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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,70 +1,74 @@ | ||
| 6d5ce007c9d25892ad605ea459e7d93b *DESCRIPTION | ||
| 26e1bb651b11630aee056146a957580e *NAMESPACE | ||
| 0705e434d9f33aa740fb707c3c3368b0 *DESCRIPTION | ||
| 7752512e691251a3bccd0e207ab5d965 *NAMESPACE | ||
| 8be455ef5a118f2940aaaa486e7d36a9 *R/RandomForest_231230.R | ||
| ad6ff0bd32b7abb263e529e8e07b34a0 *R/aicreg_230218.R | ||
| ad2014970433a9178f4bd435d57155a9 *R/ann_tab_cv_230724.R | ||
| 2916876a5349d2996468f1cd51a5abbe *R/ann_tab_cv_231230.R | ||
| 729659f3b23c47e90076141f1ff07885 *R/cv.glmnetr_230508.R | ||
| 934f013fbad6f6348ae5a6e9d1abdc6b *R/cv.stepreg_230218.R | ||
| 29482baf8fb736187c0ab5d828e09392 *R/globalVariables_230509.R | ||
| 25f44abd6d7d0901ea230579a4f90e5b *R/nested.glmnetr_230724.R | ||
| 15ab27c0f9eba24a1fb619afaa27edae *R/plot.cv.glmnetr_230508.R | ||
| c97abb495eb9d88743322b512a6effe1 *R/nested.glmnetr_231230.R | ||
| 4cc69f359aea3f9f88bf51f3c97c437a *R/plot.cv.glmnetr_231226.R | ||
| ee241bf54e9bb2c9d45a479ebbfaef84 *R/predict_ann_tab_230714.R | ||
| cc76a7a09e49c075950500d56dd0f71d *R/stepreg_230218.R | ||
| 263bdaf7c4eb37627f89eb35a8a2b225 *R/summary.cv.glmnetr_230508.R | ||
| c326045ba5e1d7dcc0c3a3dac26c8ada *R/summary.nested.glmnetr_230722.R | ||
| 4e142ce055e3074be344f4220e00ea87 *R/summary.cv.glmnetr_230508.R | ||
| aa822799b678fa8d3e985a6ef4a7101d *R/summary.nested.glmnetr_231230.R | ||
| 7ea6974f14ed567fa99c534b644458d8 *R/xgbm_tuned_230606.R | ||
| 11b89efc96b54a86c2e5927310e0af54 *build/vignette.rds | ||
| 90f5b3ec2e7dbec57a61a751d5d477ec *inst/doc/Using_ann_tab_cv_230728.pdf | ||
| fce89333e96bf7ec320fba73a50576da *inst/doc/Using_ann_tab_cv_230728.pdf.asis | ||
| bf7fe55db7c5f5ec41f31e9cd2910baf *inst/doc/Using_glmnetr_230728.pdf | ||
| 21f21b35db3650c28c96a2a3d81e6229 *inst/doc/Using_glmnetr_230728.pdf.asis | ||
| 584f11ecf28d460302093674a6da9c65 *inst/doc/Using_stepreg_230728.pdf | ||
| 11d2a2dbc54b3dc80a7e6389c7d5a9eb *inst/doc/Using_stepreg_230728.pdf.asis | ||
| 1fc1d4976d1d089cdef93fe225e5ac76 *build/vignette.rds | ||
| 18a01245b58410d00bb42f45d0a7ce25 *inst/doc/An_Overview_of_glmnetr_231230.pdf | ||
| 03ceb0a193e7e47b433efe6fbbe10eeb *inst/doc/An_Overview_of_glmnetr_231230.pdf.asis | ||
| d002647ffd957d5686a7cccfca7d8f1e *inst/doc/Using_ann_tab_cv_231230.pdf | ||
| fce89333e96bf7ec320fba73a50576da *inst/doc/Using_ann_tab_cv_231230.pdf.asis | ||
| fd700b47d6bede905c71879902b1c8e5 *inst/doc/Using_stepreg_231230.pdf | ||
| 11d2a2dbc54b3dc80a7e6389c7d5a9eb *inst/doc/Using_stepreg_231230.pdf.asis | ||
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| a818d7ba8e543d332dcfe4be3d146c60 *man/ann_tab_cv.Rd | ||
| f7b85a7f4397687d3028a7aa1664e166 *man/ann_tab_cv_best.Rd | ||
| 431c8e8a94327905fee2c13927c22242 *man/ann_tab_cv.Rd | ||
| 16df70bb8936ea69dde22108bb8c1fa0 *man/ann_tab_cv_best.Rd | ||
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| 85cfb6769c87d7807dd35f0c7afcff71 *man/factor.foldid.Rd | ||
| 5483af7c23eea9ef8473de502b9e3e95 *man/dtstndrz.Rd | ||
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| 2369ac69cb168b7768dc26985e0b8b9a *man/getlamgam.Rd | ||
| 75bf020290ee4a7864ecdeee723bf5cd *man/glmnetr.Rd | ||
