The **cv** package is designed to be extensible in
several directions. In this vignette, we discuss three kinds of
extensions, ordered by increasing general complexity: (1) adding a
cross-validation cost criterion; (2) adding a model class that’s not
directly accommodated by the `cv()`

default method or by
another directly inherited method, with separate consideration of
mixed-effects models; and (3) adding a new model-selection procedure
suitable for use with `selectModel()`

.

A cost criterion suitable for use with `cv()`

or
`cvSelect()`

should take two arguments, `y`

(the
observed response vector) and `yhat`

(a vector of fitted or
predicted response values), and return a numeric index of lack of fit.
The **cv** package supplies several such criteria:
`mse(y, yhat)`

, which returns the mean-squared prediction
error for a numeric response; `rmse(y, yhat)`

, which returns
the (square-)root mean-squared error; `medAbsErr(y, yhat)`

,
which returns the median absolute error; and
`BayesRule(y, yhat)`

(and its non-error-checking version,
`BayesRule2(y, yhat))`

, suitable for use with a binary
regression model, where `y`

is the binary response coded
`0`

for a “failure” or `1`

for a “success”; where
`yhat`

is the predicted probability of success; and where the
proportion of *incorrectly* classified cases is returned.

To illustrate using a different prediction cost criterion, we’ll base
a cost criterion on the area under the receiver operating characteristic
(“ROC”) curve for a logistic regression. The ROC curve is a graphical
representation of the classification power of a binary regression model,
and the area under the ROC curve (“AUC”), which varies from 0 to 1, is a
common summary measure based on the ROC (see
"Receiver operating characteristic", 2023). The
**Metrics** package (Hamner &
Frasco, 2018) includes a variety of measures useful for model
selection, including an `auc()`

function. We convert the AUC
into a cost measure by taking its complement:

We then apply `AUCcomp()`

to the the Mroz logistic
regression, discussed in the introductory vignette on cross-validating
regression models, which we reproduce here. Using the `Mroz`

data frame from the **carData** package (Fox & Weisberg, 2019):

```
data("Mroz", package="carData")
m.mroz <- glm(lfp ~ ., data=Mroz, family=binomial)
summary(m.mroz)
#>
#> Call:
#> glm(formula = lfp ~ ., family = binomial, data = Mroz)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 3.18214 0.64438 4.94 7.9e-07 ***
#> k5 -1.46291 0.19700 -7.43 1.1e-13 ***
#> k618 -0.06457 0.06800 -0.95 0.34234
#> age -0.06287 0.01278 -4.92 8.7e-07 ***
#> wcyes 0.80727 0.22998 3.51 0.00045 ***
#> hcyes 0.11173 0.20604 0.54 0.58762
#> lwg 0.60469 0.15082 4.01 6.1e-05 ***
#> inc -0.03445 0.00821 -4.20 2.7e-05 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 1029.75 on 752 degrees of freedom
#> Residual deviance: 905.27 on 745 degrees of freedom
#> AIC: 921.3
#>
#> Number of Fisher Scoring iterations: 4
AUCcomp(with(Mroz, as.numeric(lfp == "yes")), fitted(m.mroz))
#> [1] 0.26362
```

Cross-validating this cost measure is straightforward:

```
library("cv")
#> Loading required package: doParallel
#> Loading required package: foreach
#> Loading required package: iterators
#> Loading required package: parallel
cv(m.mroz, criterion=AUCcomp, seed=3639)
#> R RNG seed set to 3639
#> 10-Fold Cross Validation
#> method: exact
#> criterion: AUCcomp
#> cross-validation criterion = 0.27471
#> full-sample criterion = 0.26362
```

As expected, the cross-validated complement to the AUC is somewhat less optimistic than the criterion computed from the model fit to the whole data set.

As we explain in the vignette “Cross-validating regression models,”
the `cv()`

function differentiates between CV criteria that
are averages of casewise components and criteria that are not.
Computation of bias corrections and confidence intervals is limited to
the former. We show in the technical and computational vignette that the
AUC, and hence its complement, cannot be expressed as averages of
casewise components.

`cv()`

looks for a `"casewise loss"`

attribute
of the value returned by a CV criterion function. If this attribute
exists, then the criterion is treated as the mean of casewise
components, and `cv()`

uses the unexported function
`getLossFn()`

to construct a function that returns the
casewise components of the criterion.

We illustrate with the `mse()`

:

```
mse
#> function (y, yhat)
#> {
#> result <- mean((y - yhat)^2)
#> attr(result, "casewise loss") <- "(y - yhat)^2"
#> result
#> }
#> <bytecode: 0x63a6f97e4de0>
#> <environment: namespace:cv>
cv:::getLossFn(mse(rnorm(100), rnorm(100)))
#> function (y, yhat)
#> {
#> (y - yhat)^2
#> }
#> <environment: 0x63a6f9892330>
```

For this scheme to work, the “casewise loss” attribute must be a
character string (or vector of character strings), here
`"(y - yhat)^2"`

, that evaluates to an expression that is a
function of `y`

and `yhat`

, and that computes the
vector of casewise components of the CV criterion.

