| Title: | Ordered Random Forests |
|---|---|
| Description: | An implementation of the Ordered Forest estimator as developed in Lechner & Okasa (2019) <arXiv:1907.02436>. The Ordered Forest flexibly estimates the conditional probabilities of models with ordered categorical outcomes (so-called ordered choice models). Additionally to common machine learning algorithms the 'orf' package provides functions for estimating marginal effects as well as statistical inference thereof and thus provides similar output as in standard econometric models for ordered choice. The core forest algorithm relies on the fast C++ forest implementation from the 'ranger' package (Wright & Ziegler, 2017) <arXiv:1508.04409>. |
| Authors: | Gabriel Okasa [aut, cre], Michael Lechner [ctb] |
| Maintainer: | Gabriel Okasa <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.4.9000 |
| Built: | 2024-11-07 04:58:04 UTC |
| Source: | https://github.com/okasag/orf |
An implementation of the Ordered Forest estimator as developed
in Lechner & Okasa (2019). The Ordered Forest flexibly
estimates the conditional probabilities of models with ordered
categorical outcomes (so-called ordered choice models).
Additionally to common machine learning algorithms the orf
package provides functions for estimating marginal effects as well
as statistical inference thereof and thus provides similar output
as in standard econometric models for ordered choice. The core
forest algorithm relies on the fast C++ forest implementation
from the ranger package (Wright & Ziegler, 2017).
Gabriel Okasa, Michael Lechner
Lechner, M., & Okasa, G. (2019). Random Forest Estimation of the Ordered Choice Model. arXiv preprint arXiv:1907.02436. https://arxiv.org/abs/1907.02436
Goller, D., Knaus, M. C., Lechner, M., & Okasa, G. (2021). Predicting Match Outcomes in Football by an Ordered Forest Estimator. A Modern Guide to Sports Economics. Edward Elgar Publishing, 335-355. doi:10.4337/9781789906530.00026
Wright, M. N. & Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. J Stat Softw 77:1-17. doi:10.18637/jss.v077.i01.
## Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest with default settings orf_fit <- orf(X, Y) # print output of the orf estimation print(orf_fit) # show summary of the orf estimation summary(orf_fit) # plot the estimated probability distributions plot(orf_fit) # predict with the estimated orf predict(orf_fit) # estimate marginal effects of the orf margins(orf_fit)## Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest with default settings orf_fit <- orf(X, Y) # print output of the orf estimation print(orf_fit) # show summary of the orf estimation summary(orf_fit) # plot the estimated probability distributions plot(orf_fit) # predict with the estimated orf predict(orf_fit) # estimate marginal effects of the orf margins(orf_fit)
S3 generic method for estimation of marginal effects
of an Ordered Forest objects of class orf.
margins(forest, eval = NULL, inference = NULL, window = NULL, newdata = NULL)margins(forest, eval = NULL, inference = NULL, window = NULL, newdata = NULL)
forest |
estimated Ordered Forest object of class |
eval |
string, defining evaluation point for marginal effects. These can be one of "mean", "atmean", or "atmedian". (Default is "mean") |
inference |
logical, if TRUE inference on marginal effects will be conducted (default is inherited from the |
window |
numeric, share of standard deviation of X to be used for evaluation of the marginal effect (default is 0.1) |
newdata |
numeric matrix X containing the new observations for which the marginal effects should be estimated |
Gabriel Okasa
margins.orf, summary.margins.orf and print.margins.orf
## Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest orf_fit <- orf(X, Y) # estimate default marginal effects of the orf orf_margins <- margins(orf_fit)## Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest orf_fit <- orf(X, Y) # estimate default marginal effects of the orf orf_margins <- margins(orf_fit)
S3 method for estimation of marginal effects
of an Ordered Forest objects of class orf.
