R: Identify Covariates Involved in each Model Term

model.depends {spatstat.core} | R Documentation

## Identify Covariates Involved in each Model Term

### Description

Given a fitted model (of any kind), identify which of the covariates is involved in each term of the model.

### Usage

    model.depends(object)
    model.is.additive(object)
    model.covariates(object, fitted=TRUE, offset=TRUE)
    has.offset.term(object)
    has.offset(object)

### Arguments

`object` | A fitted model of any kind.
`fitted,offset` | Logical values determining which type of covariates to include.

### Details

The `object` can be a fitted model of any kind, including models of the classes `lm`, `glm` and `ppm`.

To be precise, `object` must belong to a class for which there are methods for `formula`, `terms` and `model.matrix`.

The command `model.depends` determines the relationship between the original covariates (the data supplied when `object` was fitted) and the canonical covariates (the columns of the design matrix). It returns a logical matrix, with one row for each canonical covariate, and one column for each of the original covariates, with the `i,j` entry equal to `TRUE` if the `i`th canonical covariate depends on the `j`th original covariate.

If the model formula of `object` includes offset terms (see `offset`), then the return value of `model.depends` also has an attribute `"offset"`. This is a logical value or matrix with one row for each offset term and one column for each of the original covariates, with the `i,j` entry equal to `TRUE` if the `i`th offset term depends on the `j`th original covariate.

The command `model.covariates` returns a character vector containing the names of all (original) covariates that were actually used to fit the model. By default, this includes all covariates that appear in the model formula, including offset terms as well as canonical covariate terms. To omit the offset terms, set `offset=FALSE`. To omit the canonical covariate terms, set `fitted=FALSE`.

The command `model.is.additive` determines whether the model is additive, in the sense that there is no canonical covariate that depends on two or more original covariates. It returns a logical value.

The command `has.offset.term` is a faster way to determine whether the model formula includes an `offset` term.

The functions `model.depends` and `has.offset.term` only detect `offset` terms which are present in the model formula. They do not detect numerical offsets in the model object, that were inserted using the `offset` argument in `lm`, `glm` etc. To detect the presence of offsets of both kinds, use `has.offset`.

### Value

A logical value or matrix.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

and Rolf Turner r.turner@auckland.ac.nz

### See Also

`ppm`, `model.matrix`

### Examples

       x <- 1:10
       y <- 3*x + 2
       z <- rep(c(-1,1), 5)
       fit <- lm(y ~ poly(x,2) + sin(z))
       model.depends(fit)
       model.covariates(fit)
       model.is.additive(fit)

       fitoff1 <- lm(y ~ x + offset(z))
       fitoff2 <- lm(y ~ x, offset=z)
       has.offset.term(fitoff1)
       has.offset(fitoff1)
       has.offset.term(fitoff2)
       has.offset(fitoff2)

[Package spatstat.core version 2.4-4 Index]