You were not completely clear, but it appears that you have data where each subject has
results from 8 trials, as a pair of variables is changed. If that is correct, then you
want to have a variance that corrects for the repeated measures. In R the glm command
handles the simple case but not the repeated measures one. Statisticially you can use a
generalized estimating equations approach (package gee) or a random effect per subject
approach (lme or lmer package).
Terry T.
On 05/27/2015 05:00 AM, r-help-requ...@r-project.org wrote:
I mostly use Stata 13 for my regression analysis. I want to conduct a logistic
regression on a proportion/number of success. Because I receive errors in Stata
I did not expect nor understand (if there are Stata experts who want to know
more about the problems I face and can potentially help me solve them, I would
be glad to give more details), I want to repeat the analysis in R. In Stata I
would use the command: xtlogit DEP_PROP INDEP_A INDEP_B INDEP_C, i(ID). ID is
the identifier for each subject. There are eight lines with data for each
subject because there are three within factors (INDEP_A, B, C) with two levels
each (0 and 1). I can repeat this analysis in R by using the command:
glm(DEP_SUC ~ INDEP_A + INDEP_B + INDEP_C, family = ?binomial?). DEP_SUC is
here a table with the successes and misses per row. Again, there are eight rows
for each subject. But while I know how to group these lines in Stata by using
the option i(ID ), I do not know what to do in R. I have se!
ar!
ch for more information about the i() command, but did not find any usefull
information.
So, to summarize: I want to find out how three variables (binary) influence a
proportion and use logistic regression. In Stata I can group multiple lines per
subject using the i( ) command in logistic regression. What is the equivalent
in R?
Thank you in advance!
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