Package 'ocf'

Title: Ordered Correlation Forest
Description: Machine learning estimator specifically optimized for predictive modeling of ordered non-numeric outcomes. 'ocf' provides forest-based estimation of the conditional choice probabilities and the covariates’ marginal effects. Under an "honesty" condition, the estimates are consistent and asymptotically normal and standard errors can be obtained by leveraging the weight-based representation of the random forest predictions. Please reference the use as Di Francesco (2025) <doi:10.1080/07474938.2024.2429596>.
Authors: Riccardo Di Francesco [aut, cre, cph]
Maintainer: Riccardo Di Francesco <[email protected]>
License: GPL-3
Version: 1.0.3
Built: 2025-02-28 12:37:19 UTC
Source: https://github.com/riccardo-df/ocf

Help Index

Generate Ordered Data

Description

Generate a synthetic data set with an ordered non-numeric outcome, together with conditional probabilities and covariates' marginal effects.

Usage

generate_ordered_data(n)

Arguments

n

Sample size.

Details

First, a latent outcome is generated as follows:

Yi∗=g(Xi)+ϵiY_i^* = g ( X_i ) + \epsilon_i

with:

g(Xi)=XiTβg ( X_i ) = X_i^T \beta

Xi:=(Xi,1,Xi,2,Xi,3,Xi,4,Xi,5,Xi,6)X_i := (X_{i, 1}, X_{i, 2}, X_{i, 3}, X_{i, 4}, X_{i, 5}, X_{i, 6})

Xi,1,Xi,3,Xi,5∼N(0,1)X_{i, 1}, X_{i, 3}, X_{i, 5} \sim \mathcal{N} \left( 0, 1 \right)

Xi,2,Xi,4,Xi,6∼Bernoulli(0,1)X_{i, 2}, X_{i, 4}, X_{i, 6} \sim \textit{Bernoulli} \left( 0, 1 \right)

β=(1,1,1/2,1/2,0,0)\beta = \left( 1, 1, 1/2, 1/2, 0, 0 \right)

ϵi∼logistic(0,1)\epsilon_i \sim logistic (0, 1)

Second, the observed outcomes are obtained by discretizing the latent outcome into three classes using uniformly spaced threshold parameters.

Third, the conditional probabilities and the covariates' marginal effects at the mean are generated using standard textbook formulas. Marginal effects are approximated using a sample of 1,000,000 observations.

Value

A list storing a data frame with the observed data, a matrix of true conditional probabilities, and a matrix of true marginal effects at the mean of the covariates.

Author(s)

Riccardo Di Francesco

References

See Also

ocf

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(1000)

head(data$true_probs)
data$me_at_mean

sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)

Marginal Effects for Ordered Correlation Forest

Description

Nonparametric estimation of marginal effects using an ocf object.

Usage

marginal_effects(
  object,
  data = NULL,
  these_covariates = NULL,
  eval = "atmean",
  bandwitdh = 0.1,
  inference = FALSE
)

Arguments

object

An ocf object.
data

Data set of class data.frame to estimate marginal effects. It must contain at least the same covariates used to train the forests. If NULL, marginal effects are estimated on object$full_data.
these_covariates

Named list with covariates' names as keys and strings denoting covariates' types as entries. Strings must be either "continuous" or "discrete". The names of the list indicate the covariates for which marginal effect estimation is desired. If NULL (the default), marginal effects are estimated for all covariates and covariates' types are inferred by the routine.
eval

Evaluation point for marginal effects. Either "mean", "atmean" or "atmedian".
bandwitdh

How many standard deviations x_up and x_down differ from x.
inference

Whether to extract weights and compute standard errors. The weights extraction considerably slows down the program.

Details

marginal_effects can estimate mean marginal effects, marginal effects at the mean, or marginal effects at the median, according to the eval argument.

If these_covariates is NULL (the default), the routine assumes that covariates with with at most ten unique values are categorical and treats the remaining covariates as continuous.

Value

Object of class ocf.marginal.