`cv()`

methodSuppose that we want to cross-validate a multinomial logistic
regression model fit by the `multinom()`

function in the
**nnet** package (Venables &
Ripley, 2002). We borrow an example from Fox (2016, sec. 14.2.1), with data from the
British Election Panel Study on vote choice in the 2001 British
election. Data for the example are in the `BEPS`

data frame
in the **carData** package:

```
data("BEPS", package="carData")
head(BEPS)
#> vote age economic.cond.national economic.cond.household Blair
#> 1 Liberal Democrat 43 3 3 4
#> 2 Labour 36 4 4 4
#> 3 Labour 35 4 4 5
#> 4 Labour 24 4 2 2
#> 5 Labour 41 2 2 1
#> 6 Labour 47 3 4 4
#> Hague Kennedy Europe political.knowledge gender
#> 1 1 4 2 2 female
#> 2 4 4 5 2 male
#> 3 2 3 3 2 male
#> 4 1 3 4 0 female
#> 5 1 4 6 2 male
#> 6 4 2 4 2 male
```

The polytomous (multi-category) response variable is
`vote`

, a factor with levels `"Conservative"`

,
`"Labour"`

, and `"Liberal Democrat"`

. The
predictors of `vote`

are:

`age`

, in years;`econ.cond.national`

and`econ.cond.household`

, the respondent’s ratings of the state of the economy, on 1 to 5 scales.`Blair`

,`Hague`

, and`Kennedy`

, ratings of the leaders of the Labour, Conservative, and Liberal Democratic parties, on 1 to 5 scales.`Europe`

, an 11-point scale on attitude towards European integration, with high scores representing “Euro-skepticism.”`political.knowledge`

, knowledge of the parties’ positions on European integration, with scores from 0 to 3.`gender`

,`"female"`

or`"male"`

.

The model fit to the data includes an interaction between
`Europe`

and `political.knowledge`

; the other
predictors enter the model additively:

```
library("nnet")
m.beps <- multinom(
vote ~ age + gender + economic.cond.national +
economic.cond.household + Blair + Hague + Kennedy +
Europe * political.knowledge,
data = BEPS
)
#> # weights: 36 (22 variable)
#> initial value 1675.383740
#> iter 10 value 1240.047788
#> iter 20 value 1163.199642
#> iter 30 value 1116.519687
#> final value 1116.519666
#> converged
car::Anova(m.beps)
#> Analysis of Deviance Table (Type II tests)
#>
#> Response: vote
#> LR Chisq Df Pr(>Chisq)
#> age 13.9 2 0.00097 ***
#> gender 0.5 2 0.79726
#> economic.cond.national 30.6 2 2.3e-07 ***
#> economic.cond.household 5.7 2 0.05926 .
#> Blair 135.4 2 < 2e-16 ***
#> Hague 166.8 2 < 2e-16 ***
#> Kennedy 68.9 2 1.1e-15 ***
#> Europe 78.0 2 < 2e-16 ***
#> political.knowledge 55.6 2 8.6e-13 ***
#> Europe:political.knowledge 50.8 2 9.3e-12 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
```

Most of the predictors, including the `Europe`

\(\times\) `political.knowledge`

interaction, are associated with very small \(p\)-values; the `Anova()`

function is from the **car** package (Fox & Weisberg, 2019).

Here’s an “effect plot”, using the the **effects**
package (Fox & Weisberg, 2019) to
visualize the `Europe`

\(\times\) `political.knowledge`

interaction in a “stacked-area” graph:

```
plot(
effects::Effect(
c("Europe", "political.knowledge"),
m.beps,
xlevels = list(Europe = 1:11, political.knowledge = 0:3),
fixed.predictors = list(given.values = c(gendermale = 0.5))
),
lines = list(col = c("blue", "red", "orange")),
axes = list(x = list(rug = FALSE), y = list(style = "stacked"))
)
```

To cross-validate this multinomial-logit model we need an appropriate
cost criterion. None of the criteria supplied by the **cv**
package—for example, neither `mse()`

, which is appropriate
for a numeric response, nor `BayesRule()`

, which is
appropriate for a binary response—will do. One possibility is to adapt
Bayes rule to a polytomous response:

```
head(BEPS$vote)
#> [1] Liberal Democrat Labour Labour Labour
#> [5] Labour Labour
#> Levels: Conservative Labour Liberal Democrat
yhat <- predict(m.beps, type = "class")
head(yhat)
#> [1] Labour Labour Labour Labour
#> [5] Liberal Democrat Labour
#> Levels: Conservative Labour Liberal Democrat
BayesRuleMulti <- function(y, yhat) {
result <- mean(y != yhat)
attr(result, "casewise loss") <- "y != yhat"
result
}
BayesRuleMulti(BEPS$vote, yhat)
#> [1] 0.31869
#> attr(,"casewise loss")
#> [1] "y != yhat"
```

The `predict()`

method for `"multinom"`

models
called with argument `type="class"`

reports the Bayes-rule
prediction for each case—that is, the response category with the highest
predicted probability. Our `BayesRuleMulti()`

function
calculates the proportion of misclassified cases. Because this value is
the mean of casewise components, we attach a
`"casewise loss"`

attribute to the result (as explained in
the preceding section).

The marginal proportions for the response categories are

```
xtabs(~ vote, data=BEPS)/nrow(BEPS)
#> vote
#> Conservative Labour Liberal Democrat
#> 0.30295 0.47213 0.22492
```

and so the marginal Bayes-rule prediction, that everyone will vote Labour, produces an error rate of \(1 - 0.47213 = 0.52787\). The multinomial-logit model appears to do substantially better than that, but does its performance hold up to cross-validation?

