Hi,
Try:
set.seed(494)
h<- matrix(sample(1:40,4*124,replace=TRUE),ncol=4)
set.seed(39)
m<- matrix(sample(1:100,10*124,replace=TRUE),ncol=10)
colnames(h)<- paste0("h",1:4)
colnames(m)<- paste0("m",1:10)
mat1<-combn(colnames(m),4)
res<- lapply(colnames(h),function(x) {x1<- h[,x];dat1<-
do.call(rbind,lapply(seq_len(ncol(mat1)),function(i){ x2<- m[,mat1[,i]];GG<-
lm(x1~x2[,1]+x2[,2]+x2[,3]+x2[,4]);GGsum<- summary(GG); data.frame(
Models=paste(colnames(x2),collapse=","), Multiple_Rsq= GGsum$r.squared,
Adjusted_Rsq = GGsum$adj.r.squared, Pval =
paste(GGsum$coef[-1,4],collapse=","),stringsAsFactors=FALSE) }));
dat1[rev(order(dat1[,3])),][1:10,]})
names(res)<- colnames(h)
A.K.
________________________________
From: eliza botto <eliza_bo...@hotmail.com>
To: "smartpink...@yahoo.com" <smartpink...@yahoo.com>
Sent: Thursday, October 3, 2013 11:07 AM
Subject: a simple question
Dear Arun,
I hope you are fine. I actually
wanted to discuss the following problem.
I have a linear model of the
following form.
GG<-lm(h[,any column]~m[,any
column]+m[,any other column] +m[,any other column] +m[,any other column])
where,
h is matrix with 4 columns and
124 rows
m is matrix with 10 columns and
124 rows
what I want is the following
make a loop command to run the
linear model of all the possible combinations of columns of “m” with each
column of “h”.
more precisely, if i take column
1 of matrix “h”, it should be linear modeled with every combination of 10 (210
combinations) columns of “m”.
All the columns of “h” & “m”
have certain names (you can suppose any). The summary(GG) will give Multiple
R-squared, Adjusted R-squared and 4 values of Pr(>|t|).
I want in the end a table in the
following format.
Models
Multiple R-squared Adjusted R-squared Pr(>|t|)
Name of columns of m separated by
comma Multiple R-squared Adjusted R-squared Pr(>|t|)
separated by comma
For Example
Models
Multiple R-squared Adjusted R-squared Pr(>|t|)
eliza, allen, murphy, jack
0.544 0.56
0.000114,0.000112,0.01114,0.002114
where,
eliza, allen, murphy, jack are column names.
The models are to be enlisted in the order of their Adjusted R-squared values.
The models with highest Adjusted R-squared value should be on the top and so
on. i m only interested in top 10 models. so the remaining should be ignored.
I tried to put in my question everything but if there is anything wrong plz
inform me.
Thankyou very much in advance,
Eliza
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