R: Prediction for ObliqueForest Objects

predict.ObliqueForest {aorsf}

R Documentation

Prediction for ObliqueForest Objects

Description

Compute predicted values from an oblique random forest. Predictions may be returned in aggregate (i.e., averaging over all the trees) or tree-specific.

Usage

## S3 method for class 'ObliqueForest'
predict(
  object,
  new_data = NULL,
  pred_type = NULL,
  pred_horizon = NULL,
  pred_aggregate = TRUE,
  pred_simplify = FALSE,
  oobag = FALSE,
  na_action = NULL,
  boundary_checks = TRUE,
  n_thread = NULL,
  verbose_progress = NULL,
  ...
)

Arguments

`object`	(ObliqueForest) a trained oblique random forest object (see orsf).
`new_data`	a data.frame, tibble, or data.table to compute predictions in.
`pred_type`	(character) the type of predictions to compute. Valid options for survival are: 'risk' : probability of having an event at or before `pred_horizon`. 'surv' : 1 - risk. 'chf': cumulative hazard function 'mort': mortality prediction 'time': survival time prediction For classification: 'prob': probability for each class 'class': predicted class For regression: 'mean': predicted mean, i.e., the expected value
`pred_horizon`	(double) Only relevent for survival forests. A value or vector indicating the time(s) that predictions will be calibrated to. E.g., if you were predicting risk of incident heart failure within the next 10 years, then `pred_horizon = 10`. `pred_horizon` can be `NULL` if `pred_type` is `'mort'`, since mortality predictions are aggregated over all event times
`pred_aggregate`	(logical) If `TRUE` (the default), predictions will be aggregated over all trees by taking the mean. If `FALSE`, the returned output will contain one row per observation and one column for each tree. If the length of `pred_horizon` is two or more and `pred_aggregate` is `FALSE`, then the result will be a list of such matrices, with the i'th item in the list corresponding to the i'th value of `pred_horizon`.
`pred_simplify`	(logical) If `FALSE` (the default), predictions will always be returned in a numeric matrix or a list of numeric matrices. If `TRUE`, predictions may be simplified to a vector, e.g., if `pred_type` is `'mort'` for survival or `'class'` for classification, or an array of matrices if `length(pred_horizon) > 1`.
`oobag`	(logical) If `FALSE` (the default), predictions will be computed using all trees for each observation. If `TRUE`, then out-of-bag predictions will be computed. This input parameter should only be set to `TRUE` if `new_data` is `NULL`.
`na_action`	(character) what should happen when `new_data` contains missing values (i.e., `NA` values). Valid options are: 'fail' : an error is thrown if `new_data` contains `NA` values 'pass' : the output will have `NA` in all rows where `new_data` has 1 or more `NA` value for the predictors used by `object` 'omit' : rows in `new_data` with incomplete data will be dropped 'impute_meanmode' : missing values for continuous and categorical variables in `new_data` will be imputed using the mean and mode, respectively. To clarify, the mean and mode used to impute missing values are from the training data of `object`, not from `new_data`.
`boundary_checks`	(logical) if `TRUE`, `pred_horizon` will be checked to make sure the requested values are less than the maximum observed time in `object`'s training data. If `FALSE`, these checks are skipped.
`n_thread`	(integer) number of threads to use while computing predictions. Default is 0, which allows a suitable number of threads to be used based on availability.
`verbose_progress`	(logical) if `TRUE`, progress messages are printed in the console. If `FALSE` (the default), nothing is printed.
`...`	Further arguments passed to or from other methods (not currently used).

Details

new_data must have the same columns with equivalent types as the data used to train object. Also, factors in new_data must not have levels that were not in the data used to train object.

pred_horizon values should not exceed the maximum follow-up time in object's training data, but if you truly want to do this, set boundary_checks = FALSE and you can use a pred_horizon as large as you want. Note that predictions beyond the maximum follow-up time in the object's training data are equal to predictions at the maximum follow-up time, because aorsf does not estimate survival beyond its maximum observed time.

If unspecified, pred_horizon may be automatically specified as the value used for oobag_pred_horizon when object was created (see orsf).

Value

a matrix of predictions. Column j of the matrix corresponds to value j in pred_horizon. Row i of the matrix corresponds to row i in new_data.

Examples

library(aorsf)

Classification

set.seed(329)

index_train <- sample(nrow(penguins_orsf), 150) 

penguins_orsf_train <- penguins_orsf[index_train, ]
penguins_orsf_test <- penguins_orsf[-index_train, ]

fit_clsf <- orsf(data = penguins_orsf_train, 
                 formula = species ~ .)

