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1.0.3

- Fixed documentation to mention bidirectional-forward and bidirectional-backward search as they were already implemented!
- Fixed bug that appeared when doing backward search and cross-validation
- Added Huber and L1-norm cross-validation errors to the LEGIT_cv output and choice of search_criterion in the stepwise search. These are robust criterion that handle outliers better than the usual L2-norm (which the R^2 is based on).
- Added argument to change the parameter of the Huber cross-validation error
- Added reference for Huber and L1-norm cross-validation errors
- Added newline between the AUC plot and the last line in vignette
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@AlexiaJM
AlexiaJM authored Apr 4, 2017
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6 changes: 3 additions & 3 deletions DESCRIPTION
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@@ -1,7 +1,7 @@
Package: LEGIT
Title: Latent Environmental & Genetic InTeraction (LEGIT) Model
Version: 1.0.2
Date: 2017-03-23
Version: 1.0.3
Date: 2017-04-04
Author: Alexia Jolicoeur-Martineau <[email protected]>
Maintainer: Alexia Jolicoeur-Martineau <[email protected]>
Description: Constructs genotype x environment interaction (GxE) models where
Expand All @@ -25,4 +25,4 @@ RoxygenNote: 6.0.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2017-04-02 22:06:37 UTC; Alexia
Packaged: 2017-04-04 19:13:50 UTC; ajolicoe
153 changes: 106 additions & 47 deletions R/LEGIT.R
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1 change: 1 addition & 0 deletions inst/doc/LEGIT.Rmd
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Expand Up @@ -146,4 +146,5 @@ We are a little off, especially with regards to the weights of the genetic varia
cv_5folds_bin = LEGIT_cv(train$data, train$G, train$E, y ~ G*E, cv_iter=1, cv_folds=5, classification=TRUE, family=binomial, seed=777)
pROC::plot.roc(cv_5folds_bin$roc_curve[[1]])
```

Although the weights of the genetic variants are a bit off, the model predictive power is good.
41 changes: 27 additions & 14 deletions inst/doc/LEGIT.html
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Expand Up @@ -503,12 +503,18 @@ <h2>Example 1</h2>
## 3 g2 NA 250 0.00000 Inf 1135.100 Inf 1170.314 NA
## 4 g1_bad NA 250 0.00000 Inf 1155.719 Inf 1190.933 NA
## 5 g4 NA 250 0.00013 Inf 1164.285 Inf 1199.500 NA
## cv_R2_new cv_AUC_old cv_AUC_new
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## cv_R2_new cv_AUC_old cv_AUC_new cv_Huber_old cv_Huber_new cv_L1_old
