summary {lmPerm}

Summarizing functions for linear models

Description

Replaces corresponding functions in base package.

Usage

## 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, ...)

Arguments

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.

Details

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.

Author(s)

Bob Wheeler [e-mail]