Author(s)

Riccardo Di Francesco

References

See Also

ocf

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)

## Marginal effects at the mean.
me <- marginal_effects(forests, eval = "atmean")

print(me)
print(me, latex = TRUE)
plot(me)

## Compute standard errors. This requires honest forests.
honest_forests <- ocf(Y, X, honesty = TRUE)
honest_me <- marginal_effects(honest_forests, eval = "atmean", inference = TRUE)

print(honest_me, latex = TRUE)
plot(honest_me)

## Subset covariates and select covariates' types.
my_covariates <- list("x1" = "continuous", "x2" = "discrete", "x4" = "discrete")
honest_me <- marginal_effects(honest_forests, eval = "atmean", inference = TRUE,
                              these_covariates = my_covariates)
print(honest_me)
plot(honest_me)

Accuracy Measures for Ordered Probability Predictions

Description

Accuracy measures for evaluating ordered probability predictions.

Usage

mean_squared_error(y, predictions, use.true = FALSE)

mean_absolute_error(y, predictions, use.true = FALSE)

mean_ranked_score(y, predictions, use.true = FALSE)

classification_error(y, predictions)

Arguments

y

Either the observed outcome vector or a matrix of true probabilities.
predictions

Predictions.
use.true

If TRUE, then the program treats y as a matrix of true probabilities.

Details

MSE, MAE, and RPS

When calling one of mean_squared_error, mean_absolute_error, or mean_ranked_score, predictions must be a matrix of predicted class probabilities, with as many rows as observations in y and as many columns as classes of y.

If use.true == FALSE, the mean squared error (MSE), the mean absolute error (MAE), and the mean ranked probability score (RPS) are computed as follows:

MSE=1n∑i=1n∑m=1M(1(Yi=m)−p^m(x))2MSE = \frac{1}{n} \sum_{i = 1}^n \sum_{m = 1}^M (1 (Y_i = m) - \hat{p}_m (x))^2

MAE=1n∑i=1n∑m=1M∣1(Yi=m)−p^m(x)∣MAE = \frac{1}{n} \sum_{i = 1}^n \sum_{m = 1}^M |1 (Y_i = m) - \hat{p}_m (x)|

RPS=1n∑i=1n1M−1∑m=1M(1(Yi≤m)−p^m∗(x))2RPS = \frac{1}{n} \sum_{i = 1}^n \frac{1}{M - 1} \sum_{m = 1}^M (1 (Y_i \leq m) - \hat{p}_m^* (x))^2

If use.true == TRUE, the MSE, the MAE, and the RPS are computed as follows (useful for simulation studies):

MSE=1n∑i=1n∑m=1M(pm(x)−p^m(x))2MSE = \frac{1}{n} \sum_{i = 1}^n \sum_{m = 1}^M (p_m (x) - \hat{p}_m (x))^2

MSE=1n∑i=1n∑m=1M∣pm(x)−p^m(x)∣MSE = \frac{1}{n} \sum_{i = 1}^n \sum_{m = 1}^M |p_m (x) - \hat{p}_m (x)|

RPS=1n∑i=1n1M−1∑m=1M(pm∗(x)−p^m∗(x))2RPS = \frac{1}{n} \sum_{i = 1}^n \frac{1}{M - 1} \sum_{m = 1}^M (p_m^* (x) - \hat{p}_m^* (x))^2

where:

pm(x)=P(Yi=m∣Xi=x)p_m (x) = P(Y_i = m | X_i = x)

pm∗(x)=P(Yi≤m∣Xi=x)p_m^* (x) = P(Y_i \leq m | X_i = x)

Classification error

When calling classification_error, predictions must be a vector of predicted class labels.

Classification error (CE) is computed as follows:

CE=1n∑i=1n1(Yi≠Y^i)CE = \frac{1}{n} \sum_{i = 1}^n 1 (Y_i \neq \hat{Y}_i)

where Y_i are the observed class labels.

Value

The MSE, the MAE, the RPS, or the CE of the method.

