Help for package deeptrafo

Title:

Fitting Deep Conditional Transformation Models

Version:

1.0-0

Description:

Allows for the specification of deep conditional transformation models (DCTMs) and ordinal neural network transformation models, as described in Baumann et al (2021) <doi:10.1007/978-3-030-86523-8_1> and Kook et al (2022) <doi:10.1016/j.patcog.2021.108263>. Extensions such as autoregressive DCTMs (Ruegamer et al, 2023, <doi:10.1007/s11222-023-10212-8>) and transformation ensembles (Kook et al, 2022, <doi:10.48550/arXiv.2205.12729>) are implemented. The software package is described in Kook et al (2024, <doi:10.18637/jss.v111.i10>).

Depends:

R (≥ 4.0.0), tensorflow (≥ 2.2.0), keras (≥ 2.2.0), tfprobability (≥ 0.15), deepregression (≥ 2.2.0)

Suggests:

testthat, knitr, ordinal, tram, cotram, covr

Imports:

mlt, data.table, variables, stats, purrr, survival, R6, Formula, reticulate

License:

GPL-3

Encoding:

UTF-8

RoxygenNote:

7.3.1

URL:

https://github.com/neural-structured-additive-learning/deeptrafo

BugReports:

https://github.com/neural-structured-additive-learning/deeptrafo/issues

NeedsCompilation:

Packaged:

2024-12-03 14:51:24 UTC; lkook

Author:

Lucas Kook [aut, cre], Philipp Baumann [aut], David Ruegamer [aut]

Maintainer:

Lucas Kook <lucasheinrich.kook@gmail.com>

Repository:

CRAN

Date/Publication:

2024-12-03 18:40:02 UTC

BoxCox-type neural network transformation models

Description

BoxCox-type neural network transformation models

Usage

BoxCoxNN(
  formula,
  data,
  response_type = get_response_type(data[[all.vars(formula)[1]]]),
  order = get_order(response_type, data[[all.vars(formula)[1]]]),
  addconst_interaction = 0,
  latent_distr = "normal",
  monitor_metrics = NULL,
  trafo_options = trafo_control(order_bsp = order, response_type = response_type),
  ...
)

Arguments

formula

Formula specifying the response, interaction, shift terms as response | interacting ~ shifting. auto-regressive transformation models (ATMs).

data

Named list or data.frame which may contain both structured and unstructured data.

response_type

Character; type of response. One of "continuous", "survival", "count", or "ordered". If not supplied manually it is determined by the first entry in data[[response]].

order

Integer; order of the response basis. Default 10 for Bernstein basis or number of levels minus one for ordinal responses.

addconst_interaction

Positive constant; a constant added to the additive predictor of the interaction term. If NULL, terms are left unchanged. If 0 and predictors have negative values in their design matrix, the minimum value of all predictors is added to ensure positivity. If > 0, the minimum value plus the addconst_interaction is added to each predictor in the interaction term. This ensures a monotone non-decreasing transformation function in the response when using (tensor product) spline bases in the interacting term.

latent_distr

A tfd_distribution or character; the base distribution for transformation models. If character, can be "normal", "logistic", "gumbel" or "gompertz".

monitor_metrics

See deepregression

trafo_options

Options for transformation models such as the basis function used, see trafo_control for more details.

...

Additional arguments passed to deepregression

Value

See return statement of deeptrafo

Examples

if (.Platform$OS.type != "windows" &&
    reticulate::py_available() &&
    reticulate::py_module_available("tensorflow") &&
    reticulate::py_module_available("keras") &&
    reticulate::py_module_available("tensorflow_probability")) {
    df <- data.frame(y = rnorm(50), x = rnorm(50))
    m <- BoxCoxNN(y ~ x, data = df)
    coef(m)
}

