Perform several kinds of models in one function
modeleR(df, yname, xname, modeltype, na.rm = FALSE, new_data, ...)
df | The data for which analysis is required |
---|---|
yname | The dependent variable |
xname | The independent variable. Supports formulae x1+x2+... |
modeltype | Currently one of lm, glm and aov. Other models may work with inaccuracies |
na.rm | Logical. Should missing values be removed from analysis? |
new_data | A data.frame object for which new predictions are to be made |
... | Additional arguments to the modeltype |
A list containing summary stats and a data.frame object of some stats.
This function provides a friendly way to perform any kind of model in one line. The model uses the inbuilt R functions aov and lm to make the predictions. If the target is missing in the new data frame, the function will(currently) make an empty column and fill this with predictions.
Chambers, J. M. (1992) Linear models. Chapter 4 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
Wilkinson, G. N. and Rogers, C. E. (1973). Symbolic descriptions of factorial models for analysis of variance. Applied Statistics, 22, 392-399. doi: 10.2307/2346786.