Simulation Performance Criteria and MCSE

2021-02-14

Monte Carlo simulations are computer experiments designed to study the performance of statistical methods under known data-generating conditions (Morris, White, & Crowther, 2019). Methodologists use simulations to examine questions such as: (1) how does ordinary least squares (OLS) regression perform if errors are heteroskedastic? (2) how does the presence of missing data affect treatment effect estimates from a propensity score analysis? (3) how does cluster robust variance estimation perform when the number of clusters is small? To answer such questions, we conduct experiments by simulating thousands of datasets based on pseudo-random sampling (Morris, White, & Crowther, 2019). We generate datasets under known conditions. For example, we can generate data where the errors are heteroskedastic or there is missing data present following Missing at Random (MAR) mechanism. Then, we apply statistical methods, like OLS or propensity score estimation using logistic regression, to each of the datasets and extract results like regression coefficients, p-values, and confidence intervals. We then analyze the performance of these estimation methods using criteria such as bias, Type 1 error rate, or root mean squared error. Performance measures from simulation studies are estimates, based on a finite number of repeated samples from a data-generating process, and thus include some random error. When interpreting performance measures, it is important to consider how large the estimation error is. This is typically quantified as Monte Carlo standard error (MCSE).

The goal of simhelpers is to assist in running simulation studies. This package provides a set of functions that calculate various performance measures and associated MCSE. This vignette focuses on the set of functions for calculating performance measures. Below, we explain three major categories of performance criteria: (1) absolute criteria, (2) relative criteria, and (3) criteria to evaluate hypothesis testing. We provide formulas used to calculate the performance measures and MCSE and demonstrate how to use the functions in the simhelpers package.

The notation and explanations of the performance measures largely follow notes from Dr. James Pustejovsky’s Data Analysis, Simulation and Programming in R course (Spring, 2019).

1 Performance Criteria and MCSE

1.1 Absolute Criteria

Bias characterizes whether an estimator tends to lie above or below the true parameter, on average. Variance characterizes the precision of an estimator, as the average squared deviation of the estimator from its average. Note that variance is the inverse of precision. Therefore, the higher the variance, the lower the precision. Mean squared error (MSE) and root mean squared error (RMSE) characterize the accuracy of the estimates. MSE and RMSE measure how far off, on average, an estimator is from the true parameter. Absolute criteria are in the same scale as the estimate. MSE is in squared deviation scale and RMSE is in the scale of the estimates.

Let \(T\) denote an estimator for a parameter \(\theta\). By running a simulation study, we obtain \(K\) estimates \(T_1,...,T_K\) (or realizations of the estimator) for each data generating condition. We can calculate the following sample statistics for the estimates:

Table 1 below shows each of the performance criteria, its interpretation, its formal definition, how the criterion is estimated in a simulation study, and the formula for the MCSE of the estimated performance measure.

Table 1. Absolute Performance Criteria
Criterion Measure Definition Estimate MCSE
Bias Difference from true parameter \(\text{E}(T) - \theta\) \(\bar{T} - \theta\) \(\sqrt{S_T^2/ K}\)
Variance Precision \(\text{E}\left[(T - \text{E}(T))^2\right]\) \(S_T^2\) \(S_T^2 \sqrt{\frac{k_T - 1}{K}}\)
MSE Accuracy \(\text{E}\left[(T - \theta)^2\right]\) \(\frac{1}{K}\sum_{k=1}^{K}\left(T_k - \theta\right)^2\) \(\sqrt{\frac{1}{K}\left[S_T^4 (k_T - 1) + 4 S_T^3 g_T(\bar{T} - \theta) + 4 S_T^2 (\bar{T} - \theta)^2\right]}\)
RMSE Accuracy \(\sqrt{\text{E}\left[(T - \theta)^2\right]}\) \(\sqrt{\frac{1}{K}\sum_{k=1}^{K}\left(T_k - \theta\right)^2}\) \(\sqrt{\frac{K - 1}{K} \sum_{j=1}^K \left(RMSE_{(j)} - RMSE\right)^2}\)

The equation for the MCSE of RMSE is derived using the jack-knife technique, which involves excluding a replicate \(j\) and calculating RMSE (Efron & Stein, 1981). The formula for RMSE is:

\[\sqrt{\frac{1}{K}\sum_{k=1}^{K}\left(T_k - \theta\right)^2}\]