Author(s)

Riccardo Di Francesco

See Also

mean_ranked_score

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Training-test split.
train_idx <- sample(seq_len(length(Y)), floor(length(Y) * 0.5))

Y_tr <- Y[train_idx]
X_tr <- X[train_idx, ]

Y_test <- Y[-train_idx]
X_test <- X[-train_idx, ]

## Fit ocf on training sample.
forests <- ocf(Y_tr, X_tr)

## Accuracy measures on test sample.
predictions <- predict(forests, X_test)

mean_squared_error(Y_test, predictions$probabilities)
mean_ranked_score(Y_test, predictions$probabilities)
classification_error(Y_test, predictions$classification)

Multinomial Machine Learning

Description

Estimation strategy to estimate conditional choice probabilities for multinomial outcomes.

Usage

multinomial_ml(Y = NULL, X = NULL, learner = "forest", scale = TRUE)

Arguments

Y

Outcome vector.
X

Covariate matrix (no intercept).
learner

String, either "forest" or "l1". Selects the base learner to estimate each expectation.
scale

Logical, whether to scale the covariates. Ignored if learner is not "l1".

Details

Multinomial machine learning expresses conditional choice probabilities as expectations of binary variables:

pm(Xi)=E[1(Yi=m)∣Xi]p_m \left( X_i \right) = \mathbb{E} \left[ 1 \left( Y_i = m \right) | X_i \right]

This allows us to estimate each expectation separately using any regression algorithm to get an estimate of conditional probabilities.

multinomial_ml combines this strategy with either regression forests or penalized logistic regressions with an L1 penalty, according to the user-specified parameter learner.

If learner == "l1", the penalty parameters are chosen via 10-fold cross-validation and model.matrix is used to handle non-numeric covariates. Additionally, if scale == TRUE, the covariates are scaled to have zero mean and unit variance.

Value

Object of class mml.

Author(s)

Riccardo Di Francesco

References

See Also

ordered_ml, ocf

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Training-test split.
train_idx <- sample(seq_len(length(Y)), floor(length(Y) * 0.5))

Y_tr <- Y[train_idx]
X_tr <- X[train_idx, ]

Y_test <- Y[-train_idx]
X_test <- X[-train_idx, ]

## Fit multinomial machine learning on training sample using two different learners.
multinomial_forest <- multinomial_ml(Y_tr, X_tr, learner = "forest")
multinomial_l1 <- multinomial_ml(Y_tr, X_tr, learner = "l1")

## Predict out of sample.
predictions_forest <- predict(multinomial_forest, X_test)
predictions_l1 <- predict(multinomial_l1, X_test)

## Compare predictions.
cbind(head(predictions_forest), head(predictions_l1))

Ordered Correlation Forest

Description

Nonparametric estimator for ordered non-numeric outcomes. The estimator modifies a standard random forest splitting criterion to build a collection of forests, each estimating the conditional probability of a single class.

Usage

ocf(
  Y = NULL,
  X = NULL,
  honesty = FALSE,
  honesty.fraction = 0.5,
  inference = FALSE,
  alpha = 0.2,
  n.trees = 2000,
  mtry = ceiling(sqrt(ncol(X))),
  min.node.size = 5,
  max.depth = 0,
  replace = FALSE,
  sample.fraction = ifelse(replace, 1, 0.5),
  n.threads = 1
)

Arguments

Y

Outcome vector.
X

Covariate matrix (no intercept).
honesty

Whether to grow honest forests.
honesty.fraction

Fraction of honest sample. Ignored if honesty = FALSE.
inference

Whether to extract weights and compute standard errors. The weights extraction considerably slows down the routine. honesty = TRUE is required for valid inference.
alpha

Controls the balance of each split. Each split leaves at least a fraction alpha of observations in the parent node on each side of the split.
n.trees

Number of trees.
mtry

Number of covariates to possibly split at in each node. Default is the square root of the number of covariates.
min.node.size

Minimal node size.
max.depth

Maximal tree depth. A value of 0 corresponds to unlimited depth, 1 to "stumps" (one split per tree).
replace

If TRUE, grow trees on bootstrap subsamples. Otherwise, trees are grown on random subsamples drawn without replacement.
sample.fraction

Fraction of observations to sample.
n.threads

Number of threads. Zero corresponds to the number of CPUs available.

Value

Object of class ocf.

