sumsim: Summarize Simulation Results

In dvmisc: Convenience Functions, Moving Window Statistics, and Graphics

Description

Creates table summarizing results of statistical simulations, providing common metrics of performance like mean bias, standard deviation, mean standard error, mean squared error, and confidence interval coverage.

Usage

sumsim(estimates, ses = NULL, truth = NULL, theta_0 = 0,
  statistics = c("mean_bias", "sd", "mean_se", "mse", "coverage"),
  alpha = 0.05, digits = 3, listwise_deletion = TRUE)

Arguments

estimates	Numeric matrix where each column gives the point estimates for a particular method across multiple trials.
ses	Numeric matrix where each column gives the standard errors for a particular method across multiple trials.
truth	Numeric value specifying the true value of the parameter being estimated.
theta_0	Numeric value specifying null value for hypothesis test H_0: theta = theta_0. Only used for calculating empirical power.
statistics	Numeric vector specifying which performance metrics should be calculated. Possible values are "n" for number of trials, "mean", "median", "mean_bias", "median_bias", "sd", "iqr", "mean_se" (for mean standard error), "mse" (for mean squared error), "coverage" (for confidence interval coverage), "ci_width" for median confidence interval width, and "power" for empirical power.
alpha	Numeric value specifying alpha for confidence interval. Set to 0.05 for the usual 95% CI, 0.1 for a 90% CI, and so forth.
digits	Numeric value or vector specifying the number of decimal places to include.
listwise_deletion	Logical value for whether to remove trials in which any of the estimators have missing values.

Value

Numeric matrix.

Examples

# For X ~ N(mu, sigma^2), the MLE for sigma^2 is the sample variance with n 
# in the denominator, but the unbiased version with (n - 1) is typically used 
# for its unbiasedness. Compare these estimators in 1,000 trials with n = 25. 
MLE <- c()
Unbiased <- c()
for (ii in 1: 1000) {
   x <- rnorm(n = 25)
   MLE[ii] <- sum((x - mean(x))^2) / 25
   Unbiased[ii] <- sum((x - mean(x))^2) / 24
 }
sumsim(estimates = cbind(MLE, Unbiased), truth = 1)

dvmisc documentation built on Dec. 18, 2019, 1:35 a.m.