This is approximately equivalent to:

\[RMSE = \sqrt{(\bar{T} - \theta)^2 + S^2_T}\]

The jack-knife RMSE will be calculated as:

\[RMSE_{(j)} = \sqrt{(\bar{T}_{(j)} - \theta)^2 + S^2_{T(j)}}\]

Here \(\bar{T}_{(j)}\) and \(S^2_{T(j)}\) indicate the mean and variance of the estimates leaving out replicate \(j\). Instead of calculating each jack-knife estimate, we use algebraic tricks to calculate \(\bar{T}_{(j)}\) and \(S^2_{T(j)}\) as follows:

\[\bar{T}_{(j)} = \frac{1}{K-1} \left(K \bar{T} - T_j\right)\]

\[S_{T(j)}^2 = \frac{1}{K - 2} \left[(K - 1) S_T^2 - \frac{K}{K - 1}\left(T_j - \bar{T}\right)^2\right]\]

Then, the jack-knife MCSE of RMSE will be calculated as:

\[MCSE_{RMSE(JK)} = \sqrt{\frac{K -1}{K}\sum_{j=1}^K \left(RMSE_{(j)} - RMSE\right)^2}\]

1.1.1 Example

We use the welch_res dataset included in the package. It contains the results from an example simulation study comparing the heteroskedasticity-robust Welch t-test to the usual two-sample t-test assuming equal variances. The code used to generate the data and derive results can be found here. We varied the sample sizes per group. We set the sample size of Group 1 to 50 and we varied sample size of Group 2 to be 50 and 70. We varied the mean difference parameter when generating data for two groups. We set the values to 0, 0.5, 1 and 2. We generated the data with slightly unequal variances. With each simulated dataset, we ran the usual t-test, which assumes homogeneity of variance, and we also ran Welch t-test, which does not assume homogeneity of variance. We extracted the estimates (mean differences), variances of the estimates, p-values, and the lower and upper bounds of the confidence intervals.

library(simhelpers)
library(dplyr)
library(tibble)
library(knitr)
library(dplyr)
library(kableExtra)

welch_res %>%
  glimpse()
#> Rows: 16,000
#> Columns: 11
#> $ n1          <dbl> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5…
#> $ n2          <dbl> 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 5…
#> $ mean_diff   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ iterations  <dbl> 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 100…
#> $ seed        <dbl> 204809087, 204809087, 204809087, 204809087, 204809087, 20…
#> $ method      <chr> "t-test", "Welch t-test", "t-test", "Welch t-test", "t-te…
#> $ est         <dbl> 0.025836000, 0.025836000, 0.005158587, 0.005158587, -0.07…
#> $ var         <dbl> 0.09543914, 0.09543914, 0.08481717, 0.08481717, 0.0817933…
#> $ p_val       <dbl> 0.9335212, 0.9335804, 0.9859039, 0.9859109, 0.7807543, 0.…
#> $ lower_bound <dbl> -0.5872300, -0.5899041, -0.5727856, -0.5741984, -0.647370…
#> $ upper_bound <dbl> 0.6389020, 0.6415761, 0.5831027, 0.5845155, 0.4877263, 0.…

Below, we calculate the absolute performance criteria for the estimates of the mean differences. We present the results by sample sizes, mean difference, and the t-test method. The calc_absolute() function is designed to work with the tidyeval workflow (Wickham et al., 2019). The first argument, res_dat, requires a data frame or a tibble containing the results from a simulation study. The second argument, estimates, requires the name of the column containing the estimates like mean difference or regression coefficients. The third argument, true_param, requires the name of the column containing the true parameters. The fourth argument, perfm_criteria, lets the user specify which criteria to evaluate. The criteria can be specified using a character or character vector. If the user only wants bias, they can specify perfm_criteria = "bias". If the user wants bias and root mean squared error, they can specify perfm_criteria = c("bias", "rmse"). By default, the function returns bias, variance, mean squared error (mse), and root mean squared error (rmse), perfm_criteria = c("bias", "variance", "mse", "rmse"). In the example below, we ask for all the available criteria.

We calculate the absolute performance measures only for the conventional t-test results because the mean difference is identical for the Welch t-test. We use dplyr syntax to group by the sample sizes and mean difference that were used to generate the data. We provide examples using do() and group_modify() to run calc_absolute() on results for each combination of the conditions. Results are rounded to five decimal places.