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| afd36dbe589b334821283a5e653c7ec8 *man/predict.nested.glmnetr.Rd | ||
| 542956e9b4af26a220e57ed295868630 *man/predict_ann_tab.Rd | ||
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| 56f22f5dd2fc238c351eb0a8331a7832 *man/prednn_tl.Rd | ||
| 72074968b83c105982fac03dc906a144 *man/preds_1.Rd | ||
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| 910eed30168c303a1aecb2f691b9ee5f *man/print.nested.glmnetr.Rd | ||
| a13364db342b33245ca827e3422e31b9 *man/print.rf_tune.Rd | ||
| 81b4bf043ea24091e0ad76cdeff4759f *man/rf_tune.Rd | ||
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| b11215f26e442e7ff025ef0ad97487a6 *man/summary.cv.stepreg.Rd | ||
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| 2a624034a1b8c0ec0f9de19b2a5d86d3 *man/wtlast.Rd | ||
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| 5a66f618f77aff93b6680ec862e66aa7 *man/wtzero.Rd | ||
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| 4d354079990e3f3bbd78a4f3a7441f57 *man/wtmiddle.Rd | ||
| 05db74cd06d0f0c21abe8c8ab89e1a70 *man/wtzero.Rd | ||
| df3a72008a5fccd56bf4171229b9977f *man/xgb.simple.Rd | ||
| 46f326d84ba28126b6b912db62d4c9ce *man/xgb.tuned.Rd | ||
| 635c0c9376b516054c666ee1c6cf7373 *vignettes/Using_ann_tab_cv_230728.Rmd | ||
| fce89333e96bf7ec320fba73a50576da *vignettes/Using_ann_tab_cv_230728.pdf.asis | ||
| 993571edd8eb96e4f002c3cda9e62fd3 *vignettes/Using_glmnetr_230728.Rmd | ||
| 21f21b35db3650c28c96a2a3d81e6229 *vignettes/Using_glmnetr_230728.pdf.asis | ||
| 1d7a96a7df3ec5ee04bfb4448afeda1b *vignettes/Using_stepreg_230728.Rmd | ||
| 11d2a2dbc54b3dc80a7e6389c7d5a9eb *vignettes/Using_stepreg_230728.pdf.asis | ||
| 2b2d6c660beac1aa0184cad180eff34a *vignettes/An_Overview_of_glmnetr_231230.Rmd | ||
| 03ceb0a193e7e47b433efe6fbbe10eeb *vignettes/An_Overview_of_glmnetr_231230.pdf.asis | ||
| 7835844e6366d3d192815a9e1f87e4e1 *vignettes/Using_ann_tab_cv_231230.Rmd | ||
| fce89333e96bf7ec320fba73a50576da *vignettes/Using_ann_tab_cv_231230.pdf.asis | ||
| 35fcdb1b133191c24a433403b9b14a31 *vignettes/Using_stepreg_231230.Rmd | ||
| 11d2a2dbc54b3dc80a7e6389c7d5a9eb *vignettes/Using_stepreg_231230.pdf.asis |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,200 @@ | ||
| ################################################################################ | ||
|
|
||
| #' Fit a Random Forest model on data provided in matrix and vector formats. | ||
| #' | ||
| #' @description Fit an Random Forest model using the rfsrc() function of the | ||
| #' randomForestSRC package. | ||
| #' | ||
| #' @param xs predictor input - an n by p matrix, where n (rows) is sample size, and p (columns) | ||
| #' the number of predictors. Must be in matrix form for complete data, no NA's, no Inf's, etc., | ||
| #' and not a data frame. | ||
| #' @param start an optional vector of start times in case of a Cox model. Class numeric of length same as number of patients (n) | ||
| #' @param y_ dependent variable as a vector: time, or stop time for Cox model, Y_ 0 or 1 for binomial (logistic), numeric for gaussian. | ||
| #' Must be a vector of length same as number of sample size. | ||
| #' @param event event indicator, 1 for event, 0 for census, Cox model only. | ||
| #' Must be a numeric vector of length same as sample size. | ||
