Run function nestorFit on a list of initial cliques using parallel computation (mclapply)

List.nestorFit(
  cliqueList,
  sigma_O,
  MO,
  SO,
  r,
  alpha = 0.1,
  cores = 1,
  maxIter = 20,
  eps = 0.001,
  trackJ = FALSE
)

Arguments

cliqueList	List containing all initial cliques to be tested.
sigma_O	Result of PLN estimation: variance covariance matrix of observed data.
MO	Result of PLN estimation: means matrix of observed data.
SO	Result of PLN estimation: marginal variances matrix of observed data.
r	Number of hidden variables.
alpha	Tempering parameter.
cores	Number of cores for parallel computation (uses mclapply, not available for Windows).
maxIter	Maximal number of iterations of the algorithm.
eps	Convergence precision parameter.
trackJ	Boolean for the lower bound estimation at each parameter update instead of each step.

Value

A list containing the fit of nestorFit for every clique contained in cliqueList.

Examples

 data=generate_missing_data(n=100,p=10,r=1,type="scale-free", plot=FALSE)
PLNfit<-norm_PLN(data$Y)
MO<-PLNfit$MO
SO<-PLNfit$SO
sigma_O=PLNfit$sigma_O
#-- find a list of initial cliques
findcliqueList=boot_FitSparsePCA(MO, B=5, r=1)
cliqueList=findcliqueList$cliqueList
length(cliqueList)
#> [1] 3
#-- run List.nestorFit
fitList=List.nestorFit(cliqueList, sigma_O, MO,SO, r=1)
length(fitList)
#> [1] 3
str(fitList[[1]])
#> List of 12
#>  $ M        : num [1:100, 1:11] 1.099 -0.283 0.442 -0.242 -1.104 ...
#>  $ S        : num [1:100, 1:11] 0.0441 0.124 0.0751 0.1209 0.1907 ...
#>  $ Pg       : num [1:11, 1:11] 0.0 0.0 0.0 3.6e-11 0.0 ...
#>  $ Wg       : num [1:11, 1:11] 0.00 0.00 0.00 1.71e-07 0.00 ...
#>  $ W        : num [1:11, 1:11] 0.00 0.00 0.00 5.82e-07 0.00 ...
#>  $ Omega    : num [1:11, 1:11] 3.664 -0.402 1.341 -0.404 -0.666 ...
#>  $ lowbound :'data.frame':	21 obs. of  5 variables:
#>   ..$ J        : num [1:21] -638 -613 -571 -546 -540 ...
#>   ..$ T1       : num [1:21] -984 -958 -912 -885 -877 ...
#>   ..$ T2       : num [1:21] -1.1597 -0.3514 -0.193 -0.1224 -0.0262 ...
#>   ..$ T3       : num [1:21] 347 345 342 339 337 ...
#>   ..$ parameter: chr [1:21] "complete" "complete" "complete" "complete" ...
#>  $ features :'data.frame':	20 obs. of  4 variables:
#>   ..$ diffPg   : num [1:20] 0.5 0.322 0.687 0.289 0.071 ...
#>   ..$ diffW    : num [1:20] 2430 42547 746791 4198378 33725450 ...
#>   ..$ diffOmega: num [1:20] 1.357 0.468 0.336 0.388 0.263 ...
#>   ..$ diffWg   : num [1:20] 1.02e+10 5.54e+10 8.84e+13 4.96e+16 4.61e+18 ...
#>  $ finalIter: num 20
#>  $ time     : 'difftime' num 0.948529958724976
#>   ..- attr(*, "units")= chr "secs"
#>  $ max.prec : logi FALSE
#>  $ clique   :List of 1
#>   ..$ : int [1:7] 1 2 3 5 6 7 10