RLT | R Documentation |

Fit models for regression, classification and survival analysis using reinforced splitting rules

```
RLT(
x,
y,
censor = NULL,
model = "regression",
print.summary = 0,
use.cores = 1,
ntrees = if (reinforcement) 100 else 500,
mtry = max(1, as.integer(ncol(x)/3)),
nmin = max(1, as.integer(log(nrow(x)))),
alpha = 0.4,
split.gen = "random",
nsplit = 1,
resample.prob = 0.9,
replacement = TRUE,
npermute = 1,
select.method = "var",
subject.weight = NULL,
variable.weight = NULL,
track.obs = FALSE,
importance = TRUE,
reinforcement = FALSE,
muting = -1,
muting.percent = if (reinforcement) MuteRate(nrow(x), ncol(x), speed = "aggressive",
info = FALSE) else 0,
protect = as.integer(log(ncol(x))),
combsplit = 1,
combsplit.th = 0.25,
random.select = 0,
embed.n.th = 4 * nmin,
embed.ntrees = max(1, -atan(0.01 * (ncol(x) - 500))/pi * 100 + 50),
embed.resample.prob = 0.8,
embed.mtry = 1/2,
embed.nmin = as.integer(nrow(x)^(1/3)),
embed.split.gen = "random",
embed.nsplit = 1
)
```

`x` |
A matrix or data.frame for features |

`y` |
Response variable, a numeric/factor vector or a Surv object |

`censor` |
The censoring indicator if survival model is used |

`model` |
The model type: |

`print.summary` |
Whether summary should be printed |

`use.cores` |
Number of cores |

`ntrees` |
Number of trees, |

`mtry` |
Number of variables used at each internal node, only for |

`nmin` |
Minimum number of observations required in an internal node to perform a split. Set this to twice of the desired terminal node size. |

`alpha` |
Minimum number of observations required for each child node as a portion of the parent node. Must be within |

`split.gen` |
How the cutting points are generated |

`nsplit` |
Number of random cutting points to compare for each variable at an internal node |

`resample.prob` |
Proportion of in-bag samples |

`replacement` |
Whether the in-bag samples are sampled with replacement |

`npermute` |
Number of imputations (currently not implemented, saved for future use) |

`select.method` |
Method to compare different splits |

`subject.weight` |
Subject weights |

`variable.weight` |
Variable weights when randomly sample |

`track.obs` |
Track which terminal node the observation belongs to |

`importance` |
Should importance measures be calculated |

`reinforcement` |
If reinforcement splitting rules should be used. There are default values for all tuning parameters under this feature. |

`muting` |
Muting method, |

`muting.percent` |
Only for |

`protect` |
Number of protected variables that will not be muted. These variables are adaptively selected for each tree. |

`combsplit` |
Number of variables used in a combination split. |

`combsplit.th` |
The minimum threshold (as a relative measurement compared to the best variable) for a variable to be used in the combination split. |

`random.select` |
Randomly select a variable from the top variable in the linear combination as the splitting rule. |

`embed.n.th` |
Number of observations to stop the embedded model and choose randomly from the current protected variables. |

`embed.ntrees` |
Number of embedded trees |

`embed.resample.prob` |
Proportion of in-bag samples for embedded trees |

`embed.mtry` |
Number of variables used for embedded trees, as proportion |

`embed.nmin` |
Terminal node size for embedded trees |

`embed.split.gen` |
How the cutting points are generated in the embedded trees |

`embed.nsplit` |
Number of random cutting points for embedded trees |

A `RLT`

object; a list consisting of

`FittedTrees` |
Fitted tree structure |

`FittedSurv, timepoints` |
Terminal node survival estimation and all time points, if survival model is used |

`AllError` |
All out-of-bag errors, if |

`VarImp` |
Variable importance measures, if |

`ObsTrack` |
Registration of each observation in each fitted tree |

`...` |
All the tuning parameters are saved in the fitted |

Zhu, R., Zeng, D., & Kosorok, M. R. (2015) "Reinforcement Learning Trees." Journal of the American Statistical Association. 110(512), 1770-1784.

Zhu, R., & Kosorok, M. R. (2012). Recursively imputed survival trees. Journal of the American Statistical Association, 107(497), 331-340.

```
N = 600
P = 100
X = matrix(runif(N*P), N, P)
Y = rowSums(X[,1:5]) + rnorm(N)
trainx = X[1:200,]
trainy = Y[1:200]
testx = X[-c(1:200),]
testy = Y[-c(1:200)]
# Regular ensemble trees (Extremely Randomized Trees, Geurts, et. al., 2006)
RLT.fit = RLT(trainx, trainy, model = "regression", use.cores = 6)
barplot(RLT.fit$VarImp)
RLT.pred = predict(RLT.fit, testx)
mean((RLT.pred$Prediction - testy)^2)
# Reinforcement Learning Trees, using an embedded model to find the splitting rule
Mark0 = proc.time()
RLT.fit = RLT(trainx, trainy, model = "regression", use.cores = 6, ntrees = 100,
importance = TRUE, reinforcement = TRUE, combsplit = 3, embed.ntrees = 25)
proc.time() - Mark0
barplot(RLT.fit$VarImp)
RLT.pred = predict(RLT.fit, testx)
mean((RLT.pred$Prediction - testy)^2)
```

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