Package 'imv' reference manual

Title:	Model Comparison via the 'InterModel Vigorish' ('IMV')
Description:	Computes the 'InterModel Vigorish' ('IMV'), a metric for comparing the predictive accuracy of two models for binary outcomes. The 'IMV' is derived from the expected value of a bettor using one model's predicted probabilities against those of a competing model, and is estimated via k-fold cross-validation. Methods are provided for generalized linear models, mixed-effects models ('lme4'), and item response theory models ('mirt'). See Domingue et al. (2025) <doi:10.1371/journal.pone.0316491>.
Authors:	Ben Domingue [aut, cre], Christian Jackson [ctb]
Maintainer:	Ben Domingue <[email protected]>
License:	MIT + file LICENSE
Version:	0.3.1
Built:	2026-10-15 01:53:53 UTC
Source:	https://github.com/ben-domingue/imv

Cross-validated IMV for comparing two models

Description

S3 generic that computes the InterModel Vigorish (IMV) between a baseline model m0 and an enhanced model m1 via k-fold cross-validation. For each fold, both models are refit on the training partition and evaluated on the held-out partition; the IMV is computed from those out-of-fold predictions.

imv.default provides an escape hatch for unsupported model types via predict_fn: in that case the original fitted models are used (without refitting) to obtain predictions on each test fold.

Usage

imv(m0, m1, ...)

## S3 method for class 'glm'
imv(m0, m1, data = NULL, nfold = 5, predict_fn = NULL, y = NULL,
    seed = NULL, ...)

## Default S3 method:
imv(m0, m1, data = NULL, nfold = 5, predict_fn = NULL, y = NULL,
    seed = NULL, ...)
imv(m0, m1, ...)

## S3 method for class 'glm'
imv(m0, m1, data = NULL, nfold = 5, predict_fn = NULL, y = NULL,
    seed = NULL, ...)

## Default S3 method:
imv(m0, m1, data = NULL, nfold = 5, predict_fn = NULL, y = NULL,
    seed = NULL, ...)

Arguments

m0

Baseline model.

m1

Enhanced model.

data

Data frame used for cross-validation. May be omitted for model classes that store training data internally (e.g., objects from glm() or lme4::glmer()).

nfold

Number of cross-validation folds (default 5).

predict_fn

Optional function with signature function(model, newdata) returning a numeric vector of predicted probabilities. When supplied, imv.default is invoked and models are not refit per fold.

y

Character string naming the binary outcome column in data. Required when predict_fn is supplied; otherwise inferred from the model formula.

seed

Optional integer passed to set.seed before assigning observations to folds, for reproducible cross-validation. Default NULL.

...

Additional arguments passed to methods.

Value

A named list with four elements:

folds: Numeric vector of per-fold IMV values (length nfold).
mean: Mean IMV across folds.
sd: Standard deviation of per-fold IMVs.
ci: Named numeric vector of length 2: a 95% interval computed as mean +/- 1.96 * (sd / sqrt(nfold)).

References

Domingue, B. W., Rahal, C., Faul, J., Freese, J., Kanopka, K., Rigos, A., Stenhaug, B., & Tripathi, A. S. (2025). The InterModel Vigorish (IMV) as a flexible and portable approach for quantifying predictive accuracy with binary outcomes. PloS one, 20(3), e0316491. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0316491

Examples

## --- glm ------------------------------------------------------------
set.seed(1)
x  <- rnorm(100)
y  <- rbinom(100, 1, plogis(x))
df <- data.frame(x = x, y = y)
m0 <- glm(y ~ 1, df, family = "binomial")
m1 <- glm(y ~ x, df, family = "binomial")
result <- imv(m0, m1, nfold = 2)
result$mean
result$ci

## --- custom predict_fn (escape hatch for unsupported model types) ---
pfn    <- function(model, newdata) predict(model, newdata, type = "response")
result <- imv(m0, m1, data = df, y = "y", predict_fn = pfn, nfold = 2)
result$mean