## 1 NA NA NA NA NA NA
## 2 NA NA NA NA NA NA
## 3 NA NA NA NA NA NA
## 4 NA NA NA NA NA NA
## 5 NA NA NA NA NA NA
## cv_L1_new
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## Enter the index of the variable to be added:
## No gene added
</code></pre>
Expand All @@ -530,12 +536,18 @@ <h2>Example 1</h2>
## 3 g1 250 250 0.000000 1086.685 1013.285 1121.899 1052.021
## 4 g1_g3 250 250 0.000002 1086.685 1068.051 1121.899 1106.787
## 5 g4 250 250 0.012411 1086.685 1084.133 1121.899 1122.869
## cv_R2_old cv_R2_new cv_AUC_old cv_AUC_new
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## cv_R2_old cv_R2_new cv_AUC_old cv_AUC_new cv_Huber_old cv_Huber_new
## 1 NA NA NA NA NA NA
## 2 NA NA NA NA NA NA
## 3 NA NA NA NA NA NA
## 4 NA NA NA NA NA NA
## 5 NA NA NA NA NA NA
## cv_L1_old cv_L1_new
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## Enter the index of the variable to be added:
## No gene added
</code></pre>
Expand Down Expand Up @@ -661,8 +673,9 @@ <h2>Example 2</h2>
pROC::plot.roc(cv_5folds_bin$roc_curve[[1]])
</code></pre>

<p><img 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alt="plot of chunk unnamed-chunk-12"/>
Although the weights of the genetic variants are a bit off, the model predictive power is good.</p>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAH4CAMAAACR9g9NAAAAjVBMVEUAAAAAADoAAGYAOjoAOpAAZrY6AAA6ADo6AGY6OmY6OpA6ZmY6ZrY6kNtmAABmADpmAGZmOgBmOpBmZgBmZjpmZmZmtv+QOgCQOjqQOmaQZgCQkDqQtpCQ27aQ29uQ2/+pqam2ZgC2Zjq225C2/7a2///bkDrb25Db/7bb////tmb/25D//7b//9v///880fpBAAAACXBIWXMAAAsSAAALEgHS3X78AAAPgUlEQVR4nO3dDXfiTBnGcbZ267paWleFVR/tVp/yWN6+/8eTpLCEkkIm98zcL9f1P3qw58hA+9tJJiFpJ1sG2UT7DTCdCA8a4UEjPGiEB43woBEeNMKDRnjQCA8a4UEjPGiEB43woBEeNMKDRnjQCA8a4UEjPGiEB43woBEeNMKDRnjQCA8a4UEjPGiEB00CP2GG+1EQXvBcVrgfV3QIH7Mf13QIH7IfV3UIH7Fm/054vNp1HeHhelvPEx6t/XGcGH511x4W3ryMeC6r3+H4XQq/mc/ax+Xta/JzWf1+nreRwq8fX04eU57Lqnc8X8cZj1TnPK14H7+ech/vpe75ea7qcTr5XIbwrkv4OO7UPRt8Z3F3fCmWpzwfxL4b88pLSt6u4LmwyTQv9P7zd8IXL/tkHdPZdReEL5iOcV/n19sQPkMqlin1XGclPnM33X935wfyBr7hwilN3+T6rq8Tz/jN/H70c31mF/iDeq+rlG/q1w9Po5/rIvsz+nL919NyH385t9w/++A6asJfyCX0uz66fp7wvXmd3md9eN8E4ff53Ydf6uP7ZQjfFo+87cJ9UvDwwahPunR/HDZ8tBl+2sX7IqHhw5K3XXSHhQ880fdddkeDD7mE6+2KOxQ8innTNXckeATvQ1fdceCB1Ie4w8DT/V048NrvoF5D3EHgOd/PQoBHWtUNdQ8Pj3Hw1mmge