We check first whether the default `cv()`

method works
“out-of-the-box” for the `"multinom"`

model:

```
cv(m.beps, seed=3465, criterion=BayesRuleMulti)
#> Error in GetResponse.default(model): non-vector response
```

The default method of `GetResponse()`

(a function supplied
by the **cv** package—see `?GetResponse`

) fails
for a `"multinom"`

object. A straightforward solution is to
supply a `GetResponse.multinom()`

method that returns the
factor response (using the
`get_response()`

function from the **insight**
package, Lüdecke, Waggoner, & Makowski, 2019),

```
GetResponse.multinom <- function(model, ...) {
insight::get_response(model)
}
head(GetResponse(m.beps))
#> [1] Liberal Democrat Labour Labour Labour
#> [5] Labour Labour
#> Levels: Conservative Labour Liberal Democrat
```

and to try again:

```
cv(m.beps, seed=3465, criterion=BayesRuleMulti)
#> R RNG seed set to 3465
#> # weights: 36 (22 variable)
#> initial value 1507.296060
#> iter 10 value 1134.575036
#> iter 20 value 1037.413231
#> iter 30 value 1007.705242
#> iter 30 value 1007.705235
#> iter 30 value 1007.705235
#> final value 1007.705235
#> converged
#> Error in match.arg(type): 'arg' should be one of "class", "probs"
```

A `traceback()`

(not shown) reveals that the problem is
that the default method of `cv()`

calls the
`"multinom"`

method for `predict()`

with the
argument `type="response"`

, when the correct argument should
be `type="class"`

. We therefore must write a
“`multinom`

” method for `cv()`

, but that proves to
be very simple:

```
cv.multinom <-
function (model, data, criterion = BayesRuleMulti, k, reps,
seed, ...) {
model <- update(model, trace = FALSE)
NextMethod(
type = "class",
criterion = criterion,
criterion.name = deparse(substitute(criterion))
)
}
```

That is, we simply call the default `cv()`

method with the
`type`

argument properly set. In addition to supplying the
correct `type`

argument, our method sets the default
`criterion`

for the `cv.multinom()`

method to
`BayesRuleMulti`

. Adding the argument
`criterion.name=deparse(substitute(criterion))`

is
inessential, but it insures that printed output will include the name of
the criterion function that’s employed, whether it’s the default
`BayesRuleMulti`

or something else. Prior to invoking
`NextMethod()`

, we called `update()`

with
`trace=FALSE`

to suppress the iteration history reported by
default by `multinom()`

—it would be tedious to see the
iteration history for each fold.

Then:

```
cv(m.beps, seed=3465)
#> R RNG seed set to 3465
#> 10-Fold Cross Validation
#> criterion: BayesRuleMulti
#> cross-validation criterion = 0.32459
#> bias-adjusted cross-validation criterion = 0.32368
#> 95% CI for bias-adjusted CV criterion = (0.30017, 0.34718)
#> full-sample criterion = 0.31869
```

The cross-validated polytomous Bayes-rule criterion confirms that the fitted model does substantially better than the marginal Bayes-rule prediction that everyone votes for Labour.

`cv()`

methods for independently sampled cases, such as
`cv.default()`

, `cv.lm()`

, and
`cv.glm()`

, work by setting up calls to the
`cvCompute()`

function, which is exported from the
**cv** package to support development of `cv()`

methods for additional classes of regression models. In most cases,
however, such as the preceding `cv.multinom()`

example, it
will suffice and be much simpler to set up a suitable call to
`cv.default()`

via `NextMethod()`

.

To illustrate how to use `cvCompute()`

directly, we write
an alternative, and necessarily more complicated, version of
`cv.multinom()`

.

```
cv.multinom <- function(model,
data = insight::get_data(model),
criterion = BayesRuleMulti,
k = 10,
reps = 1,
seed = NULL,
details = k <= 10,
confint = n >= 400,
level = 0.95,
ncores = 1,
start = FALSE,
...) {
f <- function(i) {
# helper function to compute to compute fitted values,
# etc., for each fold i
indices.i <- fold(folds, i)
model.i <- if (start) {
update(model,
data = data[-indices.i,],
start = b,
trace = FALSE)
} else {
update(model, data = data[-indices.i,], trace = FALSE)
}
fit.all.i <- predict(model.i, newdata = data, type = "class")
fit.i <- fit.all.i[indices.i]
# returns:
# fit.i: fitted values for the i-th fold
# crit.all.i: CV criterion for all cases based on model with
# i-th fold omitted
# coef.i: coefficients for the model with i-th fold omitted
list(
fit.i = fit.i,
crit.all.i = criterion(y, fit.all.i),
coef.i = coef(model.i)
)
}
fPara <- function(i, multinom, ...) {
# helper function for parallel computation
# argument multinom makes multinom() locally available
# ... is necessary but not used
indices.i <- fold(folds, i)
model.i <- if (start) {
update(model,
data = data[-indices.i,],
start = b,
trace = FALSE)
} else {
update(model, data = data[-indices.i,], trace = FALSE)
}
fit.all.i <- predict(model.i, newdata = data, type = "class")
fit.i <- fit.all.i[indices.i]
list(
fit.i = fit.i,
crit.all.i = criterion(y, fit.all.i),
coef.i = coef(model.i)
)
}
n <- nrow(data)
# see ?cvCompute for definitions of arguments
cvCompute(
model = model,
data = data,
criterion = criterion,
criterion.name = deparse(substitute(criterion)),
k = k,
reps = reps,
seed = seed,
details = details,
confint = confint,
level = level,
ncores = ncores,
type = "class",
start = start,
f = f,
fPara = fPara,
multinom = nnet::multinom
)
}
```