## --- glmer (requires lme4) ------------------------------------------
if (requireNamespace("lme4", quietly = TRUE)) {
  data(sleepstudy, package = "lme4")
  sleepstudy$slow <- as.integer(sleepstudy$Reaction > 300)
  m0 <- lme4::glmer(slow ~ (1 | Subject),        sleepstudy, family = binomial)
  m1 <- lme4::glmer(slow ~ Days + (1 | Subject), sleepstudy, family = binomial)
  imv(m0, m1)
}

## --- mirt (requires mirt) -------------------------------------------
if (requireNamespace("mirt", quietly = TRUE)) {
  resp <- mirt::expand.table(mirt::LSAT7)
  mod1 <- mirt::mirt(resp, 1, "Rasch", verbose = FALSE)  # 1PL
  mod2 <- mirt::mirt(resp, 1, verbose = FALSE)            # 2PL
  imv(mod1, mod2)
}


## --- ranger (requires ranger) ---------------------------------------
## Not run: 
library(ranger)
set.seed(1)
x1    <- rnorm(200); x2 <- rnorm(200)
y_fac <- factor(rbinom(200, 1, plogis(2 * x1)))
df_r  <- data.frame(y = y_fac, x1 = x1, x2 = x2)
m0 <- ranger(dependent.variable.name = "y", data = df_r[, c("y", "x2")],
             num.trees = 100, probability = TRUE)
m1 <- ranger(dependent.variable.name = "y", data = df_r,
             num.trees = 100, probability = TRUE)
imv(m0, m1, data = df_r)

## End(Not run)

## --- tidymodels workflow (requires workflows + parsnip) -------------
## Not run: 
library(workflows)
library(parsnip)
set.seed(1)
x     <- rnorm(200)
y_fac <- factor(rbinom(200, 1, plogis(x)))
df_w  <- data.frame(x = x, y = y_fac)
spec <- logistic_reg() |> set_engine("glm")
wf0  <- workflow() |> add_model(spec) |> add_formula(y ~ 1) |> fit(df_w)
wf1  <- workflow() |> add_model(spec) |> add_formula(y ~ x) |> fit(df_w)
imv(wf0, wf1, data = df_w)

## End(Not run)

## --- glm ------------------------------------------------------------
set.seed(1)
x  <- rnorm(100)
y  <- rbinom(100, 1, plogis(x))
df <- data.frame(x = x, y = y)
m0 <- glm(y ~ 1, df, family = "binomial")
m1 <- glm(y ~ x, df, family = "binomial")
result <- imv(m0, m1, nfold = 2)
result$mean
result$ci

## --- custom predict_fn (escape hatch for unsupported model types) ---
pfn    <- function(model, newdata) predict(model, newdata, type = "response")
result <- imv(m0, m1, data = df, y = "y", predict_fn = pfn, nfold = 2)
result$mean


## --- glmer (requires lme4) ------------------------------------------
if (requireNamespace("lme4", quietly = TRUE)) {
  data(sleepstudy, package = "lme4")
  sleepstudy$slow <- as.integer(sleepstudy$Reaction > 300)
  m0 <- lme4::glmer(slow ~ (1 | Subject),        sleepstudy, family = binomial)
  m1 <- lme4::glmer(slow ~ Days + (1 | Subject), sleepstudy, family = binomial)
  imv(m0, m1)
}

## --- mirt (requires mirt) -------------------------------------------
if (requireNamespace("mirt", quietly = TRUE)) {
  resp <- mirt::expand.table(mirt::LSAT7)
  mod1 <- mirt::mirt(resp, 1, "Rasch", verbose = FALSE)  # 1PL
  mod2 <- mirt::mirt(resp, 1, verbose = FALSE)            # 2PL
  imv(mod1, mod2)
}


