9/11/2023 0 Comments Random forest predictionWhere each column contains the number of times that a caseĪppears in the bootstrap sample for that tree.Ĭount of the number of times a variable was used in Matrix recording inbag membership for the test data Test data where each column contains the node number that a Matrix recording terminal node membership for the Symmetric proximity matrix of the test data. Number of terminal nodes for each tree in the Test set y-outcomes or original grow y-outcomes if none.Ī character vector of the y-outcome names.Ī character vector of the x-variable names. Sample size of test data (depends upon NA values). See theĪn object of class (rfsrc, predict), which is a list with the Error rates and VIMP are calculated by bootstrapping the testĭata and using out-of-bagging to ensure unbiased estimates. Training data, but where the predicted value is based on the testĭata. Predictor in which the topology of the forest is based solely on the In this case, the terminal nodes from the grow-forest are recalculated Y-outcomes from the test data (outcome information must be present). If outcome="test", the predictor is calculated by using This is useful, because it gives the user theĪbility to extract outputs from the forest that were not asked for in If no test data is provided, then the original training data is usedĪnd the code reverts to restore mode allowing the user to restore the Only training data is used to impute test data to avoid biasing error Setting na.action="na.impute" imputes missing test data Times can be significantly improved by setting When the dimension is high), thus if VIMP is not needed, computational That calculating VIMP can be computationally expensive (especially Single as well as joint VIMP measures can be requested. Rate and VIMP are also returned if the test data contains y-outcome Predicted values are obtained by dropping test data down the growįorest (the forest grown using the training data). Should split statistics be returned? Values can beįurther arguments passed to or from other methods. Should terminal node membership and inbag Number of seconds between updates to the user on Negative integer specifying seed for the random number Return minimal depth for each variable for each case? Record the number of times a variable is split? The safest choice is TRUE if proximity is desired. Not be valid and will depend on the context of the predict call. "all", TRUE, or FALSE - but some options may Should proximity between test observationsīe calculated? Possible choices are "inbag", "oob", See the details and examples below for more The option is also ignored whenever the test data isĭevoid of y-outcomes. Missing as the training data is used for the test data in such The default and natural choice is train which uses the Selecting na.imputeĭetermines whether the y-outcomes from the trainingĭata or the test data are used to calculate the predicted value. Only applies when the test dataĮven one of its entries is NA. Method for computing variable importance (VIMP). Specifying the target outcomes to be used. )Īn object of class (rfsrc, grow) or (rfsrc,Ĭharacter vector for multivariate families # S3 method for class 'rfsrc' predict ( object, newdata, outcome.target = NULL, importance = c ( FALSE, TRUE, "none", "permute", "random", "anti", "permute.ensemble", "random.ensemble", "anti.ensemble" ), na.action = c ( "na.omit", "na.impute" ), outcome = c ( "train", "test" ), proximity = FALSE, var.used = c ( FALSE, "all.trees", "by.tree" ), pth = c ( FALSE, "all.trees", "by.tree" ), seed = NULL, do.trace = FALSE, membership = FALSE, statistics = FALSE. wihs: Women's Interagency HIV Study (WIHS).vimp: VIMP for Single or Grouped Variables.veteran: Veteran's Administration Lung Cancer Trial.vdv: van de Vijver Microarray Breast Cancer.stat.split: Acquire Split Statistic Information.rfsrc: Random Forests for Survival, Regression and Classification.randomForestSRC_package: Random Forests for Survival, Regression and Classification.print.rfsrc: Print Summary Output of a RF-SRC Analysis.predict.rfsrc: Prediction for Random Forests for Survival, Regression, and.plot.variable: Plot Marginal Effect of Variables.plot.survival: Plot of Survival Estimates.plot.rfsrc: Plot Error Rate and Variable Importance from a RF-SRC.pbc: Primary Biliary Cirrhosis (PBC) Data.partial.rfsrc: Acquire Partial Effect of a Variable.max.subtree: Acquire Maximal Subtree Information.find.interaction: Find Interactions Between Pairs of Variables.breast: Wisconsin Prognostic Breast Cancer Data.
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