predict.bigssg {bigsplines}

Predicts for "bigssg" Objects

Description

Get fitted values and standard error estimates for generalized smoothing spline anova models.

Usage

## S3 method for class 'bigssg'
predict(object,newdata=NULL,se.lp=FALSE,include=object$tnames,
        effect=c("all","0","lin","non"),includeint=FALSE,
        design=FALSE,smoothMatrix=FALSE,intercept=NULL,...)

Arguments

object

Object of class "bigssg", which is output from bigssg.

newdata

Data frame or list containing the new data points for prediction. Variable names must match those used in the formula input of bigssg. See Details and Example. Default of newdata=NULL uses original data in object input.

se.lp

Logical indicating if the standard errors of the linear predictors (\eta) should be estimated. Default is se.lp=FALSE.

include

Which terms to include in the estimate. You can get fitted values for any combination of terms in the tnames element of an "bigssg" object.

effect

Which effect to estimate: effect="all" gives \hat{y} for given terms in include, effect="lin" gives linear portion of \hat{y} for given terms in include, and effect="non" gives nonlinear portion of \hat{y} for given terms in include. Use effect="0" to return the intercept.

includeint

Logical indicating whether the intercept should be included in the prediction. If include=object$tnames and effect="all" (default), then this input is ignored and the intercept is automatically included in the prediction.

design

Logical indicating whether the design matrix should be returned.

smoothMatrix

Logical indicating whether the smoothing matrix should be returned.

intercept

Logical indicating whether the intercept should be included in the prediction. When used, this input overrides the includeint input.

...

Ignored.

Details

Uses the coefficient and smoothing parameter estimates from a fit generalized smoothing spline anova (estimated by bigssg) to predict for new data.

Value

Returns list with elements:

fitted.values

Vector of fitted values (on data scale)

linear.predictors

Vector of fitted values (on link scale)

se.lp

Vector of standard errors of linear predictors (if se.lp=TRUE)

X

Design matrix used to create linear predictors (if design=TRUE)

ix

Index vector such that linear.predictors=X%*%object$modelspec$coef[ix] (if design=TRUE)

S

Smoothing matrix corresponding to fitted values (if smoothMatrix=TRUE)

Author(s)

Nathaniel E. Helwig <[e-mail]>

References

Gu, C. (2013). Smoothing spline ANOVA models, 2nd edition. New York: Springer.

Gu, C. and Xiang, D. (2001). Cross-validating non-Gaussian data: Generalized approximate cross-validation revisited. Journal of Computational and Graphical Statistics, 10, 581-591.

Helwig, N. E. (2017). Regression with ordered predictors via ordinal smoothing splines. Frontiers in Applied Mathematics and Statistics, 3(15), 1-13.

Helwig, N. E. and Ma, P. (2015). Fast and stable multiple smoothing parameter selection in smoothing spline analysis of variance models with large samples. Journal of Computational and Graphical Statistics, 24, 715-732.

Helwig, N. E. and Ma, P. (2016). Smoothing spline ANOVA for super-large samples: Scalable computation via rounding parameters. Statistics and Its Interface, 9, 433-444.

Examples

##########   EXAMPLE 1   ##########

# define univariate function and data
set.seed(1)
myfun <- function(x){ sin(2*pi*x) }
ndpts <- 1000
x <- runif(ndpts)

# negative binomial response (unknown dispersion)
set.seed(773)
lp <- myfun(x)
mu <- exp(lp)
y <- rnbinom(n=ndpts,size=2,mu=mu)

# fit cubic spline model
cubmod <- bigssg(y~x,family="negbin",type="cub",nknots=20)
1/cubmod$dispersion   ## dispersion = 1/size
crossprod( lp - cubmod$linear.predictor )/length(lp)

# define new data for prediction
newdata <- data.frame(x=seq(0,1,length.out=100))

# get fitted values and standard errors for new data
yc <- predict(cubmod,newdata,se.lp=TRUE)

# plot results with 95% Bayesian confidence interval (link scale)
plot(newdata$x,yc$linear.predictor,type="l")
lines(newdata$x,yc$linear.predictor+qnorm(.975)*yc$se.lp,lty=3)
lines(newdata$x,yc$linear.predictor-qnorm(.975)*yc$se.lp,lty=3)

# plot results with 95% Bayesian confidence interval (data scale)
plot(newdata$x,yc$fitted,type="l")
lines(newdata$x,exp(yc$linear.predictor+qnorm(.975)*yc$se.lp),lty=3)
lines(newdata$x,exp(yc$linear.predictor-qnorm(.975)*yc$se.lp),lty=3)

# predict constant, linear, and nonlinear effects
yc0 <- predict(cubmod,newdata,se.lp=TRUE,effect="0")
ycl <- predict(cubmod,newdata,se.lp=TRUE,effect="lin")
ycn <- predict(cubmod,newdata,se.lp=TRUE,effect="non")
crossprod( yc$linear - (yc0$linear + ycl$linear + ycn$linear) )

