Linear and Nonlinear Fixed Effects Models with a new vignette by Nel Jason Haw
Description: Linear and Nonlinear Fixed Effects Models and everything you can do with them
Original Authors: Jose C. Pinheiro and Douglas M. Bates (1997-2005), R Core Team (2005-2022)
https://jhu-statprogramming-fall-2022.github.io/biostat840-project3-pkgdown-neljasonhaw
- Applied "simplex" bootswatch
- Changed base font to "Source Sans Pro"
- Changed code font to "Source Code Pro"
- Changed theme to "github-light"
- Changed navigation bar background to "light"
remotes::install_github(repo = "jhu-statprogramming-fall-2022/biostat840-project3-pkgdown-neljasonhaw")
Descriptions provided in R Documentation
lme
: Linear Mixed-Effects Models: This generic function fits a linear mixed-effects model in the formulation described in Laird and Wre (1982) but allowing for nested random effects. The within-group errors are allowed to be correlated and/or have unequal variancesanova.lme
: Compare Likelihoods of Fitted Objects: Likelihood ratio test for two lme model objects
This is a basic example which shows you how to solve a common problem (from lme
R Documentation):
fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject)
anova(fm1)
fm2 <- update(fm1, random = pdDiag(~age))
anova(fm1, fm2)