## --- ranger (requires ranger) ---------------------------------------
## Not run: 
library(ranger)
set.seed(1)
x1    <- rnorm(200); x2 <- rnorm(200)
y_fac <- factor(rbinom(200, 1, plogis(2 * x1)))
df_r  <- data.frame(y = y_fac, x1 = x1, x2 = x2)
m0 <- ranger(dependent.variable.name = "y", data = df_r[, c("y", "x2")],
             num.trees = 100, probability = TRUE)
m1 <- ranger(dependent.variable.name = "y", data = df_r,
             num.trees = 100, probability = TRUE)
imv(m0, m1, data = df_r)

## End(Not run)

## --- tidymodels workflow (requires workflows + parsnip) -------------
## Not run: 
library(workflows)
library(parsnip)
set.seed(1)
x     <- rnorm(200)
y_fac <- factor(rbinom(200, 1, plogis(x)))
df_w  <- data.frame(x = x, y = y_fac)
spec <- logistic_reg() |> set_engine("glm")
wf0  <- workflow() |> add_model(spec) |> add_formula(y ~ 1) |> fit(df_w)
wf1  <- workflow() |> add_model(spec) |> add_formula(y ~ x) |> fit(df_w)
imv(wf0, wf1, data = df_w)

## End(Not run)

Compute IMV for binary outcomes

Description

Computes the InterModel Vigorish (IMV) comparing baseline predictions p1 to enhanced predictions p2 for binary outcomes y. A positive value indicates that p2 predicts better than p1 out of sample; a negative value indicates the reverse.

Usage

## S3 method for class 'binary'
imv(m0, m1, p2, sigma = 1e-04, ...)
## S3 method for class 'binary'
imv(m0, m1, p2, sigma = 1e-04, ...)

Arguments

m0

Integer or numeric vector of binary outcomes (0/1), preferably from a held-out test set.

m1

Numeric vector of baseline predicted probabilities (same length as m0).

p2

Numeric vector of enhanced predicted probabilities (same length as m0).

sigma

Small positive constant used to clip probabilities away from 0 and 1 to avoid numerical issues. Default 1e-4.

...

Currently unused. Accepted for consistency with the imv generic.

Details

Each vector of predictions is reduced to its mean log score $L$ and then mapped to the weight $w \in [0.5, 1]$ of the weighted coin with the same expected log score, i.e. the solution of $w \log w + (1 - w) \log(1 - w) = L$ . The IMV is $(w_1 - w_0) / w_0$ . The root is found with uniroot on the bracket $[0.5, 1]$ , where the left-hand side is strictly increasing, so the solution is accurate to about 1e-12.

A model whose mean log score is below $-\log 2$ predicts worse than a fair coin and has no weighted-coin equivalent. Such a model is treated as a fair coin ( $w = 0.5$ ) and a warning is issued. This can happen for weak models evaluated out of sample.

Value

A scalar IMV value. Positive values favour p2; negative values favour p1.

References

Examples

set.seed(1)
x  <- rnorm(1000)
y  <- rbinom(length(x), 1, plogis(x))
df <- data.frame(x = x, y = y)
m  <- glm(y ~ x, df, family = "binomial")
pr <- predict(m, data.frame(x = x), type = "response")
imv.binary(y, mean(y), pr)
set.seed(1)
x  <- rnorm(1000)
y  <- rbinom(length(x), 1, plogis(x))
df <- data.frame(x = x, y = y)
m  <- glm(y ~ x, df, family = "binomial")
pr <- predict(m, data.frame(x = x), type = "response")
imv.binary(y, mean(y), pr)

Cross-validated IMV for binomial mixed-effects models

Description

Computes the InterModel Vigorish (IMV) for binomial mixed-effects models fit with lme4::glmer() via k-fold cross-validation. Both models are refit on each training fold; predictions on the held-out fold use allow.new.levels = TRUE to handle random-effect levels not seen during training.

Only binomial family models are supported.

