Package 'abclass' reference manual

Title:	Angle-Based Large-Margin Classifiers
Description:	Multi-category angle-based large-margin classifiers. See Zhang and Liu (2014) <doi:10.1093/biomet/asu017> for details.
Authors:	Wenjie Wang [aut, cre] , Eli Lilly and Company [cph]
Maintainer:	Wenjie Wang <[email protected]>
License:	GPL (>= 3)
Version:	0.5.0.9050
Built:	2025-01-05 05:02:57 UTC
Source:	https://github.com/wenjie2wang/abclass

Multi-Category Angle-Based Large-Margin Classifiers

Description

This package provides implementations of the multi-category angle-based classifiers (Zhang & Liu, 2014) with the large-margin unified machines (Liu, et al., 2011) for high-dimensional data.

References

Zhang, C., & Liu, Y. (2014). Multicategory Angle-Based Large-Margin Classification. Biometrika, 101(3), 625–640.

Liu, Y., Zhang, H. H., & Wu, Y. (2011). Hard or soft classification? large-margin unified machines. Journal of the American Statistical Association, 106(493), 166–177.

Multi-Category Angle-Based Classification

Description

Multi-category angle-based large-margin classifiers with regularization by the elastic-net or groupwise penalty.

Usage

abclass(
  x,
  y,
  intercept = TRUE,
  weight = NULL,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = list(),
  ...
)

abclass.control(
  lambda = NULL,
  alpha = 1,
  nlambda = 50L,
  lambda_min_ratio = NULL,
  penalty_factor = NULL,
  grouped = TRUE,
  group_penalty = c("lasso", "scad", "mcp"),
  offset = NULL,
  kappa_ratio = 0.9,
  lum_a = 1,
  lum_c = 0,
  boost_umin = -5,
  maxit = 10000L,
  epsilon = 1e-07,
  standardize = TRUE,
  varying_active_set = TRUE,
  verbose = 0L,
  ...
)
abclass(
  x,
  y,
  intercept = TRUE,
  weight = NULL,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = list(),
  ...
)

abclass.control(
  lambda = NULL,
  alpha = 1,
  nlambda = 50L,
  lambda_min_ratio = NULL,
  penalty_factor = NULL,
  grouped = TRUE,
  group_penalty = c("lasso", "scad", "mcp"),
  offset = NULL,
  kappa_ratio = 0.9,
  lum_a = 1,
  lum_c = 0,
  boost_umin = -5,
  maxit = 10000L,
  epsilon = 1e-07,
  standardize = TRUE,
  varying_active_set = TRUE,
  verbose = 0L,
  ...
)

Arguments

`x`	A numeric matrix representing the design matrix. No missing valus are allowed. The coefficient estimates for constant columns will be zero. Thus, one should set the argument `intercept` to `TRUE` to include an intercept term instead of adding an all-one column to `x`.
`y`	An integer vector, a character vector, or a factor vector representing the response label.
`intercept`	A logical value indicating if an intercept should be considered in the model. The default value is `TRUE` and the intercept is excluded from regularization.
`weight`	A numeric vector for nonnegative observation weights. Equal observation weights are used by default.
`loss`	A character value specifying the loss function. The available options are `"logistic"` for the logistic deviance loss, `"boost"` for the exponential loss approximating Boosting machines, `"hinge-boost"` for hybrid of SVM and AdaBoost machine, and `"lum"` for largin-margin unified machines (LUM). See Liu, et al. (2011) for details.
`control`	A list of control parameters. See `abclass.control()` for details.
`...`	Other control parameters passed to `abclass.control()`.
`lambda`	A numeric vector specifying the tuning parameter lambda. A data-driven lambda sequence will be generated and used according to specified `alpha`, `nlambda` and `lambda_min_ratio` if this argument is left as `NULL` by default. The specified `lambda` will be sorted in decreasing order internally and only the unique values will be kept.
`alpha`	A numeric value in [0, 1] representing the mixing parameter alpha. The default value is `1.0`.
`nlambda`	A positive integer specifying the length of the internally generated lambda sequence. This argument will be ignored if a valid `lambda` is specified. The default value is `50`.
`lambda_min_ratio`	A positive number specifying the ratio of the smallest lambda parameter to the largest lambda parameter. The default value is set to `1e-4` if the sample size is larger than the number of predictors, and `1e-2` otherwise.
`penalty_factor`	A numerical vector with nonnegative values specifying the adaptive penalty factors for individual predictors (excluding intercept).
`grouped`	A logicial value. Experimental flag to apply group penalties.
`group_penalty`	A character vector specifying the name of the group penalty.
`offset`	An optional numeric matrix for offsets of the decision functions.
`kappa_ratio`	A positive number within (0, 1) specifying the ratio of reciprocal gamma parameter for group SCAD or group MCP. A close-to-zero `kappa_ratio` would give a solution close to lasso solution.
`lum_a`	A positive number greater than one representing the parameter a in LUM, which will be used only if `loss = "lum"`. The default value is `1.0`.
`lum_c`	A nonnegative number specifying the parameter c in LUM, which will be used only if `loss = "hinge-boost"` or `loss = "lum"`. The default value is `1.0`.
`boost_umin`	A negative number for adjusting the boosting loss for the internal majorization procedure.
`maxit`	A positive integer specifying the maximum number of iteration. The default value is `10^4`.
`epsilon`	A positive number specifying the relative tolerance that determines convergence. The default value is `1e-7`.
`standardize`	A logical value indicating if each column of the design matrix should be standardized internally to have mean zero and standard deviation equal to the sample size. The default value is `TRUE`. Notice that the coefficient estimates are always returned on the original scale.
`varying_active_set`	A logical value indicating if the active set should be updated after each cycle of coordinate-majorization-descent algorithm. The default value is `TRUE` for usually more efficient estimation procedure.
`verbose`	A nonnegative integer specifying if the estimation procedure is allowed to print out intermediate steps/results. The default value is `0` for silent estimation procedure.

