analyze.simstudy.lm {simitation} | R Documentation |
This function analyzes the results of simulated linear regression models, providing various summary statistics about the model coefficients, fit, and other aspects.
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"
)
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 |
summary.stats |
A data frame or data.table containing the summary
statistics from
repeated linear regression models, similar to
|
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. |
A list with several summary statistics for the linear regression model.
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")