If there are any convergence issues with estimation, please make sure to write your estimation function to output NA for the values of the point estimates, variance estimates, p-values, or confidence interval estimates. The functions in the simhelpers package will calculate performance criteria and MCSE after deleting any missing estimates and will output the number of iterations, K, which will exclude iterations with NA values for estimates. Note that K may differ by condition.

# using do()
welch_res %>%
  filter(method == "t-test") %>% # filter just conventional t-test res
  group_by(n1, n2, mean_diff) %>% # grouping 
  do(calc_absolute(., estimates = est, true_param = mean_diff)) %>% # run the function
  kable(digits = 5) # create a kable table 
n1 n2 mean_diff K bias bias_mcse var var_mcse mse mse_mcse rmse rmse_mcse
50 50 0.0 1000 -0.00890 0.01000 0.09999 0.00425 0.09997 0.00425 0.31618 0.00839
50 50 0.5 1000 -0.00555 0.01034 0.10698 0.00473 0.10690 0.00473 0.32696 0.00891
50 50 1.0 1000 -0.00726 0.00988 0.09766 0.00423 0.09761 0.00423 0.31243 0.00840
50 50 2.0 1000 0.00725 0.00981 0.09631 0.00440 0.09627 0.00439 0.31028 0.00862
50 70 0.0 1000 -0.00266 0.00867 0.07521 0.00330 0.07514 0.00331 0.27411 0.00744
50 70 0.5 1000 -0.00514 0.00895 0.08002 0.00391 0.07997 0.00391 0.28278 0.00826
50 70 1.0 1000 -0.01124 0.00899 0.08081 0.00358 0.08086 0.00358 0.28435 0.00775
50 70 2.0 1000 -0.00079 0.00883 0.07805 0.00345 0.07798 0.00345 0.27924 0.00760
# using group_modify()
welch_res %>%
  filter(method == "t-test") %>% # filter just conventional t-test res
  mutate(params = mean_diff) %>% # group_modify cannot take in a group column as an argument
  group_by(n1, n2, mean_diff) %>% # grouping 
  group_modify(~ calc_absolute(.x, estimates = est, true_param = params)) %>%
  kable(digits = 5)
n1 n2 mean_diff K bias bias_mcse var var_mcse mse mse_mcse rmse rmse_mcse
50 50 0.0 1000 -0.00890 0.01000 0.09999 0.00425 0.09997 0.00425 0.31618 0.00839
50 50 0.5 1000 -0.00555 0.01034 0.10698 0.00473 0.10690 0.00473 0.32696 0.00891
50 50 1.0 1000 -0.00726 0.00988 0.09766 0.00423 0.09761 0.00423 0.31243 0.00840
50 50 2.0 1000 0.00725 0.00981 0.09631 0.00440 0.09627 0.00439 0.31028 0.00862
50 70 0.0 1000 -0.00266 0.00867 0.07521 0.00330 0.07514 0.00331 0.27411 0.00744
50 70 0.5 1000 -0.00514 0.00895 0.08002 0.00391 0.07997 0.00391 0.28278 0.00826
50 70 1.0 1000 -0.01124 0.00899 0.08081 0.00358 0.08086 0.00358 0.28435 0.00775
50 70 2.0 1000 -0.00079 0.00883 0.07805 0.00345 0.07798 0.00345 0.27924 0.00760

1.2 Relative Criteria

Relative criteria can be useful for describing an estimator’s performance, especially if the performance varies in proportion to the true value of the target parameter. It can be only used when \(|\theta| > 0\) as we cannot divide by \(0\) (Morris, White, & Crowther, 2019).

To derive the MCSE for relative RMSE, we again used the jack-knife technique. The formula to calculate relative RMSE is:

\[rRMSE = \sqrt{\frac{(\bar{T} - \theta)^2 + S_T^2}{\theta^2}}\]

The jack-knife RMSE will be calculated as:

\[rRMSE_{(j)} = \sqrt{\frac{(\bar{T}_{(j)} - \theta)^2 + S_{T(j)}^2}{\theta^2}}\]

Here \(\bar{T}_{(j)}\) and \(S^2_{T(j)}\) are calculated as specified above when we described the algebra trick to estimate these values. The MCSE is then calculated as specified in the table below.