Value

The function abclass() returns an object of class abclass representing a trained classifier; The function abclass.control() returns an object of class abclass.control representing a list of control parameters.

References

Zhang, C., & Liu, Y. (2014). Multicategory Angle-Based Large-Margin Classification. Biometrika, 101(3), 625–640.

Liu, Y., Zhang, H. H., & Wu, Y. (2011). Hard or soft classification? large-margin unified machines. Journal of the American Statistical Association, 106(493), 166–177.

Examples

library(abclass)
set.seed(123)

## toy examples for demonstration purpose
## reference: example 1 in Zhang and Liu (2014)
ntrain <- 100 # size of training set
ntest <- 100  # size of testing set
p0 <- 5       # number of actual predictors
p1 <- 5       # number of random predictors
k <- 5        # number of categories

n <- ntrain + ntest; p <- p0 + p1
train_idx <- seq_len(ntrain)
y <- sample(k, size = n, replace = TRUE)         # response
mu <- matrix(rnorm(p0 * k), nrow = k, ncol = p0) # mean vector
## normalize the mean vector so that they are distributed on the unit circle
mu <- mu / apply(mu, 1, function(a) sqrt(sum(a ^ 2)))
x0 <- t(sapply(y, function(i) rnorm(p0, mean = mu[i, ], sd = 0.25)))
x1 <- matrix(rnorm(p1 * n, sd = 0.3), nrow = n, ncol = p1)
x <- cbind(x0, x1)
train_x <- x[train_idx, ]
test_x <- x[- train_idx, ]
y <- factor(paste0("label_", y))
train_y <- y[train_idx]
test_y <- y[- train_idx]

## Regularization through ridge penalty
control1 <- abclass.control(nlambda = 5, lambda_min_ratio = 1e-3,
                            alpha = 0, grouped = FALSE)
model1 <- abclass(train_x, train_y, loss = "logistic",
                  control = control1)
pred1 <- predict(model1, test_x, s = 5)
table(test_y, pred1)
mean(test_y == pred1) # accuracy

## groupwise regularization via group lasso
model2 <- abclass(train_x, train_y, loss = "boost",
                  grouped = TRUE, nlambda = 5)
pred2 <- predict(model2, test_x, s = 5)
table(test_y, pred2)
mean(test_y == pred2) # accuracy
library(abclass)
set.seed(123)

## toy examples for demonstration purpose
## reference: example 1 in Zhang and Liu (2014)
ntrain <- 100 # size of training set
ntest <- 100  # size of testing set
p0 <- 5       # number of actual predictors
p1 <- 5       # number of random predictors
k <- 5        # number of categories

n <- ntrain + ntest; p <- p0 + p1
train_idx <- seq_len(ntrain)
y <- sample(k, size = n, replace = TRUE)         # response
mu <- matrix(rnorm(p0 * k), nrow = k, ncol = p0) # mean vector
## normalize the mean vector so that they are distributed on the unit circle
mu <- mu / apply(mu, 1, function(a) sqrt(sum(a ^ 2)))
x0 <- t(sapply(y, function(i) rnorm(p0, mean = mu[i, ], sd = 0.25)))
x1 <- matrix(rnorm(p1 * n, sd = 0.3), nrow = n, ncol = p1)
x <- cbind(x0, x1)
train_x <- x[train_idx, ]
test_x <- x[- train_idx, ]
y <- factor(paste0("label_", y))
train_y <- y[train_idx]
test_y <- y[- train_idx]

## Regularization through ridge penalty
control1 <- abclass.control(nlambda = 5, lambda_min_ratio = 1e-3,
                            alpha = 0, grouped = FALSE)
model1 <- abclass(train_x, train_y, loss = "logistic",
                  control = control1)
pred1 <- predict(model1, test_x, s = 5)
table(test_y, pred1)
mean(test_y == pred1) # accuracy

## groupwise regularization via group lasso
model2 <- abclass(train_x, train_y, loss = "boost",
                  grouped = TRUE, nlambda = 5)
pred2 <- predict(model2, test_x, s = 5)
table(test_y, pred2)
mean(test_y == pred2) # accuracy

Estimate Propensity Score by the Angle-Based Classifiers

Description

A wrap function to estimate the propensity score by the multi-category angle-based large-margin classifiers.