## S3 method for class 'orf' margins(forest, eval = NULL, inference = NULL, window = NULL, newdata = NULL)## S3 method for class 'orf' margins(forest, eval = NULL, inference = NULL, window = NULL, newdata = NULL)
forest |
estimated Ordered Forest object of class |
eval |
string, defining evaluation point for marginal effects. These can be one of "mean", "atmean", or "atmedian". (Default is "mean") |
inference |
logical, if TRUE inference on marginal effects will be conducted (default is inherited from the |
window |
numeric, share of standard deviation of X to be used for evaluation of the marginal effect (default is 0.1) |
newdata |
numeric matrix X containing the new observations for which the marginal effects should be estimated |
margins.orf estimates marginal effects at the mean, at the median, or
the mean marginal effects, depending on the eval argument. It is advised
to increase the number of subsampling replications in the supplied orf
object as the estimation of the marginal effects is a more demanding exercise
than a simple Ordered Forest estimation/prediction. Additionally to the estimation
of the marginal effects, the weight-based inference for the effects is supported
as well. Note, that the inference procedure is much more computationally exhausting
exercise due to the computation of the forest weights. Additionally, the evaluation
window for the marginal effects can be regulated through the window argument.
Furthermore, new data for which marginal effects should be computed can be supplied
as well as long as it lies within the support of X.
object of type margins.orf with following elements
info |
info containing forest inputs and data used |
effects |
marginal effects |
variances |
variances of marginal effects |
errors |
standard errors of marginal effects |
tvalues |
t-values of marginal effects |
pvalues |
p-values of marginal effects |
Gabriel Okasa
summary.margins.orf, print.margins.orf
## Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest orf_fit <- orf(X, Y) # estimate marginal effects of the orf (default) orf_margins <- margins(orf_fit) # estimate marginal effects evaluated at the mean orf_margins <- margins(orf_fit, eval = "atmean") # estimate marginal effects with inference # (orf object has to be estimated with honesty and subsampling) orf_margins <- margins(orf_fit, inference = TRUE) # estimate marginal effects with custom window size orf_margins <- margins(orf_fit, window = 0.5) # estimate marginal effects for some new data (within support of X) orf_margins <- margins(orf_fit, newdata = X[1:10, ]) # estimate marginal effects with all custom settings orf_margins <- margins(orf_fit, eval = "atmedian", inference = TRUE, window = 0.5, newdata = X[1:10, ])## Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest orf_fit <- orf(X, Y) # estimate marginal effects of the orf (default) orf_margins <- margins(orf_fit) # estimate marginal effects evaluated at the mean orf_margins <- margins(orf_fit, eval = "atmean") # estimate marginal effects with inference # (orf object has to be estimated with honesty and subsampling) orf_margins <- margins(orf_fit, inference = TRUE) # estimate marginal effects with custom window size orf_margins <- margins(orf_fit, window = 0.5) # estimate marginal effects for some new data (within support of X) orf_margins <- margins(orf_fit, newdata = X[1:10, ]) # estimate marginal effects with all custom settings orf_margins <- margins(orf_fit, eval = "atmedian", inference = TRUE, window = 0.5, newdata = X[1:10, ])
A simulated example dataset with ordered categorical outcome variable containing different types of covariates for illustration purposes.
odataodata
A data frame with 1000 rows and 5 variables
For the exact data generating process, see the example below.