Table 2 below shows each of the relative performance criteria, its interpretation, its formal definition, how the criterion is estimated in a simulation study, and its MCSE formula.

Table 2. Relative Performance Criteria
Criterion Measure Definition Estimate MCSE
Relative Bias Relative difference from true parameter \(\text{E}(T) / \theta\) \(\bar{T} / \theta\) \(\sqrt{S_T^2 / (K\theta^2)}\)
Relative MSE Accuracy \(\text{E}\left[(T - \theta)^2\right]/ \theta^2\) \(\frac{(\bar{T} - \theta)^2 + S_T^2}{\theta^2}\) \(\sqrt{\frac{1}{K\theta^2}\left[S_T^4 (k_T - 1) + 4 S_T^3 g_T(\bar{T} - \theta) + 4 S_T^2 (\bar{T} - \theta)^2\right]}\)
Relative RMSE Accuracy \(\sqrt{\text{E}\left[(T - \theta)^2\right]/ \theta^2}\) \(\sqrt{\frac{(\bar{T} - \theta)^2 + S_T^2}{\theta^2}}\) \(\sqrt{\frac{K - 1}{K} \sum_{j=1}^K \left(rRMSE_{(j)} - rRMSE)^2\right)}\)

1.2.1 Example

Below, we calculate the relative criteria for the mean difference estimates. Note that when the mean difference is 0, the relative measures cannot be calculated and the function returns NA values. The syntax for calc_relative() is similar to the one that we used earlier for calc_absolute(). The perfm_criteria argument allows the user to specify which criteria to evaluate: perfm_criteria = c("relative bias", "relative mse", "relative rmse").

# using group_modify()
welch_res %>%
  filter(method == "t-test") %>%
  mutate(params = mean_diff) %>%
  group_by(n1, n2, mean_diff) %>%
  group_modify(~ calc_relative(.x, estimates = est, true_param = params)) %>%
  kable(digits = 5)
n1 n2 mean_diff K rel_bias rel_bias_mcse rel_mse rel_mse_mcse rel_rmse rel_rmse_mcse
50 50 0.0 1000 NA NA NA NA NA NA
50 50 0.5 1000 0.98890 0.02069 0.42803 0.00947 0.65424 0.01451
50 50 1.0 1000 0.99274 0.00988 0.09771 0.00423 0.31259 0.00679
50 50 2.0 1000 1.00363 0.00491 0.02409 0.00219 0.15522 0.00355
50 70 0.0 1000 NA NA NA NA NA NA
50 70 0.5 1000 0.98972 0.01789 0.32018 0.00783 0.56585 0.01388
50 70 1.0 1000 0.98876 0.00899 0.08094 0.00358 0.28449 0.00631
50 70 2.0 1000 0.99961 0.00442 0.01951 0.00172 0.13969 0.00309

1.3 Relative Criteria for Variance Estimators

Variance estimators are always positive, and so relative criteria are often used to characterize their performance. For variance estimators, we have \(V\) denoting the sampling variance of a point estimator \(T\). To assess the relative criteria for \(V\), we need to divide it by the true value of the sampling variance of \(T\), \(\lambda = \text{Var}(T)\), which we may not be able to calculate directly. In such scenario, we can use the sample variance of \(T\) across the replications, \(S_T^2\), to estimate the true sampling variance.

The relative bias would then be estimated by \(rB = \bar{V} / S_T^2\), the average of the variance estimates divided by the sample variance of the point estimates. To estimate MCSE of the relative bias of the variance estimator, we need to account for the uncertainty in the estimation of the true sampling variance. One way to do so is to use the jack-knife technique that we described above, which entails excluding a replicate \(j\) and calculating relative bias \(\bar{V}_{(j)}/ S_{T(j)}^2\). The Monte Carlo standard error can then be calculated as:

\[ MCSE\left(rB\right) = \sqrt{\frac{K - 1}{K} \sum_{j=1}^K \left(rB_{(j)} - rB\right)^2} \] which can be written as:

\[ MCSE\left(rB\right) = \sqrt{\frac{K - 1}{K} \sum_{j=1}^K \left(\frac{\bar{V}_{(j)}}{S_{T(j)}^2} - \frac{\bar{V}}{S_T^2}\right)^2} \]

We reformulate the MCSE using some algebra tricks similar to how we reformulated them for the RMSE MCSE formulas above.

