dheaping: Kernel density estimation for heaped data

In Kernelheaping: Kernel Density Estimation for Heaped and Rounded Data

Description

Kernel density estimation for heaped data

Usage

dheaping(
  xheaped,
  rounds,
  burnin = 5,
  samples = 10,
  setBias = FALSE,
  weights = NULL,
  bw = "nrd0",
  boundary = FALSE,
  unequal = FALSE,
  random = FALSE,
  adjust = 1,
  recall = F,
  recallParams = c(1/3, 1/3)
)

Arguments

xheaped	heaped values from which to estimate density of x
rounds	rounding values, numeric vector of length >=1
burnin	burn-in sample size
samples	sampling iteration size
setBias	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)

Kernelheaping documentation built on Jan. 27, 2022, 1:09 a.m.