Description Usage Arguments Details Value Reference See Also Examples
Conduct likelihood ratio test for comparing two different models.
1 |
d1 |
An object of class 'dis.kstest'. |
d2 |
An object of class 'dis.kstest'. |
parallel |
Whether to use multiple threads to parallelize computation. Default is FALSE. Please aware that it may take
longer time to execute the program with |
If the pvalue
of d1
and d2
are greater than the user-specified significance level, which indicates that
the original data x
may come from the two distributions in d1
and d2
, a likelihood ratio test is
desired to choose a more 'possible' distribution based on the current data. NOTE that the x
in d1
and d2
must be IDENTICAL! Besides, NOTE that the distri
in d1
and d2
must be DIFFERENT!
The distri
inherited from d1
is the null distribution and that from d2
is used as the alternative
distribution. Following Aldirawi et al. (2019), nsim
bootstrapped or simulated samples will be generated according to
bootstrap
of d1
, based on which nsim
maximum likelihood estimates of the parameters in null distribution
will be calculated. Remember that we have obtained nsim
such maximum likelihood estimates while calling function
dis.kstest
. Thus, the algorithm just adopts the mle_new
from d1
to avoid repetitive work. Using the
nsim
maximum likelihood estimates to generate nsim
new samples and calculate nsim
corresponding new
likelihood ratio test statistics. The output p-value is the proportion of new samples that have statistics greater than the
test statistic of the original data x
.
As in dis.kstest
, the computation is parallelized with the help of packages foreach
and
doParallel
.
With the output p-value smaller than the user-specified significance level, the distri
of d2
is more appropriate
for modelling x
. Otherwise, There is no significant difference between distri
of d1
and distri
of d2
,
given the current data.
The p-value of the likelihood ratio test.
H. Aldirawi, J. Yang, A. A. Metwally (2019). Identifying Appropriate Probabilistic Models for Sparse Discrete Omics Data, accepted for publication in 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).
T. Wolodzko (2019). extraDistr: Additional Univariate and Multivariate Distributions, R package version 1.8.11, https://CRAN.R-project.org/package=extraDistr.
R. Calaway, Microsoft Corporation, S. Weston, D. Tenenbaum (2017). doParallel: Foreach Parallel Adaptor for the 'parallel' Package, R package version 1.0.11, https://CRAN.R-project.org/package=doParallel.
R. Calaway, Microsoft, S. Weston (2017). foreach: Provides Foreach Looping Construct for R, R package version 1.4.4, https://CRAN.R-project.org/package=foreach.
1 2 3 4 5 | set.seed(2001)
temp1=sample.zi(N=300,phi=0.3,distri='poisson',lambda=5)
d1=dis.kstest(temp1,nsim=100,bootstrap=TRUE,distri='zip')
d2=dis.kstest(temp1,nsim=100,bootstrap=TRUE,distri='zinb')
model.lrt(d1,d2)
|
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