I'm trying to write a code that will present regression's coefficients for mixed combination of variables.After each regression another variable should be added to the regression so In the end I'll get a list that present the coefficients per a regrssion.I would like to allow only 1-4 variables in each regression. Here is my sample data:
dat <- read.table(text = " var1 var2 var3 var4
0 3 9 7
1 3 8 4
1 1 2 8
0 1 2 3
0 1 8 3
1 6 1 2
0 6 7 1
1 6 1 5
0 5 9 7
1 3 8 7
1 4 2 7
0 1 2 3
0 7 6 3
1 6 1 1
0 6 3 9
1 6 1 1 ",header = TRUE)
I manage to get a list that show the Coeffients of the regression between a spesific variable (var1) and the other variables by using this code:
t(sapply(setdiff(names(dat),"var1"),
function(x) coef(glm(reformulate(x,response="var1"),
data=dat,family=binomial(link='logit')))))
Here is the output:
(Intercept) var2
var2 -0.56394149 0.13865097
var3 1.28295290 -0.29798823
var4 0.08075091 -0.01819781
However, I would like to add regressions that use combinations of variables and present the results in a table along with it's p-value, for example:
(Intercept) var2 var3 var4 p-value
var2 -0.56394149 0.13865097 0.02
var3 1.28295290 -0.29798823 0.01
var4 0.08075091 -0.01819781 0.2
var2+var3
var2+var4
var3+var4
var2+var3+var4
Any Idea how can It be done? Thank you
You could use MuMIn for this:
mod <- glm(var1 ~ var2 + var3 + var4, data = dat, na.action = na.fail)
# Nullmodel
mod0 <- glm(var1 ~ 1, data = dat, na.action = na.fail)
require(MuMIn)
allmods <- dredge(mod, extra = c(pval = function(x) anova(x, mod0, test = 'F')[2, 'Pr(>F)']))
allmods
The p-value comes from a comparison with the nullmodel.