Author(s)

Riccardo Di Francesco

References

See Also

marginal_effects

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Training-test split.
train_idx <- sample(seq_len(length(Y)), floor(length(Y) * 0.5))

Y_tr <- Y[train_idx]
X_tr <- X[train_idx, ]

Y_test <- Y[-train_idx]
X_test <- X[-train_idx, ]

## Fit ocf on training sample.
forests <- ocf(Y_tr, X_tr)

## We have compatibility with generic S3-methods.
print(forests)
summary(forests)
predictions <- predict(forests, X_test)
head(predictions$probabilities)
table(Y_test, predictions$classification)

## Compute standard errors. This requires honest forests.
honest_forests <- ocf(Y_tr, X_tr, honesty = TRUE, inference = TRUE)
head(honest_forests$predictions$standard.errors)

## Marginal effects.
me <- marginal_effects(forests, eval = "atmean")
print(me)
print(me, latex = TRUE)
plot(me)

## Compute standard errors. This requires honest forests.
honest_me <- marginal_effects(honest_forests, eval = "atmean", inference = TRUE)
print(honest_me, latex = TRUE)
plot(honest_me)

Ordered Machine Learning

Description

Estimation strategy to estimate conditional choice probabilities for ordered non-numeric outcomes.

Usage

ordered_ml(Y = NULL, X = NULL, learner = "forest", scale = TRUE)

Arguments

Y

Outcome vector.
X

Covariate matrix (no intercept).
learner

String, either "forest" or "l1". Selects the base learner to estimate each expectation.
scale

Logical, whether to scale the covariates. Ignored if learner is not "l1".

Details

Ordered machine learning expresses conditional choice probabilities as the difference between the cumulative probabilities of two adjacent classes, which in turn can be expressed as conditional expectations of binary variables:

pm(Xi)=E[1(Yi≤m)∣Xi]−E[1(Yi≤m−1)∣Xi]p_m \left( X_i \right) = \mathbb{E} \left[ 1 \left( Y_i \leq m \right) | X_i \right] - \mathbb{E} \left[ 1 \left( Y_i \leq m - 1 \right) | X_i \right]

Then we can separately estimate each expectation using any regression algorithm and pick the difference between the m-th and the (m-1)-th estimated surfaces to estimate conditional probabilities.

ordered_ml combines this strategy with either regression forests or penalized logistic regressions with an L1 penalty, according to the user-specified parameter learner.

If learner == "forest", then the orf function is called from an external package, as this estimator has already been proposed by Lechner and Okasa (2019).

If learner == "l1", the penalty parameters are chosen via 10-fold cross-validation and model.matrix is used to handle non-numeric covariates. Additionally, if scale == TRUE, the covariates are scaled to have zero mean and unit variance.

Value

Object of class oml.

Author(s)

Riccardo Di Francesco

References

See Also

multinomial_ml, ocf

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Training-test split.
train_idx <- sample(seq_len(length(Y)), floor(length(Y) * 0.5))

Y_tr <- Y[train_idx]
X_tr <- X[train_idx, ]

Y_test <- Y[-train_idx]
X_test <- X[-train_idx, ]

## Fit ordered machine learning on training sample using two different learners.
ordered_forest <- ordered_ml(Y_tr, X_tr, learner = "forest")
ordered_l1 <- ordered_ml(Y_tr, X_tr, learner = "l1")

## Predict out of sample.
predictions_forest <- predict(ordered_forest, X_test)
predictions_l1 <- predict(ordered_l1, X_test)

## Compare predictions.
cbind(head(predictions_forest), head(predictions_l1))

Plot Method for ocf.marginal Objects

Description

Plots an ocf.marginal object.

Usage

## S3 method for class 'ocf.marginal'
plot(
  x,
  class_names = NULL,
  point_size = 2,
  facet_text_size = 12,
  legend_text_size = 10,
  ...
)

Arguments

x

An ocf.marginal object.
class_names

Character vector of length equal to x$n.classes to set class names in the plot.
point_size

Controls the points' size.
facet_text_size

Controls the facets' labels' size.
legend_text_size

Controls the legends' size.
...

Further arguments passed to or from other methods.

Details

If standard errors have been estimated, 95% confidence intervals are shown.

