Kmatrix: Matrix of Coefficients in a Linear Model

In predictmeans: Predicted Means for Linear and Semiparametric Models

Matrix of Coefficients in a Linear Model

Description

This function obtains a matrix of coefficients for parametric models such as aov, lm, glm, gls, lme, and lmer.

Usage

Kmatrix(model, modelterm, covariate=NULL, covariateV=NULL, data=NULL, prtnum=FALSE)

Arguments

model	Model object returned by aov, lm, glm, gls, lme, and lmer.
modelterm	Name (in "quotes") for indicating which model term's predicted mean to be calculated. The modelterm must be given exactly as it appears in the printed model, e.g. "A" or "A:B".
covariate	A numerical vector to specify values of covariates for calculating predicted means, default values are the means of the associated covariates. It also can be the name of one covariate in the model.
covariateV	A numeric vector or list of numeric vector, then covariatemeans will produce the result for covariate at value of covariateV.
data	In some cases, you need to provide the data set used in model fitting, especially when you have applied some variable trnasformation in the model.
prtnum	An option for printing covariate info on the screen or not. The default is FALSE.

Value

K	Coefficients matrix
fctnames	A model frame contains factor(s) info in the model.
response	The name of response variable in the model.

Author(s)

This function heavily depends on the codes from package "lsmeans".

References

Welham, S., Cullis, B., Gogel, B., Gilmour, A., & Thompson, R. (2004), Prediction in linear mixed models, Australian and New Zealand Journal of Statistics, 46(3), 325-347.

Examples

  library(predictmeans)
  data(Oats, package="nlme")
# fm <- lmer(yield ~ nitro*Variety+(1|Block/Variety), data=Oats)
  fm <- lme(yield ~ nitro*Variety, random=~1|Block/Variety, data=Oats)
  Kmatrix(fm, "Variety", prtnum=TRUE)$K
  Kmatrix(fm, "Variety", 0.5, prtnum=TRUE)$K
 # Kmatrix(fm, "Variety", "nitro")$K
  Kmatrix(fm, "Variety", "nitro", covariateV=seq(0, 0.6, 0.1))$K

predictmeans documentation built on Oct. 20, 2023, 5:07 p.m.