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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