Fit a grouped binomial outcome from a data set with case weights
Source:R/glm_grouped.R
glm_grouped.Rdstats::glm() assumes that a tabular data set with case weights corresponds
to "different observations have different dispersions" (see ?glm).
In some cases, the case weights reflect that the same covariate pattern was
observed multiple times (i.e., frequency weights). In this case,
stats::glm() expects the data to be formatted as the number of events for
each factor level so that the outcome can be given to the formula as
cbind(events_1, events_2).
glm_grouped() converts data with integer case weights to the expected
"number of events" format for binomial data.
Arguments
- formula
A formula object with one outcome that is a two-level factors.
- data
A data frame with the outcomes and predictors (but not case weights).
- weights
An integer vector of weights whose length is the same as the number of rows in
data. If it is a non-integer numeric, it will be converted to integer (with a warning).- ...
Options to pass to
stats::glm(). Iffamilyis not set, it will automatically be assigned the basic binomial family.
Value
A object produced by stats::glm().
Examples
#----------------------------------------------------------------------------
# The same data set formatted three ways
# First with basic case weights that, from ?glm, are used inappropriately.
ucb_weighted <- as.data.frame(UCBAdmissions)
ucb_weighted$Freq <- as.integer(ucb_weighted$Freq)
head(ucb_weighted)
#> Admit Gender Dept Freq
#> 1 Admitted Male A 512
#> 2 Rejected Male A 313
#> 3 Admitted Female A 89
#> 4 Rejected Female A 19
#> 5 Admitted Male B 353
#> 6 Rejected Male B 207
nrow(ucb_weighted)
#> [1] 24
# Format when yes/no data are in individual rows (probably still inappropriate)
library(tidyr)
ucb_long <- uncount(ucb_weighted, Freq)
head(ucb_long)
#> Admit Gender Dept
#> 1 Admitted Male A
#> 2 Admitted Male A
#> 3 Admitted Male A
#> 4 Admitted Male A
#> 5 Admitted Male A
#> 6 Admitted Male A
nrow(ucb_long)
#> [1] 4526
# Format where the outcome is formatted as number of events
ucb_events <-
ucb_weighted %>%
tidyr::pivot_wider(
id_cols = c(Gender, Dept),
names_from = Admit,
values_from = Freq,
values_fill = 0L
)
head(ucb_events)
#> # A tibble: 6 × 4
#> Gender Dept Admitted Rejected
#> <fct> <fct> <int> <int>
#> 1 Male A 512 313
#> 2 Female A 89 19
#> 3 Male B 353 207
#> 4 Female B 17 8
#> 5 Male C 120 205
#> 6 Female C 202 391
nrow(ucb_events)
#> [1] 12
#----------------------------------------------------------------------------
# Different model fits
# Treat data as separate Bernoulli data:
glm(Admit ~ Gender + Dept, data = ucb_long, family = binomial)
#>
#> Call: glm(formula = Admit ~ Gender + Dept, family = binomial, data = ucb_long)
#>
#> Coefficients:
#> (Intercept) GenderFemale DeptB DeptC DeptD
#> -0.58205 -0.09987 0.04340 1.26260 1.29461
#> DeptE DeptF
#> 1.73931 3.30648
#>
#> Degrees of Freedom: 4525 Total (i.e. Null); 4519 Residual
#> Null Deviance: 6044
#> Residual Deviance: 5187 AIC: 5201
# Weights produce the same statistics
glm(
Admit ~ Gender + Dept,
data = ucb_weighted,
family = binomial,
weights = ucb_weighted$Freq
)
#>
#> Call: glm(formula = Admit ~ Gender + Dept, family = binomial, data = ucb_weighted,
#> weights = ucb_weighted$Freq)
#>
#> Coefficients:
#> (Intercept) GenderFemale DeptB DeptC DeptD
#> -0.58205 -0.09987 0.04340 1.26260 1.29461
#> DeptE DeptF
#> 1.73931 3.30648
#>
#> Degrees of Freedom: 23 Total (i.e. Null); 17 Residual
#> Null Deviance: 6044
#> Residual Deviance: 5187 AIC: 5201
# Data as binomial "x events out of n trials" format. Note that, to get the same
# coefficients, the order of the levels must be reversed.
glm(
cbind(Rejected, Admitted) ~ Gender + Dept,
data = ucb_events,
family = binomial
)
#>
#> Call: glm(formula = cbind(Rejected, Admitted) ~ Gender + Dept, family = binomial,
#> data = ucb_events)
#>
#> Coefficients:
#> (Intercept) GenderFemale DeptB DeptC DeptD
#> -0.58205 -0.09987 0.04340 1.26260 1.29461
#> DeptE DeptF
#> 1.73931 3.30648
#>
#> Degrees of Freedom: 11 Total (i.e. Null); 5 Residual
#> Null Deviance: 877.1
#> Residual Deviance: 20.2 AIC: 103.1
# The new function that starts with frequency weights and gets the correct place:
glm_grouped(Admit ~ Gender + Dept, data = ucb_weighted, weights = ucb_weighted$Freq)
#>
#> Call: glm(formula = formula, family = "binomial", data = data)
#>
#> Coefficients:
#> (Intercept) GenderFemale DeptB DeptC DeptD
#> -0.58205 -0.09987 0.04340 1.26260 1.29461
#> DeptE DeptF
#> 1.73931 3.30648
#>
#> Degrees of Freedom: 11 Total (i.e. Null); 5 Residual
#> Null Deviance: 877.1
#> Residual Deviance: 20.2 AIC: 103.1