simitation (version 0.0.7)

analyze.simstudy.logistic: Analyze Simulated Logistic Regression Models

Description

This function analyzes the results of simulated logistic regression models, providing various summary statistics about the model coefficients, fit, and other aspects.

Usage

analyze.simstudy.logistic(
  the.coefs,
  summary.stats,
  conf.level = 0.95,
  the.quantiles = c(0.025, 0.1, 0.25, 0.5, 0.75, 0.9, 0.975),
  coef.name = "Coefficient",
  estimate.name = "Estimate",
  logistic.p.name = "Pr(>|z|)"
)

Value

A list with several summary statistics for the logistic regression model.

Arguments

the.coefs

A data frame or data.table containing the summary table of estimated coefficients from repeated logistic regression models. It should be structured like the output of simitation::sim.statistics.logistic$the.coefs().

summary.stats

A data.frame or data.table object of the summary statistics of repeated logistic regression models. Structure is in the form returned by the function simitation::sim.statistics.logistic$summary.stats().

conf.level

A numeric value between 0 and 1 representing the confidence level (1 - significance level).

the.quantiles

A numeric vector of values between 0 and 1. Summary statistics to analyze the tests will return the specified quantiles.

coef.name

A character value specifying the column of the.coefs that contains the names of the input variables of the logistic regression model.

estimate.name

A character value specifying the column of the.coefs that contains the estimated coefficients of the logistic regression model.

logistic.p.name

A character value specifying the column of the.coefs that contains the p-values for the tests of the estimated coefficients of the logistic regression model.

Examples

step.age <- "Age ~ N(45, 10)"
step.female <- "Female ~ binary(0.53)"
step.health.percentile <- "Health.Percentile ~ U(0,100)"
step.exercise.sessions <- "Exercise.Sessions ~ Poisson(2)"
step.diet <- "Diet ~ sample(('Light', 'Moderate', 'Heavy'),
(0.2, 0.45, 0.35))"
step.healthy.lifestyle <- "Healthy.Lifestyle ~
logistic(log(0.45) - 0.1 * (Age -45) + 0.05 * Female +
0.01 * Health.Percentile + 0.5 * Exercise.Sessions - 0.1 *
(Diet == 'Moderate') - 0.4 * (Diet == 'Heavy'))"

step.weight <- "Weight ~ lm(150 - 15 * Female + 0.5 * Age - 0.1 *
Health.Percentile - 0.2 * Exercise.Sessions  + 5 * (Diet == 'Moderate') +
15 * (Diet == 'Heavy') - 2 * Healthy.Lifestyle + N(0, 10))"

the.steps <- c(step.age, step.female, step.health.percentile,
step.exercise.sessions, step.diet, step.healthy.lifestyle, step.weight)

simdat.multivariate <- simulation.steps(the.steps = the.steps,
 n = 50, num.experiments = 2, experiment.name = "sim", seed = 41)


stats.logistic <- sim.statistics.logistic(simdat =
simdat.multivariate, the.formula =
Healthy.Lifestyle ~ Age + Female + Health.Percentile + Exercise.Sessions,
grouping.variables = "sim")


analysis.logistic <- analyze.simstudy.logistic(the.coefs =
stats.logistic$the.coefs, summary.stats =
stats.logistic$summary.stats,
conf.level = 0.95, the.quantiles = c(0.1, 0.9))