Hi Jim,
Thanks for your help, I really appreciate it.
Perhaps I'm misunderstanding, but does this formula run different ajustment
values for this function?
logit(p = doc$value, adjust = 0.025)
I'm looking to plot the p-values of different adjustment values.
Thanks so much,
Kirsten
On Wed, Jul 12, 2017 at 8:49 PM, Jim Lemon <drjimle...@gmail.com> wrote:
> Hi Kirsten,
> Perhaps this will help:
>
> set.seed(3)
> kmdf<-data.frame(group=rep(1:4,each=20),
> prop=c(runif(20,0.25,1),runif(20,0.2,0.92),
> runif(20,0.15,0.84),runif(20,0.1,0.77)))
> km.glm<-glm(prop~group,kmdf,family=quasibinomial(link="logit"))
> summary(km.glm)
> pval<-0.00845
> padjs<-NA
> npadj<-1
> # assume you have five comparisons in this family
> for(method in p.adjust.methods) {
> padjs[npadj]<-p.adjust(pval,method=method,n=5)
> npadj<-npadj+1
> }
> plot(padjs,xaxt="n",main="P plot",xlab="Method",ylab="adjusted p values")
> abline(h=0.05,col="lightgray")
> library(plotrix)
> staxlab(1,at=1:8,labels=p.adjust.methods)
>
> Jim
>
>
> On Thu, Jul 13, 2017 at 12:53 AM, Kirsten Morehouse
> <kmore...@swarthmore.edu> wrote:
> > Hi all,
> >
> > Thank you for taking the time to read my message. I'm trying to make a
> > figure that plots p-values by a range of different adjustment values.
> >
> > (Using the **logit** function in package **car**)
> >
> > My Statistical analyses were conducted on probability estimates ranging
> > from 0% to 100%. As it's not ideal to run linear models on percentages
> that
> > are bounded between 0 and 1, these estimates were logit transformed.
> >
> > However, this introduces a researcher degree of freedom. In Package
> > **Car**, the logit transformation code is
> >
> > logit(p = doc$value, adjust = 0.025)
> >
> > logit definition/Description
> >
> > Compute the logit transformation of proportions or percentages.
> >
> > Usage
> >
> > logit(p, percents=range.p[2] > 1, adjust)
> >
> > Arguments
> >
> > p a numeric vector or array of proportions or percentages.
> > percents TRUE for percentages.
> > adjust adjustment factor to avoid proportions of 0 or 1; defaults
> to
> > 0 if there are no such proportions in the data, and to .025 if there
> are.)
> >
> > I chose the default adjustment factor of .025, but I need to determine at
> > what point my values are greater than .05 to show I did not choose an
> > ajustment value that makes my results significant.
> >
> > Ultimately, I want to find the range of adjustment factors do we get P <
> > 0.05?And at what point do we get P > 0.05?
> >
> > ## The final product I'm looking for is a figure with the following
> > features:
> > ## 1) Adjustment factor on the x-axis
> > ## 2) P value on the y-axis
> >
> > Does anyone know how to do this? Thank you so much in advance.
> >
> > [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/
> posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
[[alternative HTML version deleted]]
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