# A deep dive into glmnet: predict.glmnet

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I’m writing a series of posts on various function options of the `glmnet`

function (from the package of the same name), hoping to give more detail and insight beyond R’s documentation.

In this post, instead of looking at one of the function options of `glmnet`

, we’ll look at the `predict`

method for a `glmnet`

object instead. The object returned by `glmnet`

(call it `fit`

) has class `"glmnet"`

; when we run `predict(fit)`

, it runs the `predict`

method for class `"glmnet"`

objects, i.e. `predict.glmnet(fit)`

.

For reference, here is the full signature of the `predict.glmnet`

function/method (v3.0-2):

predict(object, newx, s = NULL, type = c("link", "response", "coefficients", "nonzero", "class"), exact = FALSE, newoffset, ...)

In the above, `object`

is a fitted `"glmnet"`

object (call it `fit`

). Recall that every glmnet `fit`

has a lambda sequence associated with it: this will be important in understanding what follows. (This sequence can be accessed via `fit$lambda`

.)

For the rest of this post, we will use the following data example:

set.seed(1) n <- 100; p <- 20 x <- matrix(rnorm(n * p), nrow = n) beta <- matrix(c(rep(1, 5), rep(0, 15)), ncol = 1) y <- x %*% beta + rnorm(n) fit <- glmnet(x, y)

**Function option: newx**

`newx`

is simply the new `x`

matrix at which we want predictions for. So for example, if we want predictions for the training `x`

matrix, we would do

predict(fit, x)

If no other arguments are passed, we will get a matrix of predictions, each column corresponding to predictions for each value of in `fit$lambda`

. For our example, `fit$lambda`

has length 68 and `x`

consists of 100 rows/observations, so `predict(fit, x)`

returns a matrix.

length(fit$lambda) # [1] 68 dim(predict(fit, x)) # [1] 100 68

`newx`

must be provided except when `type="coefficients"`

or `type="nonzero"`

(more on these types later).

**Function option: newoffset**

If the original `glmnet`

call was fit with an offset, then an offset must be included in the `predict`

call under the `newoffset`

option. If not included, an error will be thrown.

set.seed(2) offset <- rnorm(n) fit2 <- glmnet(x, y, offset = offset) predict(fit2, x) # Error: No newoffset provided for prediction, yet offset used in fit of glmnet

The reverse is true, in that if the original `glmnet`

call was NOT fit with an offset, then `predict`

will not allow you to include an offset in the prediction, EVEN if you pass it the `newoffset`

option. It does not throw a warning or error, but simply ignore the `newoffset`

option. You have been warned! This is demonstrated in the code snippet below.

pred_no_offset <- predict(fit, x) pred_w_offset <- predict(fit, x, offset = offset) max(abs(pred_no_offset - pred_w_offset)) # [1] 0

**Function option: s and exact**

`s`

indicates the values for which we want predictions at. If the user does not specify `s`

, `predict`

will give predictions for each of the values in `fit$lambda`

.

(** Why is this option named s and not the more intuitive lambda?** In page 5 of this vignette, the authors say they made this choice “in case later we want to allow one to specify the model size in other ways”.

`lambda`

controls the model size in the sense that the larger it is, the more coefficients will be forced to zero. There are other ways to specify model size. For example, one could imagine a function option where we specify the number of non-zero coefficients we want in the model, or where we specify the maximum norm the coefficient vector can have. None of these other options have been implemented at the moment.)* If the user-specified * then

`s`

values all belong to `fit$lambda`

,`predict`

pulls out the coefficients corresponding to those values and returns predictions. In this case, the `exact`

option has no effect.* If the user-specified * things get interesting. If

`s`

value does NOT belong to `fit$lambda`

,`exact=FALSE`

(the default), `predict`

uses linear interpolation to make predictions. (More accurately, it does linear interpolation of the coefficients, which translates to linear interpolation of the predictions.) As stated in the documentation: “while this is often a good approximation, it can sometimes be a bit coarse”.As a demonstration: In the snippet below, we look at the predictions at a value of that lies between the two largest values in `fit$lambda`

. If the function does as the documentation says, the last line should give a value of 0 (to machine precision).

