dfrr-package: Dichotomizd functional response regression (dfrr) model
In dfrr: Dichotomized Functional Response Regression

dfrr-package

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

Y_{i}(t)=I(\beta_0(t)+\beta_1(t)*x_{1i}+\ldots+\beta_{q-1}(t)*x_{(q-1)i}+\varepsilon_{i}(t)+\epsilon_{i}(t)\times\sigma^2>0),

where I(.) is the indicator function, \varepsilon_{i} is a Gaussian random function, and \epsilon_{i}(t) are iid standard normal for each i and t independent of \varepsilon_{i}. \beta_k and x_k for k=0,1,\ldots,q-1 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]

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)

dfrr documentation built on May 31, 2023, 5:32 p.m.