Notice that separate “helper” functions are defined for non-parallel
and parallel computations.^{1} The new version of
`cv.multinom()`

produces the same results as the version that
calls `cv.default()`

:^{2}

Adding a `cv()`

method for a mixed-model class is somewhat
more complicated. We provide the `cvMixed()`

function to
facilitate this process, and to see how that works, consider the
`"lme"`

method from the **cv** package:

```
cv:::cv.lme
#> function (model, data = insight::get_data(model), criterion = mse,
#> k = NULL, reps = 1L, seed, details = NULL, ncores = 1L, clusterVariables,
#> blups = coef, fixed.effects = nlme::fixef, ...)
#> {
#> cvMixed(model, package = "nlme", data = data, criterion = criterion,
#> criterion.name = deparse(substitute(criterion)), k = k,
#> reps = reps, seed = seed, details = details, ncores = ncores,
#> clusterVariables = clusterVariables, predict.clusters.args = list(object = model,
#> newdata = data, level = 0), predict.cases.args = list(object = model,
#> newdata = data, level = 1), blups = blups, fixed.effects = fixed.effects,
#> ...)
#> }
#> <bytecode: 0x63a6fccaa7a0>
#> <environment: namespace:cv>
```

Notice that `cv.lme()`

sets up a call to
`cvMixed()`

, which does the computational work.

Most of the arguments of `cvMixed()`

are familiar:

`model`

is the mixed-model object, here of class`"lme"`

.`package`

is the name of the package in which the mixed-modeling function used to fit the model, here`lme()`

, resides—i.e.,`"nlme"`

;`cvMixed()`

uses this argument to retrieve the package namespace.`data`

is the data set to which the model is fit, by default extracted by the`get_data()`

function in the**insight**package.`criterion`

is the CV criterion, defaulting to the`mse()`

function.`k`

is the number of CV folds, defaulting to`"loo"`

for CV by clusters and`10`

for CV by cases.`reps`

is the number of times the CV process is repeated, defaulting to`1`

.`seed`

is the seed for R’s random-number generator, defaulting to a randomly selected (and saved) value.`ncores`

is the number of cores to use for parallel computation; if`1`

, the default, then the computation isn’t parallelized.`clusterVariables`

is a character vector of the names of variables defining clusters; if missing, then CV is based on cases rather than clusters.

The remaining two arguments are unfamiliar:

`predict.clusters.args`

is a named list of arguments to be passed to the`predict()`

function to obtain predictions for the full data set from a model fit to a subset of the data for cluster-based CV. The first two arguments should be`object`

and`newdata`

. It is typically necessary to tell`cvMixed()`

how to base predictions only on fixed effects; in the case of`"lme"`

models, this is done by setting`level = 0`

.Similarly,

`predict.cases.args`

is a named list of arguments to be passed to`predict()`

for case-based CV. Setting`level = 1`

includes random effects in the predictions.`blups`

and`fixed.effects`

are used to compute detailed fold-based statistics for case-based and cluster-based CV, respectively.

Finally, any additional arguments, absorbed by `...`

, are
passed to `update()`

when the model is refit with each fold
omitted. `cvMixed()`

returns an object of class
`"cv"`

.

Now imagine that we want to support a new class of mixed-effects
models. To be concrete, we illustrate with the `glmmPQL()`

function in the **MASS** package (Venables & Ripley, 2002), which fits
generalized-linear mixed-effects models by penalized quasi-likelihood.^{3} Not
coincidentally, the arguments of `glmmPQL()`

are similar to
those of `lme()`

(with an additional `family`

argument), because the former iteratively invokes the latter; so
`cv.glmmPQL()`

should resemble `cv.lme()`

.

As it turns out, neither the default method for
`GetResponse()`

nor `insight::get_data()`

work for
`"glmmPQL"`

objects. These objects include a
`"data"`

element, however, and so we can simply extract this
element as the default for the `data`

argument of our
`cv.glmmPQL()`

method.

To get the response variable is more complicated: We refit the fixed part of the model as a GLM with only the regression constant on the right-hand side, and extract the response from that; because all we need is the response variable, we limit the number of GLM iterations to 1 and suppress warning messages about non-convergence:

```
GetResponse.glmmPQL <- function(model, ...) {
f <- formula(model)
f[[3]] <- 1 # regression constant only on RHS
model <-
suppressWarnings(glm(
f,
data = model$data,
family = model$family,
control = list(maxit = 1)
))
cv::GetResponse(model)
}
```

Writing the `cv()`

method is then straightforward:

```
cv.glmmPQL <- function(model,
data = model$data,
criterion = mse,
k,
reps = 1,
seed,
ncores = 1,
clusterVariables,
blups = coef,
fixed.effects = nlme::fixef,
...) {
cvMixed(
model,
package = "MASS",
data = data,
criterion = criterion,
k = k,
reps = reps,
seed = seed,
ncores = ncores,
clusterVariables = clusterVariables,
predict.clusters.args = list(
object = model,
newdata = data,
level = 0,
type = "response"
),
predict.cases.args = list(
object = model,
newdata = data,
level = 1,
type = "response"
),
blups = blups,
fixed.effects = fixed.effects,
verbose = FALSE,
...
)
}
```

We set the argument `verbose=FALSE`

to suppress
`glmmPQL()`

’s iteration counter when `cvMixed()`

calls `update()`

.