Usage

## S3 method for class 'glmerMod'
imv(m0, m1, data = NULL, nfold = 5, predict_fn = NULL, y = NULL,
    seed = NULL, ...)
## S3 method for class 'glmerMod'
imv(m0, m1, data = NULL, nfold = 5, predict_fn = NULL, y = NULL,
    seed = NULL, ...)

Arguments

m0

A glmerMod object (binomial family) serving as the baseline model.

m1

A glmerMod object (binomial family) serving as the enhanced model. Must be fit to the same data as m0.

data

Optional data frame. If NULL, extracted from model.frame(m1).

nfold

Number of cross-validation folds. Default 5.

predict_fn

Ignored for this method.

y

Ignored for this method; the outcome is inferred from the model formula.

seed

Optional integer passed to set.seed before assigning observations to folds, for reproducible cross-validation. Default NULL.

...

Currently unused. Accepted for consistency with the generic.

Value

A named list with four elements:

folds: Numeric vector of per-fold IMV values (length nfold).
mean: Mean IMV across folds.
sd: Standard deviation of per-fold IMVs.
ci: Named numeric vector of length 2: a 95% interval computed as mean +/- 1.96 * (sd / sqrt(nfold)).

References

Examples

if (requireNamespace("lme4", quietly = TRUE)) {
  data(sleepstudy, package = "lme4")
  sleepstudy$slow <- as.integer(sleepstudy$Reaction > 300)

  m0 <- lme4::glmer(slow ~ (1 | Subject), sleepstudy, family = binomial)
  m1 <- lme4::glmer(slow ~ Days + (1 | Subject), sleepstudy, family = binomial)
  imv(m0, m1)
}
if (requireNamespace("lme4", quietly = TRUE)) {
  data(sleepstudy, package = "lme4")
  sleepstudy$slow <- as.integer(sleepstudy$Reaction > 300)

  m0 <- lme4::glmer(slow ~ (1 | Subject), sleepstudy, family = binomial)
  m1 <- lme4::glmer(slow ~ Days + (1 | Subject), sleepstudy, family = binomial)
  imv(m0, m1)
}

Cross-validated IMV for ranger random forests

Description

Computes the InterModel Vigorish (IMV) for probability random forests fit with ranger::ranger() via k-fold cross-validation. Both models are refit on each training fold and evaluated on the held-out fold.

Models must be fit with probability = TRUE and a binary outcome coded as a two-level factor. The positive class is taken as the second factor level (i.e., the higher value for 0/1-coded outcomes).

Usage

## S3 method for class 'ranger'
imv(m0, m1, data = NULL, nfold = 5, predict_fn = NULL, y = NULL,
    seed = NULL, ...)
## S3 method for class 'ranger'
imv(m0, m1, data = NULL, nfold = 5, predict_fn = NULL, y = NULL,
    seed = NULL, ...)

Arguments

m0

A ranger object (probability forest) serving as the baseline model.

m1

A ranger object (probability forest) serving as the enhanced model.

data

Data frame containing all variables used by m0 and m1. Required: ranger models do not store training data internally.

nfold

Number of cross-validation folds. Default 5.

predict_fn

Ignored for this method.

y

Ignored for this method; the outcome is taken from m1$dependent.variable.name.

seed

Optional integer passed to set.seed before assigning observations to folds, for reproducible cross-validation. Default NULL.

...

Currently unused. Accepted for consistency with the generic.

Value

A named list with four elements:

folds: Numeric vector of per-fold IMV values (length nfold).
mean: Mean IMV across folds.
sd: Standard deviation of per-fold IMVs.
ci: Named numeric vector of length 2: a 95% interval computed as mean +/- 1.96 * (sd / sqrt(nfold)).

