R help - Nov 2008 - Regression Problem for loop

Dear all,
                 I have wrote a code for a linear regression. I want to
write a loop for so, that I can get estimate for pavlues for six predictors.
But I am getting for estmate for only last one. How can I get pvalues for
all my  predictors in a loop??

Anticipating your help
Thanks
Ales



> mat<-matrix(rnorm(36),nrow=6)
> mat
            [,1]              [,2]           [,3]               [,4]
   [,5]            [,6]
[1,]  1.10536338 -0.7613770 -1.7100569 -1.8762241 -0.36579280  0.6465219
[2,] -1.34836804 -0.2174270 -0.1153477 -0.1727683 -1.88406206  1.7484955
[3,]  0.96814418 -2.1483727  0.5839668 -1.2361659  0.04592844  1.9937995
[4,]  0.01960219 -1.2339691  0.8290761  0.1002795 -0.15952881  0.3969251
[5,]  1.62343073  1.3741222 -1.2045854  0.4180127 -0.09898615  1.3575119
[6,] -0.95260509 -0.1522824 -1.4257526  1.0057412 -1.20068336
-0.4306761
> res<-rnorm(6)
> res
[1]  0.2045252 -0.9824761  0.7727004  0.6439993  1.8005737 
1.0167214
>
> pval<-NULL
>
> for(i in c(1:6))
+ {
+ reg<-lm(res~mat[,i])
+ reg
+ pval[i]<-reg$p.value
+ }
> pval
NULL
> reg
Call:
lm(formula = res ~ mat[, i])
Coefficients:
(Intercept)     mat[, i]
     0.8195      -0.2557

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Have you looked at results of str on a regression object? I would not  
think that there would be a single p.value associated with such a  
beast, but that there might be if you examined individual coefficients.

? coefficients
?coef

-- 
David Winsemius

On Nov 27, 2008, at 4:03 AM, ales grill wrote:
> Dear all,
>                 I have wrote a code for a linear regression. I want to
> write a loop for so, that I can get estimate for pavlues for six  
> predictors.
> But I am getting for estmate for only last one. How can I get  
> pvalues for
> all my  predictors in a loop??
>
> Anticipating your help
> Thanks
> Ales
>
>
>
>
>> mat<-matrix(rnorm(36),nrow=6)
>> mat
>            [,1]              [,2]           [,3]               [,4]
>   [,5]            [,6]
> [1,]  1.10536338 -0.7613770 -1.7100569 -1.8762241 -0.36579280   
> 0.6465219
> [2,] -1.34836804 -0.2174270 -0.1153477 -0.1727683 -1.88406206   
> 1.7484955
> [3,]  0.96814418 -2.1483727  0.5839668 -1.2361659  0.04592844   
> 1.9937995
> [4,]  0.01960219 -1.2339691  0.8290761  0.1002795 -0.15952881   
> 0.3969251
> [5,]  1.62343073  1.3741222 -1.2045854  0.4180127 -0.09898615   
> 1.3575119
> [6,] -0.95260509 -0.1522824 -1.4257526  1.0057412 -1.20068336  
> -0.4306761
>> res<-rnorm(6)
>> res
> [1]  0.2045252 -0.9824761  0.7727004  0.6439993  1.8005737  1.0167214
>>
>> pval<-NULL
>>
>> for(i in c(1:6))
> + {
> + reg<-lm(res~mat[,i])
> + reg
> + pval[i]<-reg$p.value
> + }
>> pval
> NULL
>> reg
>
> Call:
> lm(formula = res ~ mat[, i])
> Coefficients:
> (Intercept)     mat[, i]
>     0.8195      -0.2557
>
> 	[[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list
> stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

Dear ales,
Try this:

# Data
# set.seed(123)
mat=matrix(rnorm(36),ncol=6)
colnames(mat)=paste('x',1:6,sep="")
res=rnorm(6)

# All info for coefficients. First 4 columns # correspond to the intercept
and the next for to the slope
t(apply(mat,2,function(x){
sm=summary(lm(res~x))$coeff
res=matrix(sm,ncol=prod(dim(sm)))
names(res)=rep(colnames(sm),nrow(sm))
res
}
)
)

 Estimate Std. Error   t value  Pr(>|t|)   Estimate Std. Error   t value
Pr(>|t|)
x1 -0.3740663  0.3416852 0.2586267 0.1991565 -1.4463559  1.7156617 0.2216156
0.16136742
x2 -0.3972439 -0.5951867 0.2622604 0.3475569 -1.5146930 -1.7124872 0.2044207
0.16196830
x3 -0.1486117 -0.4973452 0.2063296 0.1890952 -0.7202638 -2.6301317 0.5112057
0.05817818
x4 -0.2408166  0.2314570 0.3159543 0.3260189 -0.7621881  0.7099497 0.4884137
0.51693194
x5 -0.4113849  0.4442002 0.1950218 0.1539688 -2.1094303  2.8850004 0.1025563
0.04478734
x6 -0.2652903  0.1900581 0.3264551 0.4945158 -0.8126393  0.3843318 0.4620218
0.72029059


HTH,

Jorge



On Thu, Nov 27, 2008 at 4:03 AM, ales grill <alesgrill@gmail.com> wrote:
> Dear all,
>                 I have wrote a code for a linear regression. I want to
> write a loop for so, that I can get estimate for pavlues for six
> predictors.
> But I am getting for estmate for only last one. How can I get pvalues for
> all my  predictors in a loop??
>
> Anticipating your help
> Thanks
> Ales
>
>
>
>
> > mat<-matrix(rnorm(36),nrow=6)
> > mat
>            [,1]              [,2]           [,3]               [,4]
>   [,5]            [,6]
> [1,]  1.10536338 -0.7613770 -1.7100569 -1.8762241 -0.36579280  0.6465219
> [2,] -1.34836804 -0.2174270 -0.1153477 -0.1727683 -1.88406206  1.7484955
> [3,]  0.96814418 -2.1483727  0.5839668 -1.2361659  0.04592844  1.9937995
> [4,]  0.01960219 -1.2339691  0.8290761  0.1002795 -0.15952881  0.3969251
> [5,]  1.62343073  1.3741222 -1.2045854  0.4180127 -0.09898615  1.3575119
> [6,] -0.95260509 -0.1522824 -1.4257526  1.0057412 -1.20068336 -0.4306761
> > res<-rnorm(6)
> > res
> [1]  0.2045252 -0.9824761  0.7727004  0.6439993  1.8005737  1.0167214
> >
> > pval<-NULL
> >
> > for(i in c(1:6))
> + {
> + reg<-lm(res~mat[,i])
> + reg
> + pval[i]<-reg$p.value
> + }
> > pval
> NULL
> > reg
>
> Call:
> lm(formula = res ~ mat[, i])
> Coefficients:
> (Intercept)     mat[, i]
>     0.8195      -0.2557
>
>        [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@r-project.org mailing list
> stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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