Like Paul said it really helps if you provide a minimal example, but I think this does what you want.
set.seed(123)
N <- 100
num_vars <- 5
df <- data.frame(lapply(1:num_vars, function(i) i = rnorm(N)))
names(df) <- c(paste0(rep("X",5), 1:num_vars ))
e <- rnorm(N)
y <- as.numeric((df$X1 + df$X2 + e) > 0.5)
pvalues <- vector(mode = "list")
singlevar <- function(var, y, df){
model <- as.formula(paste0("y ~ ", var))
pvalues[var] <- coef(summary(glm(model, family = "binomial", data = df)))[var,4]
}
sapply(colnames(df), singlevar, y, df)
X1 X2 X3 X4 X5
1.477199e-04 4.193461e-05 8.885365e-01 9.064953e-01 9.702645e-01
For comparison:
Call:
glm(formula = y ~ X2, family = "binomial", data = df)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0674 -0.8211 -0.5296 0.9218 2.5463
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.5591 0.2375 -2.354 0.0186 *
X2 1.2871 0.3142 4.097 4.19e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 130.68 on 99 degrees of freedom
Residual deviance: 106.24 on 98 degrees of freedom
AIC: 110.24
Number of Fisher Scoring iterations: 4