Summary Method for an Object of Class
mc
Usage
# S3 method for class '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)
#> $mean
#> b1 b2 b3 sigmasq sigmax1x1 sigmax2x1 sigmax3x1 sigmax2x2
#> 0.0832 0.2273 0.1142 21.0744 3619.8014 476.6385 517.6475 327.8115
#> sigmax3x2 sigmax3x3
#> 149.2978 542.8706
#>
#> $var
#> b1 b2 b3 sigmasq sigmax1x1 sigmax2x1 sigmax3x1
#> b1 0.0002 -0.0003 -0.0002 -0.0111 -4.6042 -0.5010 0.3105
#> b2 -0.0003 0.0024 -0.0005 0.0117 7.6020 -0.6670 -2.1430
#> b3 -0.0002 -0.0005 0.0015 -0.0709 4.4601 0.3034 1.7803
#> sigmasq -0.0111 0.0117 -0.0709 19.8746 -551.4091 8.3653 -161.6945
#> sigmax1x1 -4.6042 7.6020 4.4601 -551.4091 980450.9620 85188.4019 109711.6061
#> sigmax2x1 -0.5010 -0.6670 0.3034 8.3653 85188.4019 38428.4028 15576.3068
#> sigmax3x1 0.3105 -2.1430 1.7803 -161.6945 109711.6061 15576.3068 34459.7179
#> sigmax2x2 0.1017 -1.4938 0.8271 -12.9242 -1030.0465 3297.9950 1456.3705
#> sigmax3x2 -0.2361 0.1154 0.4424 -38.7005 14483.3009 7009.6589 4209.2237
#> sigmax3x3 0.2591 0.1875 -0.8235 30.7002 -7902.3786 -109.6628 278.0544
#> sigmax2x2 sigmax3x2 sigmax3x3
#> b1 0.1017 -0.2361 0.2591
#> b2 -1.4938 0.1154 0.1875
#> b3 0.8271 0.4424 -0.8235
#> sigmasq -12.9242 -38.7005 30.7002
#> sigmax1x1 -1030.0465 14483.3009 -7902.3786
#> sigmax2x1 3297.9950 7009.6589 -109.6628
#> sigmax3x1 1456.3705 4209.2237 278.0544
#> sigmax2x2 5280.2415 1270.4756 -1733.0413
#> sigmax3x2 1270.4756 2975.5309 -27.0574
#> sigmax3x3 -1733.0413 -27.0574 6099.5623
#>
#> $bias
#> b1 b2 b3 sigmasq sigmax1x1 sigmax2x1 sigmax3x1 sigmax2x2
#> -0.0010 0.0113 0.0016 -0.1704 112.6323 5.4327 7.1045 -5.4179
#> sigmax3x2 sigmax3x3
#> -1.6143 -11.5681
#>
#> $rmse
#> b1 b2 b3 sigmasq sigmax1x1 sigmax2x1 sigmax3x1 sigmax2x2
#> 0.0147 0.0499 0.0388 4.4390 991.6312 195.1247 184.8394 72.5037
#> sigmax3x2 sigmax3x3
#> 54.2990 78.5645
#>
#> $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
#>