if TRUE a rounding Bias parameter is estimated. For values above 0.5, the respondents
are more prone to round down, while for values < 0.5 they are more likely to round up
weights
optional numeric vector of sampling weights
bw
bandwidth selector method, defaults to "nrd0" see density for more options
boundary
TRUE for positive only data (no positive density for negative values)
unequal
if TRUE a probit model is fitted for the rounding probabilities with log(true value) as regressor
random
if TRUE a random effect probit model is fitted for rounding probabilities
adjust
as in density, the user can multiply the bandwidth by a certain factor such that bw=adjust*bw
recall
if TRUE a recall error is introduced to the heaping model
recallParams
recall error model parameters expression(nu) and expression(eta). Default is c(1/3, 1/3)
Value
The function returns a list object with the following objects (besides all input objects):
meanPostDensity
Vector of Mean Posterior Density
gridx
Vector Grid on which density is evaluated
resultDensity
Matrix with Estimated Density for each iteration
resultRR
Matrix with rounding probability threshold values for each iteration (on probit scale)
resultBias
Vector with estimated Bias parameter for each iteration
resultBeta
Vector with estimated Beta parameter for each iteration
resultX
Matrix of true latent values X estimates
Examples
#Simple Rounding ----------------------------------------------------------
xtrue=rnorm(3000)
xrounded=round(xtrue)
est <- dheaping(xrounded,rounds=1,burnin=20,samples=50)
plot(est,trueX=xtrue)
#####################
#####Heaping
#####################
#Real Data Example ----------------------------------------------------------
# Student learning hours per week
data(students)
xheaped <- as.numeric(na.omit(students$StudyHrs))
## Not run: est <- dheaping(xheaped,rounds=c(1,2,5,10), boundary=TRUE, unequal=TRUE,burnin=20,samples=50)
plot(est)
summary(est)
## End(Not run)
#Simulate Data ----------------------------------------------------------
Sim1 <- createSim.Kernelheaping(n=500, distribution="norm",rounds=c(1,10,100),
thresholds=c(-0.5244005, 0.5244005), sd=100)
## Not run: est <- dheaping(Sim1$xheaped,rounds=Sim1$rounds)
plot(est,trueX=Sim1$x)
## End(Not run)
#Biased rounding
Sim2 <- createSim.Kernelheaping(n=500, distribution="gamma",rounds=c(1,2,5,10),
thresholds=c(-1.2815516, -0.6744898, 0.3853205),downbias=0.2,
shape=4,scale=8,offset=45)
## Not run: est <- dheaping(Sim2$xheaped, rounds=Sim2$rounds, setBias=T, bw="SJ")
plot(est, trueX=Sim2$x)
summary(est)
tracePlots(est)
## End(Not run)
Sim3 <- createSim.Kernelheaping(n=500, distribution="gamma",rounds=c(1,2,5,10),
thresholds=c(1.84, 2.64, 3.05), downbias=0.75, Beta=-0.5, shape=4, scale=8)
## Not run: est <- dheaping(Sim3$xheaped,rounds=Sim3$rounds,boundary=TRUE,unequal=TRUE,setBias=T)
plot(est,trueX=Sim3$x)
## End(Not run)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(Kernelheaping)
Loading required package: MASS
Loading required package: ks
Loading required package: KernSmooth
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
Loading required package: misc3d
Loading required package: mvtnorm
Loading required package: rgl
Loading required package: sparr
Loading required package: spatstat
Loading required package: nlme
Loading required package: rpart
spatstat 1.45-2 (nickname: 'Caretaker Mode')
For an introduction to spatstat, type 'beginner'
Attaching package: 'spatstat'
The following object is masked from 'package:MASS':
area
Welcome to 'sparr': SPAtial Relative Risk (v0.3-8)
T.M. Davies, M.L. Hazelton & J.C. Marshall
-type 'help("sparr")' for details
-type 'citation("sparr")' for how to cite use of this package
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Kernelheaping/dheaping.Rd_%03d_medium.png", width=480, height=480)
> ### Name: dheaping
> ### Title: Kernel density estimation for heaped data
> ### Aliases: dheaping
>
> ### ** Examples
>
> #Simple Rounding ----------------------------------------------------------
> xtrue=rnorm(3000)
> xrounded=round(xtrue)
> est <- dheaping(xrounded,rounds=1,burnin=20,samples=50)
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> plot(est,trueX=xtrue)
>
> #####################
> #####Heaping
> #####################
> #Real Data Example ----------------------------------------------------------
> # Student learning hours per week
> data(students)
> xheaped <- as.numeric(na.omit(students$StudyHrs))
> ## Not run:
> ##D est <- dheaping(xheaped,rounds=c(1,2,5,10), boundary=TRUE, unequal=TRUE,burnin=20,samples=50)
> ##D plot(est)
> ##D summary(est)
> ## End(Not run)
>
> #Simulate Data ----------------------------------------------------------
> Sim1 <- createSim.Kernelheaping(n=500, distribution="norm",rounds=c(1,10,100),
+ thresholds=c(-0.5244005, 0.5244005), sd=100)
> ## Not run:
> ##D est <- dheaping(Sim1$xheaped,rounds=Sim1$rounds)
> ##D plot(est,trueX=Sim1$x)
> ## End(Not run)
>
> #Biased rounding
> Sim2 <- createSim.Kernelheaping(n=500, distribution="gamma",rounds=c(1,2,5,10),
+ thresholds=c(-1.2815516, -0.6744898, 0.3853205),downbias=0.2,
+ shape=4,scale=8,offset=45)
> ## Not run:
> ##D est <- dheaping(Sim2$xheaped, rounds=Sim2$rounds, setBias=T, bw="SJ")
> ##D plot(est, trueX=Sim2$x)
> ##D summary(est)
> ##D tracePlots(est)
> ## End(Not run)
>
> Sim3 <- createSim.Kernelheaping(n=500, distribution="gamma",rounds=c(1,2,5,10),
+ thresholds=c(1.84, 2.64, 3.05), downbias=0.75, Beta=-0.5, shape=4, scale=8)
> ## Not run:
> ##D est <- dheaping(Sim3$xheaped,rounds=Sim3$rounds,boundary=TRUE,unequal=TRUE,setBias=T)
> ##D plot(est,trueX=Sim3$x)
> ## End(Not run)
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.