dfrr-package: dfrr: Dichotomized Functional Response Regression

In asgari-fatemeh/dfrr: Dichotomized Functional Response Regression

Description

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 dfrr model.

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 dfrr model. The main function in the dfrr-package is dfrr().

Author(s)

Maintainer: Fatemeh Asgari [e-mail]

Authors:

• Saeed Hayati [e-mail] [contributor]

References

Fatemeh Asgari, Alamatsaz Mohammad Hossein, Hayati Saeed (2021). Dichotomized Functional Response Regression Model. <http://arxive.org/adress_to_paper>

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

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)

dfrr_fit<-dfrr(Y~X,yind=time)


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)

asgari-fatemeh/dfrr documentation built on Aug. 12, 2020, 3:06 a.m.