Y |
ordered outcome, classes 1, 2, and 3 |
X1 |
continuous covariate, N(0,1) |
X2 |
categorical covariate, values 1, 2, and 3 |
X3 |
binary covariate, values 0 and 1 |
X4 |
continuous covariate, N(0,10) |
# generate example data # set seed for replicability set.seed(123) # number of observations n <- 1000 # various covariates X1 <- rnorm(n, 0, 1) # continuous X2 <- rbinom(n, 2, 0.5) # categorical X3 <- rbinom(n, 1, 0.5) # dummy X4 <- rnorm(n, 0, 10) # noise # bind into matrix X <- as.matrix(cbind(X1, X2, X3, X4)) # deterministic component deterministic <- X1 + X2 + X3 # generate continuous outcome with logistic error Y <- deterministic + rlogis(n, 0, 1) # thresholds for continuous outcome cuts <- quantile(Y, c(0, 1/3, 2/3, 1)) # discretize outcome into ordered classes 1, 2, 3 Y <- as.numeric(cut(Y, breaks = cuts, include.lowest = TRUE)) # save data as a dataframe odata <- as.data.frame(cbind(Y, X)) # end of data generating# generate example data # set seed for replicability set.seed(123) # number of observations n <- 1000 # various covariates X1 <- rnorm(n, 0, 1) # continuous X2 <- rbinom(n, 2, 0.5) # categorical X3 <- rbinom(n, 1, 0.5) # dummy X4 <- rnorm(n, 0, 10) # noise # bind into matrix X <- as.matrix(cbind(X1, X2, X3, X4)) # deterministic component deterministic <- X1 + X2 + X3 # generate continuous outcome with logistic error Y <- deterministic + rlogis(n, 0, 1) # thresholds for continuous outcome cuts <- quantile(Y, c(0, 1/3, 2/3, 1)) # discretize outcome into ordered classes 1, 2, 3 Y <- as.numeric(cut(Y, breaks = cuts, include.lowest = TRUE)) # save data as a dataframe odata <- as.data.frame(cbind(Y, X)) # end of data generating
An implementation of the Ordered Forest estimator as developed
in Lechner & Okasa (2019). The Ordered Forest flexibly
estimates the conditional probabilities of models with ordered
categorical outcomes (so-called ordered choice models).
Additionally to common machine learning algorithms the orf
package provides functions for estimating marginal effects as well
as statistical inference thereof and thus provides similar output
as in standard econometric models for ordered choice. The core
forest algorithm relies on the fast C++ forest implementation
from the ranger package (Wright & Ziegler, 2017).
orf( X, Y, num.trees = 1000, mtry = NULL, min.node.size = NULL, replace = FALSE, sample.fraction = NULL, honesty = TRUE, honesty.fraction = NULL, inference = FALSE, importance = FALSE )orf( X, Y, num.trees = 1000, mtry = NULL, min.node.size = NULL, replace = FALSE, sample.fraction = NULL, honesty = TRUE, honesty.fraction = NULL, inference = FALSE, importance = FALSE )
X |
numeric matrix of features |
Y |
numeric vector of outcomes |
num.trees |
scalar, number of trees in a forest, i.e. bootstrap replications (default is 1000 trees) |
mtry |
scalar, number of randomly selected features (default is the squared root of number of features, rounded up to the nearest integer) |
min.node.size |
scalar, minimum node size, i.e. leaf size of a tree (default is 5 observations) |
replace |
logical, if TRUE sampling with replacement, i.e. bootstrap is used to grow the trees, otherwise subsampling without replacement is used (default is set to FALSE) |
sample.fraction |
scalar, subsampling rate (default is 1 for bootstrap and 0.5 for subsampling) |
honesty |
logical, if TRUE honest forest is built using sample splitting (default is set to TRUE) |
honesty.fraction |
scalar, share of observations belonging to honest sample not used for growing the forest (default is 0.5) |
inference |
logical, if TRUE the weight based inference is conducted (default is set to FALSE) |
importance |
logical, if TRUE variable importance measure based on permutation is conducted (default is set to FALSE) |
The Ordered Forest function, orf, estimates the conditional ordered choice
probabilities, i.e. P[Y=m|X=x]. Additionally, weight-based inference for
the probability predictions can be conducted as well. If inference is desired,
the Ordered Forest must be estimated with honesty and subsampling.
If prediction only is desired, estimation without honesty and with bootstrapping
is recommended for optimal prediction performance.
In order to estimate the Ordered Forest user must supply the data in form of
matrix of covariates X and a vector of outcomes 'codeY to the orf
function. These data inputs are also the only inputs that must be specified by
the user without any defaults. Further optional arguments include the classical forest
hyperparameters such as number of trees, num.trees, number of randomly
selected features, mtry, and the minimum leaf size, min.node.size.
