Provides component-wise tuning parameters using BIC for Finite Mixture of Accelerated Failure Time Regression Models and Finite Mixture of Regression Models.

1 2 3 4 5 6 7 8 | ```
fmrs.tunsel(y, delta, x, nComp, ...)
## S4 method for signature 'ANY'
fmrs.tunsel(y, delta, x, nComp, disFamily = "lnorm",
initCoeff, initDispersion, initmixProp, penFamily = "lasso",
lambRidge = 0, nIterEM = 2000, nIterNR = 2, conveps = 1e-08,
convepsEM = 1e-08, convepsNR = 1e-08, porNR = 2, gamMixPor = 1,
activeset, lambMCP, lambSICA)
``` |

`y` |
Responses (observations) |

`delta` |
Censoring indicator vector |

`x` |
Design matrix (covariates) |

`nComp` |
Order (Number of components) of mixture model |

`...` |
Other possible options |

`disFamily` |
A sub-distribution family. The options
are |

`initCoeff` |
Vector of initial values for regression coefficients including intercepts |

`initDispersion` |
Vector of initial values for standard deviations |

`initmixProp` |
Vector of initial values for proportion of components |

`penFamily` |
Penalty name that is used in variable selection method.
The available options are |

`lambRidge` |
A positive value for tuniing parameter in Ridge Regression or Elastic Net |

`nIterEM` |
Maximum number of iterations for EM algorithm |

`nIterNR` |
Maximum number of iterations for Newton-Raphson algorithm |

`conveps` |
A positive value for avoiding NaN in computing divisions |

`convepsEM` |
A positive value for threshold of convergence in EM algorithm |

`convepsNR` |
A positive value for threshold of convergence in NR algorithm |

`porNR` |
A positive interger for maximum number of searches in NR algorithm |

`gamMixPor` |
Proportion of mixing parameters in the penalty. The
value must be in the interval [0,1]. If |

`activeset` |
A matrix of zero-one that shows which intercepts and covariates are active in the fitted fmrs model |

`lambMCP` |
A positive numbers for |

`lambSICA` |
A positive numbers for |

The maximizer of penalized Log-Likelihood depends on selecting a
set of good tuning parameters which is a rather thorny issue. We
choose a value in an equally spaced set of values in
*(0, λ_{max})* for a pre-specified *λ_{max}*
that maximize the component-wise
BIC,

*\hatλ_{k} ={argmax}_{λ_{k}}BIC_k(λ_{k})=
{argmax}_{λ_{k}}≤ft\{\ell^{c}_{k, n}
(\hat{\boldsymbolΨ}_{λ_{k}, k}) -
|d_{λ_{k},k}| \log (n)\right\},*

where *d_{λ_{k},k}=\{j:\hat{β}_{λ_{k},kj}\neq 0,
j=1,…,d\}* is the active set excluding the intercept
and *|d_{λ_{k},k}|*
is its size. This approach is much faster than using an `nComp`

by `nComp`

grid to select the set *\boldsymbolλ* to
maximize the penallized Log-Likelihood.

An `fmrstunpar-class`

that includes
component-wise tuning parameter estimates that can be used in
variable selection procedure.

Farhad Shokoohi <shokoohi@icloud.com>

Shokoohi, F., Khalili, A., Asgharian, M. and Lin, S. (2016 submitted) Variable Selection in Mixture of Survival Models for Biomedical Genomic Studies

Other lnorm..norm..weibull: `fmrs.gendata`

,
`fmrs.mle`

, `fmrs.varsel`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ```
set.seed(1980)
nComp = 2
nCov = 10
nObs = 500
dispersion = c(1, 1)
mixProp = c(0.4, 0.6)
rho = 0.5
coeff1 = c( 2, 2, -1, -2, 1, 2, 0, 0, 0, 0, 0)
coeff2 = c(-1, -1, 1, 2, 0, 0, 0, 0, -1, 2, -2)
umax = 40
dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov,
coeff = c(coeff1, coeff2), dispersion = dispersion,
mixProp = mixProp, rho = rho, umax = umax,
disFamily = "lnorm")
res.mle <- fmrs.mle(y = dat$y, x = dat$x, delta = dat$delta,
nComp = nComp, disFamily = "lnorm",
initCoeff = rnorm(nComp*nCov+nComp),
initDispersion = rep(1, nComp),
initmixProp = rep(1/nComp, nComp))
res.lam <- fmrs.tunsel(y = dat$y, x = dat$x, delta = dat$delta,
nComp = nComp, disFamily = "lnorm",
initCoeff = c(coefficients(res.mle)),
initDispersion = dispersion(res.mle),
initmixProp = mixProp(res.mle),
penFamily = "adplasso")
show(res.lam)
``` |

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