Print method for summary.simsum objects
Usage
# S3 method for summary.simsum
print(x, digits = 4, mcse = TRUE, ...)Arguments
- x
An object of class
summary.simsum.- digits
Number of significant digits used for printing. Defaults to 4.
- mcse
Should Monte Carlo standard errors be reported? If
mcse = FALSE, confidence intervals based on Monte Carlo standard errors will be reported instead, seesummary.simsum(). If aNULLvalue is passed, only point estimates are printed regardless of whether Monte Carlo standard errors were computed or not. Defaults toTRUE.- ...
Ignored.
Examples
data("MIsim")
x <- simsum(
data = MIsim, estvarname = "b", true = 0.5, se = "se",
methodvar = "method"
)
#> 'ref' method was not specified, CC set as the reference
xs <- summary(x)
xs
#> Values are:
#> Point Estimate (Monte Carlo Standard Error)
#>
#> Non-missing point estimates/standard errors:
#> CC MI_LOGT MI_T
#> 1000 1000 1000
#>
#> Average point estimate:
#> CC MI_LOGT MI_T
#> 0.5168 0.5009 0.4988
#>
#> Median point estimate:
#> CC MI_LOGT MI_T
#> 0.5070 0.4969 0.4939
#>
#> Average variance:
#> CC MI_LOGT MI_T
#> 0.0216 0.0182 0.0179
#>
#> Median variance:
#> CC MI_LOGT MI_T
#> 0.0211 0.0172 0.0169
#>
#> Bias in point estimate:
#> CC MI_LOGT MI_T
#> 0.0168 (0.0048) 0.0009 (0.0042) -0.0012 (0.0043)
#>
#> Relative bias in point estimate:
#> CC MI_LOGT MI_T
#> 0.0335 (0.0096) 0.0018 (0.0083) -0.0024 (0.0085)
#>
#> Empirical standard error:
#> CC MI_LOGT MI_T
#> 0.1511 (0.0034) 0.1320 (0.0030) 0.1344 (0.0030)
#>
#> % gain in precision relative to method CC:
#> CC MI_LOGT MI_T
#> 0.0000 (0.0000) 31.0463 (3.9375) 26.3682 (3.8424)
#>
#> Mean squared error:
#> CC MI_LOGT MI_T
#> 0.0231 (0.0011) 0.0174 (0.0009) 0.0181 (0.0009)
#>
#> Model-based standard error:
#> CC MI_LOGT MI_T
#> 0.1471 (0.0005) 0.1349 (0.0006) 0.1338 (0.0006)
#>
#> Relative % error in standard error:
#> CC MI_LOGT MI_T
#> -2.6594 (2.2055) 2.2233 (2.3323) -0.4412 (2.2695)
#>
#> Coverage of nominal 95% confidence interval:
#> CC MI_LOGT MI_T
#> 0.9430 (0.0073) 0.9490 (0.0070) 0.9430 (0.0073)
#>
#> Bias-eliminated coverage of nominal 95% confidence interval:
#> CC MI_LOGT MI_T
#> 0.9400 (0.0075) 0.9490 (0.0070) 0.9430 (0.0073)
#>
#> Power of 5% level test:
#> CC MI_LOGT MI_T
#> 0.9460 (0.0071) 0.9690 (0.0055) 0.9630 (0.0060)
# Printing less significant digits:
print(xs, digits = 2)
#> Values are:
#> Point Estimate (Monte Carlo Standard Error)
#>
#> Non-missing point estimates/standard errors:
#> CC MI_LOGT MI_T
#> 1000 1000 1000
#>
#> Average point estimate:
#> CC MI_LOGT MI_T
#> 0.52 0.50 0.50
#>
#> Median point estimate:
#> CC MI_LOGT MI_T
#> 0.51 0.50 0.49
#>
#> Average variance:
#> CC MI_LOGT MI_T
#> 0.02 0.02 0.02
#>
#> Median variance:
#> CC MI_LOGT MI_T
#> 0.02 0.02 0.02
#>
#> Bias in point estimate:
#> CC MI_LOGT MI_T
#> 0.02 (0.00) 0.00 (0.00) -0.00 (0.00)
#>
#> Relative bias in point estimate:
#> CC MI_LOGT MI_T
#> 0.03 (0.01) 0.00 (0.01) -0.00 (0.01)
#>
#> Empirical standard error:
#> CC MI_LOGT MI_T
#> 0.15 (0.00) 0.13 (0.00) 0.13 (0.00)
#>
#> % gain in precision relative to method CC:
#> CC MI_LOGT MI_T
#> 0.00 (0.00) 31.05 (3.94) 26.37 (3.84)
#>
#> Mean squared error:
#> CC MI_LOGT MI_T
#> 0.02 (0.00) 0.02 (0.00) 0.02 (0.00)
#>
#> Model-based standard error:
#> CC MI_LOGT MI_T
#> 0.15 (0.00) 0.13 (0.00) 0.13 (0.00)
#>
#> Relative % error in standard error:
#> CC MI_LOGT MI_T
#> -2.66 (2.21) 2.22 (2.33) -0.44 (2.27)
#>
