Description Usage Arguments Details Value Examples

Produce solution for specific lambda of regularized finite mixture model with lasso or adaptive lasso penalty; compute the degrees of freeom, likelihood and information criteria (AIC, BIC and GIC) of the estimators. Model fitting is conducted by EM algorithm and coordinate descent.

1 2 3 4 |

`y` |
a vector of response ( |

`X` |
a matrix of covariate ( |

`m` |
number of components |

`intercept` |
indicating whether intercept should be included |

`lambda` |
value of tuning parameter |

`equal.var` |
indicating whether variances of different components are equal |

`common.var` |
indicating whether the effects over different components are the same for specific covariates |

`ic.type` |
the information criterion to be used; currently supporting "AIC", "BIC", and "GIC". |

`B` |
initial values for the rescaled coefficients with columns being the coefficients for different components |

`prob` |
initial values for prior probabilitis for different components |

`rho` |
initial values for rho vector ( |

`w` |
weight matrix for penalty function. Default option is NULL |

`control` |
a list of parameters for controlling the fitting process |

`report` |
indicating whether printing the value of objective function during EM algorithm for validation checking of initial value. |

The available elements for argument `control`

include

epsilon: Convergence threshold for generalized EM algorithm. Defaults value is 1E-6.

maxit: Maximum number of passes over the data for all lambda values. Default is 1000.

inner.maxit: Maximum number of iteration for flexmix package to compute initial values. Defaults value is 200.

n.ini: Number of initial values for EM algorithm. Default is 10. In EM algorithm, it is preferable to start from several different initial values.

A list consisting of

`y` |
vector of response |

`X` |
matrix of covariates |

`m` |
number of components |

`B.hat` |
estimated rescaled coefficient ( |

`pi.hat` |
estimated prior probabilities ( |

`rho.hat` |
estimated rho values ( |

`lambda` |
lambda used in model fitting |

`plik` |
value of penalized log-likelihood |

`loglik` |
value of log-likelihood |

`conv` |
indicator of convergence of EM algorithm |

`IC` |
values of information criteria |

`df` |
degree of freedom |

1 2 3 4 5 6 7 8 9 10 11 12 | ```
library(fmerPack)
## problem settings
n <- 100; m <- 3; p <- 5;
sigma2 <- c(0.1, 0.1, 0.4); rho <- 1 / sqrt(sigma2)
phi <- rbind(c(1, 1, 1), c(1, 1, 1), c(0, -3, 3), c(-3, 3, 0), c(3, 0, -3))
beta <- t(t(phi) / rho)
## generate response and covariates
z <- rmultinom(n, 1, prob= rep(1 / m, m))
X <- matrix(rnorm(n * p), nrow = n, ncol = p)
y <- MASS::mvrnorm(1, mu = rowSums(t(z) * X[, 1:(nrow(beta))] %*% beta),
Sigma = diag(colSums(z * sigma2)))
fmrReg(y, X, m = m, lambda = 0.01, control = list(n.ini = 10))
``` |

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