Package {ppforest2}

NCI60 Dataset

Description

cDNA microarrays were used to examine the variation in gene expression among the 60 cell lines. The cell lines are derived from tumors with different sites of origin. This dataset contain 61 observations and 30 feature variables from 8 different tissue types.

Usage

data(NCI60)

Format

A data frame with 61 rows and 31 variables

Details

Type

has 8 different tissue types, 9 cases of breast, 5 cases of central nervous system (CNS), 7 cases pf colon, 8 cases of leukemia, 8 cases of melanoma, 9 cases of non-small-cell lung carcinoma (NSCLC), 6 cases of ovarian and 9 cases of renal.

Gene1 to Gen 30

gene expression information

Source

Dudoit, S., Fridlyand, J. and Speed, T. P. (2002). Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. Journal of the American statistical Association 97 77-87.

In-bag row indices per tree.

Description

Returns a list where element 'i' is the integer vector of row indices (1-based, with replacement) drawn into the bootstrap sample of tree 'i'.

Usage

bag_samples(model)

Arguments

Value

A list of integer vectors, one per tree.

See Also

oob_samples

Disabled binarization strategy (placeholder).

Description

Placeholder binarizer for specs where binarization never fires — notably regression, where 'grouping_by_cutpoint()' always produces a 2-group partition at each node. Selecting binarize_disabled() documents that intent explicitly; if binarization is ever invoked at runtime with this strategy configured, training aborts with a clear error rather than silently passing through. Default for regression specs.

Usage

binarize_disabled()

Value

A binarize_strategy object.

See Also

binarize_largest_gap

Examples

binarize_disabled()

Largest-gap binarization strategy.

Description

Creates a binarization strategy that reduces multiclass nodes to binary by projecting group means and splitting at the largest gap. Default for classification specs.

Usage

binarize_largest_gap()

Value

A binarize_strategy object.

See Also

pptr, pprf, binarize_disabled

Examples

binarize_largest_gap()

California Housing Dataset

Description

Median house values and demographic/geographic predictors for 20,433 California block groups, derived from the 1990 US Census. Classic regression benchmark. Originally released by Pace & Barry (1997); this version drops the categorical 'ocean_proximity' column (to keep the bundled schema all-numeric, matching the rest of the package's datasets) and removes rows with missing 'total_bedrooms'. The regression target, 'median_house_value', is the last column.

Usage

data(california_housing)

Format

A data frame with 20,433 rows and 9 variables.

Details

longitude

Longitude of the block group (degrees, negative = west).

latitude

Latitude of the block group (degrees).

housing_median_age

Median age of houses in the block group (years).

total_rooms

Total number of rooms.

total_bedrooms

Total number of bedrooms.

population

Block group population.

households

Number of households.

median_income

Median household income (tens of thousands of USD).

median_house_value

Regression target: median house value (USD).

Source

Pace, R.K. and Barry, R. (1997) Sparse spatial autoregressions. Statistics & Probability Letters, 33, 291-297.

Australian Crabs Dataset

Description

Measurements on rock crabs of the genus Leptograpsus. The dataset contains 200 observations from two species of crab (blue and orange), there are 50 specimens of each sex of each species, collected on site at Fremantle, Western Australia.

Type

is the group variable and has 4 groups with the combinations of species and sex (BlueMale, BlueFemale, OrangeMale and OrangeFemale)

.

FL

the size of the frontal lobe length, in mm

RW

rear width, in mm

CL

length of midline of the carapace, in mm

CW

maximum width of carapace, in mm

BD

depth of the body; for females, measured after displacement of the abdomen, in mm

Usage

data(crab)

Format

A data frame with 200 rows and 6 variables

Source

Campbell, N. A. & Mahon, R. J. (1974), A Multivariate Study of Variation in Two Species of Rock Crab of genus Leptograpsus, Australian Journal of Zoology 22(3), 417 - 425.

Crabs Dataset

Description

Measurements of 200 crabs from the genus Leptograpsus, with 50 crabs for each of two colour forms and both sexes. Measurements include the frontal lobe size, rear width, carapace length, carapace width, and body depth.

Format

A data frame with 200 rows and 8 variables:

Type

Species code, with 'B' indicating blue and 'O' indicating orange.

sex

Sex of the crab, with 'M' indicating male and 'F' indicating female.