References

Examples

## Not run: 
library(ranger)
set.seed(1)
n  <- 300
x1 <- rnorm(n); x2 <- rnorm(n)
y  <- factor(rbinom(n, 1, plogis(2 * x1)))
df <- data.frame(y = y, x1 = x1, x2 = x2)

# m0 uses only the noise predictor; m1 uses both
m0 <- ranger(dependent.variable.name = "y", data = df[, c("y", "x2")],
             num.trees = 100, probability = TRUE)
m1 <- ranger(dependent.variable.name = "y", data = df,
             num.trees = 100, probability = TRUE)
imv(m0, m1, data = df)

## End(Not run)
## Not run: 
library(ranger)
set.seed(1)
n  <- 300
x1 <- rnorm(n); x2 <- rnorm(n)
y  <- factor(rbinom(n, 1, plogis(2 * x1)))
df <- data.frame(y = y, x1 = x1, x2 = x2)

# m0 uses only the noise predictor; m1 uses both
m0 <- ranger(dependent.variable.name = "y", data = df[, c("y", "x2")],
             num.trees = 100, probability = TRUE)
m1 <- ranger(dependent.variable.name = "y", data = df,
             num.trees = 100, probability = TRUE)
imv(m0, m1, data = df)

## End(Not run)

Cross-validated IMV for mirt IRT models

Description

Computes the InterModel Vigorish (IMV) for item response theory models fit with the mirt package via response-level k-fold cross-validation. Fold splits are at the individual response level: each held-out observation is a single person-by-item pair, and ability is estimated from the remaining responses for that person.

When a single model is supplied (m1 = NULL), predictions from m0 are compared to item-level prevalence rates (the null model). When two models are supplied, m0 serves as the baseline and m1 as the enhanced model.

Only dichotomous response models are supported.

Usage

## S3 method for class 'SingleGroupClass'
imv(m0, m1 = NULL, data = NULL, nfold = 5,
     predict_fn = NULL, y = NULL, seed = NULL,
     fscores.options = list(method = "EAP"),
     whole.matrix = TRUE,
     remove.nonvarying.items = TRUE,
     remove.allNA.rows = TRUE, ...)
## S3 method for class 'SingleGroupClass'
imv(m0, m1 = NULL, data = NULL, nfold = 5,
     predict_fn = NULL, y = NULL, seed = NULL,
     fscores.options = list(method = "EAP"),
     whole.matrix = TRUE,
     remove.nonvarying.items = TRUE,
     remove.allNA.rows = TRUE, ...)

Arguments

m0

A SingleGroupClass model object returned by mirt::mirt().

m1

An optional second SingleGroupClass model fit to the same data as m0. When NULL, m0 is compared to item prevalence.

data

Not used for mirt models. Accepted for consistency with the generic.

nfold

Number of cross-validation folds (default 5).

predict_fn

Not used for mirt models. Accepted for consistency with the generic.

y

Not used for mirt models. Accepted for consistency with the generic.

seed

Optional integer passed to set.seed before assigning observations to folds, for reproducible cross-validation. Default NULL.

fscores.options

Named list of additional arguments passed to mirt::fscores(). Default is list(method = "EAP").

whole.matrix

Logical (default TRUE). When TRUE, fold assignment is repeated until every training partition contains all participants and all items, ensuring that models can be re-identified on each fold. Ignored when m1 = NULL.

remove.nonvarying.items

Logical (default TRUE). Drop items with no response variance from each training fold's response matrix.

remove.allNA.rows

Logical (default TRUE). Drop persons whose responses are entirely missing from a training fold's response matrix.

...

Currently unused.

Value

A named list with four elements:

folds: Numeric vector of per-fold IMV values (length nfold).
mean: Mean IMV across folds.
sd: Standard deviation of per-fold IMVs.
ci: Named numeric vector of length 2: a 95% interval computed as mean +/- 1.96 * (sd / sqrt(nfold)).