The forest building scheme is regulated by the replace argument, meaning
bootstrapping if replace = TRUE or subsampling if replace = FALSE.
For the case of subsampling, sample.fraction argument regulates the subsampling
rate. Further, honest forest is estimated if the honesty argument is set to
TRUE, which is also the default. Similarly, the fraction of the sample used
for the honest estimation is regulated by the honesty.fraction argument.
The default setting conducts a 50:50 sample split, which is also generally advised
to follow for optimal performance. Inference procedure of the Ordered Forest is based on
the forest weights and is controlled by the inference argument. Note, that
such weight-based inference is computationally demanding exercise due to the estimation
of the forest weights and as such longer computation time is to be expected. Lastly,
the importance argument turns on and off the permutation based variable
importance.
orf is compatible with standard R commands such as
predict, margins, plot, summary and print.
For further details, see examples below.
object of type orf with following elements
forests |
saved forests trained for |
info |
info containing forest inputs and data used |
predictions |
predicted values for class probabilities |
variances |
variances of predicted values |
importance |
weighted measure of permutation based variable importance |
accuracy |
oob measures for mean squared error and ranked probability score |
Gabriel Okasa
Lechner, M., & Okasa, G. (2019). Random Forest Estimation of the Ordered Choice Model. arXiv preprint arXiv:1907.02436. https://arxiv.org/abs/1907.02436
Goller, D., Knaus, M. C., Lechner, M., & Okasa, G. (2021). Predicting Match Outcomes in Football by an Ordered Forest Estimator. A Modern Guide to Sports Economics. Edward Elgar Publishing, 335-355. doi:10.4337/9781789906530.00026
Wright, M. N. & Ziegler, A. (2017). ranger: A fast implementation of random forests for high dimensional data in C++ and R. J Stat Softw 77:1-17. doi:10.18637/jss.v077.i01.
summary.orf, plot.orf
predict.orf, margins.orf
## Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest with default parameters orf_fit <- orf(X, Y) # estimate Ordered Forest with own tuning parameters orf_fit <- orf(X, Y, num.trees = 2000, mtry = 3, min.node.size = 10) # estimate Ordered Forest with bootstrapping and without honesty orf_fit <- orf(X, Y, replace = TRUE, honesty = FALSE) # estimate Ordered Forest with subsampling and with honesty orf_fit <- orf(X, Y, replace = FALSE, honesty = TRUE) # estimate Ordered Forest with subsampling and with honesty # with own tuning for subsample fraction and honesty fraction orf_fit <- orf(X, Y, replace = FALSE, sample.fraction = 0.5, honesty = TRUE, honesty.fraction = 0.5) # estimate Ordered Forest with subsampling and with honesty and with inference # (for inference, subsampling and honesty are required) orf_fit <- orf(X, Y, replace = FALSE, honesty = TRUE, inference = TRUE) # estimate Ordered Forest with simple variable importance measure orf_fit <- orf(X, Y, importance = TRUE) # estimate Ordered Forest with all custom settings orf_fit <- orf(X, Y, num.trees = 2000, mtry = 3, min.node.size = 10, replace = TRUE, sample.fraction = 1, honesty = FALSE, honesty.fraction = 0, inference = FALSE, importance = FALSE)## Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest with default parameters orf_fit <- orf(X, Y) # estimate Ordered Forest with own tuning parameters orf_fit <- orf(X, Y, num.trees = 2000, mtry = 3, min.node.size = 10) # estimate Ordered Forest with bootstrapping and without honesty orf_fit <- orf(X, Y, replace = TRUE, honesty = FALSE) # estimate Ordered Forest with subsampling and with honesty orf_fit <- orf(X, Y, replace = FALSE, honesty = TRUE) # estimate Ordered Forest with subsampling and with honesty # with own tuning for subsample fraction and honesty fraction orf_fit <- orf(X, Y, replace = FALSE, sample.fraction = 0.5, honesty = TRUE, honesty.fraction = 0.5) # estimate Ordered Forest with subsampling and with honesty and with inference # (for inference, subsampling and honesty are required) orf_fit <- orf(X, Y, replace = FALSE, honesty = TRUE, inference = TRUE) # estimate Ordered Forest with simple variable importance measure orf_fit <- orf(X, Y, importance = TRUE) # estimate Ordered Forest with all custom settings orf_fit <- orf(X, Y, num.trees = 2000, mtry = 3, min.node.size = 10, replace = TRUE, sample.fraction = 1, honesty = FALSE, honesty.fraction = 0, inference = FALSE, importance = FALSE)
plot the probability distributions estimated by the Ordered Forest object of class orf
