boot-methods {rebmix} | R Documentation |
Returns as default the boot output for mixtures of conditionally independent normal,
lognormal, Weibull, gamma, binomial, Poisson, Dirac or von Mises component densities. If
x
is of class RNGMVNORM
the boot output for mixtures of multivariate normal
component densities with unrestricted variance-covariance matrices is returned.
## S4 method for signature 'REBMIX' boot(x = NULL, rseed = -1, pos = 1, Bootstrap = "parametric", B = 100, n = numeric(), replace = TRUE, prob = numeric(), ...) ## ... and for other signatures ## S4 method for signature 'REBMIX.boot' summary(object, ...) ## ... and for other signatures
x |
see Methods section below. |
rseed |
set the random seed to any negative integer value to initialize the sequence. The first bootstrap dataset corresponds to it.
For each next bootstrap dataset the random seed is decremented r_{\mathrm{seed}} = r_{\mathrm{seed}} - 1. The default value is |
pos |
a desired row number in |
Bootstrap |
a character giving the bootstrap type. One of default |
B |
number of bootstrap datasets. The default value is |
n |
number of observations. The default value is |
replace |
logical. The sampling is with replacement if |
prob |
a vector of length n containing probability weights, see also |
... |
maximum number of components |
object |
see Methods section below. |
Returns an object of class REBMIX.boot
or REBMVNORM.boot
.
signature(x = "REBMIX")
an object of class REBMIX
for mixtures of conditionally independent normal, lognormal, Weibull, gamma, binomial, Poisson, Dirac or von Mises component densities.
signature(x = "REBMVNORM")
an object of class REBMVNORM
for mixtures of multivariate normal component densities with unrestricted variance-covariance matrices.
signature(object = "REBMIX")
an object of class REBMIX
.
signature(object = "REBMVNORM")
an object of class REBMVNORM
.
Marko Nagode
G. McLachlan and D. Peel. Finite Mixture Models. John Wiley & Sons, New York, 2000.
data("weibull") n <- nrow(weibull) # Number of classes or nearest neighbours to be processed. K <- c(as.integer(1 + log2(n)), # Minimum v follows Sturges rule. as.integer(10 * log10(n))) # Maximum v follows log10 rule. # Estimate number of components, component weights and component parameters. weibullest <- REBMIX(Dataset = list(weibull), Preprocessing = "kernel density estimation", cmax = 4, Criterion = "BIC", pdf = "Weibull", K = K[1]:K[2], Restraints = "loose") # Plot finite mixture. plot(weibullest, what = c("density", "distribution", "IC", "logL", "D"), nrow = 3, ncol = 2, npts = 1000) # Bootstrap finite mixture. weibullboot <- boot(x = weibullest, Bootstrap = "nonparametric", B = 10) weibullboot