Why do we get Partial correlation values greater than 1?
I have used the default function pcor.mat :--
I have manipulated the default pcor.mat function a bit so ignore tha
variables corr_type,element1_in_no,element2_in_no,P.Please ignore the
?pairwise? section and have a look at athe ?listwise ? part i.e else part.
*pcor.mat <-
function(x,y,z,method="p",na.rm=T,corr_type,element1_in_no,element2_in_no,P){
*
* *
* print("pcor.mat")*
* x <- c(x)*
* y <- c(y)*
* z <- as.data.frame(z)*
* print(z)*
* #print("element1_in_no")*
* #print(element1_in_no)*
* *
* *
* if(dim(z)[2] == 0){ *
* stop("There should be given data\n")*
* }*
* *
* data <- data.frame(x,y,z)*
* *
* *
* if(corr_type=="pairwise")*
* {*
* *
* print("inside pairwise")*
* rxx.z
<-P[as.numeric(element1_in_no),as.numeric(element2_in_no)]*
* #print("rxx.z")*
* #print(rxx.z)*
* *
* return(rxx.z)*
* *
* }*
* else*
* {*
* print("inside listwise")*
* if(na.rm == T){*
* data = na.omit(data)*
* }*
* *
* xdata <- na.omit(data.frame(data[,c(1,2)]))
#i1,C1*
* print("printing
xdata.......................................")*
* print(xdata)*
* Sxx <- cov(xdata,xdata,m=method)*
*
print("Sxx...................................................")*
* print(Sxx )*
* *
* xzdata <- na.omit(data)*
* xdata <- data.frame(xzdata[,c(1,2)])*
* zdata <- data.frame(xzdata[,-c(1,2)])*
*
print("zdata..........................")*
* print(zdata)*
* Sxz <- cov(xdata,zdata,m=method)*
*
print("Sxz.....................................
")*
* print(Sxz)*
* *
* zdata <-
na.omit(data.frame(data[,-c(1,2)]))*
* Szz <- cov(zdata,zdata,m=method)*
* print("Szz................................
")*
* print(Szz)*
* #print("new type par corr")*
* #P<-partialCorr_matrix(data)*
* *
* }*
* *
* *
* # is Szz positive definite?*
* zz.ev <- eigen(Szz)$values*
* if(min(zz.ev)[1]<0){*
* *
* stop("\'Szz\' is not positive
definite!\n")
*
* }*
* *
* *
* # partial correlation*
* Sxx.z <- Sxx - Sxz %*% solve(Szz) %*% t(Sxz)*
* print("Sxx.z")*
* print(qr(Sxx.z))*
* rxx.z <- cov2cor(Sxx.z)[1,2]*
* *
* return(rxx.z)*
*}*
*Cov2cor function:-----------------------------------------*
*cov2cor<-function (V) *
*{*
* print("inside cov2cor")*
* *
* *
* p <- (d <- dim(V))[1]*
* if (!is.numeric(V) || length(d) != 2L || p != d[2L]) *
* stop("'V' is not a square numeric matrix")*
* Is <- sqrt(1/diag(V))*
* print("Is")*
* print(Is)*
* if (any(!is.finite(Is))) *
* warning("diag(.) had 0 or NA entries; non-finite result is
doubtful")*
* r <- V*
* r[] <- Is * V * rep(Is, each = p)*
* print("r")*
* print(r[])*
* *
* r[cbind(1L:p, 1L:p)] <- 1*
* r*
* *
*}*
* *
Sxx , Sxz , Szz all these three values I have calculated and they match
with SPSS result.
After that I am not understanding why my partial correlation result doesn?t
match with the SPSS result.
Sxx.z <- Sxx - Sxz %*% solve(Szz) %*% t(Sxz) # how to calculate in
SPSS.After this as you can see in pcor.mat function rxx.z <-
cov2cor(Sxx.z)[1,2] is computed.
Don?t know where things are going wrong.**
*I will attach the datas of categories and items which form the categories.*
*Category 1 has items -1,5 *
*Category 2 has items ? 2,4,6,7,8,9,11*
*Category 3 has items -3,12,14*
*Category 4 has items -10,13,15*
* Category values are actually mean of the items which form the category.*
Here in pcor.mat function i passed a item-category pair dataset and rets all
items and categories as predictors.
For Partial pairwise i can understand this function won't work ,because some
items which form a particular category ,cannot be partailly correlated to
that catgory.
but listwise shuld have any problem.
**
* *Is it due to LINPACK or LAPACK package.I don't have LAPACK package,which
is used in teh solve.default package.
*
*
*
*
Thanks
Moumita*
*
* *
* *
