acat {glmmLasso} | R Documentation |
Provides necessary family components to fit an adjacent categories regression model to an ordered response based on the corresponding (multivariate) binary design representation.
acat()
For a response variable Y with ordered values 1,2,…,M+1 the design of the corresponding (multivariate) binary response representation is automatically created by the glmmLasso function. The result is a linear predictor matrix η with n rows and M columns.
Based on this (n x M) predictor matrix η or on the corresponding (n x M) matrix μ the below mentioned family components can be calculated.
linkinv |
function: the inverse of the link function as a function of eta. |
deriv.mat |
function: derivative function as a function of the mean (not of eta as normally). |
SigmaInv |
function: the inverse of the variance as a function of the mean. |
family |
character: the family name. |
multivariate |
Logical. Is always set to TRUE if the family is used. |
Andreas Groll groll@math.lmu.de
Agresti, A. (2013) Categorical Data Analysis, 3rd ed. Hoboken, NJ, USA: Wiley.
Simonoff, J. S. (2003) Analyzing Categorical Data, New York: Springer-Verlag.
Tutz, G. (2012) Regression for Categorical Data, Cambridge University Press.
## Not run: data(knee) knee[,c(2,4:6)]<-scale(knee[,c(2,4:6)],center=TRUE,scale=TRUE) knee<-data.frame(knee) ## fit adjacent category model glm.obj <- glmmLasso(pain ~ time + th + age + sex, rnd = NULL, family = acat(), data = knee, lambda=10, switch.NR=TRUE, control=list(print.iter=TRUE)) summary(glm.obj) ## End(Not run)