Usage

abclass_propscore(
  x,
  treatment,
  intercept = TRUE,
  weight = NULL,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = list(),
  tuning = c("et", "cv"),
  ...
)
abclass_propscore(
  x,
  treatment,
  intercept = TRUE,
  weight = NULL,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = list(),
  tuning = c("et", "cv"),
  ...
)

Arguments

`x`	A numeric matrix representing the design matrix. No missing valus are allowed. The coefficient estimates for constant columns will be zero. Thus, one should set the argument `intercept` to `TRUE` to include an intercept term instead of adding an all-one column to `x`.
`treatment`	The assigned treatments represented by a character, integer, numeric, or factor vector.
`intercept`	A logical value indicating if an intercept should be considered in the model. The default value is `TRUE` and the intercept is excluded from regularization.
`weight`	A numeric vector for nonnegative observation weights. Equal observation weights are used by default.
`loss`	A character value specifying the loss function. The available options are `"logistic"` for the logistic deviance loss, `"boost"` for the exponential loss approximating Boosting machines, `"hinge-boost"` for hybrid of SVM and AdaBoost machine, and `"lum"` for largin-margin unified machines (LUM). See Liu, et al. (2011) for details.
`control`	A list of control parameters. See `abclass.control()` for details.
`tuning`	A character vector specifying the tuning method. This argument will be ignored if a single `lambda` is specified through `control`.
`...`	Other arguments passed to the corresponding methods.

Coefficient Estimates of A Trained Angle-Based Classifier

Description

Extract coefficient estimates from an abclass object.

Usage

## S3 method for class 'abclass'
coef(object, selection = c("cv_1se", "cv_min", "all"), ...)
## S3 method for class 'abclass'
coef(object, selection = c("cv_1se", "cv_min", "all"), ...)

Arguments

`object`	An object of class `abclass`.
`selection`	An integer vector for the indices of solution path or a character value specifying how to select a particular set of coefficient estimates from the entire solution path. If the specified `abclass` object contains the cross-validation results, one may set `selection` to `"cv_min"` (or `"cv_1se"`) for the estimates giving the smallest cross-validation error (or the set of estimates resulted from the largest lambda within one standard error of the smallest cross-validation error). The entire solution path will be returned in an array if `selection = "all"` or no cross-validation results are available in the specified `abclass` object.
`...`	Other arguments not used now.

Value

A matrix representing the coefficient estimates or an array representing all the selected solutions.

Examples

## see examples of `abclass()`.

## see examples of `abclass()`.

Coefficient Estimates of A Trained Sup-Norm Classifier

Description

Extract coefficient estimates from an supclass object.

Usage

## S3 method for class 'supclass'
coef(object, selection = c("cv_1se", "cv_min", "all"), ...)
## S3 method for class 'supclass'
coef(object, selection = c("cv_1se", "cv_min", "all"), ...)

Arguments

`object`	An object of class `supclass`.
`selection`	An integer vector for the indices of solution or a character value specifying how to select a particular set of coefficient estimates from the entire solution path. If the specified `supclass` object contains the cross-validation results, one may set `selection` to `"cv_min"` (or `"cv_1se"`) for the estimates giving the smallest cross-validation error (or the set of estimates resulted from the largest lambda within one standard error of the smallest cross-validation error). The entire solution path will be returned in an array if `selection = "all"` or no cross-validation results are available in the specified `supclass` object.
`...`	Other arguments not used now.

Value

A matrix representing the coefficient estimates or an array representing all the selected solutions.

Examples

## see examples of `supclass()`.

## see examples of `supclass()`.

Tune Angle-Based Classifiers by Cross-Validation

Description

Tune the regularization parameter for an angle-based large-margin classifier by cross-validation.

Usage

cv.abclass(
  x,
  y,
  intercept = TRUE,
  weight = NULL,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = list(),
  nfolds = 5L,
  stratified = TRUE,
  alignment = c("fraction", "lambda"),
  refit = FALSE,
  ...
)
cv.abclass(
  x,
  y,
  intercept = TRUE,
  weight = NULL,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = list(),
  nfolds = 5L,
  stratified = TRUE,
  alignment = c("fraction", "lambda"),
  refit = FALSE,
  ...
)

