`R/forest_prediction.R`

`predictCoeffsForest.Rd`

Uses the forest to predict spline coefficients. Returns a matrix of predicted spline coefficients where the columns of the returned matrix correspond to rows of the testdata. The number of rows of the returned matrix is equal to the degrees of freedom of the forest. If no testdata is provided, forest$flat_data is used. When testdata is not provided, predictions will be made according to one of three methods. The "method" parameter must be either "oob", "itb", or "all". This parameter specifies which trees are used in making a prediction for a certain datapoint. This parameter is not relevant when predicting for a testset that is distinct from the training set.

predictCoeffsForest(forest, method = "oob", testdata = NULL)

forest | A model returned from splineForest() |
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method | A string; either "oob", "itb", or "all". If "oob" (the default), predictions for a given data point are made only using trees for which this data point was "out of the bag" (not in the random subsample). If "itb", predictions for a given data point are made using only the trees for which this datapoint was "in the bag" (in the random subsample). If "all", all trees are used for every datapoint. |

testdata | The test data to make predictions for. If this is provided, then all trees are used for all datapoints. |

A matrix of predicted spline coefficients. The dimensions are forest$df x nrow(testdata). Each column of the matrix corresponds to a row of the testdata.

trainingSetPreds <- predictCoeffsForest(forest) newData <- data.frame("WHITE" = 0, "BLACK"=1, "HISP"=0, "Num_sibs"=3, "HGC_MOTHER"=12, "HGC_FATHER"=12, "SEX"=1) predictCoeffsForest(forest, testdata = newData)#> #> 1 21.426052 #> 2 5.683236 #> 3 9.120089