scrutor {semisup} | R Documentation |
This function tests whether the unlabelled observations come from a mixture of two distributions.
scrutor(Y, Z, dist = "norm", phi = NULL, pi = NULL, gamma = NULL, test = "perm", iter = NULL, kind = NULL, debug = TRUE, ...)
Y |
observations:
numeric vector of length |
Z |
class labels:
numeric vector of length |
dist |
distributional assumption:
character |
phi |
dispersion parameter(s):
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 tests a one-component (H0
)
against a two-component mixture model (H1
).
y |
index observations |
z |
index class labels |
lrts |
test statistic |
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 mixtura
for model fitting.
All other functions are internal
.
# 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 # hypothesis testing scrutor(y,z,dist="norm")