# Fit a generalized partially linear tree-based regression model Combining a generalized linear model with an additional tree part on the same scale. A four-step procedure is proposed to fit the model and test the joint effect of the selected tree part while adjusting on confounding factors. We also proposed an ensemble procedure based on the bagging to improve prediction accuracy and computed several scores of importance for variable selection. See 'Cyprien Mbogning et al.'(2014), 'Cyprien Mbogning et al.'(2015) for an overview of all the methods implemented in this package. package ## Details ||| |:--|:--| |Package:|GPLTR| |Type:|Package| |Version:|1.5| |Date:|2024-03-28| |License:|GPL(>=2.0)| ## Author(s) Cyprien Mbogning and Wilson Toussile Maintainer: Cyprien Mbogning ## References Mbogning, C., Perdry, H., Broet, P.: A Bagged partially linear tree-based regression procedure for prediction and variable selection. Human Heredity, 79(3-4):1 82-93 (2015) Mbogning, C., Perdry, H., Toussile, W., Broet, P.: A novel tree-based procedure for deciphering the genomic spectrum of clinical disease entities. Journal of Clinical Bioinformatics 4:6, (2014) Terry M. Therneau, Elizabeth J. Atkinson (2013) An Introduction to Recursive Partitioning Using the `RPART` Routines. Mayo Foundation. Chen, J., Yu, K., Hsing, A., Therneau, T.M.: A partially linear tree-based regression model for assessing complex joint gene-gene and gene-environment effects. Genetic Epidemiology 31, 238-251 (2007) ## Examples ```r ##%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## Example on a public dataset: the burn data ##%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## The burn data are also displayed in the KMsurv package ##%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ## Not run: data(burn) ## Build the rpart tree with all the variables rpart.burn <- rpart(D2 ~ Z1 + Z2 + Z3 + Z4 + Z5 + Z6 + Z7 + Z8 + Z9 + Z10 + Z11, data = burn, method = "class") plot(rpart.burn, main = 'rpart tree') text(rpart.burn, xpd = TRUE, cex = .6, use.n = TRUE) ## fit the PLTR model after adjusting on gender (Z2) using the proposed method args.rpart <- list(minbucket = 10, maxdepth = 4, cp = 0, maxcompete = 0, maxsurrogate = 0) family <- "binomial" X.names = "Z2" Y.name = "D2" G.names = c('Z1','Z3','Z4','Z5','Z6','Z7','Z8','Z9','Z10','Z11') pltr.burn <- pltr.glm(burn, Y.name, X.names, G.names, args.rpart = args.rpart, family = family, iterMax = 4, iterMin = 3, verbose = FALSE) ## Prunned back the maximal tree using either the BIC or the AIC criterion pltr.burn_prun <- best.tree.BIC.AIC(xtree = pltr.burn$tree, burn, Y.name, X.names, family = family) ## plot the BIC selected tree plot(pltr.burn_prun$tree$BIC, main = 'BIC selected tree') text(pltr.burn_prun$tree$BIC, xpd = TRUE, cex = .6, col = 'blue') ## Summary of the selected tree by a BIC criterion summary(pltr.burn_prun$tree$BIC) ## Summary of the final selected pltr model summary(pltr.burn_prun$fit_glm$BIC) ## fit the PLTR model after adjusting on gender (Z2) using the parametric ## bootstrap method ## set numWorkers = 1 on a windows plateform args.parallel = list(numWorkers = 10) best_bootstrap <- best.tree.bootstrap(pltr.burn$tree, burn, Y.name, X.names, G.names, B = 2000, BB = 2000, args.rpart = args.rpart, epsi = 0.008, iterMax = 6, iterMin = 5, family = family, LEVEL = 0.05, LB = FALSE, args.parallel = args.parallel, verbose = FALSE) plot(best_bootstrap$selected_model$tree, main = 'original method') text(best_bootstrap$selected_model$tree, xpd = TRUE) ## Bagging a set of basic unprunned pltr predictors # ?bagging.pltr Bag.burn <- bagging.pltr(burn, Y.name, X.names, G.names, family, args.rpart,epsi = 0.01, iterMax = 4, iterMin = 3, Bag = 10, verbose = FALSE, doprune = FALSE) ## The thresshold values used Bag.burn$CUT ## The set of PLTR models in the bagging procedure PLTR_BAG.burn <- Bag.burn$Glm_BAG ## The set of trees in the bagging procedure TREE_BAG.burn <- Bag.burn$Tree_BAG ## Use the bagging procedure to predict new features # ?predict_bagg.pltr Pred_Bag.burn <- predict_bagg.pltr(Bag.burn, Y.name, newdata = burn, type = "response", thresshold = seq(0, 1, by = 0.1)) ## The confusion matrix for each thresshold value using the majority vote Pred_Bag.burn$CONF1 ## The prediction error for each thresshold value Pred_Bag.burn$PRED_ERROR1 ## Compute the variable importances using the bagging procedure Var_Imp_BAG.burn <- VIMPBAG(Bag.burn, burn, Y.name) ## Importance score using the permutaion method for each thresshold value Var_Imp_BAG.burn$PIS ## Shadow plot of three proposed scores par(mfrow=c(1,3)) barplot(Var_Imp_BAG.burn$PIS$CUT5, main = 'PIS', horiz = TRUE, las = 1, cex.names = .8, col = 'lightblue') barplot(Var_Imp_BAG.burn$DIS, main = 'DIS', horiz = TRUE, las = 1, cex.names = .8, col = 'grey') barplot(Var_Imp_BAG.burn$DDIS, main = 'DDIS', horiz = TRUE, las = 1, cex.names = .8, col = 'purple') ## End(Not run) ```

GPLTR-package