#> Coverage of nominal 95% confidence interval:
#> CC MI_LOGT MI_T
#> 0.94 (0.01) 0.95 (0.01) 0.94 (0.01)
#>
#> Bias-eliminated coverage of nominal 95% confidence interval:
#> CC MI_LOGT MI_T
#> 0.94 (0.01) 0.95 (0.01) 0.94 (0.01)
#>
#> Power of 5% level test:
#> CC MI_LOGT MI_T
#> 0.95 (0.01) 0.97 (0.01) 0.96 (0.01)
# Printing confidence intervals:
print(xs, mcse = FALSE)
#> Values are:
#> Point Estimate (95% Confidence Interval based on Monte Carlo Standard Errors)
#>
#> Non-missing point estimates/standard errors:
#> CC MI_LOGT MI_T
#> 1000 1000 1000
#>
#> Average point estimate:
#> CC MI_LOGT MI_T
#> 0.5168 0.5009 0.4988
#>
#> Median point estimate:
#> CC MI_LOGT MI_T
#> 0.5070 0.4969 0.4939
#>
#> Average variance:
#> CC MI_LOGT MI_T
#> 0.0216 0.0182 0.0179
#>
#> Median variance:
#> CC MI_LOGT MI_T
#> 0.0211 0.0172 0.0169
#>
#> Bias in point estimate:
#> CC MI_LOGT MI_T
#> 0.0168 (0.0074, 0.0261) 0.0009 (-0.0073, 0.0091) -0.0012 (-0.0095, 0.0071)
#>
#> Relative bias in point estimate:
#> CC MI_LOGT MI_T
#> 0.0335 (0.0148, 0.0523) 0.0018 (-0.0145, 0.0182) -0.0024 (-0.0190, 0.0143)
#>
#> Empirical standard error:
#> CC MI_LOGT MI_T
#> 0.1511 (0.1445, 0.1577) 0.1320 (0.1262, 0.1378) 0.1344 (0.1285, 0.1403)
#>
#> % gain in precision relative to method CC:
#> CC MI_LOGT MI_T
#> 0.0000 (0.0000, 0.0000) 31.0463 (23.3290, 38.7636) 26.3682 (18.8372, 33.8991)
#>
#> Mean squared error:
#> CC MI_LOGT MI_T
#> 0.0231 (0.0209, 0.0253) 0.0174 (0.0157, 0.0191) 0.0181 (0.0163, 0.0198)
#>
#> Model-based standard error:
#> CC MI_LOGT MI_T
#> 0.1471 (0.1461, 0.1481) 0.1349 (0.1338, 0.1361) 0.1338 (0.1327, 0.1350)
#>
#> Relative % error in standard error:
#> CC MI_LOGT MI_T
#> -2.6594 (-6.9820, 1.6633) 2.2233 (-2.3480, 6.7946) -0.4412 (-4.8894, 4.0070)
#>
#> Coverage of nominal 95% confidence interval:
#> CC MI_LOGT MI_T
#> 0.9430 (0.9286, 0.9574) 0.9490 (0.9354, 0.9626) 0.9430 (0.9286, 0.9574)
#>
#> Bias-eliminated coverage of nominal 95% confidence interval:
#> CC MI_LOGT MI_T
#> 0.9400 (0.9253, 0.9547) 0.9490 (0.9354, 0.9626) 0.9430 (0.9286, 0.9574)
#>
#> Power of 5% level test:
#> CC MI_LOGT MI_T
#> 0.9460 (0.9320, 0.9600) 0.9690 (0.9583, 0.9797) 0.9630 (0.9513, 0.9747)
# Printing values only:
print(xs, mcse = NULL)
#> Values are:
#> Point Estimate
#>
#> Non-missing point estimates/standard errors:
#> CC MI_LOGT MI_T
#> 1000 1000 1000
#>
#> Average point estimate:
#> CC MI_LOGT MI_T
#> 0.5168 0.5009 0.4988
#>
#> Median point estimate:
#> CC MI_LOGT MI_T
#> 0.5070 0.4969 0.4939
#>
#> Average variance:
#> CC MI_LOGT MI_T
#> 0.0216 0.0182 0.0179
#>
#> Median variance:
#> CC MI_LOGT MI_T
#> 0.0211 0.0172 0.0169
#>
#> Bias in point estimate:
#> CC MI_LOGT MI_T
#> 0.0168 0.0009 -0.0012
#>
#> Relative bias in point estimate:
#> CC MI_LOGT MI_T
#> 0.0335 0.0018 -0.0024
#>
#> Empirical standard error:
#> CC MI_LOGT MI_T
#> 0.1511 0.1320 0.1344
#>
#> % gain in precision relative to method CC:
#> CC MI_LOGT MI_T
#> 0.0000 31.0463 26.3682
#>
#> Mean squared error:
#> CC MI_LOGT MI_T
#> 0.0231 0.0174 0.0181
#>
#> Model-based standard error:
#> CC MI_LOGT MI_T
#> 0.1471 0.1349 0.1338
#>
#> Relative % error in standard error:
#> CC MI_LOGT MI_T
#> -2.6594 2.2233 -0.4412
#>
#> Coverage of nominal 95% confidence interval:
#> CC MI_LOGT MI_T
#> 0.9430 0.9490 0.9430
#>
#> Bias-eliminated coverage of nominal 95% confidence interval:
#> CC MI_LOGT MI_T
#> 0.9400 0.9490 0.9430
#>
#> Power of 5% level test:
#> CC MI_LOGT MI_T
#> 0.9460 0.9690 0.9630