Maybe this is what you're looking for:
# x is your set of explanatory variables (10 of them):
x <- array(rnorm(10000), dim=c(1000,10))
# y is your dependent variable:
y <- rbinom(1000, 1, 0.3)
# run the regression on each column of x:
reg <- apply(x, 2, function(z) glm(y ~ z, family=binomial(link='logit')))
# the previous output is a list. Now collect whatever you need from each
list element:
bond <- lapply(reg, function(z) c(z$coeff[2], exp(z$coeff[2]),
confint(z)[2,], exp(confint(z)[2,])))
# collect everything together as a matrix:
bond <- do.call(rbind, bond)
bond
-S.
SevannaD wrote:
>
> So in my first try before I got your message, this is what I did:
>
> orconf<-list()
> ccoef<-list()
> or<-list()
> coef<-list()
> out<-list()
> for (i in 1:49){
> out[[i]]<-glm(y~var[[i]],family=binomial(link="logit"))
> coef[[i]]<-out[[i]]$coef[2]
> or[[i]]<-exp(out[[i]]$coef[2])
> bond<-matrix(out[[i]]$coef[2],
> exp(out[[i]]$coef[2]),confint(out[[i]]$coef[2]),exp(confint(out[[i]]$coef[2])))
> }
> But it did not work due to confint(out[[i]]$coef[2] and the exp one. Said
> Error in object$coefficients : $ operator is invalid for atomic vectors.
>
> would I would to identify conf_low and conf_high as two separate things?
>
> Thanks
>
> On Sun, May 8, 2011 at 4:31 PM, Scott Chamberlain-3 [via R] <
> ml-node+3508106-1763482049-235...@n4.nabble.com> wrote:
>
>> Not knowing what format your data is in or what model you are using...
>>
>> df # is your data frame with columns the variables you are running
>> regressions for
>> datout <- data.frame(coeff = NA, conf_low = NA, conf_high = NA, odd = NA)
>> #
>> a table to put your results in
>> for(i in 1:length(names(df)[2:10])) {
>> fit <- glm(data[,1] ~ data[,i], data = df, etc...)
>> datout[i,] <- fit[e.g, 1:4] # determine what values in your model output
>> are what you need
>> }
>> datout # a table with all your output for each variable
>>
>> On Sunday, May 8, 2011 at 11:58 AM, SevannaD wrote:
>>
>> I have never made a loop on my own to do anything in R. But I am hoping
>>
>> > someone can help me build one for the following issue:
>> >
>> > I need to make a univariate logistic regression for each of my
>> variables
>> > (about 62 of them), then I need to gather up each of their coefficients
>> (not
>> > the intercepts), each of their 95% confidence intervals, and each of
>> thier
>> > odds ratios and place them in a matrix to showcase them for my thesis.
>> >
>> > currently, I am writing them all out one by one with the cbond method,
>> which
>> > has taken me a better part of a day so far and I know there has to be
>> able
>> > to be a way to make a loop that can do this whole process, I just
>> havent
>> > been able to figure it out yet.
>> >
>> > Thanks in advance.
>> >
>> > --
>> > View this message in context:
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