addSE

This package provide one function: add_se which computes standard errors and confidence intervals for group-level effects adding up standard errors on the way.

You can install the package by running the following code in R

library(devtools)
install_github("lionel68/addSE")

The function can then be used with code looking like:

library(addSE)

?add_se

data("iris")
m <- lm(Sepal.Length ~ Species * Sepal.Width, iris)
#to get the fitted average Sepal length per species
add_se(m, "Species")
##                               Coef      LCI      UCI
##(Intercept):Speciessetosa 2.639001 1.518851 3.759152
##Speciesversicolor         3.539735 2.446018 4.633452
##Speciesvirginica          3.906836 2.764838 5.048835

#to get the fitted Sepal length ~ Sepal width slopes per species
add_se(m, name_f = "Species", name_x = "Sepal.Width")
##                                   Coef       LCI      UCI
##Sepal.Width:Speciessetosa     0.6904897 0.3656651 1.015314
##Speciesversicolor:Sepal.Width 0.8650777 0.4726938 1.257462
##Speciesvirginica:Sepal.Width  0.9015345 0.5197338 1.283335

These results can then be used for plotting or reporting in result tables.

So far the following models are supported: lm, glm, glm.nb (MASS), lme (nlme), lmer (lme4), glmer (lme4), merModLmerTest (lmerTest)

Please report bugs, enhancment wishes and other errors in the issues tab.

TODOs:

• support for other model types
• add plot functions?
• improve documentation
• set up tests cases