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I am using lapply to perform several glm regressions on one dependent variable by one independent variable at a time. Right now I am specifically interested in the Pr(>|z|) of each independent variable. However, I am unsure on how to report just Pr(>|z|) using the list from lapply.
If I was just running one model at a time:
coef(summary(fit))[,"Pr(>|z|)"]
or
summary(fit)$coefficients[,4]
Would work (as described here), but trying something similar with lapply does not seem to work. Can I get just the p-values using lapply and glm with an accessor method or from directly calling from the models?
#mtcars dataset
vars <- names(mtcars)[2:8]
fits <- lapply(vars, function(x) {glm(substitute(mpg ~ i, list(i = as.name(x))), family=binomial, data = mtcars)})
lapply(fits,summary) # this works
lapply(fits, coefficients) # this works
#lapply(fits, summary(fits)$coefficients[,4])# this for example does not work
208
In order to extract some data from the fitted glmmodel object, you need to figure out where that data resides (use documentation and str()for that). Some data might be available from the summary.glmobject, while more detailed data is available from the glmobject itself.
For glmer models, the summary output provides p-values based on asymptotic Wald tests (P); while this is standard practice for generalized linear models, these tests make assumptions both about the shape of the log-likelihood surface and about the accuracy of a chi-squared approximation to differences in log-likelihoods.
$\begingroup$@Ciochi: You're welcome. Feel free to accept/upvote my answer, if it is helpful. Yes, fitis new variable that gets created and initialized with the return value of the glm()function (the return value is an object of class glm).$\endgroup$
2 How do I compare coefficients from my glm when I have more than one factor variable in my formula? 1 r glm - Error in names(coef) <- xnames only for 2 columns in data 2 Select behavior dependant with other factors and its formalization
You want to do:
lapply(fits, function(f) summary(f)$coefficients[,4])
However, if each item is just a p-value, you would probably rather have a vector than a list, so you could use sapply instead of lapply:
sapply(fits, function(f) summary(f)$coefficients[,4])
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