To whom it may concern:
 
I am new to R and my knowledge is very basic.  I have a data set in which I want 
to do a generalized linear model with repeated measures.  A sample of my data is 
below, I have these measures for each of my subjects.  Each subject did 60 
trials each and I have 10 subjects.  The independent variables are true 
percentages (5), distance (3), and angle (4).  The predictor variable is log 
error.  The goal is to see what impact the independent variables have on 
judgment error (log error).  Also to see if subjects are getting better or worse 
at judgment over time.  I wanted to know if I have the write set up to do this 
in R. 
 
Thank you in Advance     
 
##Repeated Measures Model##
require(lme4)
 
 
pilotstudy.glm<-glmer(log.error~true_percentage + Distance + Angle +(1|Subject), 
data=pilotstudy)
summary(pilotstudy.glm)
anova(pilotstudy.glm)
 
Subject true_percentage Distance Angle log error 
1 60 25 180 3.33985 
1 45 35 45 4.912889 
1 45 35 70 4.651052 
1 30 35 180 4.330917 
1 15 45 25 5.134426 
1 30 35 45 4.330917 
1 45 35 180 4.651052 
1 45 45 25 2.357552 
2 60 25 180 4.330917 
2 45 35 45 4.912889 
2 45 35 70 3.918863 
2 30 35 180 4.330917 
2 15 45 25 5.326429 
2 30 35 45 3.918863 
2 45 35 180 4.098032 
2 45 45 25 4.179909 
2 30 25 25 3.33985 


      
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