| #' @param family model family, "cox", "binomial" or "gaussian" (default) | ||
| #' @param mtryc a vector (numeric) of values to search over for optimization of the | ||
| #' Random Forest fit. This if for the mtry input variable of the rfsrc() program | ||
| #' specifying the number of terms to consider in each step of teh Random Forest fit. | ||
| #' @param ntreec a vector (numeric) of 2 values, the first for the number of forests | ||
| #' (ntree from rfsrc()) to use when searhcing for a better bit and the second to use | ||
| #' when fitting the final model. More trees should give a better fit but | ||
| #' require more computations and storage for the final. | ||
| #' model. | ||
| #' @param keep 1 to keep the model fits used to select the value for mtry, or 0 | ||
| #' (default) to not keep these initial model fits. | ||
| #' @param track 1 to output a brief summary of the final selected model, 2 to | ||
| #' output a brief summary on each model fit in search of a better model or 0 | ||
| #' (default) to not output this information. | ||
| #' | ||
| #' @return a Random Forest model fit | ||
| #' | ||
| #' @importFrom randomForestSRC rfsrc | ||
| #' | ||
| #' @export | ||
| #' | ||
| rf_tune = function(xs, start=NULL, y_, event=NULL, family=NULL, mtryc=NULL, ntreec=NULL, keep=0, track=0) { | ||
|
|
||
| if (is.null(family) == 1) { | ||
| if (!is.null(event) == 1) { | ||
| family = "cox" | ||
| } else if ( (length(table(y_)) == 2) & (names(table(y_))[1] == 0) & (names(table(y_))[2] == 1) ) { family = "binomial" | ||
| } else { family = "gaussian"} | ||
| print(family) | ||
| } | ||
|
|
||
| if (track >= 1) { | ||
| time_start = diff_time() | ||
| time_split = time_start | ||
| } | ||
|
|
||
| if (family == "cox") { | ||
| df = as.data.frame(cbind(xs, y_=y_, event = event)) | ||
| } else { | ||
| df = as.data.frame(cbind(xs, y_)) | ||
| names(df) | ||
| } | ||
| # df = as.data.frame(cbind(xs, y_=y_)) | ||
| # dim(df) | ||
|
|
||
| # print(mtryc) | ||
| # print (family) | ||
|
|
||
| if (is.null(mtryc)) { | ||
| if (family %in% c("cox","binomial")) { | ||
| mtryc = round( sqrt(dim(xs)[2]) * c(0.67 , 1, 1.5, 2.25, 3.375, 5.0625) ) | ||
| mtryc = mtryc [mtryc < dim(xs)[2]] | ||
| #mtryc = mtryc [mtryc < 1] | ||
| if (length(mtryc) == 0) { mtryc = 1 } | ||
| } else { | ||
| mtryc = round( dim(xs)[2]*c(0.1, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6) ) | ||
| # mytryc = round( dim(xs)[2]*c(0.33, 0.5, 0.67) ) | ||
| } | ||
| } | ||
|
|
||
| # print(mtryc) | ||
|
|
||
| if (is.null(ntreec)) { | ||
| ntreec = c(10, 50) | ||
| if (family == "gaussian") { ntreec = c(50, 500) | ||
| } else { ntreec = c(25, 250) } | ||
| } | ||
|
|
||
| ##------------------------------------------------------------------------------ | ||
|
|
||
| rfs = list() | ||
|
|
||
| ##mtryc = mtryc[1:3] | ||
| ## k_ = 1 | ||
|
|
||
| # rfsrc does not analyze (start,stop) time survival data | ||
| if ((family == "cox") & (!is.null(start)==1)) { skip = 1 } else { skip = 0 } | ||
|
|
||
| if (skip == 0) { | ||
|
|
||
| for ( k_ in c(1:length(mtryc))) { | ||
| if (family == "cox") { | ||
| rf = rfsrc( Surv(y_, event) ~ . , data=df, mtry=mtryc[k_], nsplit=8, | ||
| ntree=ntreec[1], membership = TRUE, importance=TRUE) | ||
| } else { | ||
| rf = rfsrc( y_ ~ . , data=df, mtry=mtryc[k_], nsplit=8, | ||