# plot results with 95% Bayesian confidence intervals (link scale)
par(mfrow=c(1,2))
plot(newdata$x,ycl$linear,type="l",main="Linear effect")
lines(newdata$x,ycl$linear+qnorm(.975)*ycl$se.lp,lty=3)
lines(newdata$x,ycl$linear-qnorm(.975)*ycl$se.lp,lty=3)
plot(newdata$x,ycn$linear,type="l",main="Nonlinear effect")
lines(newdata$x,ycn$linear+qnorm(.975)*ycn$se.lp,lty=3)
lines(newdata$x,ycn$linear-qnorm(.975)*ycn$se.lp,lty=3)
         
# plot results with 95% Bayesian confidence intervals (data scale)
par(mfrow=c(1,2))
plot(newdata$x,ycl$fitted,type="l",main="Linear effect")
lines(newdata$x,exp(ycl$linear+qnorm(.975)*ycl$se.lp),lty=3)
lines(newdata$x,exp(ycl$linear-qnorm(.975)*ycl$se.lp),lty=3)
plot(newdata$x,ycn$fitted,type="l",main="Nonlinear effect")
lines(newdata$x,exp(ycn$linear+qnorm(.975)*ycn$se.lp),lty=3)
lines(newdata$x,exp(ycn$linear-qnorm(.975)*ycn$se.lp),lty=3)

         
         
##########   EXAMPLE 2   ##########

# define bivariate function and data
set.seed(1)
myfun <- function(x1v,x2v){
  sin(2*pi*x1v) + log(x2v+.1) + cos(pi*(x1v-x2v))
}
ndpts <- 1000
x1v <- runif(ndpts)
x2v <- runif(ndpts)

# binomial response (with weights)
set.seed(773)
lp <- myfun(x1v,x2v)
p <- 1/(1+exp(-lp))
w <- sample(c(10,20,30,40,50),length(p),replace=TRUE)
y <- rbinom(n=ndpts,size=w,p=p)/w   ## y is proportion correct
cubmod <- bigssg(y~x1v*x2v,family="binomial",type=list(x1v="cub",x2v="cub"),nknots=100,weights=w)
crossprod( lp - cubmod$linear.predictor )/length(lp)

# define new data for prediction
xnew <- as.matrix(expand.grid(seq(0,1,length=50),seq(0,1,length=50)))
newdata <- list(x1v=xnew[,1],x2v=xnew[,2])

# get fitted values for new data
yp <- predict(cubmod,newdata)

# plot linear predictor and fitted values
par(mfrow=c(2,2))
imagebar(seq(0,1,l=50),seq(0,1,l=50),matrix(myfun(newdata$x1v,newdata$x2v),50,50),
         xlab=expression(italic(x)[1]),ylab=expression(italic(x)[2]),
         zlab=expression(hat(italic(y))),zlim=c(-4.5,1.5),main="True Linear Predictor")
imagebar(seq(0,1,l=50),seq(0,1,l=50),matrix(yp$linear.predictor,50,50),
         xlab=expression(italic(x)[1]),ylab=expression(italic(x)[2]),
         zlab=expression(hat(italic(y))),zlim=c(-4.5,1.5),main="Estimated Linear Predictor")
newprob <- 1/(1+exp(-myfun(newdata$x1v,newdata$x2v)))
imagebar(seq(0,1,l=50),seq(0,1,l=50),matrix(newprob,50,50),
         xlab=expression(italic(x)[1]),ylab=expression(italic(x)[2]),
         zlab=expression(hat(italic(y))),zlim=c(0,0.8),main="True Probabilities")
imagebar(seq(0,1,l=50),seq(0,1,l=50),matrix(yp$fitted.values,50,50),
         xlab=expression(italic(x)[1]),ylab=expression(italic(x)[2]),
         zlab=expression(hat(italic(y))),zlim=c(0,0.8),main="Estimated Probabilities")         

# predict linear and nonlinear effects for x1v (link scale)
newdata <- list(x1v=seq(0,1,length.out=100))
yl <- predict(cubmod,newdata,include="x1v",effect="lin",se.lp=TRUE)
yn <- predict(cubmod,newdata,include="x1v",effect="non",se.lp=TRUE)

# plot results with 95% Bayesian confidence intervals (link scale)
par(mfrow=c(1,2))
plot(newdata$x1v,yl$linear,type="l",main="Linear effect")
lines(newdata$x1v,yl$linear+qnorm(.975)*yl$se.lp,lty=3)
lines(newdata$x1v,yl$linear-qnorm(.975)*yl$se.lp,lty=3)
plot(newdata$x1v,yn$linear,type="l",main="Nonlinear effect")
lines(newdata$x1v,yn$linear+qnorm(.975)*yn$se.lp,lty=3)
lines(newdata$x1v,yn$linear-qnorm(.975)*yn$se.lp,lty=3)