FL

Size of the frontal lobe in mm.

RW

Rear width in mm.

CL

Carapace length in mm.

CW

Carapace width in mm.

BD

Body depth in mm.

Source

Campbell, N.A., and Mahon, R.J. (1974) A multivariate study of variation in two species of rock crab of genus Leptograpsus. Australian Journal of Zoology, 22, 417-425.

Mean-of-means split cutpoint strategy.

Description

Creates a split cutpoint strategy that splits at the midpoint between group means. This is the default (and currently only) split cutpoint.

Usage

cutpoint_mean_of_means()

Value

A cutpoint_strategy object.

See Also

pptr, pprf, pp_pda, vars_uniform

Examples

cutpoint_mean_of_means()

Fish Catch Dataset

Description

There are 159 fishes of 7 species are caught and measured. Altogether there are 7 variables. All the fishes are caught from the same lake(Laengelmavesi) near Tampere in Finland.

Type

has 7 fish groups, with 35 cases of Bream, 11 cases of Parkki, 56 cases of Perch 17 cases of Pike, 20 cases of Roach, 14 cases of Smelt and 6 cases of Whitewish.

weight

Weight of the fish (in grams)

length1

Length from the nose to the beginning of the tail (in cm)

length2

Length from the nose to the notch of the tail (in cm)

length3

Length from the nose to the end of the tail (in cm)

height

Maximal height as % of Length3

width

Maximal width as % of Length3

Usage

data(fishcatch)

Format

A data frame with 159 rows and 7 variables

Source

https://jse.amstat.org/jse_data_archive.htm

Fitted (in-sample) predictions from a ppforest2 model.

Description

Returns predictions on the training data — a factor for classification, a numeric vector for regression. For forests, training predictions are optimistic; use oob_predictions() for an honest estimate.

Usage

## S3 method for class 'ppmodel'
fitted(object, ...)

Arguments

Value

A factor (classification) or numeric vector (regression), length equal to the number of training observations.

See Also

residuals.ppmodel, oob_predictions

Formula extractor for ppforest2 models.

Description

Formula extractor for ppforest2 models.

Usage

## S3 method for class 'ppmodel'
formula(x, ...)

Arguments

Value

The formula the model was trained with, or 'NULL' for matrix-interface fits.

Glass Dataset

Description

Contains measurements 214 observations of 6 types of glass; defined in terms of their oxide content.

Type

has 6 types of glasses

X1

refractive index

X2

Sodium (unit measurement: weight percent in corresponding oxide).

X3

Magnesium

X4

Aluminum

X5

Silicon

X6

Potassium

X7

Calcium

X8

Barium

X9

Iron

Usage

data(glass)

Format

A data frame with 214 rows and 10 variables

Cutpoint-based grouping strategy (regression).

Description

Creates a grouping strategy for regression trees: observations are split by the cutpoint in projected space, then each child's observations are sorted by the continuous response and median-split into 2 new groups for the next projection-pursuit step. This is the default grouping strategy for regression.

Usage

grouping_by_cutpoint()

Value

A grouping_strategy object.

See Also

pptr, pprf, grouping_by_label

Examples

grouping_by_cutpoint()

Label-based grouping strategy.

Description

Creates a grouping strategy that routes all observations of a group to the same child node. This is the default grouping strategy for classification.

Usage

grouping_by_label()

Value

A grouping_strategy object.

See Also

pptr, pprf

Examples

grouping_by_label()

Image Dataset

Description

Contains 2310 observations of instances from 7 outdoor images

Type

has 7 types of outdoor images, brickface, cement, foliage, grass, path, sky, and window.

X1

the column of the center pixel of the region

X2

the row of the center pixel of the region.

X3

the number of pixels in a region = 9.

X4

the results of a line extraction algorithm that counts how many lines of length 5 (any orientation) with low contrast, less than or equal to 5, go through the region.

X5

measure the contrast of horizontally adjacent pixels in the region. There are 6, the mean and standard deviation are given. This attribute is used as a vertical edge detector.

X6

X5 sd

X7

measures the contrast of vertically adjacent pixels. Used for horizontal line detection.

X8

sd X7

X9

the average over the region of (R + G + B)/3

X10

the average over the region of the R value.

X11

the average over the region of the B value.

X12

the average over the region of the G value.

