Summary Function for Bootstrap Inference for Gaussian Finite Mixture Models
summary.MclustBootstrap.Rd
Summary of bootstrap distribution for the parameters of a Gaussian mixture model providing either standard errors or percentile bootstrap confidence intervals.
Arguments
- object
An object of class
'MclustBootstrap'
as returned byMclustBootstrap
.- what
A character string:
"se"
for the standard errors;"ci"
for the confidence intervals;"ave"
for the averages.- conf.level
A value specifying the confidence level of the interval.
- ...
Further arguments passed to or from other methods.
Details
For details about the procedure used to obtain the bootstrap distribution see MclustBootstrap
.
Examples
# \donttest{
data(diabetes)
X = diabetes[,-1]
modClust = Mclust(X)
bootClust = MclustBootstrap(modClust)
summary(bootClust, what = "se")
#> ----------------------------------------------------------
#> Resampling standard errors
#> ----------------------------------------------------------
#> Model = VVV
#> Num. of mixture components = 3
#> Replications = 999
#> Type = nonparametric bootstrap
#>
#> Mixing probabilities:
#> 1 2 3
#> 0.05210496 0.05155741 0.03545805
#>
#> Means:
#> 1 2 3
#> glucose 1.070074 3.353236 16.70234
#> insulin 7.654479 29.167339 65.28345
#> sspg 7.943500 30.289487 10.18746
#>
#> Variances:
#> [,,1]
#> glucose insulin sspg
#> glucose 11.39138 51.76311 51.5165
#> insulin 51.76311 502.02447 414.8644
#> sspg 51.51650 414.86443 617.9167
#> [,,2]
#> glucose insulin sspg
#> glucose 63.29427 593.7961 432.7412
#> insulin 593.79612 7049.6150 3150.0699
#> sspg 432.74125 3150.0699 6801.4543
#> [,,3]
#> glucose insulin sspg
#> glucose 993.9233 4144.973 651.1016
#> insulin 4144.9732 19270.113 2536.4339
#> sspg 651.1016 2536.434 498.3846
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% 0.4451799 0.1528780 0.1310645
#> 97.5% 0.6510670 0.3618433 0.2684017
#>
#> Means:
#> [,,1]
#> glucose insulin sspg
#> 2.5% 88.98891 344.5995 150.2140
#> 97.5% 93.29347 375.8905 182.7606
#> [,,2]
#> glucose insulin sspg
#> 2.5% 98.94606 449.8111 259.3504
#> 97.5% 112.18129 558.6397 376.0680
#> [,,3]
#> glucose insulin sspg
#> 2.5% 199.1914 974.9909 61.89196
#> 97.5% 261.4618 1221.5805 101.83928
#>
#> Variances:
#> [,,1]
#> glucose insulin sspg
#> 2.5% 36.99433 1264.169 1509.538
#> 97.5% 81.13360 3232.756 4056.550
#> [,,2]
#> glucose insulin sspg
#> 2.5% 88.93158 3685.515 12442.53
#> 97.5% 341.33485 29834.214 38919.10
#> [,,3]
#> glucose insulin sspg
#> 2.5% 3392.779 46908.82 1347.520
#> 97.5% 7184.775 120836.47 3204.012
data(acidity)
modDens = densityMclust(acidity, plot = FALSE)
modDens = MclustBootstrap(modDens)
summary(modDens, what = "se")
#> ----------------------------------------------------------
#> Resampling standard errors
#> ----------------------------------------------------------
#> Model = E
#> Num. of mixture components = 2
#> Replications = 999
#> Type = nonparametric bootstrap
#>
#> Mixing probabilities:
#> 1 2
#> 0.03982776 0.03982776
#>
#> Means:
#> 1 2
#> 0.04577321 0.06884369
#>
#> Variances:
#> 1 2
#> 0.02352405 0.02352405
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% 0.5385733 0.2998957
#> 97.5% 0.7001043 0.4614267
#>
#> Means:
#> 1 2
#> 2.5% 4.285412 6.185831
#> 97.5% 4.457428 6.449197
#>
#> Variances:
#> 1 2
#> 2.5% 0.1408696 0.1408696
#> 97.5% 0.2322213 0.2322213
# }