Arguments

`x`	A numeric matrix representing the design matrix. No missing valus are allowed. The coefficient estimates for constant columns will be zero. Thus, one should set the argument `intercept` to `TRUE` to include an intercept term instead of adding an all-one column to `x`.
`y`	An integer vector, a character vector, or a factor vector representing the response label.
`intercept`	A logical value indicating if an intercept should be considered in the model. The default value is `TRUE` and the intercept is excluded from regularization.
`weight`	A numeric vector for nonnegative observation weights. Equal observation weights are used by default.
`loss`	A character value specifying the loss function. The available options are `"logistic"` for the logistic deviance loss, `"boost"` for the exponential loss approximating Boosting machines, `"hinge-boost"` for hybrid of SVM and AdaBoost machine, and `"lum"` for largin-margin unified machines (LUM). See Liu, et al. (2011) for details.
`control`	A list of control parameters. See `abclass.control()` for details.
`nfolds`	A positive integer specifying the number of folds for cross-validation. Five-folds cross-validation will be used by default. An error will be thrown out if the `nfolds` is specified to be less than 2.
`stratified`	A logical value indicating if the cross-validation procedure should be stratified by the response label. The default value is `TRUE` to ensure the same number of categories be used in validation and training.
`alignment`	A character vector specifying how to align the lambda sequence used in the main fit with the cross-validation fits. The available options are `"fraction"` for allowing cross-validation fits to have their own lambda sequences and `"lambda"` for using the same lambda sequence of the main fit. The option `"lambda"` will be applied if a meaningful `lambda` is specified. The default value is `"fraction"`.
`refit`	A logical value indicating if a new classifier should be trained using the selected predictors or a named list that will be passed to `abclass.control()` to specify how the new classifier should be trained.
`...`	Other control parameters passed to `abclass.control()`.

Value

An S3 object of class cv.abclass and abclass.

MOML with Cross-Validation

Description

Tune the regularization parameter for MOML by cross-validation.

Usage

cv.moml(
  x,
  treatment,
  reward,
  propensity_score,
  intercept = TRUE,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = moml.control(),
  nfolds = 5L,
  stratified = TRUE,
  alignment = c("fraction", "lambda"),
  refit = FALSE,
  ...
)
cv.moml(
  x,
  treatment,
  reward,
  propensity_score,
  intercept = TRUE,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = moml.control(),
  nfolds = 5L,
  stratified = TRUE,
  alignment = c("fraction", "lambda"),
  refit = FALSE,
  ...
)

Arguments

`x`	A numeric matrix representing the design matrix. No missing valus are allowed. The coefficient estimates for constant columns will be zero. Thus, one should set the argument `intercept` to `TRUE` to include an intercept term instead of adding an all-one column to `x`.
`treatment`	The assigned treatments represented by a character, integer, numeric, or factor vector.
`reward`	A numeric vector representing the rewards. It is assumed that a larger reward is more desirable.
`propensity_score`	A numeric vector taking values between 0 and 1 representing the propensity score.
`intercept`	A logical value indicating if an intercept should be considered in the model. The default value is `TRUE` and the intercept is excluded from regularization.
`loss`	A character value specifying the loss function. The available options are `"logistic"` for the logistic deviance loss, `"boost"` for the exponential loss approximating Boosting machines, `"hinge-boost"` for hybrid of SVM and AdaBoost machine, and `"lum"` for largin-margin unified machines (LUM). See Liu, et al. (2011) for details.
`control`	A list of control parameters. See `abclass.control()` for details.
`nfolds`	A positive integer specifying the number of folds for cross-validation. Five-folds cross-validation will be used by default. An error will be thrown out if the `nfolds` is specified to be less than 2.
`stratified`	A logical value indicating if the cross-validation procedure should be stratified by the response label. The default value is `TRUE` to ensure the same number of categories be used in validation and training.
`alignment`	A character vector specifying how to align the lambda sequence used in the main fit with the cross-validation fits. The available options are `"fraction"` for allowing cross-validation fits to have their own lambda sequences and `"lambda"` for using the same lambda sequence of the main fit. The option `"lambda"` will be applied if a meaningful `lambda` is specified. The default value is `"fraction"`.
`refit`	A logical value indicating if a new classifier should be trained using the selected predictors or a named list that will be passed to `abclass.control()` to specify how the new classifier should be trained.
`...`	Other arguments passed to the control function, which calls the `abclass.control()` internally.

Tune Sup-Norm Classifiers by Cross-Validation

Description

Tune the regularization parameter lambda for a sup-norm classifier by cross-validation.

Usage

cv.supclass(
  x,
  y,
  model = c("logistic", "psvm", "svm"),
  penalty = c("lasso", "scad"),
  start = NULL,
  control = list(),
  nfolds = 5L,
  stratified = TRUE,
  ...
)
cv.supclass(
  x,
  y,
  model = c("logistic", "psvm", "svm"),
  penalty = c("lasso", "scad"),
  start = NULL,
  control = list(),
  nfolds = 5L,
  stratified = TRUE,
  ...
)

Arguments

`x`	A numeric matrix representing the design matrix. No missing valus are allowed. The coefficient estimates for constant columns will be zero. Thus, one should set the argument `intercept` to `TRUE` to include an intercept term instead of adding an all-one column to `x`.
`y`	An integer vector, a character vector, or a factor vector representing the response label.
`model`	A charactor vector specifying the classification model. The available options are `"logistic"` for multi-nomial logistic regression model, `"psvm"` for proximal support vector machine (PSVM), `"svm"` for multi-category support vector machine.
`penalty`	A charactor vector specifying the penalty function for the sup-norms. The available options are `"lasso"` for sup-norm regularization proposed by Zhang et al. (2008) and `"scad"` for supSCAD regularization proposed by Li & Zhang (2021).
`start`	A numeric matrix representing the starting values for the quadratic approximation procedure behind the scene.
`control`	A list with named elements.
`nfolds`	A positive integer specifying the number of folds for cross-validation. Five-folds cross-validation will be used by default. An error will be thrown out if the `nfolds` is specified to be less than 2.
`stratified`	A logical value indicating if the cross-validation procedure should be stratified by the response label. The default value is `TRUE` to ensure the same number of categories be used in validation and training.
`...`	Other arguments passed to `supclass`.

