Hi there Zhang,
While there might be a better way... an ugly but generic way of
accessing this type of information is to use str() and a little
experimentation... here is a little history() of what I did to find
it...
a
str(a)
str(logr)
a[[1]]
a[[2]]
a[[3]]
a[[4]]
a[[4]][[1]]
a[[4]][1,]
a[[4]][,4]
and hey presto... we have it... :-)
now if I actually understood what was going on here I'd probably be
faster... but it is pretty generic and you can almost always get at
those things using this technique... no doubt somebody with more
knowledge will explain why it works :-)
cheers,
Sean
On 20/08/06, zhijie zhang <[EMAIL PROTECTED]> wrote:
> Dear friends,
> After running the lm() model, we can get summary resluts like the
> following:
> Coefficients:
> Estimate Std. Error t value Pr(>|t|)
> x1 0.11562 0.10994 1.052 0.2957
> x2 -0.13879 0.09674 -1.435 0.1548
> x3 0.01051 0.09862 0.107 0.9153
> x4 0.14183 0.08471 1.674 0.0975 .
> x5 0.18995 0.10482 1.812 0.0732 .
> x6 0.24832 0.10059 2.469 0.0154 *
> x7 -0.04425 0.11008 -0.402 0.6886
> x8 0.05146 0.10290 0.500 0.6182
> -------------------------------------------------------------
> **the program maybe :
> data<-matrix(rnorm(900),ncol=9) #9variables,1dependent var,8independent
> data<-data.frame(data)
> names(data)<-c('y','x1','x2','x3','x4','x5','x6','x7','x8')
> logr<-lm(y~x1+x2+x3+x4+x5+x6+x7+x8-1,data)
> a<-summary(logr)
> ------------------------------------------------------------------------------------------------------------------------
> Could i extract the p-values or t-values from the a$Coefficients, i searched
> the attributes(a), but don't find the options,how to do that?
> Thanks very much!
>
> --
> Kind Regards,
> Zhi Jie,Zhang ,
>
> [[alternative HTML version deleted]]
>
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