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Say I have some data, where a dependent variable, dv, is a function of some independent variable, iv, and a categorical predictor, cat. Here are some example data below generated in R:

a<-c(1:100)
err<-rnorm(100,sd=30)
b<-a + err
c<-a + err + 20
cat1<-rep(0,100)
cat2<-rep(1,100)
iv<-c(a,a)
dv<-c(b,c)
cat<-c(cat1,cat2)
data<- data.frame(dv,iv,cat)

I then model dv as a function of iv and cat with this code:

summary(lm(dv~iv + cat, data=data))

and get the following output

                Estimate Std. Error t value Pr(>|t|)    
(Intercept) -5.49606    5.09626  -1.078    0.282    
iv           1.18250    0.07848  15.067  < 2e-16 ***
cat         20.00000    4.53086   4.414 1.67e-05 ***

Now, I want to plot the effect of cat using a standard bar graph- means and errors. So, based on the model, I calculate what the value of dv should be when cat is 0 and when cat is 1, using a common value of iv of 50. For my particular data set, I would get dv values of 53.62894 and 73.62894, for cat levels 0 and 1, respectively.

My question is: which term from the model should I use for the error bars? Should I just use the std error estimate of the cat predictor? Or something more complex that integrates the error values of the intercept and iv parameter as well?

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Try the lsmeans package from R, which will calculate the expected value and the confidence limits:

lsmeans(mod,~iv + cat, at=list(cat=c(0,1), iv=50), data=data )

 iv cat   lsmean       SE  df lower.CL upper.CL
 50   0 54.09313 3.165838 197 47.84985 60.33642
 50   1 74.09313 3.165838 197 67.84985 80.33642

 Confidence level used: 0.95 
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