Let’s apply our newly minted method to a logistic regression with a
random intercept in an example that appears in
`?glmmPQL`

:

```
library("MASS")
m.pql <- glmmPQL(
y ~ trt + I(week > 2),
random = ~ 1 | ID,
family = binomial,
data = bacteria
)
#> iteration 1
#> iteration 2
#> iteration 3
#> iteration 4
#> iteration 5
#> iteration 6
summary(m.pql)
#> Linear mixed-effects model fit by maximum likelihood
#> Data: bacteria
#> AIC BIC logLik
#> NA NA NA
#>
#> Random effects:
#> Formula: ~1 | ID
#> (Intercept) Residual
#> StdDev: 1.4106 0.78005
#>
#> Variance function:
#> Structure: fixed weights
#> Formula: ~invwt
#> Fixed effects: y ~ trt + I(week > 2)
#> Value Std.Error DF t-value p-value
#> (Intercept) 3.4120 0.51850 169 6.5805 0.0000
#> trtdrug -1.2474 0.64406 47 -1.9367 0.0588
#> trtdrug+ -0.7543 0.64540 47 -1.1688 0.2484
#> I(week > 2)TRUE -1.6073 0.35834 169 -4.4853 0.0000
#> Correlation:
#> (Intr) trtdrg trtdr+
#> trtdrug -0.598
#> trtdrug+ -0.571 0.460
#> I(week > 2)TRUE -0.537 0.047 -0.001
#>
#> Standardized Within-Group Residuals:
#> Min Q1 Med Q3 Max
#> -5.19854 0.15723 0.35131 0.49495 1.74488
#>
#> Number of Observations: 220
#> Number of Groups: 50
```

We compare this result to that obtained from `glmer()`

in
the **lme4** package:

```
library("lme4")
#> Loading required package: Matrix
m.glmer <- glmer(y ~ trt + I(week > 2) + (1 | ID),
family = binomial, data = bacteria)
summary(m.glmer)
#> Generalized linear mixed model fit by maximum likelihood (Laplace
#> Approximation) [glmerMod]
#> Family: binomial ( logit )
#> Formula: y ~ trt + I(week > 2) + (1 | ID)
#> Data: bacteria
#>
#> AIC BIC logLik deviance df.resid
#> 202.3 219.2 -96.1 192.3 215
#>
#> Scaled residuals:
#> Min 1Q Median 3Q Max
#> -4.561 0.136 0.302 0.422 1.128
#>
#> Random effects:
#> Groups Name Variance Std.Dev.
#> ID (Intercept) 1.54 1.24
#> Number of obs: 220, groups: ID, 50
#>
#> Fixed effects:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 3.548 0.696 5.10 3.4e-07 ***
#> trtdrug -1.367 0.677 -2.02 0.04352 *
#> trtdrug+ -0.783 0.683 -1.15 0.25193
#> I(week > 2)TRUE -1.598 0.476 -3.36 0.00078 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Correlation of Fixed Effects:
#> (Intr) trtdrg trtdr+
#> trtdrug -0.593
#> trtdrug+ -0.537 0.487
#> I(wk>2)TRUE -0.656 0.126 0.064
# comparison of fixed effects:
car::compareCoefs(m.pql, m.glmer)
#> Warning in car::compareCoefs(m.pql, m.glmer): models to be compared are of
#> different classes
#> Calls:
#> 1: glmmPQL(fixed = y ~ trt + I(week > 2), random = ~1 | ID, family =
#> binomial, data = bacteria)
#> 2: glmer(formula = y ~ trt + I(week > 2) + (1 | ID), data = bacteria,
#> family = binomial)
#>
#> Model 1 Model 2
#> (Intercept) 3.412 3.548
#> SE 0.514 0.696
#>
#> trtdrug -1.247 -1.367
#> SE 0.638 0.677
#>
#> trtdrug+ -0.754 -0.783
#> SE 0.640 0.683
#>
#> I(week > 2)TRUE -1.607 -1.598
#> SE 0.355 0.476
#>
```

The two sets of estimates are similar, but not identical

Finally, we try out our `cv.glmmPQL()`

method,
cross-validating both by clusters and by cases,

```
cv(m.pql, clusterVariables="ID", criterion=BayesRule)
#> n-Fold Cross Validation based on 50 {ID} clusters
#> cross-validation criterion = 0.19545
#> bias-adjusted cross-validation criterion = 0.19545
#> full-sample criterion = 0.19545
cv(m.pql, data=bacteria, criterion=BayesRule, seed=1490)
#> R RNG seed set to 1490
#> 10-Fold Cross Validation
#> cross-validation criterion = 0.20909
#> bias-adjusted cross-validation criterion = 0.20727
#> full-sample criterion = 0.14545
```

and again compare to `glmer()`

:

```
cv(m.glmer, clusterVariables="ID", criterion=BayesRule)
#> n-Fold Cross Validation based on 50 {ID} clusters
#> criterion: BayesRule
#> cross-validation criterion = 0.19545
#> bias-adjusted cross-validation criterion = 0.19545
#> full-sample criterion = 0.19545
cv(m.glmer, data=bacteria, criterion=BayesRule, seed=1490)
#> R RNG seed set to 1490
#> 10-Fold Cross Validation
#> criterion: BayesRule
#> cross-validation criterion = 0.19545
#> bias-adjusted cross-validation criterion = 0.19364
#> full-sample criterion = 0.15
```

The `selectStepAIC()`

function supplied by the
**cv** package, which is based on the
`stepAIC()`

function from the **nnet** package
(Venables & Ripley, 2002) for stepwise
model selection, is suitable for the `procedure`

argument of
`cvSelect()`

. The use of `selectStepAIC()`

is
illustrated in the vignette on cross-validating model selection.

