get percent deviance explained for Lasso model, from n bootstraps

the training results from training were written to the object LSOLDA_dat, the summary_devidance summarises deviance explained for n bootstrap runs and also returns the best deviance matrix for plotting, as well as the best matrix with Lasso genes and coefficients

summary_deviance(object = NULL)

Arguments

object	is a list containing the training results from training

Value

a list containing three elements, with a vector of percent maximum deviance explained, a dataframe containg information for the full deviance, and a dataframe containing gene names and coefficients of the best model

Author

Quan Nguyen, 2017-11-25

Examples

c_selectID<-1
day2 <- day_2_cardio_cell_sample
mixedpop1 <-new_scGPS_object(ExpressionMatrix = day2$dat2_counts, 
    GeneMetadata = day2$dat2geneInfo, CellMetadata = day2$dat2_clusters)
day5 <- day_5_cardio_cell_sample
mixedpop2 <-new_scGPS_object(ExpressionMatrix = day5$dat5_counts, 
    GeneMetadata = day5$dat5geneInfo,
                    CellMetadata = day5$dat5_clusters)
genes <-training_gene_sample
genes <-genes$Merged_unique
LSOLDA_dat <- bootstrap_prediction(nboots = 2,mixedpop1 = mixedpop1, 
    mixedpop2 = mixedpop2, genes=genes, c_selectID, listData =list(),
    cluster_mixedpop1 = colData(mixedpop1)[,1],
    cluster_mixedpop2=colData(mixedpop2)[,1])
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> done training for bootstrap 1, moving to prediction...
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> Warning: variables are collinear
#> done training for bootstrap 2, moving to prediction...
summary_deviance(LSOLDA_dat)
#> $allDeviance
#> [1] "7.39"  "11.39"
#> 
#> $DeviMax
#>          dat_DE$Dfd          Deviance           DEgenes
#> 1                 0              7.39    genes_cluster1
#> 2                 1              7.39    genes_cluster1
#> 3                 2              7.39    genes_cluster1
#> 4                 3              7.39    genes_cluster1
#> 5                 4              7.39    genes_cluster1
#> 6 remaining DEgenes remaining DEgenes remaining DEgenes
#> 
#> $LassoGenesMax
#> NULL
#>