Hi Kirsten,
Yes, the adjustment in the "car" package is different from the
p.adjust function in the "stats" package. I used the latter as I
thought you only needed a way to plot different p-values against the
various adjustment methods. Just create a vector of method names that
you have used and pass that to "staxlab" to label each p-value along
the x-axis.
Jim
On Fri, Jul 14, 2017 at 12:52 AM, Kirsten Morehouse
<kmore...@swarthmore.edu> wrote:
> 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.
>
>
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