Deep continuous outcome logistic regression

Description

Deep continuous outcome logistic regression

Usage

ColrNN(
  formula,
  data,
  response_type = get_response_type(data[[all.vars(formula)[1]]]),
  order = get_order(response_type, data[[all.vars(formula)[1]]]),
  addconst_interaction = 0,
  latent_distr = "logistic",
  monitor_metrics = NULL,
  trafo_options = trafo_control(order_bsp = order, response_type = response_type),
  ...
)

Arguments

formula

Formula specifying the response, interaction, shift terms as response | interacting ~ shifting. auto-regressive transformation models (ATMs).

data

Named list or data.frame which may contain both structured and unstructured data.

response_type

Character; type of response. One of "continuous", "survival", "count", or "ordered". If not supplied manually it is determined by the first entry in data[[response]].

order

Integer; order of the response basis. Default 10 for Bernstein basis or number of levels minus one for ordinal responses.

addconst_interaction

latent_distr

A tfd_distribution or character; the base distribution for transformation models. If character, can be "normal", "logistic", "gumbel" or "gompertz".

monitor_metrics

See deepregression

trafo_options

Options for transformation models such as the basis function used, see trafo_control for more details.

...

Additional arguments passed to deepregression

Value

See return statement of deeptrafo

Examples

if (.Platform$OS.type != "windows" &&
    reticulate::py_available() &&
    reticulate::py_module_available("tensorflow") &&
    reticulate::py_module_available("keras") &&
    reticulate::py_module_available("tensorflow_probability")) {
    df <- data.frame(y = rnorm(50), x = rnorm(50))
    m <- ColrNN(y ~ x, data = df)
    coef(m)
}

Cox proportional hazards type neural network transformation models

Description

Cox proportional hazards type neural network transformation models

Usage

CoxphNN(
  formula,
  data,
  response_type = get_response_type(data[[all.vars(formula)[1]]]),
  order = get_order(response_type, data[[all.vars(formula)[1]]]),
  addconst_interaction = 0,
  latent_distr = "gompertz",
  monitor_metrics = NULL,
  trafo_options = trafo_control(order_bsp = order, response_type = response_type),
  ...
)

Arguments

formula

Formula specifying the response, interaction, shift terms as response | interacting ~ shifting. auto-regressive transformation models (ATMs).

data

Named list or data.frame which may contain both structured and unstructured data.

response_type

Character; type of response. One of "continuous", "survival", "count", or "ordered". If not supplied manually it is determined by the first entry in data[[response]].

order

Integer; order of the response basis. Default 10 for Bernstein basis or number of levels minus one for ordinal responses.

addconst_interaction

latent_distr

A tfd_distribution or character; the base distribution for transformation models. If character, can be "normal", "logistic", "gumbel" or "gompertz".

monitor_metrics

See deepregression

trafo_options

Options for transformation models such as the basis function used, see trafo_control for more details.

...

Additional arguments passed to deepregression

Value

See return statement of deeptrafo

Examples

if (.Platform$OS.type != "windows" &&
    reticulate::py_available() &&
    reticulate::py_module_available("tensorflow") &&
    reticulate::py_module_available("keras") &&
    reticulate::py_module_available("tensorflow_probability")) {
    df <- data.frame(y = rnorm(50), x = rnorm(50))
    m <- CoxphNN(y ~ x, data = df)
    coef(m)
}

Lehmann-type neural network transformation models

Description

Lehmann-type neural network transformation models

Usage

LehmanNN(
  formula,
  data,
  response_type = get_response_type(data[[all.vars(formula)[1]]]),
  order = get_order(response_type, data[[all.vars(formula)[1]]]),
  addconst_interaction = 0,
  latent_distr = "gumbel",
  monitor_metrics = NULL,
  trafo_options = trafo_control(order_bsp = order, response_type = response_type),
  ...
)

Arguments

formula

Formula specifying the response, interaction, shift terms as response | interacting ~ shifting. auto-regressive transformation models (ATMs).

data

Named list or data.frame which may contain both structured and unstructured data.

response_type

Character; type of response. One of "continuous", "survival", "count", or "ordered". If not supplied manually it is determined by the first entry in data[[response]].

order

Integer; order of the response basis. Default 10 for Bernstein basis or number of levels minus one for ordinal responses.

addconst_interaction

latent_distr

A tfd_distribution or character; the base distribution for transformation models. If character, can be "normal", "logistic", "gumbel" or "gompertz".

monitor_metrics

See deepregression

trafo_options

Options for transformation models such as the basis function used, see trafo_control for more details.

