fmrs.varsel-methods: fmrs.varsel method

In fmrs: Variable Selection in Finite Mixture of AFT Regression and FMR

Description

Provides variable selection and penalized MLE for Finite Mixture of Accelerated Failure Time Regression (FMAFTR) Models and Finite Mixture of Regression (FMR) Models. It also provide Ridge Regression and Elastic Net.

Usage

fmrs.varsel(y, delta, x, nComp, ...)

## S4 method for signature 'ANY'
fmrs.varsel(
  y,
  delta,
  x,
  nComp,
  disFamily = "lnorm",
  initCoeff,
  initDispersion,
  initmixProp,
  penFamily = "lasso",
  lambPen,
  lambRidge = 0,
  nIterEM = 2000,
  nIterNR = 2,
  conveps = 1e-08,
  convepsEM = 1e-08,
  convepsNR = 1e-08,
  porNR = 2,
  gamMixPor = 1,
  activeset,
  lambMCP,
  lambSICA
)

Arguments

y	Responses (observations)
delta	Censoring indicators
x	Design matrix (covariates)
nComp	Order (Number of components) of mixture model
...	Other possible options
disFamily	A sub-distribution family. The options are 'norm' for FMR models, 'lnorm' for mixture of AFT regression models with Log-Normal sub-distributions, 'weibull' for mixture of AFT regression models with Weibull sub-distributions
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 'lasso', 'adplasso', 'mcp', 'scad', 'sica' and 'hard'.
lambPen	A vector of positive numbers for tuning parameters
lambRidge	A positive value for tuning 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 gamMixPor = 0, the penalty structure is no longer mixture.
activeset	A matrix of zero-one that shows which intercepts and covariates are active in the fitted fmrs model
lambMCP	A positive numbers for mcp's extra tuning parameter
lambSICA	A positive numbers for sica's extra tuning parameter

Details

The penalized likelihood of a finite mixture of AFT regression models is written as

\tilde\ell_{n}(\boldsymbolΨ) =\ell_{n}(\boldsymbolΨ) - \mathbf{p}_{\boldsymbolλ_{n}}(\boldsymbolΨ)

where

\mathbf{p}_{\boldsymbolλ_{n}}(\boldsymbolΨ) = ∑\limits_{k=1}^{K}π_{k}^α≤ft\{ ∑\limits_{j=1}^{d}p_{λ_{n,k}}(β_{kj}) \right\}.

In the M step of EM algorithm the function

\tilde{Q}(\boldsymbolΨ,\boldsymbolΨ^{(m)}) =∑\limits_{k=1}^{K} \tilde{Q}_{k}(\boldsymbolΨ_k, \boldsymbolΨ^{(m)}_k) = ∑\limits_{k=1}^{K} ≤ft[{Q}_{k}(\boldsymbolΨ_k, \boldsymbolΨ^{(m)}_k) - π_{k}^α≤ft\{ ∑\limits_{j=1}^{d}p_{λ_{n,k}}(β_{kj}) \right\}\right]

is maximized. Since the penalty function is singular at origin, we use a local quadratic approximation (LQA) for the penalty as follows,

\mathbf{p}^\ast_{k,\boldsymbolλ_{n}} (\boldsymbolβ,\boldsymbolβ^{(m)}) =(π_{k}^{(m)})^{α}∑\limits_{j=1}^{d}≤ft\{ p_{λ_{n,k}}(β_{kj}^{(m)}) + { p^{\prime}_{λ_{n,k}} (β_{kj}^{(m)}) \over 2β_{kj}^{(m)}}(β_{kj}^{2} - {β_{kj}^{(m)}}^{2}) \right\}.

Therefore maximizing Q is equivalent to maximizing the function

{Q}^\ast(\boldsymbolΨ,\boldsymbolΨ^{(m)}) =∑\limits_{k=1}^{K} {Q}^\ast_{k}(\boldsymbolΨ_k, \boldsymbolΨ^{(m)}_k) = ∑\limits_{k=1}^{K} ≤ft[{Q}_{k}(\boldsymbolΨ_k,\boldsymbolΨ^{(m)}_k)- \mathbf{p}^\ast_{k,\boldsymbolλ_{n}}(\boldsymbolβ, \boldsymbolβ^{(m)})\right].

In case of Log-Normal sub-distributions, the maximizers of Q_k functions are as follows. Given the data and current estimates of parameters, the maximizers are

{\boldsymbolβ}^{(m+1)}_{k} =({\boldsymbol z}^{\prime}\boldsymbolτ^{(m)}_{k}{\boldsymbol z}+ \varpi_{k}(\boldsymbolβ_{kj}^{(m)}))^{-1}{\boldsymbol z}^{\prime} \boldsymbolτ^{(m)}_{k}T^{(m)}_{k},

where \varpi_{k}(\boldsymbolβ_{kj}^{(m)})={diag} ≤ft(≤ft(π_{k}^{(m+1)}\right)^α \frac{{p}^{\prime}_{λ_{n},k}(\boldsymbolβ_{kj}^{(m)})} {\boldsymbolβ_{kj}^{(m)}}\right) and σ_{k}^{(m+1)} is equal to

σ_{k}^{(m+1)} =√{\frac{∑\limits_{i=1}^{n}τ^{(m)}_{ik} (t^{(m)}_{ik} -{\boldsymbol z}_{i}\boldsymbolβ^{(m)}_{k})^{2}} {∑\limits_{i=1}^{n}τ^{(m)}_{ik} {≤ft[δ_{i} +(1-δ_{i})\{A(w^{(m)}_{ik})[A(w^{(m)}_{ik})- w^{(m)}_{ik}]\}\right]}}}.

For the Weibull distribution, on the other hand, we have \tilde{\boldsymbolΨ}^{(m+1)}_k =\tilde{\boldsymbolΨ}^{(m)}_k - 0.5^{κ}≤ft[{H_{k}^{p,(m)}}\right]^{-1}I_{k}^{p,(m)}, where H^p_{k}=H_k+h(\boldsymbolΨ_k) is the penalized version of hessian matrix and I^p_{k}=I_k+h(\boldsymbolΨ_k)\boldsymbolΨ_k is the penalized version of vector of first derivatives evaluated at \tilde{\boldsymbolΨ}_k^{(m)}.

Value

fmrsfit-class

Author(s)

Farhad Shokoohi <[e-mail]>

References

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

See Also

Other lnorm, norm, weibull: fmrs.gendata(), fmrs.mle(), fmrs.tunsel()

Examples

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$delta,
  nComp = ncomp(res.mle), disFamily = 'lnorm',
  initCoeff=c(coefficients(res.mle)),
  initDispersion = dispersion(res.mle),
  initmixProp = mixProp(res.mle),
  penFamily = 'adplasso')
res.var <- fmrs.varsel(y = dat$y, x = dat$x, delta = dat$delta,
  nComp = ncomp(res.mle), disFamily = 'lnorm',
  initCoeff=c(coefficients(res.mle)),
  initDispersion = dispersion(res.mle),
  initmixProp = mixProp(res.mle),
  penFamily = 'adplasso',
  lambPen = slot(res.lam, 'lambPen'))

coefficients(res.var)[-1,]
round(coefficients(res.var)[-1,],5)

fmrs documentation built on Nov. 8, 2020, 5:50 p.m.