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Summary of bootstrap distribution for the parameters of a Gaussian mixture model providing either standard errors or percentile bootstrap confidence intervals.

Usage

# S3 method for MclustBootstrap
summary(object, what = c("se", "ci", "ave"), conf.level = 0.95, ...)

Arguments

object

An object of class 'MclustBootstrap' as returned by MclustBootstrap.

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.

See also

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
# }