Internal function

This function fits the semi-supervised mixture model multiple times. It is called by mixtura and scrutor.

fit.wrap(y, z, dist, phi, pi, gamma, starts = 1, it.em = 100, epsilon = 1e-04)

Arguments

y	observations: numeric vector of length n
z	class labels: integer vector of length n, with entries 0, 1 and NA
dist	distributional assumption: character "norm" (Gaussian), "nbinom" (negative bionomial), or "zinb" (zero-inflated negative binomial)
phi	dispersion parameters: numeric vector of length q, or NULL
pi	zero-inflation parameter(s): numeric vector of length q, or NULL
gamma	offset: numeric vector of length n, or NULL
starts	restarts of the EM algorithm: positive integer (defaults to 1)
it.em	(maximum) number of iterations in the EM algorithm: positive integer (defaults to 100)
epsilon	convergence criterion for the EM algorithm: non-negative numeric (defaults to 1e-04)

Value

This function returns the parameter estimates, the posterior probabilities, and the likelihood.

posterior

probability of belonging to class 1: numeric vector of length n

converge

path of the log-likelihood: numeric vector with maximum length it.em

estim0

parameter estimates under H0: data frame

estim1

parameter estimates under H1: data frame

loglik0

log-likelihood under H0: numeric

loglik1

log-likelihood under H1: numeric

lrts

likelihood-ratio test statistic: positive numeric

Details

The distributions are parametrised as follows:

• Gaussian
  y ~ N(mean,sd^2)
E[y]=mean
Var[y]=sd^2

• Negative binomial
  y ~ NB(mu,phi)
E[y]=mu
Var[y]=mu+phi*mu^2

• Zero-inflated negative binomial
  y ~ ZINB(mu,phi,pi)
E[y]=(1-pi)*mu

See also

This is an internal function. The user functions are mixtura and scrutor.

Examples

# data simulation
n <- 100
z <- rep(0:1,each=n/2)
y <- rnorm(n=n,mean=2*z,sd=1)
z[(n/4):n] <- NA

# model fitting
fit.wrap(y,z,dist="norm")
#> $posterior
#>   [1] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
#>   [6] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
#>  [11] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
#>  [16] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000
#>  [21] 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.8425921935
#>  [26] 0.4084662331 0.0066437359 0.0245016147 0.0312726042 0.7853968391
#>  [31] 0.2318178842 0.0131493443 0.0449175639 0.0276646813 0.0368652934
#>  [36] 0.0325069881 0.3195242749 0.0004680964 0.2854726258 0.0387377356
#>  [41] 0.1510768584 0.3550874100 0.6020975215 0.2035236657 0.0697451110
#>  [46] 0.0713482334 0.3921015126 0.0026858865 0.3711345096 0.0049133548
#>  [51] 0.8218491385 0.8852113235 0.9470413234 0.8238578622 0.9904841501
#>  [56] 0.8625968713 0.9894537082 0.8687773658 0.5464222455 0.8422754448
#>  [61] 0.1874638518 0.9559626410 0.9323836723 0.5652770911 0.9764486990
#>  [66] 0.7183888664 0.9665881190 0.3501853431 0.6524783755 0.9383541242
#>  [71] 0.9226385563 0.9973239048 0.6918441633 0.9535939895 0.4754974138
#>  [76] 0.6311521557 0.3533258240 0.7460639013 0.3347035229 0.7490640459
#>  [81] 0.9821081960 0.9441602989 0.5505044669 0.8397911658 0.7766631154
#>  [86] 0.9445805976 0.9201395562 0.7524431750 0.9078371861 0.9357469764
#>  [91] 0.9552854771 0.9716816087 0.9469883654 0.9366126681 0.5172012021
#>  [96] 0.9727968251 0.9750004007 0.9630710243 0.3459891189 0.4238540425
#> 
#> $converge
#>  [1] -175.6058 -174.4763 -173.5012 -172.5741 -171.7009 -170.9523 -170.3971
#>  [8] -170.0464 -169.8550 -169.7608 -169.7160 -169.6936 -169.6810 -169.6729
#> [15] -169.6672 -169.6630 -169.6598 -169.6573 -169.6555 -169.6540 -169.6529
#> [22] -169.6520 -169.6513 -169.6508 -169.6504 -169.6501 -169.6499 -169.6497
#> [29] -169.6495 -169.6494 -169.6493
#> 
#> $estim0
#>   p0     mean0      sd0 p1 mean1 sd1
#> 1  1 0.9049049 1.431286  0   NaN NaN
#> 
#> $estim1
#>        p0      mean0      sd0      p1    mean1      sd1
#> 1 0.41292 0.01860373 1.093092 0.58708 2.005021 1.007327
#> 
#> $loglik0
#> [1] -177.827
#> 
#> $loglik1
#> [1] -169.6493
#> 
#> $lrts
#> [1] 16.35543
#>