\[ \begin{aligned} \bar{V}_{(j)} &= \frac{1}{K - 1}\left(K \bar{V} - V_j\right) \\ S_{T(j)}^2 &= \frac{1}{K - 2} \left[(K - 1) S_T^2 - \frac{K}{K - 1}\left(T_j - \bar{T}\right)^2\right] \end{aligned} \]

Similarly, we can estimate the MCSE of relative MSE and RMSE using the jack-knife technique. To estimate the MSE we need to estimate \(S_{V(j)}^2\), which represents the sample variance of the variance estimates leaving replicate \(j\) out. We calculate \(S_{V(j)}^2\) as

\[S_{V(j)}^2 = \frac{1}{K - 2} \left[(K - 1) S_V^2 - \frac{K}{K - 1}\left(V_j - \bar{V}\right)^2\right].\]

We can then estimate the jack-knife relative MSE and RMSE following the same logic as we described above and then calculate the MCSE.

Table 3 below lists relative performance measures for variance estimators.

Table 3. Relative Performance Criteria for Variance Estimators
Criterion Measure Definition Estimate MCSE
Relative Bias Relative difference from true parameter \(\text{E}(V) / \lambda\) \(\bar{V} / S_T^2\) \(\sqrt{\frac{K - 1}{K} \sum_{j=1}^K \left(rB_{(j)} - rB\right)^2}\)
Relative MSE Accuracy \(\text{E}\left[(V - \lambda)^2\right]/ \lambda^2\) \(\frac{(\bar{V} - S_T^2)^2 + S_V^2}{S_T^4}\) \(\sqrt{\frac{K - 1}{K} \sum_{j=1}^K \left(rMSE_{(j)} - rMSE\right)^2}\)
Relative RMSE Accuracy \(\sqrt{\text{E}\left[(V - \lambda)^2\right]/ \lambda^2}\) \(\sqrt{\frac{(\bar{V} - S_T^2)^2 + S_V^2}{S_T^4}}\) \(\sqrt{\frac{K - 1}{K} \sum_{j=1}^K \left(rRMSE_{(j)} - rRMSE\right)^2}\)

The function calc_relative_var() calculates the relative performance criteria estimates and corresponding jack-knife MCSEs for a variance estimator. The function requires res_dat, a data frame or tibble containing simulation results, estimates, the name of the column containing point estimates, var_estimates, the name of the column containing the variance estimates, and perfm_criteria, the criteria to be evaluated: perfm_criteria = c("relative bias", "relative mse", "relative rmse").

Below we demonstrate the use of the calc_relative_var() function. The variance estimates are expected to be different when derived from the conventional t-test as opposed to when they are derived from the Welch t-test. We group by the sample sizes, mean difference, and the t-test method. Note the difference between the conventional t-test and the Welch t-test results, especially when the group sample sizes are unequal.

If there are any convergence issues with estimation, please again make sure that the estimation function returns NA values for the variance estimate and the point estimate. The calc_relative_var() function will omit the pair of point estimate and variance estimate if either value is NA before calculating the performance measure and MCSE.

1.3.1 Example

welch_res %>%
  group_by(n1, n2, mean_diff, method) %>%
  group_modify(~ calc_relative_var(.x, estimates = est, var_estimates = var)) %>%
  kable(digits = 5)
n1 n2 mean_diff method K rel_bias_var rel_bias_var_mcse rel_mse_var rel_mse_var_mcse rel_rmse_var rel_rmse_var_mcse
50 50 0.0 Welch t-test 1000 0.99449 0.04264 0.02941 0.00259 0.17148 0.00756
50 50 0.0 t-test 1000 0.99449 0.04264 0.02941 0.00259 0.17148 0.00756
50 50 0.5 Welch t-test 1000 0.93705 0.04202 0.02839 0.00313 0.16849 0.00929
50 50 0.5 t-test 1000 0.93705 0.04202 0.02839 0.00313 0.16849 0.00929
50 50 1.0 Welch t-test 1000 1.02571 0.04508 0.03103 0.00534 0.17616 0.01512
50 50 1.0 t-test 1000 1.02571 0.04508 0.03103 0.00534 0.17616 0.01512
50 50 2.0 Welch t-test 1000 1.04180 0.04785 0.03211 0.00717 0.17918 0.01990
50 50 2.0 t-test 1000 1.04180 0.04785 0.03211 0.00717 0.17918 0.01990
50 70 0.0 Welch t-test 1000 1.02720 0.04555 0.02079 0.00452 0.14420 0.01561
50 70 0.0 t-test 1000 1.25701 0.05583 0.10150 0.03217 0.31859 0.05013
50 70 0.5 Welch t-test 1000 0.96512 0.04765 0.01795 0.00157 0.13398 0.00586
50 70 0.5 t-test 1000 1.18047 0.05831 0.06131 0.02440 0.24760 0.04864
50 70 1.0 Welch t-test 1000 0.95326 0.04270 0.01921 0.00245 0.13859 0.00885
50 70 1.0 t-test 1000 1.16667 0.05225 0.05714 0.02032 0.23905 0.04221
50 70 2.0 Welch t-test 1000 0.98888 0.04413 0.01938 0.00126 0.13919 0.00451
50 70 2.0 t-test 1000 1.21007 0.05409 0.07709 0.02594 0.27765 0.04640

