':
    print(x, digits=4, ...)

### Arguments

`formula` |
`family` |
`data` |
`weights` |
`subset` |
`na.action` |
`start` |
`offset` |
`control` |
`model` |
`method` |
`x` |
`y` |
`contrasts` | see `glm`; for `print`, `x` is the result of `glmD`
`...` | ignored for `print`
`digits` | number of significant digits to print

### Value

a fit object like that produced by `glm` but with `Design` attributes and a `class` of `"Design"`, `"glmD"`, and `"glm"` or `"glm.null"`.

### See Also

`glm`,`Design`

### Examples

    ## Dobson (1990) Page 93: Randomized Controlled Trial :
    counts <- c(18,17,15,20,10,20,25,13,12)
    outcome <- gl(3,1,9)
    treatment <- gl(3,3)
    f <- glm(counts ~ outcome + treatment, family=poisson())
    f
    anova(f)
    summary(f)
    f <- glmD(counts ~ outcome + treatment, family=poisson())
    # could have had rcs( ) etc. if there were continuous predictors
    f
    anova(f)
    summary(f, outcome=c('1','2','3'), treatment=c('1','2','3'))

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