mixtura {semisup} | R Documentation |
This function fits a semi-supervised mixture model. It simultaneously estimates two mixture components, and assigns the unlabelled observations to these.
mixtura(y, z, dist = "norm", phi = NULL, pi = NULL, gamma = NULL, test = NULL, iter = 100, kind = 0.05, debug = TRUE, ...)
y |
observations:
numeric vector of length |
z |
class labels:
integer vector of length |
dist |
distributional assumption:
character |
phi |
dispersion parameters:
numeric vector of length |
pi |
zero-inflation parameter(s):
numeric vector of length |
gamma |
offset:
numeric vector of length |
test |
resampling procedure:
character |
iter |
(maximum) number of resampling iterations :
positive integer, or |
kind |
resampling accuracy:
numeric between |
debug |
verification of arguments:
|
... |
settings |
By default, phi
and pi
are estimated by the maximum likelihood method,
and gamma
is replaced by a vector of ones.
This function fits and compares a one-component (H0
)
and a two-component (H1
) mixture model.
posterior |
probability of belonging to class 1:
numeric vector of length |
converge |
path of the log-likelihood:
numeric vector with maximum length
|
estim0 |
parameter estimates under |
estim1 |
parameter estimates under |
loglik0 |
log-likelihood under |
loglik1 |
log-likelihood under |
lrts |
likelihood-ratio test statistic: positive numeric |
p.value |
|
A Rauschenberger, RX Menezes, MA van de Wiel, NM van Schoor, and MA Jonker (2020). "Semi-supervised mixture test for detecting markers associated with a quantitative trait", Manuscript in preparation.
Use scrutor
for hypothesis testing.
All other functions are internal
.
# data simulation n <- 100 z <- rep(0:1,each=n/2) y <- rnorm(n=n,mean=2,sd=1) z[(n/4):n] <- NA # model fitting mixtura(y,z,dist="norm",test="perm")