analyze.simstudy.lm {simitation}

Analyze Simulated Linear Regression Models

Description

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

Usage

analyze.simstudy.lm(
  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",
  lm.p.name = "Pr(>|t|)",
  f.p.name = "f.pvalue"
)

Arguments

the.coefs

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

summary.stats

A data frame or data.table containing the summary statistics from repeated linear regression models, similar to simitation::sim.statistics.lm$summary.stats().

conf.level

A numeric value for the confidence level (1 - significance level). Default is 0.95.

the.quantiles

Numeric vector of quantile values for which statistics are required.

coef.name

Column name in 'the.coefs' that has input variable names of the regression model.

estimate.name

Column name in 'the.coefs' for estimated coefficients of the regression model.

lm.p.name

Column name in 'the.coefs' for p-values of coefficient tests.

f.p.name

Column name in 'summary.stats' for the F-test p-value.

Value

A list with several summary statistics for the linear 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.lm <- sim.statistics.lm(simdat = simdat.multivariate, the.formula =
Weight ~ Age + Female + Health.Percentile + Exercise.Sessions +
Healthy.Lifestyle, grouping.variables = "sim")


analysis.lm <- analyze.simstudy.lm(the.coefs = stats.lm$the.coefs,
summary.stats = stats.lm$summary.stats, conf.level = 0.95,
the.quantiles = c(0.25, 0.75), coef.name = "Coefficient",
estimate.name = "Estimate", lm.p.name = "Pr(>|t|)", f.p.name = "f.pvalue")