Having issues in transforming my data for further analysis In R

Question

I have a dataset here:

'''dataset

I want to perform linear and multiple regression.MoralRelationship and SkeletalP are both dependent variables while others are independent. I tried all the various method of Transformation I know but it did not yield any meaningful result from my diagnostic plot

I did this:

lm1<- lm(MoralRelationship ~ RThumb + RTindex + RTmid + RTFourth + RTFifth + Lthumb + Lindex
+ LTMid + LTFourth + LTfifth + BldGRP1 + BlDGR2, data=data)

I did same for SkeletalP

I did adiagnostic plot for both. then Tried to normalize the variables because there is correlation nor linearity. I took square term, log ,Sqrtof all independent variables also,log,1/x but no better output. I also did

`lm(SkeletalP ~ RThumb + I(RThumb^2), data=data)` 

if i will get a better result with one variable.

The independent variables are right skewed except for ANB which is normally distributed.

is there method I can use to transform my data? most importantly, to be uniformly distributed so that i can perform other statistical test.

Answer 1

Your dataset is kind of small. You can try dimensionality reduction like PCA, but I don't think it's appropriate here. It's also harder to interpret.

Have you tried other models? Tuning might help the fit of your regression models (e.g. Lasso/Ridge L1/L2 regulation)