...

Additional arguments passed to deepregression

Value

See return statement of deeptrafo

Examples

if (.Platform$OS.type != "windows" &&
    reticulate::py_available() &&
    reticulate::py_module_available("tensorflow") &&
    reticulate::py_module_available("keras") &&
    reticulate::py_module_available("tensorflow_probability")) {
    df <- data.frame(y = rnorm(50), x = rnorm(50))
    m <- LehmanNN(y ~ 0 + x, data = df)
    coef(m)
}

Deep normal linear regression

Description

Deep normal linear regression

Usage

LmNN(
  formula,
  data,
  response_type = get_response_type(data[[all.vars(formula)[1]]]),
  order = get_order(response_type, data[[all.vars(formula)[1]]]),
  addconst_interaction = 0,
  latent_distr = "normal",
  monitor_metrics = NULL,
  trafo_options = trafo_control(order_bsp = 1L, response_type = response_type,
    y_basis_fun = eval_lin, y_basis_fun_lower = .empty_fun(eval_lin), y_basis_fun_prime =
    eval_lin_prime, basis = "shiftscale"),
  ...
)

Arguments

formula

Formula specifying the response, interaction, shift terms as response | interacting ~ shifting. auto-regressive transformation models (ATMs).

data

Named list or data.frame which may contain both structured and unstructured data.

response_type

Character; type of response. One of "continuous", "survival", "count", or "ordered". If not supplied manually it is determined by the first entry in data[[response]].

order

Integer; order of the response basis. Default 10 for Bernstein basis or number of levels minus one for ordinal responses.

addconst_interaction

latent_distr

A tfd_distribution or character; the base distribution for transformation models. If character, can be "normal", "logistic", "gumbel" or "gompertz".

monitor_metrics

See deepregression

trafo_options

Options for transformation models such as the basis function used, see trafo_control for more details.

...

Additional arguments passed to deepregression

Value

See return statement of deeptrafo

Examples

if (.Platform$OS.type != "windows" &&
    reticulate::py_available() &&
    reticulate::py_module_available("tensorflow") &&
    reticulate::py_module_available("keras") &&
    reticulate::py_module_available("tensorflow_probability")) {
    set.seed(1)
    df <- data.frame(y = 10 + rnorm(50), x = rnorm(50))
    m <- LmNN(y ~ 0 + x, data = df)

    optimizer <- optimizer_adam(learning_rate = 0.01, decay = 4e-4)
    m <- LmNN(y ~ 0 + x, data = df, optimizer = optimizer)
    library(tram)
    fit(m, epochs = 900L, validation_split = 0)
    logLik(mm <- Lm(y ~ x, data = df)); logLik(m)
    coef(mm, with_baseline = TRUE); unlist(c(coef(m, which = "interacting"),
                                             coef(m, which = "shifting")))

}

Deep (proportional odds) logistic regression

Description

Deep (proportional odds) logistic regression

Usage

PolrNN(
  formula,
  data,
  response_type = get_response_type(data[[all.vars(formula)[1]]]),
  order = get_order(response_type, data[[all.vars(formula)[1]]]),
  addconst_interaction = 0,
  latent_distr = "logistic",
  monitor_metrics = NULL,
  trafo_options = trafo_control(order_bsp = order, response_type = response_type),
  ...
)

Arguments

formula

Formula specifying the response, interaction, shift terms as response | interacting ~ shifting. auto-regressive transformation models (ATMs).

data

Named list or data.frame which may contain both structured and unstructured data.

response_type

Character; type of response. One of "continuous", "survival", "count", or "ordered". If not supplied manually it is determined by the first entry in data[[response]].

order

Integer; order of the response basis. Default 10 for Bernstein basis or number of levels minus one for ordinal responses.

addconst_interaction

latent_distr

A tfd_distribution or character; the base distribution for transformation models. If character, can be "normal", "logistic", "gumbel" or "gompertz".

monitor_metrics

See deepregression

trafo_options

Options for transformation models such as the basis function used, see trafo_control for more details.

