[R] How to make a figure plotting p-values by range of different adjustment values?

[Jim Lemon]

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
<kmoreho1 at 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]]
>
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