I'm trying to do LASSO in R with the package glmpath. However, I'm not sure
if I am using the accompanying prediction function *predict.glmpath()*
correctly.
Suppose I fit some regularized binomial regression model like so:


library(glmpath);load(heart.data);attach(heart.data);

fit <- glmpath(x, y, family=binomial)

Then I can use predict.glmpath() to estimate the value of the response
variable y at x for varying values of lambda through

pred <- predict.glmpath(fit, newx = x, mode="lambda",
s=seq(0,10,1),type="response")

However, in the help file it can be seen that there is also an option
*newy*. How should one interpret the result when calling
*predict.glmpath()* with *newy = some.y*?


Additionally, in the help file it can be seen that there exist
numerous choices for the option "type":

                     description in help file
"response"            the estimated responses are returned"loglik"
         the log-likelihoods are returned"coefficients"        the
coefficients are returned. The coefficients for the initial input
variables are returned (rather than the standardized
coefficients)"link"(default)       the linear predictors are returned

How should I understand these options? To which linear predictors and
coefficients are they referring to? Surely not those of the original
model?


Thanks in advance!

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