X13

measure the excess red: (2R - (G + B))

X14

measure the excess blue: (2B - (G + R))

X15

measure the excess green: (2G - (R + B))

X16

3-d nonlinear transformation of RGB. (Algorithm can be found in Foley and VanDam, Fundamentals of Interactive Computer Graphics)

X17

mean of X16

X18

hue mean

Usage

data(image)

Format

A data frame contains 2310 observations and 19 variables

Majority-vote leaf strategy.

Description

Creates a leaf strategy that assigns the majority group label as the leaf prediction. When groups are tied, the smallest label wins. This is the default leaf strategy for classification.

Usage

leaf_majority_vote()

Value

A leaf_strategy object.

See Also

pptr, pprf

Examples

leaf_majority_vote()

Mean-response leaf strategy.

Description

Creates a leaf strategy that predicts the mean of the continuous response values for the observations in the leaf. Used for regression trees; requires a numeric response.

Usage

leaf_mean_response()

Value

A leaf_strategy object.

See Also

pptr, pprf, leaf_majority_vote

Examples

leaf_mean_response()

Leukemia Dataset

Description

This dataset comes from a study of gene expression in two types of acute leukemias, acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). Gene expression levels were measured using Affymetrix high density oligonucleotide arrays containing 6817 human genes. A dataset containing 72 observations from 3 leukemia type groups.

Type

has 3 groups with 38 cases of B-cell ALL, 25 cases of AML and 9 cases of T-cell ALL

.

Gene1 to Gen 40

gene expression levels

Usage

data(leukemia)

Format

A data frame with 72 rows and 41 variables

Source

Dudoit, S., Fridlyand, J. and Speed, T. P. (2002). Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. Journal of the American statistical Association 97 77-87.

Load a model from a JSON file.

Description

Deserializes a pptr or pprf model from a JSON file. The model can be used for prediction immediately. If variable importance metrics and OOB fields were saved, they are restored into the model's lazy-computation cache so later accessor calls (oob_error(), permuted_importance(), etc.) return the stored values without recomputation.

Usage

load_json(path)

Arguments

Details

Note that formula, x, and y are not stored in the JSON and will be NULL on the loaded model. Formula-based prediction and any accessor that requires training data will error unless the user re-attaches those fields.

Value

A pptr or pprf model (with the appropriate _classification / _regression subclass).

See Also

save_json, pptr, pprf

Examples

model <- pptr(Species ~ ., data = iris, seed = 0)
path <- tempfile(fileext = ".json")
save_json(model, path)
loaded <- load_json(path)
predict(loaded, as.matrix(iris[, 1:4]))

Lymphoma Dataset

Description

Gene expression in the three most prevalent adult lymphoid malignancies: B-cell chronic lymphocytic leukemia (B-CLL), follicular lymphoma (FL), and diffuse large B-cell lym- phoma (DLBCL). Gene expression levels were measured using a specialized cDNA microarray, the Lymphochip, containing genes that are preferentially expressed in lymphoid cells or that are of known immunologic or oncologic importance. This dataset contain 80 observations from 3 lymphoma types.

Type

Group variable has 3 groups with 29 cases of B-cell ALL (B-CLL), 42 cases of diffuse large B-cell lymphoma (DLBCL) and 9 cases of follicular lymphoma (FL)

.

Gene1 to Gen 50

gene expression

Usage

data(lymphoma)

Format

A data frame with 80 rows and 51 variables

Source

Dudoit, S., Fridlyand, J. and Speed, T. P. (2002). Comparison of Discrimination Methods for the Classification of Tumors Using Gene Ex- pression Data. Journal of the American statistical Association 97 77-87.

Number of observations used to fit a ppforest2 model.

Description

Implements the standard stats::nobs() contract so downstream tools (step(), broom's glance(), information criteria) can ask for the training-sample size.

Usage

## S3 method for class 'ppmodel'
nobs(object, ...)

Arguments

Value

Integer scalar. Returns NA_integer_ for models loaded from JSON without their original training data.