1.4 Hypothesis Testing and Confidence Intervals

When doing hypothesis tests, we are often interested in whether the Type 1 error rate is adequately controlled and whether the test has enough power to detect an effect size of substantive interest. The rejection rate of a hypothesis test captures the proportion of times the p-value is below a specified \(\alpha\) level—that is, the proportion of times we reject the null hypothesis. When the specified effect size is 0, we can examine Type 1 error rates and when the magnitude of the effect is greater than 0, we can examine power. We are also interested in confidence interval coverage, the proportion of intervals that contain the true parameter, and the interval width, which is an indicator of the precision of the interval estimator.

Table 4 below presents the performance criteria used to evaluate hypothesis tests. In the table, let \(P_k\) denote the p-value from simulation replication \(k\), for \(k = 1,...,K\). Suppose that the confidence intervals are for the target parameter \(\theta\) and have coverage level \(\beta\). Let \(A_k\) and \(B_k\) denote the lower and upper end-points of the confidence interval from simulation replication \(k\), and let \(W_k = B_k − A_k\), for \(k = 1,...,K\).

Table 4. Hypothesis Testing and Confidence Intervals Performance Criteria
Criterion Measure Definition Estimate MCSE
Rejection Rate Type 1 error or power \(\rho_\alpha = Pr(P_k) < \alpha\) \(r_\alpha = \frac{1}{K} \sum_{k=1}^K I(P_k < \alpha)\) \(\sqrt{r_\alpha(1 - r_\alpha) / K}\)
Coverage Proportion of intervals containing true parameter \(\omega_\beta = Pr(A \leq \theta \leq B)\) \(c_\beta = \frac{1}{K}\sum_{k=1}^K I(A_k \leq \theta \leq B_k)\) \(\sqrt{c_\beta (1 - c_\beta) / K}\)
Width Precision \(\text{E}(W) = \text{E}(B - A)\) \(\bar{W} = \bar{B} - \bar{A}\) \(\sqrt{S_W^2 / K}\)

1.4.1 Example

Below we calculate the rejection rates for the hypothesis tests conducted using the conventional two-sample t-test and the Welch t-test. The null hypothesis is that the two groups have the same means. The calc_rejection() function requires a data frame or tibble containing simulation results as the first argument. The second argument, p_values, requires the name of the column containing p-values. The third argument, alpha, lets the user specify a value for \(\alpha\). The default value is set to the conventional 0.05.

# using group_modify()
welch_res %>%
  group_by(n1, n2, mean_diff, method) %>%
  group_modify(~ calc_rejection(.x, p_values = p_val)) %>%
  kable(digits = 5)
n1 n2 mean_diff method K rej_rate rej_rate_mcse
50 50 0.0 Welch t-test 1000 0.047 0.00669
50 50 0.0 t-test 1000 0.048 0.00676
50 50 0.5 Welch t-test 1000 0.335 0.01493
50 50 0.5 t-test 1000 0.340 0.01498
50 50 1.0 Welch t-test 1000 0.871 0.01060
50 50 1.0 t-test 1000 0.876 0.01042
50 50 2.0 Welch t-test 1000 1.000 0.00000
50 50 2.0 t-test 1000 1.000 0.00000
50 70 0.0 Welch t-test 1000 0.039 0.00612
50 70 0.0 t-test 1000 0.027 0.00513
50 70 0.5 Welch t-test 1000 0.426 0.01564
50 70 0.5 t-test 1000 0.341 0.01499
50 70 1.0 Welch t-test 1000 0.937 0.00768
50 70 1.0 t-test 1000 0.904 0.00932
50 70 2.0 Welch t-test 1000 1.000 0.00000
50 70 2.0 t-test 1000 1.000 0.00000