...

Additional arguments passed to deepregression

Value

See return statement of deeptrafo

Examples

if (.Platform$OS.type != "windows" &&
    reticulate::py_available() &&
    reticulate::py_module_available("tensorflow") &&
    reticulate::py_module_available("keras") &&
    reticulate::py_module_available("tensorflow_probability")) {
    df <- data.frame(y = ordered(sample.int(5, 50, replace = TRUE)),
                     x = rnorm(50))
    m <- PolrNN(y ~ x, data = df)
    coef(m)
}

Deep parametric survival regression

Description

Deep parametric survival regression

Usage

SurvregNN(
  formula,
  data,
  response_type = get_response_type(data[[all.vars(formula)[1]]]),
  order = get_order(response_type, data[[all.vars(formula)[1]]]),
  addconst_interaction = 0,
  latent_distr = "gompertz",
  monitor_metrics = NULL,
  trafo_options = NULL,
  ...
)

Arguments

formula

Formula specifying the response, interaction, shift terms as response | interacting ~ shifting. auto-regressive transformation models (ATMs).

data

Named list or data.frame which may contain both structured and unstructured data.

response_type

Character; type of response. One of "continuous", "survival", "count", or "ordered". If not supplied manually it is determined by the first entry in data[[response]].

order

Integer; order of the response basis. Default 10 for Bernstein basis or number of levels minus one for ordinal responses.

addconst_interaction

latent_distr

A tfd_distribution or character; the base distribution for transformation models. If character, can be "normal", "logistic", "gumbel" or "gompertz".

monitor_metrics

See deepregression

trafo_options

Options for transformation models such as the basis function used, see trafo_control for more details.

...

Additional arguments passed to deepregression

Value

See return statement of deeptrafo

Examples

if (.Platform$OS.type != "windows" &&
    reticulate::py_available() &&
    reticulate::py_module_available("tensorflow") &&
    reticulate::py_module_available("keras") &&
    reticulate::py_module_available("tensorflow_probability")) {
    set.seed(1)
    df <- data.frame(y = abs(1 + rnorm(50)), x = rnorm(50))
    m <- SurvregNN(y ~ 0 + x, data = df)

    optimizer <- optimizer_adam(learning_rate = 0.01, decay = 4e-4)
    m <- SurvregNN(y ~ 0 + x, data = df, optimizer = optimizer)
    library(tram)
    fit(m, epochs = 500L, validation_split = 0)
    logLik(mm <- Survreg(y ~ x, data = df, dist = "loglogistic")); logLik(m)
    coef(mm, with_baseline = TRUE); unlist(c(coef(m, which = "interacting"),
                                             coef(m, which = "shifting")))

}

Initializes the Processed Additive Predictor for ATMs

Description

Initializes the Processed Additive Predictor for ATMs

Usage

atm_init(atmnr, h1nr)

Arguments

atmnr, h1nr

positions of the atm and h1 formula

Value

returns a subnetwork_init function with pre-defined arguments

S3 methods for deep conditional transformation models

Description

S3 methods for deep conditional transformation models

Usage

## S3 method for class 'deeptrafo'
coef(
  object,
  which_param = c("shifting", "interacting", "autoregressive"),
  type = NULL,
  ...
)

## S3 method for class 'deeptrafo'
predict(
  object,
  newdata = NULL,
  type = c("trafo", "pdf", "cdf", "interaction", "shift", "terms"),
  batch_size = NULL,
  K = 100,
  q = NULL,
  pred_grid = FALSE,
  ...
)