Olive Dataset

Description

Contains 572 observations and 10 variables

Region

Three super-groups of Italy: North, South and the island of Sardinia

area

Nine collection areas: three from North, four from South and 2 from Sardinia

palmitic

fatty acids percent x 100

palmitoleic

fatty acids percent x 100

stearic

fatty acids percent x 100

oleic

fatty acids percent x 100

linoleic

fatty acids percent x 100

linolenic

fatty acids percent x 100

arachidic

fatty acids percent x 100

eicosenoic

fatty acids percent x 100

Usage

data(olive)

Format

A data frame contains 573 observations and 10 variables

Out-of-bag error for a random forest.

Description

Computes (or returns the cached) OOB error using the training data stored on the model. For classification, this is the misclassification rate in '[0, 1]'. For regression, it is the mean squared error against the continuous response.

Usage

oob_error(model)

Arguments

Value

A numeric scalar in '[0, 1]' for classification or '[0, Inf)' for regression. Returns 'NA_real_' when no observation has any out-of-bag tree (e.g. a degenerate forest where every tree saw every row). Callers should check with 'is.na()' rather than comparing against a sentinel value; in earlier versions this condition was signalled as '-1', which was not distinguishable from a (mathematically impossible but representable) error rate.

See Also

oob_predictions, oob_samples

Out-of-bag predictions for a random forest.

Description

Returns predictions for each training row using only trees that did not see that row in their bootstrap sample. Observations with no OOB tree are represented as 'NA' in both modes: 'NA' at the factor level for classification, and 'NA_real_' for regression. Filter with the standard 'is.na()' idiom.

Usage

oob_predictions(model)

Arguments

Value

A factor (classification) or numeric vector (regression), length 'n'.

See Also

oob_error, oob_samples

Out-of-bag row indices per tree.

Description

Returns a list where element 'i' is the integer vector of row indices (1-based) that were **not** in the bootstrap sample of tree 'i'.

Usage

oob_samples(model)

Arguments

Value

A list of integer vectors, one per tree.

See Also

bag_samples, oob_error

Parkinson Dataset

Description

A dataset containing 195 observations from 2 parkinson types.

Type

Group variable has 2 groups, there are 48 cases of healthy people and 147 cases with Parkinson. The feature variables are biomedical voice measures

.

X1

Average vocal fundamental frequency

X2

Maximum vocal fundamental frequency

X3

Minimum vocal fundamental frequency

X4

MDVP:Jitter(%) measures of variation in fundamental frequency

X5

MDVP:Jitter(Abs) measures of variation in fundamental frequency

X6

MDVP:RAP measures of variation in fundamental frequency

X7

MDVP:PPQ measures of variation in fundamental frequency

X8

Jitter:DDP measures of variation in fundamental frequency

X9

MDVP:Shimmer measures of variation in amplitude

X10

MDVP:Shimmer(dB) measures of variation in amplitude

X11

Shimmer:APQ3 measures of variation in amplitude

X12

Shimmer:APQ5 measures of variation in amplitude

X13

MDVP:APQ measures of variation in amplitude

X14

Shimmer:DDA measures of variation in amplitude

X15

NHR measures of ratio of noise to tonal components in the voice

X16

HNR measures of ratio of noise to tonal components in the voice

X17

RPDE nonlinear dynamical complexity measures

X18

D2 nonlinear dynamical complexity measures

X19

DFA - Signal fractal scaling exponent

X20

spread1 Nonlinear measures of fundamental frequency variation

X21

spread2 Nonlinear measures of fundamental frequency variation

X22

PPE Nonlinear measures of fundamental frequency variation

Usage

data(parkinson)

Format

A data frame with 195 rows and 23 variables

Source

https://archive.ics.uci.edu/ml/datasets/Parkinsons

Permuted variable importance for a random forest.

Description

For each feature, measures the drop in OOB accuracy (classification) or the increase in normalised MSE (regression) after randomly permuting that feature across the OOB observations. Computed lazily from the training data stored on the model; the result is cached.

Usage

permuted_importance(model)

Arguments

Details

**Sign semantics.** Entries may be **negative**. That is not an error and not a sentinel: it means permuting the feature did not degrade OOB fit on average — the feature's signal sits at or below the noise floor of the permutation procedure. Interpret negative or near-zero entries as "no evidence of importance"; rely on the ranking rather than clipping at zero or normalizing. The scale is already comparable within a fitted model.

Value

A numeric vector, one entry per feature. Negative values are meaningful (see Sign semantics above).

Plot a pprf model.

Description

Visualizes a pprf model. By default, shows variable importance with one plot per metric side by side. Use metric to show a single importance metric.