3B4OPbB7rHhwdC3Ce5x4eHmetNw96jweNv4pgT3gPAgJ+N7SnGPBw/LnuYeEV7lZfVLcyd8lBLdCR+kVHfCxyjZPRw813UDiwQPu6Af4R4JHpV9lHss+MovaKRR7mHgUWf7WPco8LCb+bHuceArvpilxroT3nej3YPAg27mBe4x4Omennt43GWdyN09PN1H5h++xqtYTObuHR51tovdncPTfXSe4WH37hncPcPjLusyuDuGh1XP4u4XHne6Z3H3DF90eMNlcXcLz/kuzCU88LIul7tLeLpnyBs88Ln5pmzu3uDpnitX8NDo26zuruDpnjFH8ODsed19wWce0Fd53R3Bg0/4zO6u4POO56vc7n7gsSd8dnc38Ngr+vzuXuCh2Uu4y+FXd5PPi8nk5mXEc4dH99xJ4Tffn7aLzzv/r6/Jzx0e9IQv4i6GXz++bBf3b4+pzx0e3bPnZcbnG8tZhdyz7OPvi+/jceFLuftY1ePu4ou5O4BH/gi+nHs2+M7iLu9f8QRmL+luf8bDqpd1tw4PPN3LumdZ1TeVWdVzO18s8XH8fNY+Lm8LHMfjqhd3z3LmrvuY8twrAc/28u6GZzzdiybex6+nZfbxyOwV3O2u6uleNsLbq4a7WXjgLX0Vd8Pwkmd7ro474a1Vyd0qPOyWvpa7XXjBkx1XzZ3wpqrnbhQedEtf0d0mPN3LZxRe8LJuq+puDx72Eru67ubg6V4pe/CCV3RcbXdr8JizXcHdGDzoZl7B3Ry84PX8puBOeANpuBNePxV3wqun40547ZTcCa+cljvhdVNzJ7xqeu624NFO3yi6W4MXvJy/NN1NwYNNeFV3Y/CCV3OXrjvhtVJ2J7xS2u6G4KE+klV3twMPdcmVvrsleMFLOcuAO+EVsuBuBJ7b+eqZgKd7/YzAC17GWUbcCV85K+6Er5sZd8JXzY67CXiYlZ0hdwvwdNfIBLzgRRxlyp3w1bLlTvhaGXMnfKWsuRO+TubcCV8le+6Er5FBdwPw8Q/jLbqbgBe8hodMuhO+eDbdCV86o+6EL5xVd3342Gs7s+4W4AUvYT277oQvmWF3whfMsjvhy2XanfDFsu1O+FIZdyd8oay7y+FXd7PNfDI5//Px0PDm3cXwm/ls+zzb+X89kx8kGvP8jX13Mfz68WXz/al9TH5u+3+iu07iTf1uui/vt9vl5xHP3caE9+CeYXH33N7kfO4OC+/CXX1VHw/eh7s2fLy1nRP3bPCdxd3k0IBXDwfvxV13xodj9+OuCk93xTKcuWs36zdnh/FD4K+O7itH7lnO3DUtz8/ZXntuuAnvyT3HmbuTx4Sho8G7clec8cHYnbnL9/Hr6ch9fDB4Z+6Kq/pY8N7cCZ8nd+6Ez5I/d8LnyKF7Gvx62vPp6+ihw8B7dE+d8cvJ5NNTpqGjwLt0H7Gpby6tnOUYOgi8T/dU+NVdM+N7TtONGDoGvFP31H18z1XUo4cOAe/VPRG+nenD5vu1oWOcqXfrngK/Pznbe/NE8tAxPqLx6z5mxmcZOgK7Z3etEzic79qlbOoff/3wk7jUoemundqMFwxsI9/uhB+bc3et43j38N7dR5y567tNLnlo7/Du3cds6pfyxZ33Rb1/d6UZT3f1dPbxvuEjuCut6l3Dh3BXOoHjGT6GO2d8akHclT6W9QsfxV3pY1m38GHclT6W9Qofx537+JQCuXNVn1Akd8744YVyJ/zgYrknwi9uXxcDb6eIBh/MPXFV//C0+8/qi3Qf7/DDuWjuyYdzuzmfA37QAIYK5566qZ98elqKN/X+Jnw8d5XFHd0NRPjrRXRPhF9mOYHjDD6ke+oVOAN375eH9gUf013lQxpX8EHdEzf1z/c5hvYEH9U9dVOPto8P685V/cXiuhP+UoHdE+E388nt/x4G/r4z9/CR3dPgN/P71dfXnt9QnjS0lzO2od2TD+d28MKrbL389pvY7mNm/EI0452wR3dP38cPvrr6Q/hhz1Yuunv9Vb0P+PDuhO8tvnsSfPNby1d3Q0/cOYYHcE+Cf75v/+jUYuBvRnALj+CedkPFy7a53k52OGcfHsI9Fb45lJNdbGkeHsM9bVM/2x3I7x4km3rzh/Eg7omLu90x/PC/S9M3tPnTdijutQ/njLPjuBP+JBx3wncDcq8Mb3sPj+SeCb73CK8Xfth4KkG5i+F//kak8xO5Z8+1vaTHcpfP+PX05mXYjKe7pTJs6tfT29+GwQ99UwqhuefZx6/u+j6x8wQP515zVW8YHs+d8E2A7tngO5/VTg69H8sqPKI7ZzyoO+FB3eXwzV+p6b+D1gc8qLsYvrkIr6nnvioX8Kju8lO2+0Vdz4V4HuBh3cFnPK57jnP1fvfxwO7Qq3pkd2R4aHdgeGx3XHhwd1h4dHdUeHh3UHi6Y8LTHROe7tua8GausaV7U014wVgZo3sbHDzd30KDp/s+MHi6H8KCp/vPoODpfgwJnu6dgODp3g0Hnu4nwcDT/TQUeLq/CwSe7u/DgKf7WRDwdD8PAZ7uPQHA072v+PB07y08PN37iw5P9w8KDk/3j4oNT/cPCw1P94+LDE/3CwWGp/ul4sLT/WJh4el+uajwdL9SNfi6t87R/VoV4QVDpUb3q4WEp/v1IsLTfUAB4ek+pHjwdB9UOHi6DysaPN0HFgye7kOLBU/3wYWCp/vwIsHTPaFA8HRPKQ483ZMKA0/3tKLA0z2xIPB0Ty0GPN2TCwFP9/QiwNN9RAHg6T4m//B0H5V7eLqPyzs83UfmHJ7uYxPDr+4mn5622/Xj2d8RrwBP99FJ4Tfz2e6/9zrwdB+fFP4N/PmzBjzdBeWY8bsWv/tSHZ7uksT7+PX0vnlY3NSGp7sot6t6usvyCk93YbngO4u7yaHTkbLC012azxlPd3Eu4ekuL8eZu6bzRX05eLpnKNNx/HZ5+3r5ufng6Z6jPGfuap6rp3uW3M14uucpw5m7qvt4umfK2aqe7rnyBU/3bLmCp3u+PMHTPWOO4OmeMz/wdM+aG3i6580LPN0z5wSe7rnzAU/37LmAp3v+PMDTvUAO4OleIvvwdC+SeXi6l8k6PN0LZRye7qWyDU/3YpmGp3u5LMPTvWCG4eleMrvwdC+aWXi6l80qPN0LZxSe7qWzCU/34pmEp3v5LMLTvUIG4eleI3vwdK+SOXi618kaPN0rZQye7rWyBU/3apmCp3u9LMHTvWKG4OleMzvwdK+aGXi6180KPN0rZwSe7rWzAU/36pmAp3v9LMDTXSED8HTXSB+e7iqpw9NdJ214uiulDE93rXTh6a6WKjzd9dKEp7tiivB010wPnu6qqcHTXTcteLorpwRPd+104Omungo83fXTgKe7gRTg6W6h+vB0N5EYfnWX9mfE6W4jKfxmPmsfl7evl5+7h6e7kaTw68eXk8cPn/sGT3cr1Z3xdDeTeB+/ng7fx9PdTjVX9XQ3VEV4ulsqF3xncTc5dDqS5N8Qy161Gc/5bqta8HQ3VqUzd3S3Vp3jeLqbq8qZO7rbq8aMp7vBKpy5o7vFyq/q6W6y4vB0t1lpeLobrTA83a1WFp7uZisKT3e7lYSnu+EKwv+YMMOVgy83VN7BzL4xzcEIDzoY4UEHIzzoYIQHHYzwoIMRHnQwwoMOxovhQSM8aIQHjfCgER40woNGeNAIDxrhQcsCv/qyv79uOjm/0Sqt4xCru767d8aNtZlPPj3lemPH28oyDNbci5xtsJSffw745V6o+XksPouGOg6xfnjaLkT/jDpv53nWd+/fyMG224XQ6vS7XP1e9G/yOFjSzz8D/POnX95mfHND7WHyj+w4xOrra98duqPGko3zbrDdW/vDn2Xwx8GWjdOzaLTTb3Pwzz/npr61ehD98z0OIZ/xx7FWX/8h3dR3vrfN938LN/WnP6hsP7Kkn39O+GZrKvwuOkOIFwzHsVZ3s/ankueNLe6l+/iTH9Rmfp9psKSfv9kZ3+z5lqLV3cipMGAwKXz3/aynMncLMz7rPl6++ejs/L6J4Y+DLdqL1kVanR9UszGSZWAf32y0xKv6wxDyGd95O8/iTf3J9yad8Z3vUuzeGSzp558NvvlvtuP4ZrDlRLogO461+1/CcwKdwbIdx+8Ge9t8CI8Rut9m1eN45jDCg0Z40AgPGuFBIzxohAeN8KARHjTCg0Z40AgPGuFBg4Nvfv3+B5/Urb68bOaTPx4+0d59Kby6wHJo8OvpbHvhWr530oQPU0u5fnhaP/6z/bh//0n97uHT0+rLr7uHX94+2N5/efMX8TUcNkOD38zfZvt6evvaXODz3F600lxZsbz9rd22txv845fL++1SeFWcydDgt/sre5pN/ub7U3Ml3vpxvy9fHeC7X66/vf5HeBOOyfDgt+31fO21l8+z9i8t7bbq7cb8CN/5cvP9X98Cbunh4NtbVxrxHXwz49/usbkw47eLv0Xc0sPBt6v6ZnE3/dxewb3bx+8emp366st/u/v4w5fSW9ushgbfHsc3+/iHvx5W9T8fjtP98OVmfvOy+XvIQzo4+H3D769Y/anoG9GK8FdaSG+sNxoqPHyEB43woBEeNMKDRnjQCA8a4UEjPGj/ByIid9EuoTifAAAAAElFTkSuQmCC" alt="plot of chunk unnamed-chunk-12"/></p>

<p>Although the weights of the genetic variants are a bit off, the model predictive power is good.</p>

</body>

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