Value

Plots an ocf.marginal object.

Author(s)

Riccardo Di Francesco

References

See Also

ocf, marginal_effects.

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)

## Marginal effects at the mean.
me <- marginal_effects(forests, eval = "atmean")
plot(me)

## Add standard errors.
honest_forests <- ocf(Y, X, honesty = TRUE)
honest_me <- marginal_effects(honest_forests, eval = "atmean", inference = TRUE)
plot(honest_me)

Prediction Method for mml Objects

Description

Prediction method for class mml.

Usage

## S3 method for class 'mml'
predict(object, data = NULL, ...)

Arguments

object

An mml object.
data

Data set of class data.frame. It must contain the same covariates used to train the base learners. If data is NULL, then object$X is used.
...

Further arguments passed to or from other methods.

Details

If object$learner == "l1", then model.matrix is used to handle non-numeric covariates. If we also have object$scaling == TRUE, then data is scaled to have zero mean and unit variance.

Value

Matrix of predictions.

Author(s)

Riccardo Di Francesco

References

See Also

multinomial_ml, ordered_ml

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Training-test split.
train_idx <- sample(seq_len(length(Y)), floor(length(Y) * 0.5))

Y_tr <- Y[train_idx]
X_tr <- X[train_idx, ]

Y_test <- Y[-train_idx]
X_test <- X[-train_idx, ]

## Fit multinomial machine learning on training sample using two different learners.
multinomial_forest <- multinomial_ml(Y_tr, X_tr, learner = "forest")
multinomial_l1 <- multinomial_ml(Y_tr, X_tr, learner = "l1")

## Predict out of sample.
predictions_forest <- predict(multinomial_forest, X_test)
predictions_l1 <- predict(multinomial_l1, X_test)

## Compare predictions.
cbind(head(predictions_forest), head(predictions_l1))

Prediction Method for ocf Objects

Description

Prediction method for class ocf.

Usage

## S3 method for class 'ocf'
predict(object, data = NULL, type = "response", ...)

Arguments

object

An ocf object.
data

Data set of class data.frame. It must contain at least the same covariates used to train the forests. If data is NULL, then object$full_data is used.
type

Type of prediction. Either "response" or "terminalNodes".
...

Further arguments passed to or from other methods.

Details

If type == "response", the routine returns the predicted conditional class probabilities and the predicted class labels. If forests are honest, the predicted probabilities are honest.

If type == "terminalNodes", the IDs of the terminal node in each tree for each observation in data are returned.

Value

Desired predictions.

Author(s)

Riccardo Di Francesco

References

See Also

ocf, marginal_effects

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Training-test split.
train_idx <- sample(seq_len(length(Y)), floor(length(Y) * 0.5))

Y_tr <- Y[train_idx]
X_tr <- X[train_idx, ]

Y_test <- Y[-train_idx]
X_test <- X[-train_idx, ]

## Fit ocf on training sample.
forests <- ocf(Y_tr, X_tr)

## Predict on test sample.
predictions <- predict(forests, X_test)
head(predictions$probabilities)
predictions$classification

## Get terminal nodes.
predictions <- predict(forests, X_test, type = "terminalNodes")
predictions$forest.1[1:10, 1:20] # Rows are observations, columns are forests.

Prediction Method for oml Objects

Description

Prediction method for class oml.

Usage

## S3 method for class 'oml'
predict(object, data = NULL, ...)

Arguments

object

An oml object.
data

Data set of class data.frame. It must contain the same covariates used to train the base learners. If data is NULL, then object$X is used.
...

Further arguments passed to or from other methods.

Details

If object$learner == "l1", then model.matrix is used to handle non-numeric covariates. If we also have object$scaling == TRUE, then data is scaled to have zero mean and unit variance.

Value

Matrix of predictions.

