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## Author: Daniel Sabanes Bove [daniel *.* sabanesbove *a*t* campus *.* lmu *.* de]
## Time-stamp: <[createData.R] by DSB Die 10/03/2009 11:06 (GMT) on daniel@puc-home>
##
## Description:
## Create the data for the example BayesX run.
##
## History:
## 10/03/2009 file creation
#####################################################################################
### setup
nObs <- 300
set.seed(991)
### create covariates and their effects
## smooth functions in x1 and x2
x1 <- round(runif(n=nObs,
min=-pi, max=pi),
2)
x1Effect <- sin(x1) * cos(x1)
plot(x1Effect[order(x1)] ~ x1[order(x1)],
type="l")
x2 <- round(runif(n=nObs),
2)
x2Effect <- (x2 - 0.5)^2
plot(x2Effect[order(x2)] ~ x2[order(x2)],
type="l")
## linear functions in x3 and x4
x3 <- rnorm(n=nObs)
x3Effect <- 9.2 * x3
x4 <- rexp(n=nObs)
x4Effect <- 5.1 * x4
## spatial effect from the district in Tanzania
library(BayesX)
tanzania <- read.bnd(file="tanzania.bnd")
tanzaniaEffects <- rnorm(n=length(tanzania))
names(tanzaniaEffects) <- names(tanzania)
drawmap(map=tanzania,
data=
data.frame(x=names(tanzaniaEffects),
y=tanzaniaEffects),
regionvar="x",
plotvar="y")
district <- sample(x=names(tanzania),
size=nObs,
replace=TRUE)
districtEffect <- tanzaniaEffects[district]
### now generate the response
linearPredictor <- x1Effect + x2Effect + x3Effect + x4Effect + districtEffect
y <- linearPredictor + rnorm(n=nObs)
### write data into text file
data <- data.frame(x1=x1,
x2=x2,
x3=x3,
x4=x4,
district=district,
y=y)
write.table(x=data, file="data.txt",
quote=FALSE, col.names=TRUE, row.names=FALSE)
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