dfrr (version 0.1.5)

dfrr-package: 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.

Author

Maintainer: Fatemeh Asgari

Authors:

• Saeed Hayati [contributor]

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().

See Also

Useful links:

• https://github.com/asgari-fatemeh/dfrr

• Report bugs at https://github.com/asgari-fatemeh/dfrr/issues

Examples

set.seed(2000)
N<-50;M<-24
N<-30;M<-12
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)