When running the data.frame command in R (shown below - please note that "Macro" is my variable of interest within the model), I get an output for my variable, fit, se, lower, and upper. I am aware of what each output is telling me except fit.
> data.frame(effect(c("Macro"), model))
Macro fit se lower upper
1 C 45.30041 5.650558 34.14164 56.45918
2 R 33.73317 4.394917 25.05406 42.41229
When I run the effect command (which I originally thought was giving me my standard deviation) I get the same numbers as fit:
> effect(c("macro"), model)
Macro effect
Macro
C R
45.30041 33.73317
Is this truly the standard deviation, or is fit more representative of the mean? And, of course, there is always the option that I am completely off base with both of these potential interpretations.
fit represents your fitted or predicted values given your regression model. In your case with categorical predictors, it's the mean:
library(effects) ## to access the effect() function
m1 <- lm(weight ~ group, data = PlantGrowth)
data.frame(effect(c("group"), m1))
group fit se lower upper
1 ctrl 5.032 0.1971284 4.627526 5.436474
2 trt1 4.661 0.1971284 4.256526 5.065474
3 trt2 5.526 0.1971284 5.121526 5.930474
# CALCULATE MEANS
aggregate(weight ~ group, data = PlantGrowth, mean)
group weight
1 ctrl 5.032
2 trt1 4.661
3 trt2 5.526
Not sure why you thought that effect() will give you the standard deviation. Have a look at ?effect to see what you will get when using the function.