Identify Covariates Involved in each Model Term

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

models, spatial
model.covariates(object, fitted=TRUE, offset=TRUE)
A fitted model of any kind.
Logical values determining which type of covariates to include.

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 ith canonical covariate depends on the jth 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 ith offset term depends on the jth 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 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.


  • A logical value or matrix.

See Also

ppm, model.matrix

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

   fitoff1 <- lm(y ~ x + offset(z))
   fitoff2 <- lm(y ~ x, offset=z)
Documentation reproduced from package spatstat, version 1.42-2, License: GPL (>= 2)

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