Predict probability for each class or the predicted class:

# predicted probabilities, the default
predict(fit_clsf, 
        new_data = penguins_orsf_test[1:5, ],
        pred_type = 'prob')

##         Adelie  Chinstrap      Gentoo
## [1,] 0.9405310 0.04121955 0.018249405
## [2,] 0.9628988 0.03455909 0.002542096
## [3,] 0.9032074 0.08510528 0.011687309
## [4,] 0.9300133 0.05209040 0.017896329
## [5,] 0.7965703 0.16243492 0.040994821

# predicted class (as a matrix by default)
predict(fit_clsf, 
        new_data = penguins_orsf_test[1:5, ],
        pred_type = 'class')

##      [,1]
## [1,]    1
## [2,]    1
## [3,]    1
## [4,]    1
## [5,]    1

# predicted class (as a factor if you use simplify)
predict(fit_clsf, 
        new_data = penguins_orsf_test[1:5, ],
        pred_type = 'class',
        pred_simplify = TRUE)

## [1] Adelie Adelie Adelie Adelie Adelie
## Levels: Adelie Chinstrap Gentoo

Regression

set.seed(329)

index_train <- sample(nrow(penguins_orsf), 150) 

penguins_orsf_train <- penguins_orsf[index_train, ]
penguins_orsf_test <- penguins_orsf[-index_train, ]

fit_regr <- orsf(data = penguins_orsf_train, 
                 formula = bill_length_mm ~ .)

Predict the mean value of the outcome:

predict(fit_regr, 
        new_data = penguins_orsf_test[1:5, ], 
        pred_type = 'mean')

##          [,1]
## [1,] 37.74136
## [2,] 37.42367
## [3,] 37.04598
## [4,] 39.89602
## [5,] 39.14848

Survival

Begin by fitting an oblique survival random forest:

set.seed(329)

index_train <- sample(nrow(pbc_orsf), 150) 

pbc_orsf_train <- pbc_orsf[index_train, ]
pbc_orsf_test <- pbc_orsf[-index_train, ]

fit_surv <- orsf(data = pbc_orsf_train, 
                 formula = Surv(time, status) ~ . - id,
                 oobag_pred_horizon = 365.25 * 5)

Predict risk, survival, or cumulative hazard at one or several times:

# predicted risk, the default
predict(fit_surv, 
        new_data = pbc_orsf_test[1:5, ], 
        pred_type = 'risk', 
        pred_horizon = c(500, 1000, 1500))

##             [,1]        [,2]       [,3]
## [1,] 0.013648562 0.058393393 0.11184029
## [2,] 0.003811413 0.026857586 0.04774151
## [3,] 0.030548361 0.100600301 0.14847107
## [4,] 0.040381075 0.169596943 0.27018952
## [5,] 0.001484698 0.006663576 0.01337655

# predicted survival, i.e., 1 - risk
predict(fit_surv, 
        new_data = pbc_orsf_test[1:5, ], 
        pred_type = 'surv',
        pred_horizon = c(500, 1000, 1500))

##           [,1]      [,2]      [,3]
## [1,] 0.9863514 0.9416066 0.8881597
## [2,] 0.9961886 0.9731424 0.9522585
## [3,] 0.9694516 0.8993997 0.8515289
## [4,] 0.9596189 0.8304031 0.7298105
## [5,] 0.9985153 0.9933364 0.9866235

# predicted cumulative hazard function
# (expected number of events for person i at time j)
predict(fit_surv, 
        new_data = pbc_orsf_test[1:5, ], 
        pred_type = 'chf',
        pred_horizon = c(500, 1000, 1500))

##             [,1]        [,2]       [,3]
## [1,] 0.015395388 0.067815817 0.14942956
## [2,] 0.004022524 0.028740305 0.05424314
## [3,] 0.034832754 0.127687156 0.20899732
## [4,] 0.059978334 0.233048809 0.42562310
## [5,] 0.001651365 0.007173177 0.01393016

Predict mortality, defined as the number of events in the forest’s population if all observations had characteristics like the current observation. This type of prediction does not require you to specify a prediction horizon

predict(fit_surv, 
        new_data = pbc_orsf_test[1:5, ], 
        pred_type = 'mort')

##           [,1]
## [1,] 23.405016
## [2,] 15.362916
## [3,] 26.180648
## [4,] 36.515629
## [5,]  5.856674

[Package aorsf version 0.1.5 Index]