Value

An S3 object of class cv.supclass.

Tune Angle-Based Classifiers by ET-Lasso

Description

Tune the regularization parameter for an angle-based large-margin classifier by the ET-Lasso method (Yang, et al., 2019).

Usage

et.abclass(
  x,
  y,
  intercept = TRUE,
  weight = NULL,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = list(),
  nstages = 2L,
  nfolds = 0L,
  stratified = TRUE,
  alignment = c("fraction", "lambda"),
  refit = FALSE,
  ...
)
et.abclass(
  x,
  y,
  intercept = TRUE,
  weight = NULL,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = list(),
  nstages = 2L,
  nfolds = 0L,
  stratified = TRUE,
  alignment = c("fraction", "lambda"),
  refit = FALSE,
  ...
)

Arguments

`x`	A numeric matrix representing the design matrix. No missing valus are allowed. The coefficient estimates for constant columns will be zero. Thus, one should set the argument `intercept` to `TRUE` to include an intercept term instead of adding an all-one column to `x`.
`y`	An integer vector, a character vector, or a factor vector representing the response label.
`intercept`	A logical value indicating if an intercept should be considered in the model. The default value is `TRUE` and the intercept is excluded from regularization.
`weight`	A numeric vector for nonnegative observation weights. Equal observation weights are used by default.
`loss`	A character value specifying the loss function. The available options are `"logistic"` for the logistic deviance loss, `"boost"` for the exponential loss approximating Boosting machines, `"hinge-boost"` for hybrid of SVM and AdaBoost machine, and `"lum"` for largin-margin unified machines (LUM). See Liu, et al. (2011) for details.
`control`	A list of control parameters. See `abclass.control()` for details.
`nstages`	A positive integer specifying for the number of stages in the ET-Lasso procedure. By default, two rounds of tuning by random permutations will be performed as suggested in Yang, et al. (2019).
`nfolds`	A positive integer specifying the number of folds for cross-validation. Five-folds cross-validation will be used by default. An error will be thrown out if the `nfolds` is specified to be less than 2.
`stratified`	A logical value indicating if the cross-validation procedure should be stratified by the response label. The default value is `TRUE` to ensure the same number of categories be used in validation and training.
`alignment`	A character vector specifying how to align the lambda sequence used in the main fit with the cross-validation fits. The available options are `"fraction"` for allowing cross-validation fits to have their own lambda sequences and `"lambda"` for using the same lambda sequence of the main fit. The option `"lambda"` will be applied if a meaningful `lambda` is specified. The default value is `"fraction"`.
`refit`	A logical value indicating if a new classifier should be trained using the selected predictors or a named list that will be passed to `abclass.control()` to specify how the new classifier should be trained.
`...`	Other control parameters passed to `abclass.control()`.

Details

The ET-Lasso procedure is intended for tuning the lambda parameter solely. The arguments regarding cross-validation, nfolds, stratified, and alignment, allow one to estimate the prediction accuracy by cross-validation for the model estimates resulted from the ET-Lasso procedure, which can be helpful for one to choose other tuning parameters (e.g., alpha).

Value

An S3 object of class et.abclass and abclass.

References

Yang, S., Wen, J., Zhan, X., & Kifer, D. (2019). ET-Lasso: A new efficient tuning of lasso-type regularization for high-dimensional data. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 607–616).

MOML with ET-Lasso

Description

Tune the regularization parameter for MOML by the ET-Lasso method (Yang, et al., 2019).

Usage

et.moml(
  x,
  treatment,
  reward,
  propensity_score,
  intercept = TRUE,
  weight = NULL,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = list(),
  nstages = 2,
  refit = list(lambda = 1e-06),
  ...
)
et.moml(
  x,
  treatment,
  reward,
  propensity_score,
  intercept = TRUE,
  weight = NULL,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = list(),
  nstages = 2,
  refit = list(lambda = 1e-06),
  ...
)

