I can't help with the questions on pooling regressions, but for the question
about data-frame usage, I'll note two things.
One,
> x = data.frame(y = 1:3, z = 4:6)
> is.data.frame(x)
TRUE
> is.data.frame(t(x))
FALSE
> is.data.frame(as.data.frame(t(x)))
TRUE
Two, you'll need to check that your row and column names for the transposed
data frame are what you expect them to be.
Once you do both,( results2 = as.data.frame(t(results)) & names ) ,
results2$intercept should work fine.
Hope this helps,
Michael Weylandt
On Mon, Aug 15, 2011 at 8:32 AM, Johannes Radinger <jradin...@gmx.at> wrote:
> Just to inform:
> I posted that before in R-sig-ecology but as it might be interesting also
> for other useRs, I post it also to the general r-user list:
>
>
> Hello Alexandre,
>
> thank you very much. I also found another way to extract summarizing
> information from lm results over e.g. 1000 repeated model runs:
>
> results2 <- t(as.data.frame(results))
> summary(results2)
>
> Although some questions popped up in my analysis (procedure see below) :
>
> 1) it seems that I can't get an element with the usual
> command like results2$intercept ... that doesn't work here, why?
> That would be useful for plotting an element like R-squared etc.
> It can be done with e.g hist(results2[,1]) but then I've to know which
> column is which coefficient.
>
> 2) is there a correct way to get a "mean" p - value or any
> other mean of the regression coefficients? What is the statistically proper
> way to calculate summaries for regression coefficients of many model runs
> Or can I just use the mean from the summary? Is there any restriction when
> using a log transformation like: lm(log(Y)=log(X1)+X2())?
>
> 3) How can I check for normality of the residuals in my overall model?
> I know how to run lillie.test(resid(lm.model)). But how to do that in my
> case with 1000 model runs? Perform the test for each run and get an average
> or pool the residuals?
>
> 4)with single lm-runs it is possible to compare the results of
> two models with an anova...how can I compare the results of lets
> say two models with each 1000 model runs?
>
>
> Thank you very much
>
> /johannes
>
>
> -------- Original-Nachricht --------
> > Datum: Mon, 15 Aug 2011 08:33:52 -0300
> > Von: Alexandre Camargo Martensen <acmarten...@gmail.com>
> > An: Johannes Radinger <jradin...@gmx.at>
> > Betreff: Re: [R-sig-eco] Extracting information from lm results
>
> > Hi Johannes,
> >
> > try this
> >
> > results.df<-data.frame(results)
> > #results.df
> >
> > p<-t(results.df[4,])
> > r<-t(results.df[5,])
> >
> > mean(p)
> > mean(r)
> >
> > HTH,
> >
> > Alexandre
> >
> >
> > On Mon, Aug 15, 2011 at 8:07 AM, Johannes Radinger <jradin...@gmx.at>
> > wrote:
> >
> > > Hello Peter,
> > >
> > > I just found a mail list correspondence from you in R-sig-eco and
> > > found your information and code snipped about extracting information
> > from
> > > lm results very useful. I do have a similar challenge to perform
> several
> > > lm-regression and in the end I'd like to get e.g a mean value for
> > R-squared
> > > or p or any other of the regression coefficients.
> > >
> > > So far I managed to write following code example:
> > >
> > > #Defining variables
> > > Y=c(15,14,23,18,19,9,19,13)
> > > X1=c(0.2,0.6,0.45,0.27,0.6,0.14,0.1,0.52)
> > > X2a=c(17,22,21,18,19,25,8,19)
> > > X2b=c(22,22,29,34,19,26,17,22)
> > > X2 <- function()runif(length(X2a), X2a, X2b)
> > >
> > > #create empty list
> > > mod <- list()
> > > #perfrom n-model runs for lm
> > > for(i in 1:10) {
> > > mod[[paste("run",i,sep="")]] <- lm(Y~X1+X2())
> > > }
> > >
> > > #define coeficients and how to extract them from lm
> > > All_Model_runs <- function(lm){
> > > out <- c(lm$coefficients[1],
> > > lm$coefficients[2],
> > > summary(lm)$coefficients[2,2],
> > > pf(summary(lm)$fstatistic[1], summary(lm)$fstatistic[2],
> > > summary(lm)$fstatistic[3], lower.tail = FALSE),
> > > summary(lm)$r.squared)
> > > names(out) <-
> > > c("intercept","slope","slope.SE","p.value","r.squared")
> > > return(out)
> > > }
> > >
> > > #create empty list
> > > results <- list()
> > > #get coeffiencts for all model runs into list: results
> > > for (i in 1:length(mod)) results[[names(mod)[i]]] <-
> > > All_Model_runs(mod[[i]])
> > >
> > >
> > > Now my questions you might probably be able to answer:
> > >
> > > 1) Is that so far good what I was doing (according to your code)?
> > > 2) How can I get a mean/average p value for example or an average
> > R-squared
> > > over all model runs?
> > > 3) Is there any special function to call a mean of these coefficients?
> > >
> > > Thank you very much
> > > /Johannes
> > >
> > > --
> > >
> > > _______________________________________________
> > > R-sig-ecology mailing list
> > > r-sig-ecol...@r-project.org
> > > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
> > >
>
> --
>
>
>
>
> --
>
> ______________________________________________
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>
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