References

Examples


if (requireNamespace("mirt", quietly = TRUE)) {
  set.seed(1)
  resp <- mirt::expand.table(mirt::LSAT7)

  # Single model vs prevalence baseline
  mod1 <- mirt::mirt(resp, 1, "Rasch", verbose = FALSE)
  imv(mod1)

  # Two models
  mod2 <- mirt::mirt(resp, 1, verbose = FALSE)
  imv(mod1, mod2)

  # Priors specified as a variable are handled correctly
  my_prior <- list(a1 = c(0, 1, 0.25, 3))
  mod3 <- mirt::mirt(resp, 1, prior.list = my_prior, verbose = FALSE)
  imv(mod3)
}

if (requireNamespace("mirt", quietly = TRUE)) {
  set.seed(1)
  resp <- mirt::expand.table(mirt::LSAT7)

  # Single model vs prevalence baseline
  mod1 <- mirt::mirt(resp, 1, "Rasch", verbose = FALSE)
  imv(mod1)

  # Two models
  mod2 <- mirt::mirt(resp, 1, verbose = FALSE)
  imv(mod1, mod2)

  # Priors specified as a variable are handled correctly
  my_prior <- list(a1 = c(0, 1, 0.25, 3))
  mod3 <- mirt::mirt(resp, 1, prior.list = my_prior, verbose = FALSE)
  imv(mod3)
}

Cross-validated IMV for tidymodels workflows

Description

Computes the InterModel Vigorish (IMV) for fitted workflow objects (from the workflows package) via k-fold cross-validation. Both workflows are refit on each training fold using workflows::fit() and predictions are obtained with predict(..., type = "prob").

Works with any parsnip engine that supports probability predictions (logistic regression, random forests, boosted trees, etc.). The outcome must be a binary factor; the positive class is the second factor level.

Usage

## S3 method for class 'workflow'
imv(m0, m1, data = NULL, nfold = 5, predict_fn = NULL, y = NULL,
    seed = NULL, ...)
## S3 method for class 'workflow'
imv(m0, m1, data = NULL, nfold = 5, predict_fn = NULL, y = NULL,
    seed = NULL, ...)

Arguments

m0

A fitted workflow object serving as the baseline model.

m1

A fitted workflow object serving as the enhanced model.

data

Data frame used for cross-validation. Required: workflow models do not store training data internally.

nfold

Number of cross-validation folds. Default 5.

predict_fn

Ignored for this method.

y

Character string naming the binary outcome column in data. For formula-based workflows, y is inferred automatically via workflows::extract_preprocessor(). Required for recipe-based workflows.

seed

Optional integer passed to set.seed before assigning observations to folds, for reproducible cross-validation. Default NULL.

...

Currently unused. Accepted for consistency with the generic.

Value

A named list with four elements:

folds: Numeric vector of per-fold IMV values (length nfold).
mean: Mean IMV across folds.
sd: Standard deviation of per-fold IMVs.
ci: Named numeric vector of length 2: a 95% interval computed as mean +/- 1.96 * (sd / sqrt(nfold)).

References

Examples

## Not run: 
library(workflows)
library(parsnip)
set.seed(1)
n  <- 300
x1 <- rnorm(n); x2 <- rnorm(n)
y  <- factor(rbinom(n, 1, plogis(2 * x1)))
df <- data.frame(y = y, x1 = x1, x2 = x2)

spec <- logistic_reg() |> set_engine("glm")

wf0 <- workflow() |> add_model(spec) |> add_formula(y ~ x2)
wf0 <- fit(wf0, data = df)

wf1 <- workflow() |> add_model(spec) |> add_formula(y ~ x1 + x2)
wf1 <- fit(wf1, data = df)

imv(wf0, wf1, data = df)