Arguments

`x`	A numeric matrix representing the design matrix. No missing valus are allowed. The coefficient estimates for constant columns will be zero. Thus, one should set the argument `intercept` to `TRUE` to include an intercept term instead of adding an all-one column to `x`.
`treatment`	The assigned treatments represented by a character, integer, numeric, or factor vector.
`reward`	A numeric vector representing the rewards. It is assumed that a larger reward is more desirable.
`propensity_score`	A numeric vector taking values between 0 and 1 representing the propensity score.
`intercept`	A logical value indicating if an intercept should be considered in the model. The default value is `TRUE` and the intercept is excluded from regularization.
`weight`	A numeric vector for nonnegative observation weights. Equal observation weights are used by default.
`loss`	A character value specifying the loss function. The available options are `"logistic"` for the logistic deviance loss, `"boost"` for the exponential loss approximating Boosting machines, `"hinge-boost"` for hybrid of SVM and AdaBoost machine, and `"lum"` for largin-margin unified machines (LUM). See Liu, et al. (2011) for details.
`control`	A list of control parameters. See `abclass.control()` for details.
`nstages`	A positive integer specifying for the number of stages in the ET-Lasso procedure. By default, two rounds of tuning by random permutations will be performed as suggested in Yang, et al. (2019).
`refit`	A logical value indicating if a new classifier should be trained using the selected predictors or a named list that will be passed to `abclass.control()` to specify how the new classifier should be trained.
`...`	Other arguments passed to the control function, which calls the `abclass.control()` internally.

References

Multi-Category Outcome-Weighted Margin-Based Learning (MOML)

Description

Performs the outcome-weighted margin-based learning for multicategory treatments proposed by Zhang, et al. (2020).

Usage

moml(
  x,
  treatment,
  reward,
  propensity_score,
  intercept = TRUE,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = moml.control(),
  ...
)

moml.control(...)
moml(
  x,
  treatment,
  reward,
  propensity_score,
  intercept = TRUE,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = moml.control(),
  ...
)

moml.control(...)

Arguments

`x`	A numeric matrix representing the design matrix. No missing valus are allowed. The coefficient estimates for constant columns will be zero. Thus, one should set the argument `intercept` to `TRUE` to include an intercept term instead of adding an all-one column to `x`.
`treatment`	The assigned treatments represented by a character, integer, numeric, or factor vector.
`reward`	A numeric vector representing the rewards. It is assumed that a larger reward is more desirable.
`propensity_score`	A numeric vector taking values between 0 and 1 representing the propensity score.
`intercept`	A logical value indicating if an intercept should be considered in the model. The default value is `TRUE` and the intercept is excluded from regularization.
`loss`	A character value specifying the loss function. The available options are `"logistic"` for the logistic deviance loss, `"boost"` for the exponential loss approximating Boosting machines, `"hinge-boost"` for hybrid of SVM and AdaBoost machine, and `"lum"` for largin-margin unified machines (LUM). See Liu, et al. (2011) for details.
`control`	A list of control parameters. See `abclass.control()` for details.
`...`	Other arguments passed to the control function, which calls the `abclass.control()` internally.

References

Zhang, C., Chen, J., Fu, H., He, X., Zhao, Y., & Liu, Y. (2020). Multicategory outcome weighted margin-based learning for estimating individualized treatment rules. Statistica Sinica, 30, 1857–1879.

Prediction by A Trained Angle-Based Classifier

Description

Predict class labels or estimate conditional probabilities for the specified new data.

Usage

## S3 method for class 'abclass'
predict(
  object,
  newx,
  type = c("class", "probability", "link"),
  selection = c("cv_1se", "cv_min", "all"),
  newoffset = NULL,
  ...
)
## S3 method for class 'abclass'
predict(
  object,
  newx,
  type = c("class", "probability", "link"),
  selection = c("cv_1se", "cv_min", "all"),
  newoffset = NULL,
  ...
)

Arguments

`object`	An object of class `abclass`.
`newx`	A numeric matrix representing the design matrix for predictions.
`type`	A character value specifying the desired type of predictions. The available options are `"class"` for predicted labels, `"probability"` for class conditional probability estimates, and `"link"` for decision functions.
`selection`	An integer vector for the solution indices or a character value specifying how to select a particular set of coefficient estimates from the entire solution path for prediction. If the specified `object` contains the cross-validation results, one may set `selection` to `"cv_min"` (or `"cv_1se"`) for using the estimates giving the smallest cross-validation error (or the set of estimates resulted from the largest lambda within one standard error of the smallest cross-validation error) or prediction. The prediction for the entire solution path will be returned in a list if `selection = "all"` or no cross-validation results are available in the specified `object`.
`newoffset`	An optional numeric matrix for the offsets.
`...`	Other arguments not used now.

Value

A vector representing the predictions or a list containing the predictions for each set of estimates along the solution path.

Examples

## see examples of `abclass()`.

## see examples of `abclass()`.

Predictions from A Trained Sup-Norm Classifier

Description

Predict class labels or estimate conditional probabilities for the specified new data.

