predict_hrf: Prediction
In htree: Historical Tree Ensembles for Longitudinal Data
Description
Usage
Arguments
Value
See Also
Examples
Description
Prediction functions for hrf and htb.
Usage
1
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Arguments
object
An object of class htree.
x
A data frame or matrix containing new data. If NULL then training data in object is used.
time
A vector of observation times.
id
A vector of length nrow(x) identifying the subjects.
type
If response then predictions on same scale as response are given.
ntrees
The number of trees to use in prediction.
all.trees
If TRUE then predictions associated with each tree are returned.
se
If TRUE then standard errors of predictions are returned.
Value
Returns predictions.
See Also
Examples
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## Not run:
# ------------------------------- #
# Simulated data example #
# ------------------------------- #
# library(htree)
p=5;sigma_e=.5;sigma_a=.5;n=500;pnoise=2
random_intercept=as.numeric(mapply(rep,rnorm(n,sd=sigma_a),times=p))
dat=data.frame(time=rep(1:p,n),
x=(random_intercept+rnorm(n*p,sd=sigma_e)),
znoise=matrix(rnorm(n*p*pnoise),ncol=pnoise))
id=sort(rep(1:n,p))
# fit historical random forest
hb=hrf(x=dat,time=dat$time,id=id,yindx=2,se=TRUE)
# get predictions with standard errors
pred=predict_hrf(hb,se=TRUE)
# ------------------------------------------------------------------ #
# Comparison of SE-estimates with actual standard errors for 'hrf'
# ------------------------------------------------------------------ #
## -- evaluation points
n=200
datp=data.frame(y=rep(0,n),w=seq(-2,2,length=n),z=rep(0,n))
## -- estimate model on 50 simulated data sets
pred=NULL
pred_se=NULL
nsim=20
## -- B=100 bootstrap samples, ensemble size of R=10 on each
control=list(ntrees=500,B=100,R=10,se=TRUE,nodesize=5)
for(k in 1:nsim){
if(is.element(k,seq(1,nsim,by=10)))
cat(paste("simulation: ",k," of ",nsim," \n",sep=""))
# -- simulation model -- #
dat=data.frame(y=(4*datp$w+rnorm(n)),x=datp$w,z=rnorm(n))
# ---------------------- #
h=hrf(x=dat,yindx="y",control=control)
mm=predict_hrf(object=h,x=datp,se=TRUE)
pred=cbind(pred,mm[,1])
pred_se=cbind(pred_se,mm[,2])
}
# --- Actual Standard errors at datp
pred_se_true=apply(pred,1,sd)
# --- Mean of estimated standard errors
pred_se_est=apply(pred_se,1,mean)
pred_se_lower=apply(pred_se,1,quantile,prob=.1)
pred_se_upper=apply(pred_se,1,quantile,prob=.9)
# -- Plot estimated SE and true SE (+smooth)
z=c(pred_se_true,pred_se_est,pred_se_lower,pred_se_upper)
ylim=c(min(z),max(z))
plot(datp$w,pred_se_est,ylim=ylim,col="blue",xlab="w",
ylab="Standard error",type="l",main=" SE-true (red) SE-est (blue)")
points(datp$w,pred_se_lower,col="blue",type="l",lty=2)
points(datp$w,pred_se_upper,col="blue",type="l",lty=2)
points(datp$w,pred_se_true,col="red",type="l")
# ------------------------------------------------------------------ #
# Comparison of SE-estimates with actual standard errors for 'htb'
# ------------------------------------------------------------------ #
## -- evaluation points
n=200
datp=data.frame(y=rep(0,n),w=seq(-2,2,length=n),z=rep(0,n))
## -- estimate model on 50 simulated data sets
pred=NULL
pred_se=NULL
nsim=20
for(k in 1:nsim){
if(is.element(k,seq(1,nsim,by=10)))
cat(paste("simulation: ",k," of ",nsim," \n",sep=""))
# -- simulation model -- #
dat=data.frame(y=(4*datp$w+rnorm(n)),x=datp$w,z=rnorm(n))
# ---------------------- #
h=htb(x=dat,yindx="y",ntrees=200,cv.fold=10)
mm=predict_htb(object=h,x=datp,se=TRUE)
pred=cbind(pred,mm[,1])
pred_se=cbind(pred_se,mm[,2])
}
# --- Actual Standard errors at datp
pred_se_true=apply(pred,1,sd)
# --- Mean of estimated standard errors
pred_se_est=apply(pred_se,1,mean)
pred_se_lower=apply(pred_se,1,quantile,prob=.1)
pred_se_upper=apply(pred_se,1,quantile,prob=.9)
# -- Plot estimated SE and true SE (+smooth)
z=c(pred_se_true,pred_se_est,pred_se_lower,pred_se_upper)
ylim=c(min(z),max(z))
plot(datp$w,pred_se_est,ylim=ylim,col="blue",xlab="w",
ylab="Standard error",type="l",main=" SE-true (red) SE-est (blue)")
points(datp$w,pred_se_lower,col="blue",type="l",lty=2)
points(datp$w,pred_se_upper,col="blue",type="l",lty=2)
points(datp$w,pred_se_true,col="red",type="l")
## End(Not run)
htree documentation built on May 1, 2019, 9:11 p.m.
