dfrr-package {dfrr} | R Documentation |
Dichotomizd functional response regression (dfrr) model
Description
Implementing Function-on-Scalar Regression model, in which the response function is dichotomized and observed sparsely. This function fits the dichotomized functional response regression (dfrr) model as
where is the indicator function,
is a Gaussian random function, and
are iid standard normal for each
and
independent of
.
and
for
are the functional regression coefficients and scalar covariates, respectively.
Details
@details Implementing Function-on-Scalar Regression model in which the response function is dichotomized and observed sparsely. This package provides smooth estimations of functional regression coefficients and principal components for the dichotomized funtional response regression (dfrr) model. The main function in the dfrr-package is dfrr().
Author(s)
Maintainer: Fatemeh Asgari fatemeh.asgari@medisin.uio.no
Authors:
Saeed Hayati saeed.hayati@medisin.uio.no [contributor]
See Also
Useful links:
Examples
set.seed(2000)
N<-50;M<-24
X<-rnorm(N,mean=0)
time<-seq(0,1,length.out=M)
Y<-simulate_simple_dfrr(beta0=function(t){cos(pi*t+pi)},
beta1=function(t){2*t},
X=X,time=time)
#The argument T_E indicates the number of EM algorithm.
#T_E is set to 1 for the demonstration purpose only.
#Remove this argument for the purpose of converging the EM algorithm.
dfrr_fit<-dfrr(Y~X,yind=time,T_E=1)
coefs<-coef(dfrr_fit)
plot(coefs)
fitteds<-fitted(dfrr_fit)
plot(fitteds)
resids<-residuals(dfrr_fit)
plot(resids)
fpcs<-fpca(dfrr_fit)
plot(fpcs,plot.contour=TRUE,plot.3dsurface = TRUE)
newdata<-data.frame(X=c(1,0))
preds<-predict(dfrr_fit,newdata=newdata)
plot(preds)
newdata<-data.frame(X=c(1,0))
newydata<-data.frame(.obs=rep(1,5),.index=c(0.0,0.1,0.2,0.3,0.7),.value=c(1,1,1,0,0))
preds<-predict(dfrr_fit,newdata=newdata,newydata = newydata)
plot(preds)