Usage

## S3 method for class 'supclass'
predict(
  object,
  newx,
  type = c("class", "probability", "link"),
  selection = c("cv_1se", "cv_min", "all"),
  ...
)
## S3 method for class 'supclass'
predict(
  object,
  newx,
  type = c("class", "probability", "link"),
  selection = c("cv_1se", "cv_min", "all"),
  ...
)

Arguments

`object`	An object of class `abclass`.
`newx`	A numeric matrix representing the design matrix for predictions.
`type`	A character value specifying the desired type of predictions. The available options are `"class"` for predicted labels, `"probability"` for class conditional probability estimates, and `"link"` for decision functions.
`selection`	An integer vector for the solution indices or a character value specifying how to select a particular set of coefficient estimates from the entire solution path for prediction. If the specified `object` contains the cross-validation results, one may set `selection` to `"cv_min"` (or `"cv_1se"`) for using the estimates giving the smallest cross-validation error (or the set of estimates resulted from the largest lambda within one standard error of the smallest cross-validation error) or prediction. The prediction for the entire solution path will be returned in a list if `selection = "all"` or no cross-validation results are available in the specified `object`.
`...`	Other arguments not used now.

Value

A vector representing the predictions or a list containing the predictions for each set of estimates.

Examples

## see examples of `supclass()`.

## see examples of `supclass()`.

Multi-Category Classifiers with Sup-Norm Regularization

Description

Experimental implementations of multi-category classifiers with sup-norm penalties proposed by Zhang, et al. (2008) and Li & Zhang (2021).

Usage

supclass(
  x,
  y,
  model = c("logistic", "psvm", "svm"),
  penalty = c("lasso", "scad"),
  start = NULL,
  control = list(),
  ...
)

supclass.control(
  lambda = 0.1,
  adaptive_weight = NULL,
  scad_a = 3.7,
  maxit = 50,
  epsilon = 1e-04,
  shrinkage = 1e-04,
  ridge_lambda = 1,
  warm_start = TRUE,
  standardize = TRUE,
  Rglpk = list(verbose = TRUE, tm_limit = 6e+05),
  ...
)
supclass(
  x,
  y,
  model = c("logistic", "psvm", "svm"),
  penalty = c("lasso", "scad"),
  start = NULL,
  control = list(),
  ...
)

supclass.control(
  lambda = 0.1,
  adaptive_weight = NULL,
  scad_a = 3.7,
  maxit = 50,
  epsilon = 1e-04,
  shrinkage = 1e-04,
  ridge_lambda = 1,
  warm_start = TRUE,
  standardize = TRUE,
  Rglpk = list(verbose = TRUE, tm_limit = 6e+05),
  ...
)

Arguments

`x`	A numeric matrix representing the design matrix. No missing valus are allowed. The coefficient estimates for constant columns will be zero. Thus, one should set the argument `intercept` to `TRUE` to include an intercept term instead of adding an all-one column to `x`.
`y`	An integer vector, a character vector, or a factor vector representing the response label.
`model`	A charactor vector specifying the classification model. The available options are `"logistic"` for multi-nomial logistic regression model, `"psvm"` for proximal support vector machine (PSVM), `"svm"` for multi-category support vector machine.
`penalty`	A charactor vector specifying the penalty function for the sup-norms. The available options are `"lasso"` for sup-norm regularization proposed by Zhang et al. (2008) and `"scad"` for supSCAD regularization proposed by Li & Zhang (2021).
`start`	A numeric matrix representing the starting values for the quadratic approximation procedure behind the scene.
`control`	A list with named elements.
`...`	Optional control parameters passed to the `supclass.control()`.
`lambda`	A numeric vector specifying the tuning parameter lambda. The default value is `0.1`. Users should tune this parameter for a better model fit. The specified lambda will be sorted in decreasing order internally and only the unique values will be kept.
`adaptive_weight`	A numeric vector or matrix representing the adaptive penalty weights. The default value is `NULL` for equal weights. Zhang, et al. (2008) proposed two ways to employ the adaptive weights. The first approach applies the weights to the sup-norm of coefficient estimates, while the second approach applies element-wise multiplication to the weights and coefficient estimates inside the sup-norms. The first or second approach will be applied if a numeric vector or matrix is specified, respectively. The adaptive weights are supported for lasso penalty only.
`scad_a`	A positive number specifying the tuning parameter a in the SCAD penalty.
`maxit`	A positive integer specifying the maximum number of iteration. The default value is `50` as suggested in Li & Zhang (2021).
`epsilon`	A positive number specifying the relative tolerance that determines convergence. The default value is `1e-7`.
`shrinkage`	A nonnegative tolerance to shrink estimates with sup-norm close enough to zero (within the specified tolerance) to zeros. The default value is `1e-4`.
`ridge_lambda`	TODO The tuning parameter lambda of the ridge penalty used to set the starting values for multinomial logistic models.
`warm_start`	A logical value indicating if the estimates from last lambda should be used as the starting values for the next lambda. If `FALSE`, the user-specified starting values will be used instead.
`standardize`	A logical value indicating if a standardization procedure should be performed so that each column of the design matrix has mean zero and standardization
`Rglpk`	A named list that consists of control parameters passed to `Rglpk_solve_LP()`.

Details

For the multinomial logistic model or the proximal SVM model, this function utilizes the function quadprog::solve.QP() to solve the equivalent quadratic problem; For the multi-class SVM, this function utilizes GNU GLPK to solve the equivalent linear programming problem via the package Rglpk. It is recommended to use a recent version of GLPK.

