Hi,
I guess you wanted something like this:
res2 <- do.call(cbind,lapply(lst2,
function(x)
rollapply(x,width=32,FUN=function(z) {z1 <- as.data.frame(z);
if(!sum(!!rowSums(is.na(z1 {l1 <-lm(r~F.1+F.2+F.3,data=z1);
c(coef(l1), pval=summary(l1)$coef[,4], rsquare=summary(l1)$r.squared) }
else
Hi,
Try:
dat1 <- read.table(text="A B C D
r.1 x1 x2 x3
r.1 x4 x5 x6
r.2 x7 x8 x9
r.2 x10 x11 x12
r.3 x13 x14 x15
r.3 x16 x17 x18",header=TRUE,stringsAsFactors=FALSE)
dat2 <- do.call(cbind,split(dat1,dat1$A))
colnames(dat2) <- gsub(".*\\.","",colnames(dat2))
A.
Yes. Thank you once again. With this:
> res2 <- do.call(cbind,lapply(lst2,
> function(x)
> rollapply(x,width=32,FUN=function(z) {z1 <- as.data.frame(z);
> if(!sum(!!rowSums(is.na(z1 {l1 <-lm(r~F.1+F.2+F.3,data=z1);
> c(coef(l1), pval=summary(l1)$coef[,4], rsquare=summary(l1)$r.squared) }
> el
Hi,
No problem.
In that case, each column will be a list. For example if I take the first
element of `lst2`
dW1 <- rollapply(lst2[[1]],width=32,FUN=function(z) {z1 <- as.data.frame(z);
if(!sum(!!rowSums(is.na(z1 {l1 <-lm(r~F.1+F.2+F.3,data=z1);
durbinWatsonTest(l1,max.lag=3) } else rep(NA,
Hey!
Yes,
only the D-W test takes so much time, did not check it yet
I checked results (estimates) with manually run regressions (in excel) and
they are correct.
I only change the "width" to 31 and "each=123" to 124, cause it should be
((154-31)+1) x 334 = 41416 matrix
with the lag in D-W test I
Hi,
I was able to read the file after saving it as .csv. It seems to work without
any errors.
dat1<-read.csv("Book2.csv", header=T)
###same as previous
lst1 <- lapply(paste("r",1:334,sep="."),function(x)
cbind(dat1[,c(1:3)],dat1[x]))
lst2 <- lapply(lst1,function(x) {colnames(x)[4] <-"r";x} )
Hi,
#1 & 2:
set.seed(432)
dat1 <- as.data.frame(matrix(sample(c(1:60,NA),154*337,replace=TRUE),ncol=337))
colnames(dat1) <- c(paste("F",1:3,sep="."),paste("r",1:334,sep="."))
lst1 <- lapply(paste("r",1:334,sep="."),function(x)
cbind(dat1[,c(1:3)],dat1[x]))
lst2 <- lapply(lst1,function(x) {
Hi,
Thanks for reply!
Three things:
1.
I did not write that some of the data has more then 31 NA in the column and
then it is not possible to run lm()
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
0 (non-NA) cases
In this case program should return "NA" symbol and go
Hi,
The link seems to be not working. From the description, it looks like:
set.seed(432)
dat1 <- as.data.frame(matrix(sample(200,154*337,replace=TRUE),ncol=337))
colnames(dat1) <- c(paste("F",1:3,sep="."),paste("r",1:334,sep="."))
lst1 <- lapply(paste("r",1:334,sep="."),function(x)
cbind(dat1[,c
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