summary: Summarizing functions for linear models
In lmPerm: Permutation Tests for Linear Models

Description Usage Arguments Details Author(s)

Replaces corresponding functions in base package.

## S3 method for class 'lmp'
summary(object, correlation = FALSE, symbolic.cor = FALSE, ...)
## S3 method for class 'mlmp'
summary(object, ...)
## S3 method for class 'summary.lmp'
print(x, digits = max(3, getOption("digits") - 3),
              symbolic.cor = x$symbolic.cor,
	      signif.stars= getOption("show.signif.stars"),	...)
## S3 method for class 'aovp'
summary(object, intercept = FALSE, split,
                        expand.split = TRUE, keep.zero.df = TRUE, ...)
## S3 method for class 'lmp'
anova(object, ...)

Same as for the corresponding functions in base package:

`object`	an object of class `"lm"`, usually, a result of a call to `lm`.
`x`	an object of class `"summary.lm"`, usually, a result of a call to `summary.lm`.
`correlation`	logical; if `TRUE`, the correlation matrix of the estimated parameters is returned and printed.
`digits`	the number of significant digits to use when printing.
`symbolic.cor`	logical. If `TRUE`, print the correlations in a symbolic form (see `symnum`) rather than as numbers.
`signif.stars`	logical. If `TRUE`, “significance stars” are printed for each coefficient.
`intercept`	logical: should intercept terms be included?
`split`	an optional named list, with names corresponding to terms in the model. Each component is itself a list with integer components giving contrasts whose contributions are to be summed.
`expand.split`	logical: should the split apply also to interactions involving the factor?
`keep.zero.df`	logical: should terms with no degrees of freedom be included?
`...`	further arguments passed to or from other methods.

These modified functions are needed because the perm values, which are attached to the object, replace the usual test columns in the output from these functions.

Bob Wheeler rwheeler@echip.com

lmPerm documentation built on May 2, 2019, 12:35 p.m.