We’ll employ `selectStepAIC()`

as a “template” for writing
a CV model-selection procedure. To see the code for this function, type
`cv::selectStepAIC`

at the R command prompt, or examine the
sources for the **cv** package at https://github.com/gmonette/cv (the code for
`selectStepAIC()`

is in https://github.com/gmonette/cv/blob/main/R/cv-select.R).

Another approach to model selection is all-subsets regression. The
`regsubsets()`

function in the **leaps** package
(Lumley & Miller, 2020) implements an
efficient algorithm for selecting the best-fitting linear least-squares
regressions for subsets of predictors of all sizes, from 1 through the
maximum number of candidate predictors.^{4} To illustrate the use
of `regsubsets()`

, we employ the `swiss`

data
frame supplied by the **leaps** package:

```
library("leaps")
head(swiss)
#> Fertility Agriculture Examination Education Catholic
#> Courtelary 80.2 17.0 15 12 9.96
#> Delemont 83.1 45.1 6 9 84.84
#> Franches-Mnt 92.5 39.7 5 5 93.40
#> Moutier 85.8 36.5 12 7 33.77
#> Neuveville 76.9 43.5 17 15 5.16
#> Porrentruy 76.1 35.3 9 7 90.57
#> Infant.Mortality
#> Courtelary 22.2
#> Delemont 22.2
#> Franches-Mnt 20.2
#> Moutier 20.3
#> Neuveville 20.6
#> Porrentruy 26.6
nrow(swiss)
#> [1] 47
```

The data set includes the following variables, for each of 47 French-speaking Swiss provinces circa 1888:

`Fertility`

: A standardized fertility measure.`Agriculture`

: The percentage of the male population engaged in agriculture.`Examination`

: The percentage of draftees into the Swiss army receiving the highest grade on an examination.`Education`

: The percentage of draftees with more than a primary-school education.`Catholic`

: The percentage of the population who were Catholic.`Infant.Mortality`

: The infant-mortality rate, expressed as the percentage of live births surviving less than a year.

Following Lumley & Miller (2020),
we treat `Fertility`

as the response and the other variables
as predictors in a linear least-squares regression:

```
m.swiss <- lm(Fertility ~ ., data=swiss)
summary(m.swiss)
#>
#> Call:
#> lm(formula = Fertility ~ ., data = swiss)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -15.274 -5.262 0.503 4.120 15.321
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 66.9152 10.7060 6.25 1.9e-07 ***
#> Agriculture -0.1721 0.0703 -2.45 0.0187 *
#> Examination -0.2580 0.2539 -1.02 0.3155
#> Education -0.8709 0.1830 -4.76 2.4e-05 ***
#> Catholic 0.1041 0.0353 2.95 0.0052 **
#> Infant.Mortality 1.0770 0.3817 2.82 0.0073 **
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 7.17 on 41 degrees of freedom
#> Multiple R-squared: 0.707, Adjusted R-squared: 0.671
#> F-statistic: 19.8 on 5 and 41 DF, p-value: 5.59e-10
cv(m.swiss, seed=8433)
#> R RNG seed set to 8433
#> 10-Fold Cross Validation
#> method: Woodbury
#> criterion: mse
#> cross-validation criterion = 59.683
#> bias-adjusted cross-validation criterion = 58.846
#> full-sample criterion = 44.788
```

Thus, the MSE for the model fit to the complete data is considerably smaller than the CV estimate of the MSE. Can we do better by selecting a subset of the predictors, taking account of the additional uncertainty induced by model selection?

First, let’s apply best-subset selection to the complete data set:

```
swiss.sub <- regsubsets(Fertility ~ ., data=swiss)
summary(swiss.sub)
#> Subset selection object
#> Call: regsubsets.formula(Fertility ~ ., data = swiss)
#> 5 Variables (and intercept)
#> Forced in Forced out
#> Agriculture FALSE FALSE
#> Examination FALSE FALSE
#> Education FALSE FALSE
#> Catholic FALSE FALSE
#> Infant.Mortality FALSE FALSE
#> 1 subsets of each size up to 5
#> Selection Algorithm: exhaustive
#> Agriculture Examination Education Catholic Infant.Mortality
#> 1 ( 1 ) " " " " "*" " " " "
#> 2 ( 1 ) " " " " "*" "*" " "
#> 3 ( 1 ) " " " " "*" "*" "*"
#> 4 ( 1 ) "*" " " "*" "*" "*"
#> 5 ( 1 ) "*" "*" "*" "*" "*"
(bics <- summary(swiss.sub)$bic)
#> [1] -19.603 -28.611 -35.656 -37.234 -34.553
which.min(bics)
#> [1] 4
car::subsets(swiss.sub, legend="topright")
```

The graph, produced by the `subsets()`

function in the
**car** package, shows that the model with the smallest BIC
is the “best” model with 4 predictors, including
`Agriculture`