References

Zhang, H. H., Liu, Y., Wu, Y., & Zhu, J. (2008). Variable selection for the multicategory SVM via adaptive sup-norm regularization. Electronic Journal of Statistics, 2, 149–167.

Li, N., & Zhang, H. H. (2021). Sparse learning with non-convex penalty in multi-classification. Journal of Data Science, 19(1), 56–74.

Examples

library(abclass)
set.seed(123)

## toy examples for demonstration purpose
## reference: example 1 in Zhang and Liu (2014)
ntrain <- 100 # size of training set
ntest <- 1000 # size of testing set
p0 <- 2       # number of actual predictors
p1 <- 2       # number of random predictors
k <- 3        # number of categories

n <- ntrain + ntest; p <- p0 + p1
train_idx <- seq_len(ntrain)
y <- sample(k, size = n, replace = TRUE)         # response
mu <- matrix(rnorm(p0 * k), nrow = k, ncol = p0) # mean vector
## normalize the mean vector so that they are distributed on the unit circle
mu <- mu / apply(mu, 1, function(a) sqrt(sum(a ^ 2)))
x0 <- t(sapply(y, function(i) rnorm(p0, mean = mu[i, ], sd = 0.25)))
x1 <- matrix(rnorm(p1 * n, sd = 0.3), nrow = n, ncol = p1)
x <- cbind(x0, x1)
train_x <- x[train_idx, ]
test_x <- x[- train_idx, ]
y <- factor(paste0("label_", y))
train_y <- y[train_idx]
test_y <- y[- train_idx]

## regularization with the supnorm lasso penalty
options("mc.cores" = 1)
model <- supclass(train_x, train_y, model = "psvm", penalty = "lasso")
pred <- predict(model, test_x)
table(test_y, pred)
mean(test_y == pred) # accuracy
library(abclass)
set.seed(123)

## toy examples for demonstration purpose
## reference: example 1 in Zhang and Liu (2014)
ntrain <- 100 # size of training set
ntest <- 1000 # size of testing set
p0 <- 2       # number of actual predictors
p1 <- 2       # number of random predictors
k <- 3        # number of categories

n <- ntrain + ntest; p <- p0 + p1
train_idx <- seq_len(ntrain)
y <- sample(k, size = n, replace = TRUE)         # response
mu <- matrix(rnorm(p0 * k), nrow = k, ncol = p0) # mean vector
## normalize the mean vector so that they are distributed on the unit circle
mu <- mu / apply(mu, 1, function(a) sqrt(sum(a ^ 2)))
x0 <- t(sapply(y, function(i) rnorm(p0, mean = mu[i, ], sd = 0.25)))
x1 <- matrix(rnorm(p1 * n, sd = 0.3), nrow = n, ncol = p1)
x <- cbind(x0, x1)
train_x <- x[train_idx, ]
test_x <- x[- train_idx, ]
y <- factor(paste0("label_", y))
train_y <- y[train_idx]
test_y <- y[- train_idx]

## regularization with the supnorm lasso penalty
options("mc.cores" = 1)
model <- supclass(train_x, train_y, model = "psvm", penalty = "lasso")
pred <- predict(model, test_x)
table(test_y, pred)
mean(test_y == pred) # accuracy

Simplex Vertices for The Angle-Based Classification

Description

Simplex Vertices for The Angle-Based Classification

Usage

vertex(k)
vertex(k)

Arguments

`k`	Number of classes, a positive integer that is greater than one.

Value

A (k-1) by k matrix that consists of vertices in columns.

References

Lange, K., & Tong Wu, Tong (2008). An MM algorithm for multicategory vertex discriminant analysis. Journal of Computational and Graphical Statistics, 17(3), 527–544.

Package 'abclass'

Help Index

Multi-Category Angle-Based Large-Margin Classifiers

Description

References

Multi-Category Angle-Based Classification

Description

Usage

Arguments

Value

References

Examples

Estimate Propensity Score by the Angle-Based Classifiers

Description

Usage

Arguments

Coefficient Estimates of A Trained Angle-Based Classifier

Description

Usage

Arguments

Value

Examples

Coefficient Estimates of A Trained Sup-Norm Classifier

Description

Usage

Arguments

Value

Examples

Tune Angle-Based Classifiers by Cross-Validation

Description

Usage

Arguments

Value

MOML with Cross-Validation

Description

Usage

Arguments

Tune Sup-Norm Classifiers by Cross-Validation

Description

Usage

Arguments

Value

Tune Angle-Based Classifiers by ET-Lasso

Description

Usage

Arguments

Details

Value

References

MOML with ET-Lasso

Description

Usage

Arguments

References

Multi-Category Outcome-Weighted Margin-Based Learning (MOML)

Description

Usage

Arguments

References

Prediction by A Trained Angle-Based Classifier

Description

Usage

Arguments

Value

Examples

Predictions from A Trained Sup-Norm Classifier

Description

Usage

Arguments

Value

Examples

Multi-Category Classifiers with Sup-Norm Regularization

Description

Usage

Arguments

Details

References

Examples

Simplex Vertices for The Angle-Based Classification

Description