Below we calculate the confidence interval coverage rates and widths for the estimates of the mean difference. The calc_coverage() function requires a data frame or tibble containing the simulation results as the first argument. The second and third arguments, lower_bound and upper_bound, take in the name of the columns that contain the lower and upper bound estimates of the confidence intervals. The true_param argument requires the name of the column containing the true parameters. Like calc_absolute(), calc_relative() and calc_relative_var(), calc_coverage() also has an argument, perfm_criteria, where the user can specify which criteria to evaluate: perfm_criteria = c("coverage", "width").

# using group_modify()
welch_res %>%
  mutate(params = mean_diff) %>%
  group_by(n1, n2, mean_diff, method) %>%
  group_modify(~ calc_coverage(.x, lower_bound = lower_bound, upper_bound = upper_bound, true_param = params)) %>%
  kable(digits = 5)
n1 n2 mean_diff method K coverage coverage_mcse width width_mcse
50 50 0.0 Welch t-test 1000 0.953 0.00669 1.25266 0.00343
50 50 0.0 t-test 1000 0.952 0.00676 1.24697 0.00338
50 50 0.5 Welch t-test 1000 0.944 0.00727 1.25800 0.00336
50 50 0.5 t-test 1000 0.943 0.00733 1.25223 0.00332
50 50 1.0 Welch t-test 1000 0.953 0.00669 1.25741 0.00339
50 50 1.0 t-test 1000 0.952 0.00676 1.25169 0.00335
50 50 2.0 Welch t-test 1000 0.959 0.00627 1.25859 0.00335
50 50 2.0 t-test 1000 0.958 0.00634 1.25287 0.00331
50 70 0.0 Welch t-test 1000 0.961 0.00612 1.09943 0.00241
50 70 0.0 t-test 1000 0.973 0.00513 1.21433 0.00288
50 70 0.5 Welch t-test 1000 0.952 0.00676 1.09938 0.00234
50 70 0.5 t-test 1000 0.972 0.00522 1.21412 0.00276
50 70 1.0 Welch t-test 1000 0.940 0.00751 1.09791 0.00239
50 70 1.0 t-test 1000 0.963 0.00597 1.21280 0.00283
50 70 2.0 Welch t-test 1000 0.951 0.00683 1.09886 0.00245
50 70 2.0 t-test 1000 0.971 0.00531 1.21376 0.00289

2 How to Evaluate MCSE

Generally, the associated MCSE should be small compared to the performance measure. Below is an example from Frane (2015) of how to write up MCSE results as part of simulation study results. Frane (2015) compared various methods to control Type 1 error rates when conducting analysis on multiple outcome variables. In describing the simulation results, he wrote:

Only Bonferroni and MP strictly controlled the PFER; the observed maximum PFER was 0.050 for Bonferroni, 0.051 for Sidák, 0.061 for Holm, 0.100 for Hochberg, and 0.050 for MP (estimated SE ≤ 0.0002 for all estimates).

The PFER refers to per family error rate, the expected number of Type 1 errors out of \(m\) comparisons. MP refers to multivariate analysis of variance (MANOVA) protected tests. The SE refers to the MCSE. The number of replications was 1,000,000 in Frane (2015). We note that the number is quite high and generally 1,000 to 10,000 replications are enough. The resulting MCSEs in Frane (2015), therefore, are exceedingly small compared to the values for PFER.

For more information for performance criteria and MCSE, we recommend Morris, White, & Crowther (2019).

References

Efron, B., & Stein, C. (1981). The jackknife estimate of variance. The Annals of Statistics, 586–596.
Frane, A. V. (2015). Power and type i error control for univariate comparisons in multivariate two-group designs. Multivariate Behavioral Research, 50(2), 233–247.
Morris, T. P., White, I. R., & Crowther, M. J. (2019). Using simulation studies to evaluate statistical methods. Statistics in Medicine, 38(11), 2074–2102.
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L. D., François, R., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686