Using some of the lambda values, you can print out the different coefficients for each model.
This will also help you choose a lambda value that is good for your model:
coef(mod.result,s=0.1957000) # 4 of 5 coefficient are at 0, too much shrinkagecoef(mod.result,s=0.0131800) # only 1 coefficient is set to 0coef(mod.result,s=0.0120100) # All coefficients are includedcoef(mod.result,s=0.01)
Using a lambda of .01 will use all of the variables, so let's print out the coefficients using that value:
> coef(mod.result,s=0.01) 7 x 1 sparse Matrix of class "dgCMatrix" 1 (Intercept) -17.57297008856023268208df.Duration -0.000962546 df.TreatmentB -0.33638216397784859168 df.TreatmentP 2.82134076268210698402 ...