I have several dataframes (with different names) like those below, with the same number of rows and columns but different names for the last column.
df1:
ID matching_variable STATUS code_1
1 1 1 1
2 1 0 1
3 2 1 0
4 2 1 0
df2:
ID matching_variable STATUS code_2
1 1 1 1
2 1 0 0
3 2 1 0
4 2 1 1
I have about a dozen df's like this and I would like to do a logistic regression of this style for each df:
fit1<-clogit(STATUS~code_1+strata(matching_variable),data=df1)
fit2<-clogit(STATUS~code_2+strata(matching_variable),data=df2)
etc….
I would like to make a function to "automate" this (without having to write all the regressions) and have all the outputs of the regressions in a new table.
I thought of using something like this function: (but as I have different names for the df and for the last column, I get stuck...)
list<-list(df1,df2)
results<- lapply(list, function(x) {clogit(STATUS ~ code_??? + strata(matching_variable), data=???, l)})
Thank you in advance.
Another possible solution, based on purrr::map:
library(purrr)
library(survival)
map(list(df1, df2), ~ clogit(STATUS ~ .x[,4] + strata(matching_variable), data=.x))
#> Warning in coxexact.fit(X, Y, istrat, offset, init, control, weights =
#> weights, : Ran out of iterations and did not converge
#> [[1]]
#> Call:
#> clogit(STATUS ~ .x[, 4] + strata(matching_variable), data = .x)
#>
#> coef exp(coef) se(coef) z p
#> .x[, 4] NA NA 0 NA NA
#>
#> Likelihood ratio test=0 on 0 df, p=1
#> n= 4, number of events= 3
#>
#> [[2]]
#> Call:
#> clogit(STATUS ~ .x[, 4] + strata(matching_variable), data = .x)
#>
#> coef exp(coef) se(coef) z p
#> .x[, 4] 2.020e+01 5.943e+08 2.438e+04 0.001 0.999
#>
#> Likelihood ratio test=1.39 on 1 df, p=0.239
#> n= 4, number of events= 3