Usage

## S3 method for class 'pprf'
plot(x, type = "importance", metric = NULL, tree_index = 1L, node = 1L, ...)

Arguments

Value

A ggplot2-compatible object (invisibly). The importance grid returns a patchwork object that works with ggplot2::ggsave().

Examples

if (requireNamespace("ggplot2", quietly = TRUE) &&
    requireNamespace("patchwork", quietly = TRUE)) {
  forest <- pprf(Species ~ ., data = iris, size = 10)
  plot(forest)                                    # all metrics side by side
  plot(forest, metric = "permuted")               # single metric
  plot(forest, type = "structure", tree_index = 1)
  plot(forest, type = "projection", tree_index = 1)
}

Plot a pptr model.

Description

Visualizes a pptr model. By default, shows a mosaic overview with tree structure, decision boundaries, and variable importance. Use type to show individual plots.

Usage

## S3 method for class 'pptr'
plot(x, type = NULL, metric = NULL, node = 1L, ...)

Arguments

Value

A ggplot2-compatible object (invisibly). The mosaic layout returns a patchwork object that works with ggplot2::ggsave().

Examples

if (requireNamespace("ggplot2", quietly = TRUE) &&
    requireNamespace("patchwork", quietly = TRUE)) {
  model <- pptr(Species ~ ., data = iris)
  plot(model)                         # mosaic overview
  plot(model, type = "structure")     # tree structure only
  plot(model, type = "importance")    # variable importance
  plot(model, type = "projection")    # projection histogram
  plot(model, type = "boundaries")    # decision boundaries
}

PDA projection pursuit strategy.

Description

Creates a Penalized Discriminant Analysis (PDA) projection pursuit strategy for use with pptr or pprf.

Usage

pp_pda(lambda = 0)

Arguments

Value

A pp_strategy object.

See Also

pptr, pprf, vars_uniform, vars_all, cutpoint_mean_of_means

Examples

# PDA with lambda = 0.5
pp_pda(0.5)

# Use with pptr
pptr(Species ~ ., data = iris, pp = pp_pda(0.5))

# Use with pprf
pprf(Species ~ ., data = iris, pp = pp_pda(0.5), vars = vars_uniform(n_vars = 2))

Parsnip model specification for pprf.

Description

Creates a model specification for a Projection Pursuit random forest. Use set_engine("ppforest2") to select the ppforest2 engine.

Usage

pp_rand_forest(
  mode = "classification",
  trees = NULL,
  mtry = NULL,
  mtry_prop = NULL,
  penalty = NULL
)

Arguments

Value

A parsnip model specification.

See Also

pprf for the underlying training function, pp_tree for single trees

Examples

if (requireNamespace("parsnip", quietly = TRUE)) {
  library(parsnip)
  spec <- pp_rand_forest(trees = 50, mtry = 2) %>% set_engine("ppforest2")
  fit <- spec %>% fit(Species ~ ., data = iris)
  predict(fit, iris)
  predict(fit, iris, type = "prob")
}

Parsnip model specification for pptr.

Description

Creates a model specification for a single Projection Pursuit decision tree. Use set_engine("ppforest2") to select the ppforest2 engine.

Usage

pp_tree(mode = "classification", penalty = NULL)

Arguments

Value

A parsnip model specification.

See Also

pptr for the underlying training function, pp_rand_forest for forests

Examples

if (requireNamespace("parsnip", quietly = TRUE)) {
  library(parsnip)
  spec <- pp_tree(penalty = 0) %>% set_engine("ppforest2")
  fit <- spec %>% fit(Species ~ ., data = iris)
  predict(fit, iris)
}

Trains a Random Forest of Projection-Pursuit oblique decision trees.

Description

This function trains a Random Forest of Projection-Pursuit oblique decision tree using either a formula and data frame interface or a matrix-based interface. When using the formula interface, specify the model formula and the data frame containing the variables. For the matrix-based interface, provide matrices for the features and labels directly. The number of trees is controlled by the size parameter. Each tree is trained on a stratified bootstrap sample drawn from the data. The number of variables to consider at each split is controlled by the n_vars parameter. If lambda = 0, the model is trained using Linear Discriminant Analysis (LDA). If lambda > 0, the model is trained using Penalized Discriminant Analysis (PDA).

