Dear helpers please provide me some helpful answer to my problem while I m
trying to run a program .I m attaching both the program and the data to
which I have to obtain my estimation results.
"Motives.dat" is the data file, and "OBTfile4.3" is the complete code of
program. by Running this
//
rawdata<-matrix(scan(inputFile, n = nsubj*ncomp), nsubj, ncomp, byrow = TRUE)
\\
The error appears to be
//
Error in file(file, "r") : unable to open connection
In addition: Warning message:
cannot open file 'motives_pc.dat', reason 'No such file or directory' in:
file(file, "r")
\\
where # name of preferences data file is assigned as,
inputFile <- "motives_pc.dat
Thanking Regards
SYED ADIL HUSSAIN (+923455205402)
QUAID-E-AZAM UNIVERSITY
ISLAMABAD, PAKISTAN.
This is Virus Free Email Scanned by QAU's McAfee Virus Scanner
""THIS COMMUNICATION MAY CONTAIN CONFIDENTIAL AND/OR OTHERWISE PROPRIETARY
MATERIAL
and is thus for use only by the intended recipient. If you received this in
error, please contact the sender and delete the e-mail
and its attachments from all computers."
www.qau.edu.pk
5 4 3 3 1 3 0 0 0 0
3 5 5 1 5 1 1 2 1 3
1 1 5 1 4 1 1 1 0 1
0 6 6 -9 -9 0 0 3 -9 0
3 3 5 3 4 3 1 0 0 0
6 5 2 5 1 4 4 2 3 3
6 5 1 5 0 4 5 0 1 0
1 6 1 1 5 1 5 1 5 1
1 5 5 5 4 4 0 0 0 0
3 5 3 3 1 0 6 5 3 6
5 5 5 1 0 0 6 4 3 6
6 3 2 3 3 4 6 6 6 6
3 5 4 0 2 4 0 0 3 6
0 3 5 2 4 4 0 0 0 0
1 2 2 1 2 4 3 2 5 3
6 6 0 5 5 4 6 3 6 3
1 5 5 1 3 0 2 3 4 3
1 2 5 1 3 1 2 3 2 3
3 5 6 2 5 1 4 5 3 5
3 6 6 1 1 1 4 2 1 5
0 0 3 0 3 0 0 3 3 4
2 5 2 2 4 3 1 3 2 1
6 5 1 6 4 6 4 1 3 1
1 4 4 1 5 0 1 2 1 2
1 4 4 1 4 2 0 0 0 0
1 3 0 1 0 0 3 0 6 4
4 5 4 3 1 0 3 0 0 3
4 5 2 4 2 3 1 0 1 0
5 6 4 6 2 3 5 3 5 5
0 3 6 0 4 0 0 4 0 2
1 3 6 0 4 2 0 4 1 3
5 4 4 6 3 4 6 3 4 3
2 4 3 5 5 5 0 0 0 0
3 3 3 1 2 2 0 0 0 1
6 5 2 4 4 3 6 3 3 0
2 3 4 2 3 2 2 3 2 2
5 6 3 4 1 0 1 0 0 1
2 3 2 2 3 2 3 3 4 5
2 2 4 2 2 3 2 2 4 4
3 4 2 4 3 4 4 4 4 5
1 6 6 1 5 1 0 0 0 0
0 2 4 0 2 1 0 1 2 2
2 2 -9 2 1 2 2 2 5 5
4 6 3 2 1 2 1 0 0 0
3 3 3 3 3 3 3 3 3 3
5 6 6 2 4 1 0 0 0 0
1 4 4 1 2 1 0 0 0 1
1 5 5 3 4 6 0 0 0 0
0 1 5 0 1 1 1 1 1 5
2 5 5 1 1 1 4 4 1 5
2 4 5 1 1 1 4 2 2 5
0 3 6 1 5 3 0 3 0 1
0 4 6 0 4 0 3 3 3 2
1 1 5 -9 1 1 -9 3 2 5
3 3 3 3 3 3 2 2 2 2
1 4 5 1 1 0 -9 -9 -9 -9
3 4 3 2 1 1 4 4 2 5
1 5 2 3 3 4 3 3 3 3
6 6 2 6 5 5 3 1 0 0
2 2 4 4 4 5 4 4 2 2
4 6 3 6 4 6 3 2 1 0
1 6 6 1 2 0 1 2 0 4
0 1 0 0 0 0 0 0 0 3
6 6 3 3 1 3 3 0 3 0
0 3 6 1 3 0 0 3 0 3
0 6 6 0 2 0 0 0 0 0
3 2 1 0 0 0 0 0 0 1
1 6 5 3 4 0 4 5 3 4
2 2 3 2 2 2 1 1 1 2
5 3 1 5 3 5 0 0 0 0
1 -9 0 3 5 6 1 1 1 1
3 2 2 4 3 4 2 2 3 2
1 3 5 1 4 3 1 3 3 3
6 6 1 6 1 5 1 0 0 0
1 6 6 1 0 0 5 5 1 5
4 5 3 5 3 3 0 0 0 0
5 5 1 5 3 5 2 1 2 1
3 3 3 0 0 0 3 0 0 6
2 6 0 1 6 0 4 4 2 2
3 5 5 5 5 5 2 1 1 1
4 5 4 2 3 3 1 0 2 1
4 4 2 2 4 3 2 1 1 1
2 1 3 3 3 3 3 2 4 4
4 5 3 0 0 1 1 1 1 3
4 3 1 3 1 0 2 1 1 2
0 3 6 0 0 0 0 0 0 6
1 2 5 0 1 0 1 4 1 4
6 4 2 4 4 3 0 0 0 0
6 4 1 5 3 5 1 0 0 0
6 6 2 -9 -9 3 -9 -9 -9 -9
