Summary Method for an Object of Class
mc
Usage
# S3 method for mc
summary(object, digits = 4, ...)Arguments
- object
Object of Class
mc, that is, the output of theMC()function.- digits
Digits to print.
- ...
additional arguments.
Value
Returns a list with the following elements:
- mean
Mean of the sampling distribution of \(\boldsymbol{\hat{\theta}}\).
- var
Variance of the sampling distribution of \(\boldsymbol{\hat{\theta}}\).
- bias
Monte Carlo simulation bias.
- rmse
Monte Carlo simulation root mean square error.
- location
Location parameter used in the Monte Carlo simulation.
- scale
Scale parameter used in the Monte Carlo simulation.
Examples
# Fit the regression model
object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = nas1982)
mc <- MC(object, R = 100)
summary(mc)
#> MC(object = object, R = 100)
#> $mean
#> b1 b2 b3 sigmasq sigmax1x1 sigmax2x1 sigmax3x1 sigmax2x2
#> 0.0855 0.2192 0.1144 21.0793 3536.5569 466.4309 528.7742 342.2669
#> sigmax3x2 sigmax3x3
#> 155.7474 557.1612
#>
#> $var
#> b1 b2 b3 sigmasq sigmax1x1 sigmax2x1 sigmax3x1
#> b1 0.0002 -0.0004 -0.0001 -0.0079 -7.7167 -0.5720 0.0516
#> b2 -0.0004 0.0029 -0.0005 -0.0004 8.0391 -0.2946 -2.9652
#> b3 -0.0001 -0.0005 0.0013 -0.0584 6.4137 0.2717 1.7175
#> sigmasq -0.0079 -0.0004 -0.0584 16.9411 -314.3656 34.8260 -74.0046
#> sigmax1x1 -7.7167 8.0391 6.4137 -314.3656 1123104.2354 83493.9236 100323.7300
#> sigmax2x1 -0.5720 -0.2946 0.2717 34.8260 83493.9236 31672.0469 14133.3509
#> sigmax3x1 0.0516 -2.9652 1.7175 -74.0046 100323.7300 14133.3509 31909.1440
#> sigmax2x2 0.0337 -0.9942 0.4918 18.5096 5453.7952 2730.9280 4642.5670
#> sigmax3x2 -0.2628 0.4262 0.1824 -15.3243 11307.9487 5844.6613 4136.8488
#> sigmax3x3 0.2602 0.5481 -0.9642 67.8322 -15249.8855 -1129.7055 255.9731
#> sigmax2x2 sigmax3x2 sigmax3x3
#> b1 0.0337 -0.2628 0.2602
#> b2 -0.9942 0.4262 0.5481
#> b3 0.4918 0.1824 -0.9642
#> sigmasq 18.5096 -15.3243 67.8322
#> sigmax1x1 5453.7952 11307.9487 -15249.8855
#> sigmax2x1 2730.9280 5844.6613 -1129.7055
#> sigmax3x1 4642.5670 4136.8488 255.9731
#> sigmax2x2 5151.3870 1066.1870 -1859.1171
#> sigmax3x2 1066.1870 2911.9506 -102.7055
#> sigmax3x3 -1859.1171 -102.7055 8272.2350
#>
#> $bias
#> b1 b2 b3 sigmasq sigmax1x1 sigmax2x1 sigmax3x1 sigmax2x2
#> 0.0013 0.0032 0.0018 -0.1655 29.3878 -4.7749 18.2312 9.0374
#> sigmax3x2 sigmax3x3
#> 4.8353 2.7225
#>
#> $rmse
#> b1 b2 b3 sigmasq sigmax1x1 sigmax2x1 sigmax3x1 sigmax2x2
#> 0.0148 0.0533 0.0360 4.0987 1054.8634 177.1387 178.6685 71.9830
#> sigmax3x2 sigmax3x3
#> 53.9093 90.5369
#>
#> $location
#> b1 b2 b3 sigmasq sigmax1x1 sigmax2x1 sigmax3x1 sigmax2x2
#> 0.0842 0.2160 0.1126 21.2448 3507.1691 471.2058 510.5430 333.2295
#> sigmax3x2 sigmax3x3
#> 150.9121 554.4386
#>
#> $scale
#> b1 b2 b3 sigmasq sigmax1x1 sigmax2x1 sigmax3x1
#> b1 0.0002 -0.0003 -0.0002 -0.0073 -6.4514 -0.1818 0.0793
#> b2 -0.0003 0.0027 -0.0006 0.0097 5.4783 0.6385 -1.2960
#> b3 -0.0002 -0.0006 0.0015 -0.0510 4.7717 -0.4470 1.1153
#> sigmasq -0.0073 0.0097 -0.0510 15.5891 -623.3795 -69.6223 -115.0921
#> sigmax1x1 -6.4514 5.4783 4.7717 -623.3795 1234077.9191 70017.7837 135353.8871
#> sigmax2x1 -0.1818 0.6385 -0.4470 -69.6223 70017.7837 32380.2229 19033.6847
#> sigmax3x1 0.0793 -1.2960 1.1153 -115.0921 135353.8871 19033.6847 43294.1991
#> sigmax2x2 0.1241 -1.2801 0.5313 11.0921 -161.6877 2275.8820 3152.0325
#> sigmax3x2 -0.1904 0.6456 0.2802 -18.6658 10148.7795 6598.7503 5669.9978
#> sigmax3x3 0.2132 0.6850 -0.9885 42.7723 -8922.3944 920.0158 1333.8790
#> sigmax2x2 sigmax3x2 sigmax3x3
#> b1 0.1241 -0.1904 0.2132
#> b2 -1.2801 0.6456 0.6850
#> b3 0.5313 0.2802 -0.9885
#> sigmasq 11.0921 -18.6658 42.7723
#> sigmax1x1 -161.6877 10148.7795 -8922.3944
#> sigmax2x1 2275.8820 6598.7503 920.0158
#> sigmax3x1 3152.0325 5669.9978 1333.8790
#> sigmax2x2 5980.8603 1092.1986 -1134.2969
#> sigmax3x2 1092.1986 3700.7183 704.7217
#> sigmax3x3 -1134.2969 704.7217 7350.2416
#>