I've got this model:
model <- lm (time~radius_mean+texture_mean+perimeter_mean+area_mean +smoothness_mean+compactness_mean+concavity_mean +concave_points_mean+symmetry_mean+fractal_dimension_mean+radius_se +texture_se+perimeter_se+area_se+smoothness_se+compactness_se +concavity_se+concave_points_se+symmetry_se+fractal_dimension_se +radius_worst+texture_worst+perimeter_worst+area_worst+smoothness_worst +smoothness_worst+compactness_worst+concavity_worst+concave_points_worst +symmetry_worst+fractal_dimension_worst+tumor_size+lymph_node, model) summary(model)
And I want to check what transformations should I do over the variables. I tried a Box-Cox to check if a transformation over the response variable would be necessary:
require(MASS) boxcox(model, plotit=T) boxcox(model, plotit=T, lambda=seq(0.2,0.7,by=0.05))
But the graph says no. At this point, how can I check if a transformation over the independent variables is necessary?
Thank you for your answer. Maybe I explained myself wrong. I just want to consider if it is necessary to make any transformation over a variable and what alternative models would should be applied.
The point is that I am fully lost about transformation and I don't know how to check it with this huge amount of variables.
I guess I should make a response transformation. So what would be the next point for this?