Main statistics from the fitted base and random-effects models
rndEffModelStats(
theta_null,
theta_rndEff,
V,
M,
M_bdiag,
y,
VCNs,
nObs,
verbose = TRUE
)A vector of statistics associated to the fitted base and random-effects models:
nPar | number of parameters of the base(null) model |
cll | value of the conditional log-likelihood, in this case just the log-likelihood |
mll | value of the marginal log-likelihood, in this case just the log-likelihood |
cAIC | conditional Akaike Information Criterion (cAIC), in this case simply the AIC. |
mAIC | marginal Akaike Information Criterion (mAIC), in this case simply the AIC. |
Chi2 | value of the \(\chi^2\) statistic \((y - M\theta)'S^{-1}(y - M\theta)\). |
p-value | p-value of the \(\chi^2\) test for the null hypothesis that Chi2 follows a \(\chi^2\) distribution with n - nPar degrees of freedom. |
KLdiv | Kullback-Leibler divergence of the random-effects model from the null model. |
KLdiv/N | Rescaled Kullback-Leibler divergence of the random-effects model from the null model. |
BhattDist_nullCond | Bhattacharyya distance between the random-effects model and the null model. |
BhattDist_nullCond/N | Rescaled Bhattacharyya distance between the random-effects model and the null model. |
the estimated p-dimensional vector parameter for the base (null) model.
the estimated p-dimensional vector parameter for the random-effects model.
A \(n \times K\) dimensional (design) matrix.
A\(n \times Jp\) dimensional block-diagonal design matrix. Each j-th block (\(j = 1,\dots,J\)) is a \(n_j \times p\) dimensional design matrix for the j-th clone.
n-dimensional vector of the time-adjacent cellular increments
A n-dimensional vector including values of the vector copy number corresponding to the cell counts of y.
A K-dimensional vector including the frequencies of each clone k (\(k = 1,\dots,K\)).
(defaults to TRUE) Logical value. If TRUE, then information messages on the progress of the algorithm are printed to the console.