I'm learning by building models on UCI datasets and I've found interesting and difficult (for me ;-)) dataset
I'd like to build 6 regression models (or use multivariable regression) and find out which input variables have the biggest impact on each of dependent variable.
I've found the function
varImp from the caret package but I have no idea how to implement regression.
There are target variables that reflect the weight of the stock-picking concept and the weights sum up to 1. Assuming I want to build a regression model with normalized Anuual Return as a dependent variable, how to implement these input variables that are ratios?
Should I just omit one independent variable in order to estimate coefficients or transform them??
The second issue: Dependent variables have values from 0 to 1 (only six different values), what kind of regression should I implement? Which distribution is relevant?
I'm a beginner in R, so I would appreciate your help and simple hints :)