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)
#> MC(object = object, R = 100)
#> $mean
#> b1 b2 b3 sigmasq sigmax1x1 sigmax2x1 sigmax3x1 sigmax2x2
#> 0.0832 0.2273 0.1142 21.1065 3611.1447 476.0296 517.5181 330.6449
#> sigmax3x2 sigmax3x3
#> 150.8569 543.0762
#>
#> $var
#> b1 b2 b3 sigmasq sigmax1x1 sigmax2x1 sigmax3x1
#> b1 0.0002 -0.0003 -0.0002 -0.0120 -4.2726 -0.4293 0.3883
#> b2 -0.0003 0.0025 -0.0006 0.0141 6.8647 -1.2025 -2.8625
#> b3 -0.0002 -0.0006 0.0015 -0.0663 2.6280 0.2757 1.8956
#> sigmasq -0.0120 0.0141 -0.0663 19.1385 -268.3510 49.9355 -127.4505
#> sigmax1x1 -4.2726 6.8647 2.6280 -268.3510 871595.5465 68865.0344 95797.0266
#> sigmax2x1 -0.4293 -1.2025 0.2757 49.9355 68865.0344 37344.1407 15439.5289
#> sigmax3x1 0.3883 -2.8625 1.8956 -127.4505 95797.0266 15439.5289 35468.7569
#> sigmax2x2 0.1309 -1.7208 0.9148 -3.3713 -5075.7348 3483.6566 1882.7798
#> sigmax3x2 -0.2104 -0.0873 0.4911 -26.8380 9765.8579 6961.7183 4439.5420
#> sigmax3x3 0.2514 0.2417 -0.8208 28.7762 -6929.1966 -60.4522 252.8172
#> sigmax2x2 sigmax3x2 sigmax3x3
#> b1 0.1309 -0.2104 0.2514
#> b2 -1.7208 -0.0873 0.2417
#> b3 0.9148 0.4911 -0.8208
#> sigmasq -3.3713 -26.8380 28.7762
#> sigmax1x1 -5075.7348 9765.8579 -6929.1966
#> sigmax2x1 3483.6566 6961.7183 -60.4522
#> sigmax3x1 1882.7798 4439.5420 252.8172
#> sigmax2x2 5388.2098 1439.8419 -1607.3905
#> sigmax3x2 1439.8419 3113.2530 57.5257
#> sigmax3x3 -1607.3905 57.5257 6116.1742
#>
#> $bias
#> b1 b2 b3 sigmasq sigmax1x1 sigmax2x1 sigmax3x1 sigmax2x2
#> -0.0010 0.0113 0.0016 -0.1384 103.9756 4.8238 6.9751 -2.5846
#> sigmax3x2 sigmax3x3
#> -0.0552 -11.3625
#>
#> $rmse
#> b1 b2 b3 sigmasq sigmax1x1 sigmax2x1 sigmax3x1 sigmax2x2
#> 0.0147 0.0513 0.0390 4.3550 934.7141 192.3382 187.5173 73.0822
#> sigmax3x2 sigmax3x3
#> 55.5169 78.6392
#>
#> $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
#>