Nothing

```
# Standardized coefficient extraction
#
# Extract model coefficients in a standardized format (as a named numeric vector).
#
# @section Warning:
# The Binomial models in glmboost return coefficients which are 1/2 the coefficients
# fit by a call to glm(..., family=binomial(...)), because the response variable is
# internally recoded to -1 and +1. sqlscore multiplies the returned coefficients by 2
# to put them back on the same scale as glm, and adds the glmboost offset to the
# intercept before multiplying.
#
# @param object An object for which the extraction of model coefficients is meaningful.
#
# @return Model coefficients as a named numeric vector.
extract_coef <-
function(object)
{
UseMethod("extract_coef")
}
#' @export
extract_coef.default <-
function(object)
{
stats::coef(object)
}
#' @export
extract_coef.glmboost <-
function(object)
{
# suppress coef.glmboost's message so we can print our own
cf <- captureConditions(stats::coef(object, off2int=TRUE))$value
# mboost internally recodes DVs to -1 and +1 in one particular case (the
# logit fit from Binomial_adaboost()), so the coefficients are half those
# returned by glm. If we have a binomial model, let's fix this and return
# twice the fitted coefficients, with a message
if(object$family@name == "Negative Binomial Likelihood (logit link)")
{
message("\nNOTE: Coefficients from glmboost's Binomial_adaboost logit model \n",
"are 1/2 the coefficients from a model fit by glm(... , family = 'binomial').\n",
"sqlscore scales these coefficients by 2 to put them on the same scale as glm.\n")
sc <- 2
}
else
{
sc <- 1
}
sc * cf
}
#' @export
extract_coef.cv.glmnet <-
function(object)
{
cf <- stats::coef(object)
val <- as.vector(cf)
names(val) <- rownames(cf)
#Return only the coefficients that weren't regularized to 0
val[which(val != 0)]
}
```

**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.