Usage

pprf(
  formula = NULL,
  data = NULL,
  x = NULL,
  y = NULL,
  mode = NULL,
  size = 100,
  lambda = 0.5,
  n_vars = NULL,
  p_vars = NULL,
  seed = NULL,
  max_retries = 3L,
  threads = NULL,
  pp = NULL,
  vars = NULL,
  cutpoint = NULL,
  stop = NULL,
  binarize = NULL,
  grouping = NULL,
  leaf = NULL
)

Arguments

Details

Mode is taken from the mode argument when explicit, and otherwise auto-detected from 'y' (factor/character → classification, numeric → regression). Pass mode = "classification" to force classification on integer labels (e.g. binary 0/1), or mode = "regression" to assert intent on numeric responses.

OOB error, OOB predictions, permuted variable importance, and weighted variable importance are computed lazily on first access via the accessor functions ('oob_error()', 'oob_predictions()', 'permuted_importance()', 'weighted_importance()'). Training itself is fast because these OOB-based computations are deferred.

Value

A pprf model. Its S3 class vector is c("pprf_classification", "pprf", "ppmodel") or c("pprf_regression", "pprf", "ppmodel") depending on the mode.

See Also

predict.pprf_classification, predict.pprf_regression, formula.ppmodel, oob_error, save_json, load_json, pp_rand_forest for parsnip integration, vignette("introduction") for a tutorial

Examples

# Example 1: formula interface with the `iris` dataset
pprf(Species ~ ., data = iris)

# Example 2: formula interface with the `iris` dataset with regularization
pprf(Species ~ ., data = iris, lambda = 0.5)

# Example 3: matrix interface with the `iris` dataset
pprf(x = iris[, 1:4], y = iris[, 5])

# Example 4: matrix interface with the `iris` dataset with regularization
pprf(x = iris[, 1:4], y = iris[, 5], lambda = 0.5)

# Example 5: formula interface with the `crabs` dataset
pprf(Type ~ ., data = crabs)

# Example 6: formula interface with the `crabs` dataset with regularization
pprf(Type ~ ., data = crabs, lambda = 0.5)

Trains a Projection-Pursuit oblique decision tree.

Description

This function trains a Projection-Pursuit oblique decision tree using either a formula and data frame interface or a matrix-based interface. When using the formula interface, specify the model formula and the data frame containing the variables. For the matrix-based interface, provide matrices for the features and labels directly. If lambda = 0, the model is trained using Linear Discriminant Analysis (LDA). If lambda > 0, the model is trained using Penalized Discriminant Analysis (PDA).

Usage

pptr(
  formula = NULL,
  data = NULL,
  x = NULL,
  y = NULL,
  mode = NULL,
  lambda = 0.5,
  seed = NULL,
  pp = NULL,
  cutpoint = NULL,
  stop = NULL,
  binarize = NULL,
  grouping = NULL,
  leaf = NULL
)

Arguments

Details

Mode is taken from the mode argument when explicit, and otherwise auto-detected from 'y' (factor/character → classification, numeric → regression). Pass mode = "classification" to force classification on integer labels (e.g. binary 0/1), or mode = "regression" to assert intent on numeric responses.

Value

A pptr model. Its S3 class vector is c("pptr_classification", "pptr", "ppmodel") or c("pptr_regression", "pptr", "ppmodel") depending on the mode.

See Also

predict.pptr_classification, predict.pptr_regression, formula.ppmodel, print.pptr, save_json, load_json, pp_tree for parsnip integration

Examples

# Example 1: formula interface with the `iris` dataset
pptr(Species ~ ., data = iris)

# Example 2: formula interface with the `iris` dataset with regularization
pptr(Species ~ ., data = iris, lambda = 0.5)

# Example 3: matrix interface with the `iris` dataset
pptr(x = iris[, 1:4], y = iris[, 5])

Predicts labels or vote proportions from a pprf model (classification mode).

Description

Predicts labels or vote proportions from a pprf model (classification mode).

Usage

## S3 method for class 'pprf_classification'
predict(object, new_data = NULL, type = NULL, ...)

Arguments

Value

If type = "class", a factor of predicted labels. If type = "prob", a data frame with one column per group, each row summing to 1.

