From 659d0c68dcc8a30b2f0573edcd7ca8772289b9fe Mon Sep 17 00:00:00 2001 From: Cesaire Joris Kuete Fouodo Date: Wed, 17 Jul 2024 17:11:21 +0200 Subject: [PATCH] Predicting --- README.Rmd | 2 ++ README.md | 94 ++++++++++++++++++++++++++---------------------------- 2 files changed, 47 insertions(+), 49 deletions(-) diff --git a/README.Rmd b/README.Rmd index c7b94e6..e0671fd 100644 --- a/README.Rmd +++ b/README.Rmd @@ -245,3 +245,5 @@ new_data_me <- NewData$new(id = "methylation", new_predictions <- train_study$predict(new_study = new_study) print(new_predictions) ``` + +`©` 2024 Institute of Medical Biometry and Statistics (IMBS). All rights reserved. diff --git a/README.md b/README.md index 7f0695c..c538594 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,3 @@ ---- -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) @@ -226,15 +217,15 @@ print(var_sel_res) ## Layer variable ## 1 geneexpr ACACA - ## 2 geneexpr BAP1 - ## 3 geneexpr EIF4E - ## 4 geneexpr MAP2K1 - ## 5 geneexpr MAPK14 - ## 6 geneexpr PCNA - ## 7 geneexpr SMAD4 - ## 8 geneexpr SQSTM1 - ## 9 geneexpr YWHAE - ## 10 geneexpr YWHAZ + ## 2 geneexpr ASNS + ## 3 geneexpr BAP1 + ## 4 geneexpr CHEK2 + ## 5 geneexpr EIF4E + ## 6 geneexpr MAP2K1 + ## 7 geneexpr MAPK14 + ## 8 geneexpr PCNA + ## 9 geneexpr SMAD4 + ## 10 geneexpr YWHAE ## 11 proteinexpr Bap1.c.4 ## 12 proteinexpr Bid ## 13 proteinexpr Cyclin_E2 @@ -262,13 +253,15 @@ print(var_sel_res) ## 35 proteinexpr 14.3.3_epsilon ## 36 methylation cg20139214 ## 37 methylation cg18457775 - ## 38 methylation cg01306510 - ## 39 methylation cg02412050 - ## 40 methylation cg07566050 - ## 41 methylation cg02630105 - ## 42 methylation cg20849549 - ## 43 methylation cg25539131 - ## 44 methylation cg07064406 + ## 38 methylation cg09637363 + ## 39 methylation cg01306510 + ## 40 methylation cg02412050 + ## 41 methylation cg25984124 + ## 42 methylation cg07566050 + ## 43 methylation cg02630105 + ## 44 methylation cg20849549 + ## 45 methylation cg25539131 + ## 46 methylation cg07064406 For each layer, the variable selection results show the chosen variables. In this example, we perform variable selection on the entire @@ -373,7 +366,7 @@ print(model_ge) ## Layer : geneexpr ## ind. id. : IDS ## target : disease - ## n : 25 + ## n : 27 ## Missing : 0 ## p : 11 @@ -424,26 +417,29 @@ print(new_predictions) ## ## $predicted_values ## IDS geneexpr proteinexpr methylation meta_layer - ## 1 subject4 0.6067187 0.6119083 0.33182817 0.5209286 - ## 2 subject7 0.4109321 0.2189310 0.61729762 0.4040821 - ## 3 subject8 0.6746929 0.8667262 0.80640714 0.7894835 - ## 4 subject10 0.6585460 0.7638556 0.66543492 0.7006365 - ## 5 subject13 0.4947683 0.2539440 0.08529286 0.2728232 - ## 6 subject15 0.6994488 0.8390187 0.32866032 0.6339475 - ## 7 subject16 0.6408147 0.2740290 0.32936230 0.4024482 - ## 8 subject18 0.5568742 0.2851813 0.05248452 0.2929357 - ## 9 subject23 0.6719992 0.1901524 0.71083929 0.5018748 - ## 10 subject24 0.4724123 0.5691786 0.53698690 0.5296822 - ## 11 subject27 0.4899246 0.2185917 0.59058452 0.4192783 - ## 12 subject31 0.3499429 0.7916210 0.50772579 0.5676212 - ## 13 subject32 0.5065488 0.7755845 0.73835317 0.6824607 - ## 14 subject35 0.4434528 0.7836210 0.60108056 0.6226296 - ## 15 subject36 0.3183730 0.1848798 0.52778135 0.3346572 - ## 16 subject50 0.6447103 0.5143079 0.77826746 0.6379506 - ## 17 subject54 0.5750107 0.5990496 0.82990119 0.6654878 - ## 18 subject55 0.6246929 0.2048667 0.56081071 0.4452689 - ## 19 subject59 0.3740976 0.2233389 0.55631111 0.3751600 - ## 20 subject62 0.4220766 0.3033536 0.40324762 0.3710933 - ## 21 subject63 0.3846024 0.7639377 0.85401865 0.6781510 - ## 22 subject66 0.6744151 0.6113643 0.94513651 0.7369564 - ## 23 subject70 0.2530921 0.3034790 0.37938611 0.3124967 + ## 1 subject4 0.4725151 0.6599369 0.3956623 0.5086549 + ## 2 subject7 0.6207952 0.2535532 0.4593956 0.4343414 + ## 3 subject8 0.7858988 0.8617183 0.5170813 0.7124039 + ## 4 subject10 0.7736159 0.8144282 0.7356651 0.7736219 + ## 5 subject13 0.5496202 0.3388135 0.1889472 0.3432458 + ## 6 subject15 0.7520679 0.8456587 0.3008671 0.6170995 + ## 7 subject16 0.6741667 0.3009167 0.4049599 0.4456820 + ## 8 subject18 0.7936623 0.3036036 0.1069857 0.3701630 + ## 9 subject23 0.7684464 0.3103667 0.6047968 0.5498365 + ## 10 subject24 0.4487278 0.6280996 0.4865853 0.5246098 + ## 11 subject27 0.3982817 0.2857008 0.4592833 0.3820728 + ## 12 subject31 0.4925512 0.8178683 0.4383095 0.5846202 + ## 13 subject32 0.5856746 0.7545948 0.5088413 0.6154420 + ## 14 subject35 0.4182294 0.8120417 0.5838194 0.6153982 + ## 15 subject36 0.4785877 0.2373167 0.4264560 0.3760711 + ## 16 subject50 0.8579135 0.5765698 0.5744472 0.6558340 + ## 17 subject54 0.6482135 0.7085040 0.8919758 0.7593542 + ## 18 subject55 0.6911262 0.2668444 0.5258881 0.4835815 + ## 19 subject59 0.5783048 0.2353687 0.4648218 0.4179925 + ## 20 subject62 0.2776175 0.3502944 0.3815302 0.3411933 + ## 21 subject63 0.4978976 0.8386833 0.6942627 0.6881890 + ## 22 subject66 0.7175944 0.6737528 0.7879369 0.7285500 + ## 23 subject70 0.3779357 0.3989810 0.2578671 0.3406884 + +`©` 2024 Institute of Medical Biometry and Statistics (IMBS). All +rights reserved.