b1 <- as.numeric(predict(fit, x, s = fit$lambda[1])) b2 <- as.numeric(predict(fit, x, s = fit$lambda[2])) b3 <- as.numeric(predict(fit, x, s = 0.3*fit$lambda[1] + 0.7*fit$lambda[2])) max(abs(b3 - (0.3*b1 + 0.7*b2))) # [1] 3.885781e-16

** What happens if we have values in ** First, I would recommend using

`s`

that are not within the range of `fit$lambda`

?`exact=TRUE`

because extrapolation beyond the range of `fit$lambda`

is dangerous in general. In my little experiments, it looks like `predict`

simply returns the predictions for the value in `fit$lambda`

that is closest to `s`

.If `exact=TRUE`

, `predict`

merges `s`

with `fit$lambda`

to get a single (decreasing) sequence, refits the glmnet model, then returns predictions at the values in `s`

. If your training data is very large, this refitting could take a long time.

One note when using `exact=TRUE`

is that you have to pass in additional arguments in order for the refitting to happen. That’s because the fitted `glmnet`

object does not contain all the ingredients needed to do refitting. For our example, to predict for `fit`

we need to supply `x`

and `y`

as well. For more complicated glmnet calls, more options have to be provided.

predict(fit, x, s = fit$lambda[68] / 2, exact = TRUE) # Error: used coef.glmnet() or predict.glmnet() with `exact=TRUE` # so must in addition supply original argument(s) x and y in order to # safely rerun glmnet predict(fit, x, s = fit$lambda[68] / 2, exact = TRUE, x = x, y = y) # glmnet correctly returns predictions...

**Function option: type**

The `type`

option determines the type of prediction returned. `type="coefficients"`

returns the model coefficients for the values in `s`

as a sparse matrix. `type="nonzero"`

returns a list, with each element being a vector of the features which have non-zero features. For example, the code snippet below shows that for the second and third values in `fit$lambda`

, the features that have non-zero coefficients are feature 5 and features 3 and 5 respectively.

predict(fit, type = "nonzero", s = fit$lambda[2:3]) # $`1` # [1] 5 # # $`2` # [1] 3 5

For `type="coefficients"`

and `type="nonzero"`

, the user does not have to provide a `newx`

argument since the return value does not depend on where we want the predictions. For the rest of the possible values of `type`

, `newx`

is required.

For `type="link"`

(the default) and `type="response"`

it helps to know a little GLM theory. For a observation having values , `type="link"`

returns , where is the coefficient vector corresponding to a value in `s`

.

For `type="response"`

, is passed through the GLM’s inverse link function to return predictions on the `y`

scale. For “gaussian” family it is still . For “binomial” and “poisson” families it is and respectively. For “multinomial” it returns fitted probabilities and for “cox” it returns fitted relative risk.

The final possibility, `type="class"`

, applies only to “binomial” and “multinomial” families. For each observation, it simply returns the class with the highest predicted probability.

**Bonus: The coef method**

The `coef`

method for glmnet is actually just a special case of the `predict`

method. This can be seen from the source code:

coef.glmnet # function (object, s = NULL, exact = FALSE, ...) # predict(object, s = s, type = "coefficients", exact = exact, # ...) # <bytecode: 0x7ff3ae934f20> # <environment: namespace:glmnet>

**Bonus: predict.elnet, predict.lognet, …**

If you inspect the class of the object returned by a `glmnet`

call, you will realize that it has more than one class. In the code below, we see that “gaussian” family results in an “elnet” class object. (“binomial” family returns a “lognet” object, “poisson” family returns a “fishnet” object, etc.)

class(fit) # [1] "elnet" "glmnet"

These classes have their own `predict`

methods as well, but they draw on this base `predict.glmnet`

call. As an example, here is the code for `predict.fishnet`

:

glmnet:::predict.fishnet # function (object, newx, s = NULL, type = c("link", "response", # "coefficients", "nonzero"), exact = FALSE, newoffset, ...) # { # type = match.arg(type) # nfit = NextMethod("predict") # switch(type, response = exp(nfit), nfit) # } # <bytecode: 0x7ff3ab622040> # <environment: namespace:glmnet>

What happens here is that `predict.glmnet`

is first called. If `type`

is not `"response"`

, then we simply return whatever `predict.glmnet`

would have returned. However, if `type="response"`

, then (i) we call `predict.glmnet`

, and (ii) the predictions are passed through the function before being returned.

This is how `predict`

is able to give the correct return output across the different `family`

and `type`

options.

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