, `Education`

, `Catholic`

,
and `Infant.Mortality`

, but not `Examination`

:

```
m.best <- update(m.swiss, . ~ . - Examination)
summary(m.best)
#>
#> Call:
#> lm(formula = Fertility ~ Agriculture + Education + Catholic +
#> Infant.Mortality, data = swiss)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -14.676 -6.052 0.751 3.166 16.142
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 62.1013 9.6049 6.47 8.5e-08 ***
#> Agriculture -0.1546 0.0682 -2.27 0.0286 *
#> Education -0.9803 0.1481 -6.62 5.1e-08 ***
#> Catholic 0.1247 0.0289 4.31 9.5e-05 ***
#> Infant.Mortality 1.0784 0.3819 2.82 0.0072 **
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 7.17 on 42 degrees of freedom
#> Multiple R-squared: 0.699, Adjusted R-squared: 0.671
#> F-statistic: 24.4 on 4 and 42 DF, p-value: 1.72e-10
cv(m.best, seed=8433) # use same folds as before
#> R RNG seed set to 8433
#> 10-Fold Cross Validation
#> method: Woodbury
#> criterion: mse
#> cross-validation criterion = 58.467
#> bias-adjusted cross-validation criterion = 57.778
#> full-sample criterion = 45.916
```

The MSE for the selected model is (of course) slightly higher than for the full model fit previously, but the cross-validated MSE is a bit lower; as we explain in the vignette on cross-validating model selection, however, it isn’t kosher to select and cross-validate a model on the same data.

Here’s a function named `selectSubsets()`

, meant to be
used with `cvSelect()`

, suitable for cross-validating the
model-selection process:

```
selectSubsets <- function(data = insight::get_data(model),
model,
indices,
criterion = mse,
details = TRUE,
seed,
save.model = FALSE,
...) {
if (inherits(model, "lm", which = TRUE) != 1)
stop("selectSubsets is appropriate only for 'lm' models")
y <- GetResponse(model)
formula <- formula(model)
X <- model.matrix(model)
if (missing(indices)) {
if (missing(seed) || is.null(seed))
seed <- sample(1e6, 1L)
# select the best model from the full data by BIC
sel <- leaps::regsubsets(formula, data = data, ...)
bics <- summary(sel)$bic
best <- coef(sel, 1:length(bics))[[which.min(bics)]]
x.names <- names(best)
# fit the best model; intercept is already in X, hence - 1:
m.best <- lm(y ~ X[, x.names] - 1)
fit.all <- predict(m.best, newdata = data)
return(list(
criterion = criterion(y, fit.all),
model = if (save.model)
m.best # return best model
else
NULL
))
}
# select the best model omitting the i-th fold (given by indices)
sel.i <- leaps::regsubsets(formula, data[-indices,], ...)
bics.i <- summary(sel.i)$bic
best.i <- coef(sel.i, 1:length(bics.i))[[which.min(bics.i)]]
x.names.i <- names(best.i)
m.best.i <- lm(y[-indices] ~ X[-indices, x.names.i] - 1)
# predict() doesn't work here:
fit.all.i <- as.vector(X[, x.names.i] %*% coef(m.best.i))
fit.i <- fit.all.i[indices]
# return the fitted values for i-th fold, CV criterion for all cases,
# and the regression coefficients
list(
fit.i = fit.i,
# fitted values for i-th fold
crit.all.i = criterion(y, fit.all.i),
# CV crit for all cases
coefficients = if (details) {
# regression coefficients
coefs <- coef(m.best.i)
# fix coefficient names
names(coefs) <- sub("X\\[-indices, x.names.i\\]", "",
names(coefs))
coefs
} else {
NULL
}
)
}
```

A slightly tricky point is that because of scoping issues,
`predict()`

doesn’t work with the model fit omitting the
\(i\)th fold, and so the fitted values
for all cases are computed directly as \(\widehat{\mathbf{y}}_{-i} = \mathbf{X}
\mathbf{b}_{-i}\), where \(\mathbf{X}\) is the model-matrix for all of
the cases, and \(\mathbf{b}_{-i}\) is
the vector of least-squares coefficients for the selected model with the
\(i\)th fold omitted.

Additionally, the command
`lm(y[-indices] ~ X[-indices, x.names.i] - 1)`

, which is the
selected model with the \(i\)th fold
deleted, produces awkward coefficient names like
`"X[-indices, x.names.i]Infant.Mortality"`

. Purely for
aesthetic reasons, the command
`sub("X\\[-indices, x.names.i\\]", "", names(coefs))`

fixes
these awkward names, removing the extraneous text,
`"X[-indices, x.names.i]"`

.