## S3 method for class 'orf' plot(x, ...)## S3 method for class 'orf' plot(x, ...)
x |
estimated Ordered Forest object of class |
... |
further arguments (currently ignored) |
plot.orf generates probability distributions, i.e. density plots of estimated
ordered probabilities by the Ordered Forest for each outcome class considered.
The plots effectively visualize the estimated probability density in contrast to
a real observed ordered outcome class and as such provide a visual inspection of
the overall in-sample estimation accuracy. The dashed lines locate the means of
the respective probability distributions.
Gabriel Okasa
# Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest orf_fit <- orf(X, Y) # plot the estimated probability distributions plot(orf_fit)# Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest orf_fit <- orf(X, Y) # plot the estimated probability distributions plot(orf_fit)
Prediction for new observations based on estimated Ordered Forest of class orf
## S3 method for class 'orf' predict(object, newdata = NULL, type = NULL, inference = NULL, ...)## S3 method for class 'orf' predict(object, newdata = NULL, type = NULL, inference = NULL, ...)
object |
estimated Ordered Forest object of class |
newdata |
numeric matrix X containing the observations for which the outcomes should be predicted |
type |
string, specifying the type of the prediction, These can be either "probs" or "p" for probabilities and "class" or "c" for classes. (Default is "probs"). |
inference |
logical, if TRUE variances for the predictions will be estimated (only feasible for probability predictions). |
... |
further arguments (currently ignored) |
predict.orf estimates the conditional ordered choice probabilities,
i.e. P[Y=m|X=x] for new data points (matrix X containing new observations
of covariates) based on the estimated Ordered Forest object of class orf.
Furthermore, weight-based inference for the probability predictions can be
conducted as well. If inference is desired, the supplied Ordered Forest must be
estimated with honesty and subsampling. If prediction only is desired, estimation
without honesty and with bootstrapping is recommended for optimal prediction
performance. Additionally to the probability predictions, class predictions can
be estimated as well using the type argument. In this case, the predicted
classes are obtained as classes with the highest predicted probability.
object of class orf.prediction with following elements
info |
info containing forest inputs and data used |
predictions |
predicted values |
variances |
variances of predicted values |
Gabriel Okasa
summary.orf.prediction, print.orf.prediction
# Ordered Forest require(orf) # load example data data(odata) # specify response and covariates for train and test idx <- sample(seq(1, nrow(odata), 1), 0.8*nrow(odata)) # train set Y_train <- as.numeric(odata[idx, 1]) X_train <- as.matrix(odata[idx, -1]) # test set Y_test <- as.numeric(odata[-idx, 1]) X_test <- as.matrix(odata[-idx, -1]) # estimate Ordered Forest orf_fit <- orf(X_train, Y_train) # predict the probabilities with the estimated orf orf_pred <- predict(orf_fit, newdata = X_test) # predict the probabilities with estimated orf together with variances orf_pred <- predict(orf_fit, newdata = X_test, inference = TRUE) # predict the classes with estimated orf orf_pred <- predict(orf_fit, newdata = X_test, type = "class")# Ordered Forest require(orf) # load example data data(odata) # specify response and covariates for train and test idx <- sample(seq(1, nrow(odata), 1), 0.8*nrow(odata)) # train set Y_train <- as.numeric(odata[idx, 1]) X_train <- as.matrix(odata[idx, -1]) # test set Y_test <- as.numeric(odata[-idx, 1]) X_test <- as.matrix(odata[-idx, -1]) # estimate Ordered Forest orf_fit <- orf(X_train, Y_train) # predict the probabilities with the estimated orf orf_pred <- predict(orf_fit, newdata = X_test) # predict the probabilities with estimated orf together with variances orf_pred <- predict(orf_fit, newdata = X_test, inference = TRUE) # predict the classes with estimated orf orf_pred <- predict(orf_fit, newdata = X_test, type = "class")
