diff --git a/README.Rmd b/README.Rmd index 8065208..aa274bf 100644 --- a/README.Rmd +++ b/README.Rmd @@ -52,7 +52,7 @@ library(ranger) data("entities") # This is a list containing two lists of data: training and test. # Each sublist contains three entities. -str(object = entities, max.level = 2) +str(object = entities, max.level = 2L) ``` Variable selection, training and prediction are the main functionalities of `fuseMLR`. As variable selection and training are performed for a training study, predictions are made for a new study. @@ -115,7 +115,8 @@ We need to set up variable selection methods to our training study. Note that th ```{r varsel_param, include=TRUE, eval=TRUE} same_param_varsel <- ParamVarSel$new(id = "ParamVarSel", - param_list = list(num.trees = 1000, mtry = 3)) + param_list = list(num.trees = 1000L, + mtry = 3L)) print(same_param_varsel) ``` @@ -159,8 +160,8 @@ We can now train our study using the subset of selected variables. Users can cho ```{r lrner_param, include=TRUE, eval=TRUE} same_param <- ParamLrner$new(id = "ParamRanger", param_list = list(probability = TRUE, - mtry = 1), - hyperparam_list = list(num.trees = 1000)) + mtry = 2L), + hyperparam_list = list(num.trees = 1000L)) ``` - Set up learners for each layer. We will use a weighted sum for the meta-analysis. @@ -197,7 +198,7 @@ lrner_meta <- Lrner$new(id = "weighted", disease <- train_study$getTargetValues()$disease trained_study <- train_study$train(resampling_method = "caret::createFolds", resampling_arg = list(y = disease, - k = 2), + k = 2L), use_var_sel = TRUE) # Let us now check the status of our study. print(trained_study) diff --git a/README.md b/README.md index f835ef0..3957ac5 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,12 @@ +--- +title: "fuseMLR" +author: Cesaire J. K. Fouodo +output: + md_document: + variant: gfm + preserve_yaml: true +--- + [![R-CMD-check](https://github.com/imbs-hl/fuseMLR/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/imbs-hl/fuseMLR/actions/workflows/R-CMD-check.yaml) @@ -64,7 +73,7 @@ library(ranger) data("entities") # This is a list containing two lists of data: training and test. # Each sublist contains three entities. -str(object = entities, max.level = 2) +str(object = entities, max.level = 2L) ``` ## List of 2 @@ -178,7 +187,8 @@ For simplicity, we will set up the same method on all layers. ``` r same_param_varsel <- ParamVarSel$new(id = "ParamVarSel", - param_list = list(num.trees = 1000, mtry = 3)) + param_list = list(num.trees = 1000L, + mtry = 3L)) print(same_param_varsel) ``` @@ -224,54 +234,47 @@ print(var_sel_res) ## 1 geneexpr ACACA ## 2 geneexpr ASNS ## 3 geneexpr BAP1 - ## 4 geneexpr CDH3 - ## 5 geneexpr CHEK2 - ## 6 geneexpr EIF4E - ## 7 geneexpr MAP2K1 - ## 8 geneexpr MAPK14 - ## 9 geneexpr PCNA - ## 10 geneexpr SMAD4 - ## 11 geneexpr SQSTM1 - ## 12 geneexpr YWHAE - ## 13 geneexpr YWHAZ - ## 14 proteinexpr Bap1.c.4 - ## 15 proteinexpr Bid - ## 16 proteinexpr Cyclin_E2 - ## 17 proteinexpr P.Cadherin - ## 18 proteinexpr Chk1 - ## 19 proteinexpr