cv.predict {cvDSA} | R Documentation |
'cv.predict' is used to calculate the predicted values of the given model for the covariate 'w'.
cv.predict(w, model, family='gaussian')
w |
covariates matrix. |
model |
a list description of the model, e.g., model=list(formula=, coef= ). $formula can be in two formats: a list of integer arrays or a character string. Please see Examples for detail. |
family |
a description of the error distribution. Availible choices are 'gaussian' and 'binomial'. |
cvMSM
, cvGLM
, cvDCY
, check.ETA
, create.obs.data
# Example 1. n<-2000; w1<-runif(n); w2<-runif(n); w3<-runif(n); w4<-runif(n); w<-cbind(w1,w2,w3,w4); # Let E(Y|W)=-1+w1+w2+w1*w3; model <- list(formula=list(c(1,0,0,0),c(0,1,0,0),c(1,0,1,0)), coef=c(-1, 1, 1, 1)); y.hat <- cv.predict(w, model) # Or model <- list(formula="w1+w2+w1:w3", coef=c(-1, 1, 1, 1)); y.hat <- cv.predict(w, model) # Example 2. # Let g(A|W) = logit^(-1) (-1 + w1 - w2 + w1*w3) p.vec <- diag(4) model.aw <- list(formula = list(p.vec[1,], p.vec[2,], p.vec[1,]+p.vec[3,]), coef = c(-1, 1, -5, 1)) y.hat <- cv.predict(w, model.aw, 'binomial') # Or model.aw <- list(formula = "w1+w2+w1:w3", coef = c(-1, 1, -5, 1)) y.hat <- cv.predict(w, model.aw, 'binomial')