Aliases: summary.MclustBootstrap print.summary.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
0.05363625 0.05185538 0.03527195
Means:
1 2 3
glucose 1.004756 3.413582 17.01845
insulin 7.430516 28.182851 66.10707
sspg 8.106513 30.718650 10.74794
Variances:
[,,1]
glucose insulin sspg
glucose 10.95963 50.90677 51.17487
insulin 50.90677 503.87541 409.82846
sspg 51.17487 409.82846 635.09289
[,,2]
glucose insulin sspg
glucose 66.00937 613.1517 438.7881
insulin 613.15171 7209.8507 3150.2910
sspg 438.78814 3150.2910 6661.4139
[,,3]
glucose insulin sspg
glucose 990.7973 4002.643 650.6575
insulin 4002.6433 18213.237 2460.1400
sspg 650.6575 2460.140 524.9188
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% 0.4384351 0.1527799 0.1324220
97.5% 0.6512555 0.3575926 0.2704934
Means:
[,,1]
glucose insulin sspg
2.5% 89.27196 344.0561 151.0396
97.5% 93.15499 373.4189 183.3973
[,,2]
glucose insulin sspg
2.5% 99.11248 449.9440 259.8014
97.5% 112.97868 560.6903 382.3826
[,,3]
glucose insulin sspg
2.5% 194.1506 965.0184 60.62543
97.5% 261.3953 1221.9379 103.24247
Variances:
[,,1]
glucose insulin sspg
2.5% 37.17972 1249.048 1471.655
97.5% 79.91694 3247.110 3982.171
[,,2]
glucose insulin sspg
2.5% 83.37289 3243.396 11919.65
97.5% 366.98948 29474.824 37657.86
[,,3]
glucose insulin sspg
2.5% 3427.136 48325.93 1357.947
97.5% 7373.119 118509.20 3393.834
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
0.03953645 0.03953645
Means:
1 2
0.04586968 0.06602324
Variances:
1 2
0.02387101 0.02387101
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% 0.5406538 0.3028423
97.5% 0.6971577 0.4593462
Means:
1 2
2.5% 4.282953 6.184107
97.5% 4.455493 6.440165
Variances:
1 2
2.5% 0.1441611 0.1441611
97.5% 0.2361556 0.2361556
## End(No test)