Chk1_pS345 - ## 20 proteinexpr EGFR - ## 21 proteinexpr EGFR_pY1173 - ## 22 proteinexpr HER3_pY1289 - ## 23 proteinexpr MIG.6 - ## 24 proteinexpr ETS.1 - ## 25 proteinexpr MEK1_pS217_S221 - ## 26 proteinexpr p38_MAPK - ## 27 proteinexpr c.Met_pY1235 - ## 28 proteinexpr N.Ras - ## 29 proteinexpr PCNA - ## 30 proteinexpr PEA15_pS116 - ## 31 proteinexpr PKC.delta_pS664 - ## 32 proteinexpr Rad50 - ## 33 proteinexpr C.Raf_pS338 - ## 34 proteinexpr p70S6K - ## 35 proteinexpr p70S6K_pT389 - ## 36 proteinexpr Smad4 - ## 37 proteinexpr STAT3_pY705 - ## 38 proteinexpr 14.3.3_epsilon - ## 39 methylation cg20139214 - ## 40 methylation cg18457775 - ## 41 methylation cg24747396 - ## 42 methylation cg01306510 - ## 43 methylation cg11861730 - ## 44 methylation cg02412050 - ## 45 methylation cg07566050 - ## 46 methylation cg02630105 - ## 47 methylation cg20849549 - ## 48 methylation cg00547829 - ## 49 methylation cg25539131 - ## 50 methylation cg07064406 - ## 51 methylation cg11816577 + ## 4 geneexpr CHEK2 + ## 5 geneexpr EIF4E + ## 6 geneexpr MAP2K1 + ## 7 geneexpr MAPK14 + ## 8 geneexpr PCNA + ## 9 geneexpr YWHAE + ## 10 proteinexpr Bap1.c.4 + ## 11 proteinexpr Bid + ## 12 proteinexpr Cyclin_E2 + ## 13 proteinexpr P.Cadherin + ## 14 proteinexpr Chk1 + ## 15 proteinexpr Chk1_pS345 + ## 16 proteinexpr EGFR + ## 17 proteinexpr EGFR_pY1173 + ## 18 proteinexpr HER3_pY1289 + ## 19 proteinexpr MIG.6 + ## 20 proteinexpr ETS.1 + ## 21 proteinexpr MEK1_pS217_S221 + ## 22 proteinexpr p38_MAPK + ## 23 proteinexpr c.Met_pY1235 + ## 24 proteinexpr N.Ras + ## 25 proteinexpr PEA15_pS116 + ## 26 proteinexpr PKC.delta_pS664 + ## 27 proteinexpr Rad50 + ## 28 proteinexpr C.Raf_pS338 + ## 29 proteinexpr p70S6K + ## 30 proteinexpr p70S6K_pT389 + ## 31 proteinexpr Smad4 + ## 32 proteinexpr STAT3_pY705 + ## 33 proteinexpr 14.3.3_epsilon + ## 34 methylation cg20139214 + ## 35 methylation cg18457775 + ## 36 methylation cg24747396 + ## 37 methylation cg01306510 + ## 38 methylation cg02412050 + ## 39 methylation cg07566050 + ## 40 methylation cg02630105 + ## 41 methylation cg20849549 + ## 42 methylation cg25539131 + ## 43 methylation cg07064406 + ## 44 methylation cg11816577 For each layer, the variable selection results show the chosen variables. In this example, we perform variable selection on the entire @@ -289,8 +292,8 @@ will use the same learner for all layers. ``` r same_param <- ParamLrner$new(id = "ParamRanger", param_list = list(probability = TRUE, - mtry = 1), - hyperparam_list = list(num.trees = 1000)) + mtry = 2L), + hyperparam_list = list(num.trees = 1000L)) ``` - Set up learners for each layer. We will use a weighted sum for the @@ -328,7 +331,7 @@ lrner_meta <- Lrner$new(id = "weighted", disease <- train_study$getTargetValues()$disease trained_study <- train_study$train(resampling_method = "caret::createFolds", resampling_arg = list(y = disease, - k = 2), + k = 2L), use_var_sel = TRUE) # Let us now check the status of our study. print(trained_study) @@ -376,9 +379,9 @@ print(model_ge) ## Layer : geneexpr ## ind. id. : IDS ## target : disease - ## n : 22 + ## n : 25 ## Missing : 0 - ## p : 14 + ## p : 10 #### C) Predicting @@ -427,29 +430,29 @@ print(new_predictions) ## ## $predicted_values ## IDS geneexpr proteinexpr methylation meta_layer - ## 1 subject4 0.6484115 0.6232698 0.31071389 0.5184565 - ## 2 subject7 0.4769810 0.2258972 0.54508016 0.4157126 - ## 3 subject8 0.7140984 0.8477976 0.66553690 0.7423027 - ## 4 subject10 0.6940758 0.7736833 0.64894563 0.7050413 - ## 5 subject13 0.5576877 0.3196278 0.12406032 0.3205452 - ## 6 subject15 0.7106389 0.8446940 0.27681587 0.6005008 - ## 7 subject16 0.6214357 0.3434575 0.24932183 0.3927695 - ## 8 subject18 0.6862171 0.2557313 0.09267262 0.3258879 - ## 9 subject23 0.6038460 0.2769857 0.55668968 0.4752774 - ## 10 subject24 0.4790024 0.6149226 0.45314762 0.5161260 - ## 11 subject27 0.4629325 0.2620349 0.51638849 0.4135396 - ## 12 subject31 0.4329060 0.8181782 0.47140833 0.5783116 - ## 13 subject32 0.5698889 0.7577599 0.65391865 0.6642461 - ## 14 subject35 0.4604948 0.7791980 0.45331230 0.5667484 - ## 15 subject36 0.3806321 0.1859825 0.46996190 0.3462660 - ## 16 subject50 0.7118357 0.5917563 0.64517460 0.6468713 - ## 17 subject54 0.5519837 0.6745282 0.67802460 0.6391312 - ## 18 subject55 0.6578337 0.2442829 0.44351508 0.4395823 - ## 19 subject59 0.4506401 0.2893857 0.45415952 0.3968377 - ## 20 subject62 0.4784972 0.3452619 0.33005794 0.3796489 - ## 21 subject63 0.4671940 0.8077119 0.82196270 0.7109832 - ## 22 subject66 0.6660956 0.7094044 0.85581270 0.7490700 - ## 23 subject70 0.3423341 0.3459401 0.31369524 0.3332727 + ## 1 subject4 0.3486429 0.7254452 0.39821270 0.5065696 + ## 2 subject7 0.6563234 0.1913813 0.60077143 0.4633997 + ## 3 subject8 0.7697984 0.8252417 0.72493373 0.7749673 + ## 4 subject10 0.6770103 0.8271147 0.75742738 0.7609400 + ## 5 subject13 0.4164071 0.2048258 0.09259405 0.2251955 + ## 6 subject15 0.5557313 0.9379647 0.36718214 0.6331977 + ## 7 subject16 0.5701992 0.3528000 0.26069683 0.3817718 + ## 8 subject18 0.6497238 0.2932206 0.02371310 0.2996893 + ## 9 subject23 0.6612794 0.2485218 0.60419841 0.4873196 + ## 10 subject24 0.3336988 0.5391282 0.46320079 0.4552991 + ## 11 subject27 0.2748909 0.2293552 0.55319563 0.3542913 + ## 12 subject31 0.4272877 0.8598790 0.59272540 0.6461933 + ## 13 subject32 0.4388905 0.8694258 0.76771111 0.7136272 + ## 14 subject35 0.2083437 0.7910008 0.56550437 0.5497232 + ## 15 subject36 0.6707909 0.1662433 0.56591627 0.4459862 + ## 16 subject50 0.7561679 0.6132381 0.79458849 0.7160860 + ## 17 subject54 0.5675159 0.7214897 0.78051508 0.6988200 + ## 18 subject55 0.5815325 0.2319464 0.58723532 0.4529200 + ## 19 subject59 0.4597877 0.2770992 0.50990794 0.4089069 + ## 20 subject62 0.2760032 0.2478115 0.41481548 0.3135595 + ## 21 subject63 0.5786218 0.8238242 0.89740000 0.7806484 + ## 22 subject66 0.5528552 0.6825976 0.94541984 0.7373127 + ## 23 subject70 0.2690111 0.3042032 0.27120873 0.2829184 © 2024 Institute of Medical Biometry and Statistics (IMBS). 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