6 0 0 4 6 5 -9 -9 1 -9
-9 6 6 -9 -9 -9 -9 -9 -9 -9
1 6 5 1 1 1 1 1 0 -9
5 5 3 5 3 5 6 3 2 4
2 5 4 6 6 5 1 3 1 1
4 5 3 1 2 1 4 0 4 2
5 2 3 4 5 3 3 4 4 -9
0 2 2 0 3 1 0 1 1 2
4 4 2 4 4 3 0 0 0 0
-9 -9 6 0 0 0 0 0 0 5
########################################################################
# OBTdesign.R #
# #
# generates design matrix for simple and ordinal paired BT model #
# (first column in output file is dependent variable) #
# the designmatrix is stored in the dataframe "dm" and is written #
#
# to "outFile" (see below) #
# #
# preference responses are assumed to be coded #
# 0,...,nrespcat-1, #
# where 0 denotes (highest) preference for #
# object i in comparison (object i, object j) #
# #
# the order of comparisons in the preferences data file is #
# assumed to be #
# (1,2) (1,3) (2,3) (1,4) (2,4) (3,4) (1,5) .... (J-1,J) #
# #
# if model with subject covariates: #
# #
# factors are assumed with levels #
# 1,2,3,..,K #
# for models with continuos subject covariates #
# choose casewise==TRUE #
# corresponding element of covlevels is NA (see below) #
# #
########################################################################
########################################################################
# BEGIN USER SPECIFICATIONS #
########################################################################
# names for objects:
objnames<-c("reseach","career","education","title","transition")
# name of preferences data file
inputFile <- "motives_pc.dat"
# number of rows in preferences data file
nsubj=100
# number of response categories
nrespcat<- 7
# name of output file (dep.variable + design matrix)
outFile <- "motives_design.dat"
# number of subject covariates
ncov <- 3
# remaining specifications are ignored if ncov is zero:
# for models with continuos subject covariates or
# if nsubj < number of cells in subject factor crossclassification:
# casewise <- TRUE
# otherwise all covariates are crossclassified
casewise <- FALSE
# name of subject covariates file
covFile <- "motives_covs.dat"
# labels for covariates
covnames<-c("gender","break","faculty")
# number of factorlevels of covariates, NA for continuos covariates
# (only if casewise==TRUE)
covlevels <- c(2,2,2)
########################################################################
# END USER SPECIFICATIONS #
########################################################################
nobj<-length(objnames)
ncomp <- nobj * (nobj-1) / 2
nrows <- ncomp * nrespcat # number of rows for 1 design matrix (1
subject or no covs)
nrespcat <- nrespcat - 1 # response categories start with 0
if (ncov == 0 ) {
totlev <- 1
ones.totlev <- 1
} else if (casewise){
totlev<-nsubj
ones.totlev <- rep(1,nsubj)
} else {
totlev <- prod(covlevels) # total number of covariate levels
ones.totlev<-rep(1,totlev) # vector for kronecker products
}
ones.ncomp<-rep(1,ncomp) # vector for kronecker products
