hypr (version 0.2.8)

cmat: Retrieve or set contrast matrix

Description

Use these functions to retrieve or set a hypr object’s contrast matrix. If used for updating, the hypothesis matrix and equations are derived automatically.

Usage

cmat(x, add_intercept = FALSE, remove_intercept = FALSE, as_fractions = TRUE)
cmat(x, add_intercept = FALSE, remove_intercept = FALSE) <- value
contr.hypothesis(
  ...,
  add_intercept = FALSE,
  remove_intercept = NULL,
  as_fractions = FALSE
)
# S4 method for factor,ANY,hypr
contrasts(x, how.many = NULL) <- value
# S4 method for factor,ANY,hypr_cmat
contrasts(x, how.many = NULL) <- value
contr.hypothesis(
  x,
  add_intercept = NULL,
  remove_intercept = FALSE,
  as_fractions = FALSE
) <- value

Value

A matrix of contrast codes with contrasts as columns and levels as rows.

Arguments

x

A hypr object

add_intercept

Add additional intercept column to contrast matrix

remove_intercept

If TRUE, tries to find an intercept column (all codes equal) and removes it from the matrix. If NULL, does the same but does not throw an exception if no intercept is found. FALSE explicitly disables this functionality. A numeric argument explicitly identifies the index of the column to be removed.

as_fractions

Should the returned matrix be formatted as fractions (using MASS::as.fractions())?

value

contrast matrix

...

A list of hypothesis equations for which to retrieve a contrast matrix

how.many

see stats::contrasts()

Functions

• cmat(x, add_intercept = FALSE, remove_intercept = FALSE) <- value: Set contrast matrix

• contr.hypothesis(): Retrieve contrast matrix with sensible intercept default to override factor contrasts

• contrasts(x = factor, how.many = ANY) <- value: Update factor contrasts

• contrasts(x = factor, how.many = ANY) <- value: Update factor contrasts

• contr.hypothesis( x, add_intercept = NULL, remove_intercept = FALSE, as_fractions = FALSE ) <- value: Update contrast matrix with sensible intercept default

Details

Basic specification of contrasts in R is accomplished with basic R functions stats::contrasts() and stats::C() (Chambers & Hastie, 1992). Other relevant packages for this topic are multcomp (Bretz et al., 2010), contrast (Kuhn et al., 2016), and, including also various vignettes, emmeans (Lenth, 2019).

References

Chambers, J. M. and Hastie, T. J. (1992) Statistical models. Chapter 2 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

Frank Bretz, Torsten Hothorn and Peter Westfall (2010), Multiple Comparisons Using R, CRC Press, Boca Raton.

Max Kuhn, contributions from Steve Weston, Jed Wing, James Forester and Thorn Thaler (2016). contrast: A Collection of Contrast Methods. R package version 0.21. https://CRAN.R-project.org/package=contrast

Lenth, R. (2019). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.4.1. https://CRAN.R-project.org/package=emmeans

See Also

hypr

Examples

h <- hypr(mu1~0, mu2~mu1)
cmat(h) # retrieve the contrast matrix

contr.hypothesis(h) # by default without intercept (removes first column)
contr.hypothesis(mu1~0, mu2~mu1)