or_glm {oddsratio}

Calculate Odds Ratios of Generalized Linear (Mixed) Models

Description

This function calculates odds ratio(s) for specific increment steps of GLMs.

Usage

or_glm(data, model, incr, ci = 0.95)

Arguments

data

The data used for model fitting.

model

A fitted GLM(M).

incr

Increment values of each predictor given in a named list.

ci

Which confidence interval to calculate. Must be between 0 and 1. Default to 0.95

Details

ci_low and ci_high are only calculated for GLM models because MASS::glmmPQL() does not return confident intervals due to its penalizing behavior.

Currently supported functions: stats::glm,MASS::glmmPQL

Value

A data frame with five columns:

predictor

Predictor name(s)

oddsratio

Calculated odds ratio(s)

ci_low

Lower confident interval of odds ratio

ci_high

Higher confident interval of odds ratio

increment

Increment of the predictor(s)

See Also

or_gam()

Examples

## Example with glm()
library(oddsratio)
# load data (source: http://www.ats.ucla.edu/stat/r/dae/logit.htm) and
# fit model
fit_glm <- glm(admit ~ gre + gpa + rank,
  data = data_glm,
  family = "binomial"
) # fit model

# Calculate OR for specific increment step of continuous variable
or_glm(data = data_glm, model = fit_glm, incr = list(gre = 380, gpa = 5))

# Calculate OR and change the confidence interval level
or_glm(
  data = data_glm, model = fit_glm,
  incr = list(gre = 380, gpa = 5), ci = .70
)

## Example with MASS:glmmPQL()
# load data
library(MASS)
data(bacteria)
fit_glmmPQL <- glmmPQL(y ~ trt + week,
  random = ~ 1 | ID,
  family = binomial, data = bacteria,
  verbose = FALSE
)

# Apply function
or_glm(data = bacteria, model = fit_glmmPQL, incr = list(week = 5))