sumsim {dvmisc} | R Documentation |
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.
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)
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
|
statistics |
Numeric vector specifying which performance metrics should
be calculated. Possible values are |
alpha |
Numeric value specifying alpha for confidence interval. Set to
|
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. |
Numeric matrix.
# 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)