Description Usage Arguments Value Examples
Performs MEM test given the data for y and x on the null hypothesis H_0: m = m_0. Using this function is equivalent to calling normalmixMEMtest with regressors specified by x as a parameter.
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y |
n by 1 vector of data for y |
x |
n by q matrix of data for x |
m |
The number of components in the mixture defined by a null hypothesis, m_0 |
z |
n by p matrix of regressor associated with gamma |
tauset |
A set of initial tau value candidates |
an |
a term used for penalty function |
ninits |
The number of randomly drawn initial values. |
crit.method |
Method used to compute the variance-covariance matrix,
one of |
nbtsp |
The number of bootstrap observations; by default, set as 199. |
cl |
Cluster used for parallelization; if it is |
parallel |
Determines what percentage of available cores are used, represented by a double in [0,1]. 0.75 is default. |
A list of class normalMix
with items:
coefficients |
A vector of parameter estimates. Ordered as 'α_1,…,α_m,μ_1,…,μ_m,σ_1,…,σ_m,\gam. |
parlist |
The parameter estimates as a list containing alpha, mu, and sigma (and gamma if z is included in the model). |
vcov |
The estimated variance-covariance matrix. |
loglik |
The maximized value of the log-likelihood. |
penloglik |
The maximized value of the penalized log-likelihood. |
aic |
Akaike Information Criterion of the fitted model. |
bic |
Bayesian Information Criterion of the fitted model. |
postprobs |
n by m matrix of posterior probabilities for observations |
components |
n by 1 vector of integers that indicates the indices of components each observation belongs to based on computed posterior probabilities |
call |
The matched call. |
m |
The number of components in the mixture. |
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