See Also

pprf, predict.pprf_regression

Examples

model <- pprf(Species ~ ., data = iris)
predict(model, iris)
predict(model, iris, type = "prob")

Predicts numeric responses from a pprf model (regression mode).

Description

Predicts numeric responses from a pprf model (regression mode).

Usage

## S3 method for class 'pprf_regression'
predict(object, new_data = NULL, type = NULL, ...)

Arguments

Value

A numeric vector of mean predictions across the forest's trees.

See Also

pprf, predict.pprf_classification

Predicts labels or per-group one-hot proportions from a pptr model (classification mode).

Description

Predicts labels or per-group one-hot proportions from a pptr model (classification mode).

Usage

## S3 method for class 'pptr_classification'
predict(object, new_data = NULL, type = NULL, ...)

Arguments

Value

A factor or data frame.

See Also

pptr, predict.pptr_regression

Predicts numeric responses from a pptr model (regression mode).

Description

Predicts numeric responses from a pptr model (regression mode).

Usage

## S3 method for class 'pptr_regression'
predict(object, new_data = NULL, type = NULL, ...)

Arguments

Value

A numeric vector.

See Also

pptr, predict.pptr_classification

Prints a compact summary of a pprf forest.

Description

Prints a compact summary of a pprf forest.

Usage

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

Arguments

Value

Invisibly returns the input pprf model x (unchanged). Called for its side effect of printing a compact description of the forest (call, number of trees, mode, group names, formula) to the console.

See Also

summary.pprf

Prints the structure of a pptr tree.

Description

Prints the structure of a pptr tree.

Usage

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

Arguments

Value

Invisibly returns the input pptr model x (unchanged). Called for its side effect of printing the tree structure – the oblique split rules and leaf predictions – to the console.

Projection-coefficient variable importance.

Description

The projection-based importance (VI2): each split's scaled absolute projection coefficients ('|a_j| * sigma_j') aggregated into a per-feature score, averaged over the non-degenerate trees of a forest. Unlike permuted_importance and weighted_importance, this measure is not OOB-based — it depends only on the fitted projector geometry, is computed eagerly at fit time (cheap), and is available for both single trees (pptr) and forests (pprf).

Usage

projection_importance(model)

Arguments

Details

**Sign semantics.** Entries are non-negative by construction (absolute coefficients scaled by each feature's standard deviation). Rely on the ranking rather than re-normalizing.

Value

A non-negative numeric vector, one entry per feature.

See Also

permuted_importance, weighted_importance

Residuals from a regression ppforest2 model.

Description

Returns y - fitted(model). Only defined for regression models — classification residuals have no canonical scalar form, so this method errors on classification models rather than inventing a convention.

Usage

## S3 method for class 'ppmodel'
residuals(object, ...)

Arguments

Value

A numeric vector of length nobs(object).

See Also

fitted.ppmodel

Save a model to a JSON file.

Description

Serializes a pptr or pprf model to JSON format compatible with the C++ CLI. The JSON includes the model structure, group labels, training parameters, and optionally variable importance metrics.

Usage

save_json(model, path, ...)

## S3 method for class 'ppmodel'
save_json(model, path, include_metrics = TRUE, ...)

Arguments

Value

Invisibly returns the file path that was written (a length-one character string). Called for its side effect of serializing model to a JSON file at path.

See Also

load_json, pptr, pprf

Examples

model <- pptr(Species ~ ., data = iris, seed = 0)
path <- tempfile(fileext = ".json")
save_json(model, path)

Composite stopping rule (logical OR).

Description

Creates a composite stopping rule that fires when any of the child rules fires. Useful for combining multiple criteria, e.g. stop_any(stop_min_size(5), stop_min_variance(0.01)) for regression.

Usage

stop_any(...)

Arguments

Value

A stop_strategy object.

See Also

stop_min_size, stop_min_variance, stop_pure_node

Examples

stop_any(stop_min_size(5), stop_min_variance(0.01))

Maximum-depth stopping rule.

Description

Creates a stopping rule that stops splitting when a node's depth reaches max_depth. Depth is zero-based at the root, so max_depth(k) allows at most k + 1 levels. Mode-agnostic: useful for bounding tree complexity in both classification and regression trees.

Usage

stop_max_depth(max_depth)

Arguments

Value

A stop_strategy object.

See Also

stop_min_size, stop_any

Examples

stop_max_depth(5)

Minimum-size stopping rule.

