Description Usage Arguments Value Author(s) References Examples
The function calculates the binary weighted z-Difference for a binary reference variable (ref) and a binary variable (x)
1 | zdifference_binary(x,ref,w=NULL,na.rm=TRUE,r)
|
x |
The binary variable for which the weighted z-Difference should be calculated. |
ref |
The binary reference variable as a vector. |
w |
The weights to calculate the weighted binary z-Difference |
na.rm |
Should NAs be removed or not. If NAs exists in dataset and na.rm=FALSE then an error will occure. |
r |
digits to round the returned value, default is 2 |
The function returns the calculated z-Difference as a numeric value.
Tim Filla
For standard z-difference (unweighted) https://pubmed.ncbi.nlm.nih.gov/23972521/
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | #generate the data. The weights are taken from uniform #distribution and the
#values of x are generated from a bernoulli distribution with
#success rate 0.3. The reference variable
#is chosen from a bernoulli distribution with success rate 0.8.
ref<-sample(0:1,1000,replace=TRUE,prob=c(0.2,0.8))
erg<-unlist(lapply(1:1000,function(z){
w<-runif(1000)
x<-rbinom(1000,1,0.3)
zdifference_binary(x,ref,w)
}))
hist(erg,breaks=50,main="z-difference for continuous data")
plot(seq(0.005,0.97,0.01),quantile(erg,seq(0.005,0.97,0.01)),
type="l",lwd=3,xlab=c("quantile"),ylab=c("x-value"))
points(seq(0.005,0.97,0.01),qnorm(seq(0.005,0.97,0.01)),col="red",type="l",lwd=3,lty=2)
legend("topleft",legend=c("N(0,1) distribution","sample distribution"),lty=c(2,1),
lwd=c(3,3),col=c("red","black"),cex=1.3)
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