print of estimated marginal effects of the Ordered Forest of class margins.orf
## S3 method for class 'margins.orf' print(x, ...)## S3 method for class 'margins.orf' print(x, ...)
x |
estimated Ordered Forest Marginal Effect object of type |
... |
further arguments (currently ignored) |
print.margins.orf provides a first glimpse of the Ordered Forest
marginal effects, printed directly to the R console. The printed information
contains the results for the marginal effects for each covariate and each outcome class.
Gabriel Okasa
## Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest orf_fit <- orf(X, Y) # estimate marginal effects of the orf orf_margins <- margins(orf_fit) # print marginal effects print(orf_margins)## Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest orf_fit <- orf(X, Y) # estimate marginal effects of the orf orf_margins <- margins(orf_fit) # print marginal effects print(orf_margins)
print of an estimated Ordered Forest object of class orf
## S3 method for class 'orf' print(x, ...)## S3 method for class 'orf' print(x, ...)
x |
estimated Ordered Forest object of class |
... |
further arguments (currently ignored) |
print.orf provides a first glimpse of the Ordered Forest estimation,
printed directly to the R console. The printed information contains
the main inputs of the orf function.
Gabriel Okasa
# Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest orf_fit <- orf(X, Y) # print output of the orf estimation print(orf_fit)# Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest orf_fit <- orf(X, Y) # print output of the orf estimation print(orf_fit)
print of Ordered Forest predictions of class orf.prediction
## S3 method for class 'orf.prediction' print(x, ...)## S3 method for class 'orf.prediction' print(x, ...)
x |
predicted Ordered Forest object of class |
... |
further arguments (currently ignored) |
print.orf.prediction provides a first glimpse of the Ordered Forest
prediction, printed directly to the R console. The printed information
contains the main inputs of the predict.orf function.
Gabriel Okasa
# Ordered Forest require(orf) # load example data data(odata) # specify response and covariates for train and test idx <- sample(seq(1, nrow(odata), 1), 0.8*nrow(odata)) # train set Y_train <- as.numeric(odata[idx, 1]) X_train <- as.matrix(odata[idx, -1]) # test set Y_test <- as.numeric(odata[-idx, 1]) X_test <- as.matrix(odata[-idx, -1]) # estimate Ordered Forest orf_fit <- orf(X_train, Y_train) # predict the probabilities with the estimated orf orf_pred <- predict(orf_fit, newdata = X_test) # print the prediction object print(orf_pred)# Ordered Forest require(orf) # load example data data(odata) # specify response and covariates for train and test idx <- sample(seq(1, nrow(odata), 1), 0.8*nrow(odata)) # train set Y_train <- as.numeric(odata[idx, 1]) X_train <- as.matrix(odata[idx, -1]) # test set Y_test <- as.numeric(odata[-idx, 1]) X_test <- as.matrix(odata[-idx, -1]) # estimate Ordered Forest orf_fit <- orf(X_train, Y_train) # predict the probabilities with the estimated orf orf_pred <- predict(orf_fit, newdata = X_test) # print the prediction object print(orf_pred)
summary of estimated marginal effects of the Ordered Forest of class margins.orf
## S3 method for class 'margins.orf' summary(object, latex = FALSE, ...)## S3 method for class 'margins.orf' summary(object, latex = FALSE, ...)
object |
estimated Ordered Forest Marginal Effect object of type |
latex |
logical, if TRUE latex coded summary will be generated (default is FALSE) |
... |
further arguments (currently ignored) |
summary.margins.orf provides estimation results of the Ordered Forest
marginal effects. The summary contains the results for the marginal effects
for each covariate and each outcome class, optionally with inference as well.