## End(Not run)
## Not run: 
library(workflows)
library(parsnip)
set.seed(1)
n  <- 300
x1 <- rnorm(n); x2 <- rnorm(n)
y  <- factor(rbinom(n, 1, plogis(2 * x1)))
df <- data.frame(y = y, x1 = x1, x2 = x2)

spec <- logistic_reg() |> set_engine("glm")

wf0 <- workflow() |> add_model(spec) |> add_formula(y ~ x2)
wf0 <- fit(wf0, data = df)

wf1 <- workflow() |> add_model(spec) |> add_formula(y ~ x1 + x2)
wf1 <- fit(wf1, data = df)

imv(wf0, wf1, data = df)

## End(Not run)

IMV for a GLM compared to a prevalence baseline

Description

Legacy function. Computes the IMV for a fitted glm model against a null model (intercept only) via k-fold cross-validation. Both the full and null models are refit on each training fold and evaluated on the held-out fold.

For new code, prefer imv(m0, m1) with an explicit null model as m0.

Usage

imv0glm(m, nfold = 5)
imv0glm(m, nfold = 5)

Arguments

m

A glm object fit with a binomial family. Must have been called with an explicit data argument.

nfold

Number of cross-validation folds. Default 5.

Value

A numeric vector of length nfold containing the per-fold IMV values.

References

Examples

set.seed(1)
x  <- rnorm(1000)
y  <- rbinom(length(x), 1, plogis(x))
df <- data.frame(x = x, y = y)
m  <- glm(y ~ x, df, family = "binomial")
imv0glm(m)
set.seed(1)
x  <- rnorm(1000)
y  <- rbinom(length(x), 1, plogis(x))
df <- data.frame(x = x, y = y)
m  <- glm(y ~ x, df, family = "binomial")
imv0glm(m)

IMV for a GLM versus the same model with one variable removed

Description

Legacy function. Computes the IMV comparing a full glm model to a reduced model with var.nm dropped from the formula, via k-fold cross-validation. Both models are refit on each training fold and evaluated on the held-out fold.

For new code, prefer constructing both models explicitly and calling imv(m0, m1).

Usage

imvglm.rmvar(m, nfold = 5, var.nm)
imvglm.rmvar(m, nfold = 5, var.nm)

Arguments

m

A glm object fit with a binomial family. Must have been called with an explicit data argument.

nfold

Number of cross-validation folds. Default 5.

var.nm

Character string naming the variable to remove from the formula. Must match exactly the term as it appears in the original glm call.

Value

A numeric vector of length nfold containing the per-fold IMV values. The reduced model (without var.nm) serves as the baseline; the full model serves as the enhanced model. A positive mean indicates that var.nm improves out-of-sample prediction.

References

Examples

set.seed(1)
x  <- rnorm(1000)
z  <- rnorm(1000)
y  <- rbinom(length(x), 1, plogis(x))
df <- data.frame(x = x, z = z, y = y)
m  <- glm(y ~ x + z, df, family = "binomial")
imvglm.rmvar(m, var.nm = "z")
set.seed(1)
x  <- rnorm(1000)
z  <- rnorm(1000)
y  <- rbinom(length(x), 1, plogis(x))
df <- data.frame(x = x, z = z, y = y)
m  <- glm(y ~ x + z, df, family = "binomial")
imvglm.rmvar(m, var.nm = "z")

Package 'imv'

Help Index

Cross-validated IMV for comparing two models

Description

Usage

Arguments

Value

References

See Also

Examples

Compute IMV for binary outcomes

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Cross-validated IMV for binomial mixed-effects models

Description

Usage

Arguments

Value

References

See Also

Examples

Cross-validated IMV for ranger random forests

Description

Usage

Arguments

Value

References

See Also

Examples

Cross-validated IMV for mirt IRT models

Description

Usage

Arguments

Value

References

See Also

Examples

Cross-validated IMV for tidymodels workflows

Description

Usage

Arguments

Value

References

See Also

Examples

IMV for a GLM compared to a prevalence baseline

Description

Usage

Arguments

Value

References

See Also

Examples

IMV for a GLM versus the same model with one variable removed

Description

Usage

Arguments

Value

References

See Also

Examples