ones.nrows<-rep(1,nrows) # vector for kronecker products
#
# design matrix for objects
#
obj<-matrix(c(0:0),nrows,nobj)
row<-1
for (j in 2:nobj) {
for (i in 1:(j-1) ){
d <- nrespcat
for (c in 0:nrespcat) {
obj[row + c, i] <- d
obj[row + c, j] <- -d
d <- d-2
}
row <- row + nrespcat + 1
}
}
obj<- ones.totlev %x% obj # stack object design matrix totlev times
#
# mu - factor for comparisons
#
mu<-rep.int(1:ncomp, rep.int((nrespcat+1),ncomp))
mu<-factor(rep(mu,totlev)) # stack mu totlev times
#mu<-gl(ncomp,nrespcat+1,totlev*ncomp*(nrespcat+1))
#
# design matrix for gammas
#
I <- diag(nrespcat+1)
g <- ones.ncomp %x% I # stack gamma matrix ncomp times for 1
subject
g <- ones.totlev %x% g # stack gammas totlev times
gamnames<-paste("G", 0:nrespcat, sep = "")
#
# read data into responsevector
# (count frequencies of preferences)
#
# read ascii data into datamatrix
rawdata<-matrix(scan(inputFile, n = nsubj*ncomp), nsubj, ncomp, byrow =
TRUE)
# case: no subject covariates
#
if (ncov == 0 ) {
y<-rep(0,nrows)
for (i in 1:nsubj) {
k<-1
for (j in 1:ncomp) {
t<-rawdata[i,j]
if (t >= 0 && t <= nrespcat) {
y[k+t]=y[k+t]+1
}
k <- k + nrespcat + 1
}
}
# case: categorical subject covariates
#
} else if (!casewise) {
# read subject covariates data into datamatrix
cov.case<-matrix(scan(covFile, n = nsubj*ncov), nr=nsubj, byrow = TRUE)
#
# transform response data into responsevector y
# according to subject covariates
# (count frequencies of preferences)
#
indx <- ncov:1
if (ncov == 1) {
baslev <- nrows
} else {
baslev <- c(covlevels[2:ncov],nrows)
}
levmult <- rev(cumprod(baslev[indx]))
y<-rep(0,totlev * nrows)
for (i in 1:nsubj) {
y.address <- sum((cov.case[i,]-1)*levmult)
for (j in 1:ncomp) {
t<-rawdata[i,j]
if (t >= 0 && t <= nrespcat) {
y[y.address+t+1]=y[y.address+t+1]+1
}
y.address <- y.address + nrespcat+1
}
}
#
# transform subject covariates data into covariate vectors
#
cov<-NULL
for (j in 1:ncov) {
scov<-gl(covlevels[j],levmult[j],totlev*nrows)
cov<-cbind(cov,scov)
}
# case: metric (and categorical) subject covariates
#
} else {
# read subject covariates data into datamatrix
cov.case<-matrix(scan(covFile, n = nsubj*ncov), nr=nsubj, byrow = TRUE)
cov<-cov.case %x% ones.nrows
case<-rep.int(1:nsubj, rep.int(nrows,nsubj))
case<-factor(case) # factor for cases
cov<-cbind(cov,case)
covnames<-c(covnames,"CASE")
covlevels<-c(covlevels,nsubj)
k<-1
y<-rep(0,nrows * totlev)
for (i in 1:nsubj) {
for (j in 1:ncomp) {
t<-rawdata[i,j]
if (t >= 0 && t <= nrespcat) {
y[k+t]=y[k+t]+1
}
k <- k + nrespcat + 1
}
}
}
#
# prepare dataframe and export
#
if (ncov == 0) {
dm<-data.frame(y,mu,g,obj)
varnames<-c("mu",gamnames,objnames)
} else {
dm<-data.frame(y,mu,g,obj,cov)
varnames<-c("mu",gamnames,objnames,covnames)
}
names(dm)<-c("!y",varnames)
write.table(dm,outFile,quote=F,row.names=F)
################################################################______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
