MOR

Overview

The goal of MOR is to provides post-estimation function to calculate the Median Odds Ratio (MOR) from a multilevel binary logistic regression model fit. Currently this package provides the MOR point estimate and confidence interval only in case of two- and three-level random intercept model where the model is fitted using the R packages lme4 or GLMMadaptive or glmmTMB.

Installation

MOR is not on CRAN yet. You can install the development version of MOR from GitHub with:

# install.packages("pak")
pak::pak("shafayetShafee/MOR")

or,

# install.packages("remotes")
remotes::install_github("shafayetShafee/MOR")

Example

library(MOR)

data("mlm_data1", package = "MOR")
data("mlm_data2", package = "MOR")

# fitting two level random intercept model using lme4 package
model <- lme4::glmer(Yij ~ X1c + X2b + (1 | cluster),
  family = "binomial", data = mlm_data1
)

mor(model)
#> # A tibble: 1 × 4
#>   term        estimate ci_lower ci_upper
#>   <chr>          <dbl>    <dbl>    <dbl>
#> 1 mor_cluster     4.42     3.51     5.58
## to get 90% CI
mor(model, conf.level = 0.90)
#> # A tibble: 1 × 4
#>   term        estimate ci_lower ci_upper
#>   <chr>          <dbl>    <dbl>    <dbl>
#> 1 mor_cluster     4.42     3.64     5.38

# fitting two level random intercept model using GLMMadaptive package
model1 <- GLMMadaptive::mixed_model(
  fixed = Yij ~ X1c + X2b,
  random = ~ 1 | cluster,
  family = binomial("logit"), data = mlm_data1
)

mor(model1)
#> # A tibble: 1 × 4
#>   term        estimate ci_lower ci_upper
#>   <chr>          <dbl>    <dbl>    <dbl>
#> 1 mor_cluster     4.43     3.51     5.60


# fitting two level random intercept model using glmmTMB package
model2 <- glmmTMB::glmmTMB(Yij ~ X1c + X2b + (1 | cluster),
  family = binomial("logit"), data = mlm_data1
)

mor(model2)
#> # A tibble: 1 × 4
#>   term        estimate ci_lower ci_upper
#>   <chr>          <dbl>    <dbl>    <dbl>
#> 1 mor_cluster     4.42     3.50     5.59


# fitting three level random intercept model using glmmTMB package
model3 <- glmmTMB::glmmTMB(Yijk ~ X1c + X2b + (1 | ea) + (1 | ea:hh),
  family = "binomial", data = mlm_data2
)

mor(model3)
#> # A tibble: 2 × 4
#>   term      estimate ci_lower ci_upper
#>   <chr>        <dbl>    <dbl>    <dbl>
#> 1 mor_ea:hh     4.21     3.78     4.68
#> 2 mor_ea        6.29     5.38     7.36


# fitting three level random intercept model using lme4 package
model4 <- lme4::glmer(Yijk ~ X1c + X2b + (1 | ea) + (1 | ea:hh),
  family = "binomial", data = mlm_data2
)

mor(model4)
#> # A tibble: 2 × 4
#>   term      estimate ci_lower ci_upper
#>   <chr>        <dbl>    <dbl>    <dbl>
#> 1 mor_ea:hh     4.21     3.78     4.68
#> 2 mor_ea        6.29     5.38     7.36

## to get 90% CI
mor(model4, conf.level = 0.90)
#> # A tibble: 2 × 4
#>   term      estimate ci_lower ci_upper
#>   <chr>        <dbl>    <dbl>    <dbl>
#> 1 mor_ea:hh     4.21     3.85     4.60
#> 2 mor_ea        6.29     5.52     7.18

Code of Conduct

Please note that the MOR project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.