Cannot plot p-value on simple logistic regression-CodePudding

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 (

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))))

CodePudding user response：

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")