Applying `selectSubsets()`

to the full data produces the
full-data cross-validated MSE (which we obtained previously):

```
selectSubsets(model=m.swiss)
#> $criterion
#> [1] 45.916
#> attr(,"casewise loss")
#> [1] "(y - yhat)^2"
#>
#> $model
#> NULL
```

Similarly, applying the function to an imaginary “fold” of 5 cases returns the MSE for the cases in the fold, based on the model selected and fit to the cases omitting the fold; the MSE for all of the cases, based on the same model; and the coefficients of the selected model, which includes 4 or the 5 predictors (and the intercept):

```
selectSubsets(model=m.swiss, indices=seq(5, 45, by=10))
#> $fit.i
#> [1] 62.922 67.001 73.157 83.778 32.251
#>
#> $crit.all.i
#> [1] 46.297
#> attr(,"casewise loss")
#> [1] "(y - yhat)^2"
#>
#> $coefficients
#> (Intercept) Agriculture Education Catholic
#> 63.80452 -0.15895 -1.04218 0.13066
#> Infant.Mortality
#> 1.01895
```

Then, using `selectSubsets()`

in cross-validation,
invoking the `cv.function()`

method for `cv()`

, we
get:

```
(cv.swiss <- cv(
selectSubsets,
working.model = m.swiss,
data = swiss,
seed = 8433 # use same folds
))
#> R RNG seed set to 8433
#> 10-Fold Cross Validation
#> criterion: mse
#> cross-validation criterion = 65.835
#> bias-adjusted cross-validation criterion = 63.644
#> full-sample criterion = 45.916
```

Cross-validation shows that model selection exacts a penalty in MSE.
Examining the models selected for the 10 folds reveals that there is
some uncertainty in identifying the predictors in the “best” model, with
`Agriculture`

sometimes appearing and sometimes not:

```
compareFolds(cv.swiss)
#> CV criterion by folds:
#> fold.1 fold.2 fold.3 fold.4 fold.5 fold.6 fold.7 fold.8
#> 76.6964 61.3105 131.1616 9.0662 52.9403 41.0853 51.8768 136.9498
#> fold.9 fold.10
#> 24.1808 82.2587
#>
#> Coefficients by folds:
#> (Intercept) Catholic Education Infant.Mortality Agriculture
#> Fold 1 59.0852 0.1397 -1.0203 1.2985 -0.17
#> Fold 2 67.0335 0.1367 -1.0499 0.9413 -0.20
#> Fold 3 55.0453 0.1221 -0.8757 1.3541 -0.15
#> Fold 4 62.5543 0.1236 -0.9719 1.0679 -0.16
#> Fold 5 50.4643 0.1057 -0.7863 1.2144
#> Fold 6 68.0289 0.1195 -1.0073 0.8294 -0.17
#> Fold 7 66.5219 0.1357 -1.0827 0.9523 -0.19
#> Fold 8 46.3507 0.0776 -0.7637 1.4463
#> Fold 9 62.2632 0.1230 -1.0067 1.1000 -0.17
#> Fold 10 52.5112 0.1005 -0.7232 1.0809
```

As well, the fold-wise MSE varies considerably, reflecting the small
size of the `swiss`

data set (47 cases).

Fox, J. (2016). *Applied regression analysis and generalized linear
models* (Second edition). Thousand Oaks CA: Sage.

Fox, J., & Weisberg, S. (2019). *An R companion to
applied regression* (Third edition). Thousand Oaks CA:
Sage.

Hamner, B., & Frasco, M. (2018). *Metrics: Evaluation metrics for
machine learning*. Retrieved from https://CRAN.R-project.org/package=Metrics

Lüdecke, D., Waggoner, P., & Makowski, D. (2019). insight: A unified interface to access information
from model objects in R. *Journal of Open Source
Software*, *4*(38), 1412.

Lumley, T., & Miller, A. (2020). *leaps: Regression subset selection*. Retrieved
from https://CRAN.R-project.org/package=leaps

"Receiver operating characteristic". (2023). Receiver operating
characteristic—Wikipedia, the free
encyclopedia. Retrieved from https://en.wikipedia.org/wiki/Receiver_operating_characteristic

Venables, W. N., & Ripley, B. D. (2002). *Modern applied
statistics with S* (Fourth edition). New York:
Springer.

Try the following, for example, with both versions of

`cv.multinom()`

(possibly replacing`ncores=2`

with a larger number):

↩︎`system.time(print(cv1 <- cv(m.beps, k="loo"))) system.time(print(cv2 <- cv(m.beps, k="loo", ncores=2))) all.equal(cv1, cv2)`

A subtle point is that we added a

`multinom`

argument to the local function`fPara()`

, which is passed to the`fPara`

argument of`cvCompute()`

. There is also a`multinom`

argument to`cvCompute()`

, which is set to the`multinom`

function in the**nnet**package. The`multinom`

argument isn’t directly defined in`cvCompute()`

(examine the definition of this function), but is passed through the`...`

argument.`cvCompute()`

, in turn, will pass`multinom`

to`fPara()`

via`...`

, allowing`fPara()`

to find this function when it calls`update()`

to refit the model with each fold`i`

omitted. This scoping issue arises because`cvCompute()`

uses`foreach()`

for parallel computations, even though the**nnet**package is attached to the search path in the current R session via`library("nnet")`

.`cv.default()`

is able to handle the scoping issue transparently by automatically locating`multinom()`

.↩︎This example is somewhat artificial in that

`glmmPQL()`

has largely been superseded by computationally superior functions, such the`glmer()`

function in the**lme4**package. There is, however, one situation in which`glmmPQL()`

might prove useful: to specify serial dependency in case-level errors within clusters for longitudinal data, which is not currently supported by`glmer()`

.↩︎The

`regsubsets()`

function computes several measures of model predictive performance, including the \(R^2\) and \(R^2\) adjusted for degrees of freedom, the residual sums of squares, Mallows’s \(C_p\), and the BIC. Several of these are suitable for comparing models with differing numbers of coefficients—we use the BIC below—but all necessarily agree when comparing models with the*same*number of coefficients.↩︎