Description

Creates a stopping rule that stops splitting when a node has fewer than min_size observations. Used primarily for regression trees.

Usage

stop_min_size(min_size = 5L)

Arguments

Value

A stop_strategy object.

See Also

stop_min_variance, stop_any

Examples

stop_min_size(5)

Minimum-variance stopping rule.

Description

Creates a stopping rule that stops splitting when the within-node response variance falls below threshold. Used primarily for regression trees; requires a continuous response.

Usage

stop_min_variance(threshold = 0.01)

Arguments

Value

A stop_strategy object.

See Also

stop_min_size, stop_any

Examples

stop_min_variance(0.01)

Pure-node stopping rule.

Description

Creates a stopping rule that stops splitting when a node contains only one group. This is the default stopping rule for classification.

Usage

stop_pure_node()

Value

A stop_strategy object.

See Also

pptr, pprf

Examples

stop_pure_node()

Summary of a pprf forest (shared header + VI).

Description

Summary of a pprf forest (shared header + VI).

Usage

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

Arguments

Value

Invisibly returns the input pprf model object (unchanged). Called for its side effect of printing a detailed summary – the training specification, data summary, and variable-importance table (plus, for classification, the training/OOB confusion matrices) – to the console.

Update a pp_rand_forest model specification.

Description

Implements parsnip's update protocol so that tune::tune_grid() (and any other caller that finalises a spec via update()) can fill in the tuned values for trees, mtry, and penalty. Without this method, update() falls back to stats::update.default and fails with "need an object with call component".

Usage

## S3 method for class 'pp_rand_forest'
update(
  object,
  parameters = NULL,
  trees = NULL,
  mtry = NULL,
  mtry_prop = NULL,
  penalty = NULL,
  fresh = FALSE,
  ...
)

Arguments

Value

An updated pp_rand_forest model specification.

Update a pp_tree model specification.

Description

Companion to update.pp_rand_forest — exists for the same reason (tune::tune_grid() relies on update() to finalise the spec at each grid point).

Usage

## S3 method for class 'pp_tree'
update(object, parameters = NULL, penalty = NULL, fresh = FALSE, ...)

Arguments

Value

An updated pp_tree model specification.

All-variables selection strategy.

Description

Creates a variable selection strategy that uses all variables at each split. This is the default for single trees (pptr).

Usage

vars_all()

Value

A vars_strategy object.

See Also

pptr, vars_uniform, pp_pda, cutpoint_mean_of_means

Examples

vars_all()

Uniform random variable selection strategy.

Description

Creates a variable selection strategy that randomly selects a subset of variables at each split. Used with pprf for random forests.

Usage

vars_uniform(n_vars = NULL, p_vars = NULL)

Arguments

Details

Exactly one of n_vars or p_vars may be specified. When p_vars is used, it is stored as-is and resolved to an integer count later by pprf once the number of features is known.

Value

A vars_strategy object.

See Also

pprf, vars_all, pp_pda, cutpoint_mean_of_means

Examples

# Select 2 variables at each split
vars_uniform(n_vars = 2)

# Select half the variables at each split
vars_uniform(p_vars = 0.5)

# Use with pprf
pprf(Species ~ ., data = iris, vars = vars_uniform(n_vars = 2))

Weighted projection variable importance for a random forest.

Description

Weights each tree's projection-based importance by a per-tree OOB quality score — '1 - error_rate' in '[0, 1]' for classification, and 'max(0, 1 - NMSE)' in '[0, 1]' for regression — then aggregates 'I_s × |a_j|' over splits. Computed lazily from the training data stored on the model; the result is cached.

Usage

weighted_importance(model)

Arguments

Details

**Sign semantics.** Entries are non-negative by construction (weights and per-split contributions are both non-negative). A zero entry means "this feature never appeared in a weighted OOB-contributing split," not "within noise." Contrast with permuted_importance, where negative values are meaningful. Do not re-normalize — rely on the ranking.

Value

A non-negative numeric vector, one entry per feature.

Wine Dataset

Description

A dataset containing 178 observations from 3 wine grown cultivares in Italy.

Usage

data(wine)

Format

A data frame with 178 rows and 14 variables

Details

Type

Group variable has 3 groups that are 3 different wine grown cultivars in Italy.

X1 to X13

Check vbles