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Predicting
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fouodo committed Jul 17, 2024
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2 changes: 2 additions & 0 deletions README.Rmd
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Expand Up @@ -245,3 +245,5 @@ new_data_me <- NewData$new(id = "methylation",
new_predictions <- train_study$predict(new_study = new_study)
print(new_predictions)
```

`&copy;` 2024 Institute of Medical Biometry and Statistics (IMBS). All rights reserved.
94 changes: 45 additions & 49 deletions README.md
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@@ -1,12 +1,3 @@
---
title: "fuseMLR"
author: Cesaire J. K. Fouodo
output:
md_document:
variant: gfm
preserve_yaml: true
---

<!-- badges: start -->

[![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)
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -373,7 +366,7 @@ print(model_ge)
## Layer : geneexpr
## ind. id. : IDS
## target : disease
## n : 25
## n : 27
## Missing : 0
## p : 11

Expand Down Expand Up @@ -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

`&copy;` 2024 Institute of Medical Biometry and Statistics (IMBS). All
rights reserved.

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