Cannot plot p-value on simple logistic regression - r

Question

I am trying to plot a simple logistic regression in R.
I am following this tutorial to conduct the logistic regression and calculate a P-value (https://mgimond.github.io/Stats-in-R/Logistic.html). I am trying to use ggplot2 and ggpmisc to plot the regression. I have been trying to use this guide (http://cran.nexr.com/web/packages/ggpmisc/vignettes/user-guide-1.html#stat_fit_glance) to stat_fit_glance to display a p-value
require(cowplot)
require(ggplot2)
library(ggpmisc)
library(rms)
dataset=read.table('input.txt', header=TRUE)
model <- glm(variable ~ ancestry, data=dataset, family=binomial)
summary(model)
#plot logistic regression curve
plot <- ggplot(dataset, aes(x=ancestry, y=variable)) +
geom_point(alpha=.5, color=dataset$colorsite) +
stat_smooth(method="glm", se=FALSE, method.args = list(family=binomial)) + stat_fit_glance(method = "glm", method.args = list(formula = formula), geom = "text", aes(label = paste("P-value = ", signif(..p.value.., digits = 4), sep = "")))
ggsave("output.pdf")
The output however comes out as
> source("C:/Users/Deven/Desktop/logistic/script.R")
Saving 7 x 7 in image
`geom_smooth()` using formula 'y ~ x'
Warning message:
Computation failed in `stat_fit_glance()`:
object of type 'closure' is not subsettable
I have also tried stat_cor from ggpubr, but that seem to be generating different p-values and R^2 values from what I have calculated.
UPDATE BASED ON COMMENTS:
+ stat_poly_eq(formula = y ~ x, method="glm", aes(x = ancestry, y = variable, label = paste(..p.value.label..,sep = "~~~~")),parse = TRUE) fails due to
1: Computation failed in `stat_poly_eq()`:
Method 'glm' not yet implemented.
If I remove method it defaults to a linear regresssion (and gives p values that do not correspond to a logistic regression).
SECOND UPDATE
model <- glm(variable ~ ancestry, data=dataset, family=binomial)
summary(model)
#plot logistic regression curve
plot <- ggplot(dataset, aes(x=ancestry, y=variable)) +
geom_point(alpha=.5, color=dataset$colorsite) +
stat_smooth(method="glm", se=FALSE, method.args = list(family=binomial)) + stat_fit_tidy(method = "glm",method.args = list(family=binomial,formula=y~x), mapping = aes(label = sprintf("Coef = %.3g\np-value = %.3g",after_stat(x_estimate),after_stat(x_p.value))))
ggsave("variable.pdf")
yields the following error:
Saving 7 x 7 in image
`geom_smooth()` using formula 'y ~ x'
Warning message:
Computation failed in `stat_fit_tidy()`:
no applicable method for 'tidy' applied to an object of class "c('glm', 'lm')"
YET ANOTHER UPDATE
library(ggplot2)
library(ggpmisc)
da =read.table('data.txt', header=TRUE)
model = glm(variable ~ ancestry,family=binomial,data=da)
summary(model)
ggplot(da,aes(x = ancestry,y = variable)) + geom_point() +
stat_smooth(method="glm",se=FALSE,method.args = list(family=binomial)) +
stat_fit_tidy(method = "glm",method.args = list(family=binomial,formula=y~x),
mapping = aes(label = sprintf("Coef = %.3g\np-value = %.3g",
after_stat(x_estimate),after_stat(x_p.value))))
ggsave("test.pdf")
works in theory, but the p-value it gives me is very different from the p value that I calculated manually (which corresponds to the one from lrm(variable ~ ancestry, dataset))...
Not sure at all what is going on here...

Answer 1

There is a table on ggpmisc help page that specifies what can be applied to each type of models.
You have a glm, so glance() from tidy will not give you a p-value. Using an example:
library(ggplot2)
library(ggpmisc)
da = MASS::Pima.tr
da$label = as.numeric(da$type=="Yes")
model = glm(label ~ bmi,family=binomial,data=da)
summary(model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.11156 0.92806 -4.430 9.41e-06 ***
bmi 0.10482 0.02738 3.829 0.000129 ***
You can see glance will not give you a p-value :
broom::glance(model)
# A tibble: 1 x 8
null.deviance df.null logLik AIC BIC deviance df.residual nobs
<dbl> <int> <dbl> <dbl> <dbl> <dbl> <int> <int>
1 256. 199 -120. 244. 251. 240. 198 200
You need to use tidy() and as #JonSpring mentioned in the comment, provide the formula, so something like this:
ggplot(da,aes(x = bmi,y = label)) + geom_point() +
stat_smooth(method="glm",se=FALSE,method.args = list(family=binomial)) +
stat_fit_tidy(method = "glm",method.args = list(family=binomial,formula=y~x),
mapping = aes(label = sprintf("Coef = %.3g\np-value = %.3g",
after_stat(x_estimate),after_stat(x_p.value))))

Answer 2

Thank you for all the help, but unfortunately nothing automated worked, so I came up with this instead
require(cowplot)
require(ggplot2)
library(ggpmisc)
library(rms)
dataset=read.table('data.txt', header=TRUE)
model <- glm(variable ~ ancestry, data=dataset, family=binomial)
summary(model)
M1 <- glm(variable ~ ancestry, dataset, family = binomial)
M1
M1$null.deviance
M1$deviance
modelChi <- M1$null.deviance - M1$deviance
pseudo.R2 <- modelChi / M1$null.deviance
pseudo.R2
test <-lrm(variable ~ ancestry, dataset)
Chidf <- M1$df.null - M1$df.residual
chisq.prob <- 1 - pchisq(modelChi, Chidf)
chisq.prob
#plot logistic regression curve
all_variable <- ggplot(dataset, aes(x=ancestry, y=variable)) +
geom_point(alpha=.5, color=dataset$colorsite) +
stat_smooth(method="glm", se=FALSE, method.args = list(family=binomial)) + annotate("text", x=-Inf, y=Inf, hjust = 0, vjust = 2.5, label=paste("p-value: ",signif(chisq.prob, digits = 3),"\nR2: ",signif(pseudo.R2, digits = 3),sep="") )+
ggtitle("Title not relevant to Stack Overflow")
ggsave("variable.pdf")