| ntree=ntreec[1], membership = TRUE, importance=TRUE) | ||
| } | ||
|
|
||
| rfs[[paste0("m.", k_)]] <- rf | ||
|
|
||
| if (k_ ==1) { | ||
| k_best = 1 | ||
| mtry_best = mtryc[k_] | ||
| err.rate = rf$err.rate[length(rf$err.rate)] ## should be OOB error rate | ||
| err.rate.best = err.rate | ||
| err.ratev = err.rate | ||
| rf_best = rf | ||
| } else if (rf$err.rate[length(rf$err.rate)] < err.rate.best) { | ||
| k_best = k_ | ||
| mtry_best = mtryc[k_] | ||
| err.rate.best = rf$err.rate[length(rf$err.rate)] | ||
| rf_best = rf | ||
| } | ||
|
|
||
| if (k_ > 1) { err.ratev = c(err.ratev, rf$err.rate[length(rf$err.rate)]) } | ||
|
|
||
| if (track >= 2) { | ||
| print( c(k_, mtryc[k_], mtry_best, rf$err.rate[length(rf$err.rate)] ) ) | ||
| time_split = diff_time(time_start, time_split) | ||
| } | ||
| } | ||
|
|
||
| if (ntreec[2] > ntreec[1]) { | ||
| if (family == "cox") { | ||
| rf_tuned = rfsrc( Surv(y_, event) ~ . , data=df, mtry=mtryc[k_best], nsplit=8, | ||
| ntree=ntreec[2], membership = TRUE, importance=TRUE) | ||
| } else { | ||
| rf_tuned = rfsrc( y_ ~ . , data=df, mtry=mtryc[k_best], nsplit=8, | ||
| ntree=ntreec[2], membership = TRUE, importance=TRUE) | ||
| } | ||
| } else { | ||
| rf_tuned = rf_best | ||
| } | ||
|
|
||
| if (track >= 1) { | ||
| print( c(mtry_best, rf_tuned$err.rate[length(rf_tuned$err.rate)] ) ) | ||
| time_split = diff_time(time_start, time_split) | ||
| } | ||
|
|
||
| if (keep == 1) { | ||
| rffit = list(rf_tuned=rf_tuned, rfs=rfs, err.ratev=err.ratev, err.rate=rf_tuned$err.rate[length(rf_tuned$err.rate)], | ||
| mtryc=mtryc, ntreec=ntreec ) | ||
| } else { | ||
| rffit = list(rf_tuned=rf_tuned, err.ratev=err.ratev, rf_tuned$err.rate[length(rf_tuned$err.rate)], | ||
| mtryc=mtryc, ntreec=ntreec ) | ||
| } | ||
|
|
||
| } else { rffit = list(rffit="NONE") } | ||
|
|
||
| class(rffit) <- c("rf_tune") | ||
|
|
||
| return(rffit) | ||
|
|
||
| } | ||
|
|
||
| ################################################################################ | ||
| #' | ||
| #' Summarize output from rf_tune() function | ||
| #' | ||
| #' @param object output from an rf_tune() function | ||
| #' @param ... optional pass through parameters to pass to summary.rfsrc() | ||
| #' | ||
| #' @return summary to console | ||
| #' | ||
| #' @export | ||
| #' | ||
| summary.rf_tune = function(object, ...) { | ||
| cat(paste0("\n search set for mtry and ntree : \n\n")) | ||
| print(object$mtryc) | ||
| print(object$ntreec) | ||
| cat(paste0("\n selected vale for for mtry from search: ", object$rf_tuned$mtry, "\n\n")) | ||
| rf_tuned = object$rf_tuned | ||
| summary(rf_tuned) | ||
| } | ||
|
|
||
| ################################################################################ | ||
| #' | ||
| #' Print output from rf_tune() function | ||
| #' | ||
| #' @param x output from an rf_tune() function | ||
| #' @param ... optional pass through parameters to pass to print.rfsrc() | ||
| #' | ||
| #' @return summary to console | ||
| #' | ||
| #' @export | ||
| #' | ||
| #' | ||
| print.rf_tune = function(x, ...) { | ||
| rf_tuned = x$rf_tuned | ||
| print( rf_tuned ) | ||
| } | ||
|
|
||
| ################################################################################ | ||
|
|
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