Furthermore, summary output as a LaTeX table is supported in order to directly
extract the results for the documentation.
Gabriel Okasa
## Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest orf_fit <- orf(X, Y) # estimate marginal effects of the orf orf_margins <- margins(orf_fit) # summary of marginal effects summary(orf_margins) # summary of marginal effects coded in LaTeX summary(orf_margins, latex = TRUE)## Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest orf_fit <- orf(X, Y) # estimate marginal effects of the orf orf_margins <- margins(orf_fit) # summary of marginal effects summary(orf_margins) # summary of marginal effects coded in LaTeX summary(orf_margins, latex = TRUE)
summary of an estimated Ordered Forest object of class orf
## S3 method for class 'orf' summary(object, latex = FALSE, ...)## S3 method for class 'orf' summary(object, latex = FALSE, ...)
object |
estimated Ordered Forest object of class |
latex |
logical, if TRUE latex coded summary will be generated (default is FALSE) |
... |
further arguments (currently ignored) |
summary.orf provides a short summary of the Ordered Forest estimation,
including the input information regarding the values of hyperparameters as
well as the output information regarding the prediction accuracy.
Gabriel Okasa
# Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest orf_fit <- orf(X, Y) # show summary of the orf estimation summary(orf_fit) # show summary of the orf estimation coded in LaTeX summary(orf_fit, latex = TRUE)# Ordered Forest require(orf) # load example data data(odata) # specify response and covariates Y <- as.numeric(odata[, 1]) X <- as.matrix(odata[, -1]) # estimate Ordered Forest orf_fit <- orf(X, Y) # show summary of the orf estimation summary(orf_fit) # show summary of the orf estimation coded in LaTeX summary(orf_fit, latex = TRUE)
summary of Ordered Forest predictions of class orf.prediction
## S3 method for class 'orf.prediction' summary(object, latex = FALSE, ...)## S3 method for class 'orf.prediction' summary(object, latex = FALSE, ...)
object |
predicted Ordered Forest object of class |
latex |
logical, if TRUE latex coded summary will be generated (default is FALSE) |
... |
further arguments (currently ignored) |
summary.orf.prediction provides a main summary of the Ordered Forest
prediction, including the input information regarding the values of hyperparameters
as well as the inputs of the predict.orf function.
Gabriel Okasa
# Ordered Forest require(orf) # load example data data(odata) # specify response and covariates for train and test idx <- sample(seq(1, nrow(odata), 1), 0.8*nrow(odata)) # train set Y_train <- as.numeric(odata[idx, 1]) X_train <- as.matrix(odata[idx, -1]) # test set Y_test <- as.numeric(odata[-idx, 1]) X_test <- as.matrix(odata[-idx, -1]) # estimate Ordered Forest orf_fit <- orf(X_train, Y_train) # predict the probabilities with the estimated orf orf_pred <- predict(orf_fit, newdata = X_test) # summary of the prediction object summary(orf_pred) # show summary of the orf prediction coded in LaTeX summary(orf_pred, latex = TRUE)# Ordered Forest require(orf) # load example data data(odata) # specify response and covariates for train and test idx <- sample(seq(1, nrow(odata), 1), 0.8*nrow(odata)) # train set Y_train <- as.numeric(odata[idx, 1]) X_train <- as.matrix(odata[idx, -1]) # test set Y_test <- as.numeric(odata[-idx, 1]) X_test <- as.matrix(odata[-idx, -1]) # estimate Ordered Forest orf_fit <- orf(X_train, Y_train) # predict the probabilities with the estimated orf orf_pred <- predict(orf_fit, newdata = X_test) # summary of the prediction object summary(orf_pred) # show summary of the orf prediction coded in LaTeX summary(orf_pred, latex = TRUE)