Aliases: MclustBootstrap print.MclustBootstrap
### ** Examples ## No test: data(diabetes) X <- diabetes[,-1] modClust <- Mclust(X)
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bootClust <- MclustBootstrap(modClust)
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summary(bootClust, what = "se")
----------------------------------------------------------
Resampling standard errors
----------------------------------------------------------
Model = VVV
Num. of mixture components = 3
Replications = 999
Type = nonparametric bootstrap
Mixing probabilities:
1 2 3
0.05449595 0.05147794 0.03573644
Means:
1 2 3
glucose 0.9922335 3.505836 16.42449
insulin 7.5602606 29.586662 64.83873
sspg 7.6647058 30.532942 10.12077
Variances:
[,,1]
glucose insulin sspg
glucose 10.83259 51.94925 51.90574
insulin 51.94925 524.77975 405.74270
sspg 51.90574 405.74270 617.56853
[,,2]
glucose insulin sspg
glucose 66.14502 618.032 451.4526
insulin 618.03199 7288.897 3275.2162
sspg 451.45264 3275.216 6737.9977
[,,3]
glucose insulin sspg
glucose 978.3007 4017.226 654.3212
insulin 4017.2257 18805.952 2453.6784
sspg 654.3212 2453.678 519.9762
summary(bootClust, what = "ci")
----------------------------------------------------------
Resampling confidence intervals
----------------------------------------------------------
Model = VVV
Num. of mixture components = 3
Replications = 999
Type = nonparametric bootstrap
Confidence level = 0.95
Mixing probabilities:
1 2 3
2.5% 0.4401435 0.1508056 0.1319888
97.5% 0.6542836 0.3513697 0.2712947
Means:
[,,1]
glucose insulin sspg
2.5% 89.14054 344.8964 150.4223
97.5% 93.04696 373.9385 182.0138
[,,2]
glucose insulin sspg
2.5% 98.58659 444.7872 260.5233
97.5% 112.35412 558.3293 378.5292
[,,3]
glucose insulin sspg
2.5% 196.7928 965.009 61.68052
97.5% 262.6493 1220.807 102.36323
Variances:
[,,1]
glucose insulin sspg
2.5% 37.98588 1265.577 1527.566
97.5% 80.50810 3456.357 4057.870
[,,2]
glucose insulin sspg
2.5% 82.95281 3215.854 12573.6
97.5% 358.31916 30797.805 39527.8
[,,3]
glucose insulin sspg
2.5% 3420.188 47351.4 1286.948
97.5% 7296.295 121713.1 3284.069
data(acidity) modDens <- densityMclust(acidity, plot = FALSE)
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modDens <- MclustBootstrap(modDens)
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summary(modDens, what = "se")
----------------------------------------------------------
Resampling standard errors
----------------------------------------------------------
Model = E
Num. of mixture components = 2
Replications = 999
Type = nonparametric bootstrap
Mixing probabilities:
1 2
0.03953244 0.03953244
Means:
1 2
0.04453392 0.06764683
Variances:
1 2
0.02366756 0.02366756
summary(modDens, what = "ci")
----------------------------------------------------------
Resampling confidence intervals
----------------------------------------------------------
Model = E
Num. of mixture components = 2
Replications = 999
Type = nonparametric bootstrap
Confidence level = 0.95
Mixing probabilities:
1 2
2.5% 0.5416178 0.3044539
97.5% 0.6955461 0.4583822
Means:
1 2
2.5% 4.282831 6.184306
97.5% 4.456801 6.458102
Variances:
1 2
2.5% 0.1408165 0.1408165
97.5% 0.2338780 0.2338780
## End(No test)