Description Usage Arguments Examples
View source: R/computeDeltas.R
This function takes the important estimated model components and returns
the two necessary arguments for computeD
.
1 2 | computeDeltas(theta_glmm, sigma_glmm, idx_glmm, theta_clogit, sigma_clogit,
idx_clogit, ...)
|
theta_glmm |
estimated glmer coefficients |
sigma_glmm |
estimated variance w/r/t glmer model |
idx_glmm |
indices of specific glmer coefficients that vary between clusters |
theta_clogit |
estimated clogit coefficients |
sigma_clogit |
estimated variance w/r/t clogit model |
idx_clogit |
indices of clogit model (generally, all of them) that line
up with |
... |
additional arguments |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | library(glmerGOF)
set.seed(1)
n <- 50
m <- 4
beta <- c(2, 2)
id <- rep(1:n, each=m)
x <- rnorm(m*n)
b <- rep(rnorm(n), each=m)
y <- rbinom(m*n, 1, plogis(cbind(1, x) %*% beta + b))
my_data <- data.frame(y,x,id)
variable_names <- list(DV = "y", grouping = "id")
library(lme4)
fit_glmm <- lme4::glmer(
formula = y ~ x + (1|id),
family = "binomial",
data = my_data
)
library(survival)
fit_clogit <- survival::clogit(
formula = y ~ x + strata(id),
data = my_data,
method = "exact"
)
test_results <- testGOF(
data = my_data,
fitted_model_clogit = fit_clogit,
fitted_model_glmm = fit_glmm,
var_names = variable_names,
gradient_derivative_method = "simple"
)
test_results
deltas <- do.call(computeDeltas, test_results$fits)
deltas
test_results$fits[c("delta", "sigma_delta")]
|
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