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Description Usage Arguments Value See Also Examples
Description
Prediction functions for hrf and htb.
Usage
1 2 3 4 5 |
Arguments
object |
An object of class |
x |
A data frame or matrix containing new data. If |
time |
A vector of observation times. |
id |
A vector of length |
type |
If |
ntrees |
The number of trees to use in prediction. |
all.trees |
If |
se |
If |
Value
Returns predictions.
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | ## Not run:
# ------------------------------- #
# Simulated data example #
# ------------------------------- #
# library(htree)
p=5;sigma_e=.5;sigma_a=.5;n=500;pnoise=2
random_intercept=as.numeric(mapply(rep,rnorm(n,sd=sigma_a),times=p))
dat=data.frame(time=rep(1:p,n),
x=(random_intercept+rnorm(n*p,sd=sigma_e)),
znoise=matrix(rnorm(n*p*pnoise),ncol=pnoise))
id=sort(rep(1:n,p))
# fit historical random forest
hb=hrf(x=dat,time=dat$time,id=id,yindx=2,se=TRUE)
# get predictions with standard errors
pred=predict_hrf(hb,se=TRUE)
# ------------------------------------------------------------------ #
# Comparison of SE-estimates with actual standard errors for 'hrf'
# ------------------------------------------------------------------ #
## -- evaluation points
n=200
datp=data.frame(y=rep(0,n),w=seq(-2,2,length=n),z=rep(0,n))
## -- estimate model on 50 simulated data sets
pred=NULL
pred_se=NULL
nsim=20
## -- B=100 bootstrap samples, ensemble size of R=10 on each
control=list(ntrees=500,B=100,R=10,se=TRUE,nodesize=5)
for(k in 1:nsim){
if(is.element(k,seq(1,nsim,by=10)))
cat(paste("simulation: ",k," of ",nsim," \n",sep=""))
# -- simulation model -- #
dat=data.frame(y=(4*datp$w+rnorm(n)),x=datp$w,z=rnorm(n))
# ---------------------- #
h=hrf(x=dat,yindx="y",control=control)
mm=predict_hrf(object=h,x=datp,se=TRUE)
pred=cbind(pred,mm[,1])
pred_se=cbind(pred_se,mm[,2])
}
# --- Actual Standard errors at datp
pred_se_true=apply(pred,1,sd)
# --- Mean of estimated standard errors
pred_se_est=apply(pred_se,1,mean)
pred_se_lower=apply(pred_se,1,quantile,prob=.1)
pred_se_upper=apply(pred_se,1,quantile,prob=.9)
# -- Plot estimated SE and true SE (+smooth)
z=c(pred_se_true,pred_se_est,pred_se_lower,pred_se_upper)
ylim=c(min(z),max(z))
plot(datp$w,pred_se_est,ylim=ylim,col="blue",xlab="w",
ylab="Standard error",type="l",main=" SE-true (red) SE-est (blue)")
points(datp$w,pred_se_lower,col="blue",type="l",lty=2)
points(datp$w,pred_se_upper,col="blue",type="l",lty=2)
points(datp$w,pred_se_true,col="red",type="l")
# ------------------------------------------------------------------ #
# Comparison of SE-estimates with actual standard errors for 'htb'
# ------------------------------------------------------------------ #
## -- evaluation points
n=200
datp=data.frame(y=rep(0,n),w=seq(-2,2,length=n),z=rep(0,n))
## -- estimate model on 50 simulated data sets
pred=NULL
pred_se=NULL
nsim=20
for(k in 1:nsim){
if(is.element(k,seq(1,nsim,by=10)))
cat(paste("simulation: ",k," of ",nsim," \n",sep=""))
# -- simulation model -- #
dat=data.frame(y=(4*datp$w+rnorm(n)),x=datp$w,z=rnorm(n))
# ---------------------- #
h=htb(x=dat,yindx="y",ntrees=200,cv.fold=10)
mm=predict_htb(object=h,x=datp,se=TRUE)
pred=cbind(pred,mm[,1])
pred_se=cbind(pred_se,mm[,2])
}
# --- Actual Standard errors at datp
pred_se_true=apply(pred,1,sd)
# --- Mean of estimated standard errors
pred_se_est=apply(pred_se,1,mean)
pred_se_lower=apply(pred_se,1,quantile,prob=.1)
pred_se_upper=apply(pred_se,1,quantile,prob=.9)
# -- Plot estimated SE and true SE (+smooth)
z=c(pred_se_true,pred_se_est,pred_se_lower,pred_se_upper)
ylim=c(min(z),max(z))
plot(datp$w,pred_se_est,ylim=ylim,col="blue",xlab="w",
ylab="Standard error",type="l",main=" SE-true (red) SE-est (blue)")
points(datp$w,pred_se_lower,col="blue",type="l",lty=2)
points(datp$w,pred_se_upper,col="blue",type="l",lty=2)
points(datp$w,pred_se_true,col="red",type="l")
## End(Not run)
|
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