I would like to find predictors for a continuous dependent variable out of a set of 30 independent variables. I am using Lasso regression as implemented in the glmnet package in R. Here is some dummy code:
# generate a dummy dataset with 30 predictors (10 useful & 20 useless) y=rnorm(100) x1=matrix(rnorm(100*20),100,20) x2=matrix(y+rnorm(100*10),100,10) x=cbind(x1,x2) # use crossvalidation to find the best lambda library(glmnet) cv <- cv.glmnet(x,y,alpha=1,nfolds=10) l <- cv$lambda.min alpha=1 # fit the model fits <- glmnet( x, y, family="gaussian", alpha=alpha, nlambda=100) res <- predict(fits, s=l, type="coefficients") res
My questions is how to interpret the output:
Is it correct to say that in the final output all predictors that show a coefficient different from zero are related to the dependent variable?
Would that be a sufficient report in the context of a journal publication? Or is it expected to provide test-statistics for the significance of the coefficients? (The context is human genetics)
Is it reasonable to calculate p-values or other test-statistic to claim significance? How would that be possible? Is a procedure implemented in R?
Would a simple regression plot (data points plotted with a linear fit) for every predictor be a suitable way to visualize this data?
Maybe someone can provide some easy examples of published articles showing the use of Lasso in the context of some real data & how to report this in a journal?