Description Usage Arguments Value References Examples
An implementation of AMFA algorithm from \insertCiteWangWan-Lun2020AlomautoMFA. The number of factors, q, is estimated during the fitting process of each MFA model.
The best value of g is chosen as the model with the minimum BIC of all candidate models in the range gmin
<= g <= gmax
.
1 2 3 4 5 6 7 8 9 10 11 12 |
Y |
An n by p data matrix, where n is the number of observations and p is the number of dimensions of the data. |
gmin |
The smallest number of components for which an MFA model will be fitted. |
gmax |
The largest number of components for which an MFA model will be fitted. |
eta |
The smallest possible entry in any of the error matrices D_i \insertCiteJian-HuaZhao2008FMEfautoMFA. |
itmax |
The maximum number of ECM iterations allowed for the estimation of each MFA model. |
nkmeans |
The number of times the k-means algorithm will be used to initialise models for each combination of g and q. |
nrandom |
The number of randomly initialised models that will be used for each combination of g and q. |
tol |
The ECM algorithm terminates if the measure of convergence falls below this value. |
conv_measure |
The convergence criterion of the ECM algorithm. The default |
varimax |
Boolean indicating whether the output factor loading matrices should be constrained using varimax rotation or not. |
A list containing the following elements:
model
: A list specifying the final MFA model. This contains:
B
: A p by p by q array containing the factor loading matrices for each component.
D
: A p by p by g array of error variance matrices.
mu
: A p by g array containing the mean of each cluster.
pivec
: A 1 by g vector containing the mixing
proportions for each FA in the mixture.
numFactors
: A 1 by g vector containing the number of factors for each FA.
clustering
: A list specifying the clustering produced by the final model. This contains:
responsibilities
: A n by g matrix containing the probability
that each point belongs to each FA in the mixture.
allocations
: A n by 1 matrix containing which
FA in the mixture each point is assigned to based on the responsibilities.
diagnostics
: A list containing various pieces of information related to the fitting process of the algorithm. This contains:
bic
: The BIC of the final model.
logL
: The log-likelihood of the final model.
times
: A data frame containing the amount of time taken to fit each MFA model.
totalTime
: The total time taken to fit the final model.
WangWan-Lun2020AlomautoMFA
\insertRefJian-HuaZhao2008FMEfautoMFA
1 2 | RNGversion('4.0.3'); set.seed(3)
MFA.fit <- AMFA(autoMFA::MFA_testdata,3,3, nkmeans = 3, nrandom = 3, itmax = 100)
|
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