Author(s)

Riccardo Di Francesco

References

See Also

multinomial_ml, ordered_ml

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Training-test split.
train_idx <- sample(seq_len(length(Y)), floor(length(Y) * 0.5))

Y_tr <- Y[train_idx]
X_tr <- X[train_idx, ]

Y_test <- Y[-train_idx]
X_test <- X[-train_idx, ]

## Fit ordered machine learning on training sample using two different learners.
ordered_forest <- ordered_ml(Y_tr, X_tr, learner = "forest")
ordered_l1 <- ordered_ml(Y_tr, X_tr, learner = "l1")

## Predict out of sample.
predictions_forest <- predict(ordered_forest, X_test)
predictions_l1 <- predict(ordered_l1, X_test)

## Compare predictions.
cbind(head(predictions_forest), head(predictions_l1))

Print Method for ocf Objects

Description

Prints an ocf object.

Usage

## S3 method for class 'ocf'
print(x, ...)

Arguments

x

An ocf object.
...

Further arguments passed to or from other methods.

Value

Prints an ocf object.

Author(s)

Riccardo Di Francesco

References

See Also

ocf

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)

## Print.
print(forests)

Print Method for ocf.marginal Objects

Description

Prints an ocf.marginal object.

Usage

## S3 method for class 'ocf.marginal'
print(x, latex = FALSE, ...)

Arguments

x

An ocf.marginal object.
latex

If TRUE, prints LATEX code.
...

Further arguments passed to or from other methods.

Details

Compilation of the LATEX code requires the following packages: booktabs, float, adjustbox. If standard errors have been estimated, they are printed in parenthesis below each point estimate.

Value

Prints an ocf.marginal object.

Author(s)

Riccardo Di Francesco

References

See Also

ocf, marginal_effects.

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)

## Marginal effects at the mean.
me <- marginal_effects(forests, eval = "atmean")
print(me)
print(me, latex = TRUE)

## Add standard errors.
honest_forests <- ocf(Y, X, honesty = TRUE)
honest_me <- marginal_effects(honest_forests, eval = "atmean", inference = TRUE)
print(honest_me, latex = TRUE)

Summary Method for ocf Objects

Description

Summarizes an ocf object.

Usage

## S3 method for class 'ocf'
summary(object, ...)

Arguments

object

An ocf object.
...

Further arguments passed to or from other methods.

Value

Summarizes an ocf object.

Author(s)

Riccardo Di Francesco

References

See Also

ocf, marginal_effects

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)

## Summary.
summary(forests)

Summary Method for ocf.marginal Objects

Description

Summarizes an ocf.marginal object.

Usage

## S3 method for class 'ocf.marginal'
summary(object, latex = FALSE, ...)

Arguments

object

An ocf.marginal object.
latex

If TRUE, prints LATEX code.
...

Further arguments passed to or from other methods.

Details

Compilation of the LATEX code requires the following packages: booktabs, float, adjustbox. If standard errors have been estimated, they are printed in parenthesis below each point estimate.

Value

Summarizes an ocf.marginal object.

Author(s)

Riccardo Di Francesco

References

See Also

ocf, marginal_effects.

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)

## Marginal effects at the mean.
me <- marginal_effects(forests, eval = "atmean")
summary(me)
summary(me, latex = TRUE)

## Add standard errors.
honest_forests <- ocf(Y, X, honesty = TRUE)
honest_me <- marginal_effects(honest_forests, eval = "atmean", inference = TRUE)
summary(honest_me, latex = TRUE)

Tree Information in Readable Format

Description

Extracts tree information from a ocf.forest object.

Usage

tree_info(object, tree = 1)

Arguments

object

ocf.forest object.
tree

Number of the tree of interest.

Details

Nodes and variables IDs are 0-indexed, i.e., node 0 is the root node.

All values smaller than or equal to splitval go to the left and all values larger go to the right.

Value

A data.frame with the following columns:

nodeID

Node IDs.
leftChild

IDs of the left child node.
rightChild

IDs of the right child node.
splitvarID

IDs of the splitting variable.
splitvarName

Name of the splitting variable.
splitval

Splitting value.
terminal

Logical, TRUE for terminal nodes.
prediction

One column with the predicted conditional class probabilities.

Author(s)

Riccardo Di Francesco

References

See Also

ocf

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(1000)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Fit ocf.
forests <- ocf(Y, X)

## Extract information from tenth tree of first forest.
info <- tree_info(forests$forests.info$forest.1, tree = 10)
head(info)