## S3 method for class 'deeptrafo'
fitted(
  object,
  newdata = NULL,
  batch_size = NULL,
  convert_fun = as.matrix,
  call_create_lags = TRUE,
  ...
)

## S3 method for class 'deeptrafo'
logLik(
  object,
  newdata = NULL,
  convert_fun = function(x, ...) -sum(x, ...),
  ...
)

## S3 method for class 'deeptrafo'
residuals(object, newdata = NULL, return_gradients = FALSE, ...)

## S3 method for class 'deeptrafo'
simulate(object, nsim = 1, seed = NULL, newdata = NULL, ...)

## S3 method for class 'deeptrafo'
print(x, print_model = FALSE, print_coefs = TRUE, with_baseline = FALSE, ...)

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

Arguments

object

Object of class "deeptrafo".

which_param

Character; either "shifting", "interacting", or "autoregressive" (only for autoregressive transformation models).

type

Either NULL (all types of coefficients are returned), "linear" for linear coefficients or "smooth" for coefficients of; Note that type is currently not used for "interacting".

...

Further arguments supplied to print.deeptrafo

newdata

Named list or data.frame; optional new data.

batch_size

Integer; optional, useful if data is too large.

K

Integer; grid length for the response to evaluate predictions at, if newdata does not contain the response.

q

Numeric or factor; user-supplied grid of response values to evaluate the predictions. Defaults to NULL. If overwritten, K is ignored.

pred_grid

Logical; set TRUE, if user provides a predefined grid for an atp/atm model through newdata which holds two attributes. The first attribute, rname, should hold the column name (string) of the response variable while the second attribute, y, should hold the grid name.

convert_fun

Function; applied to the log-likelihood values of all observations.

call_create_lags

Logical; lags may already be computed by a different method (e.g. plot)

return_gradients

Return individual gradients instead of the summed gradients; the residuals are 0.5 * rowSums(gradients)

nsim

Integer; number of simulations; defaults to 1.

seed

Seed for generating samples; defaults to NULL.

x

Object of class "deeptrafo".

print_model

Logical; print keras model.

print_coefs

Logical; print coefficients.

with_baseline

Logical; print baseline coefs.

Details

If no new data is supplied, predictions are computed on the training data (i.e. in-sample). If new data is supplied without a response, predictions are evaluated on a grid of length K.

Value

Returns vector or matrix of predictions, depending on the supplied type.

Returns matrix of fitted values.

Deep distribution-free count regression

Description

Deep distribution-free count regression

Usage

cotramNN(
  formula,
  data,
  response_type = get_response_type(data[[all.vars(formula)[1]]]),
  order = get_order(response_type, data[[all.vars(formula)[1]]]),
  addconst_interaction = 0,
  latent_distr = "logistic",
  monitor_metrics = NULL,
  ...
)

Arguments

formula

Formula specifying the response, interaction, shift terms as response | interacting ~ shifting. auto-regressive transformation models (ATMs).

data

Named list or data.frame which may contain both structured and unstructured data.

response_type

Character; type of response. One of "continuous", "survival", "count", or "ordered". If not supplied manually it is determined by the first entry in data[[response]].

order

Integer; order of the response basis. Default 10 for Bernstein basis or number of levels minus one for ordinal responses.

addconst_interaction

latent_distr

A tfd_distribution or character; the base distribution for transformation models. If character, can be "normal", "logistic", "gumbel" or "gompertz".

monitor_metrics

See deepregression

...

Additional arguments passed to deepregression

Value

See return statement of deeptrafo

Examples

if (.Platform$OS.type != "windows" &&
    reticulate::py_available() &&
    reticulate::py_module_available("tensorflow") &&
    reticulate::py_module_available("keras") &&
    reticulate::py_module_available("tensorflow_probability")) {
    set.seed(1)
    df <- data.frame(y = as.integer(abs(1 + rnorm(50, sd = 10))), x = rnorm(50))
    m <- cotramNN(y ~ 0 + x, data = df, order = 6)

    optimizer <- optimizer_adam(learning_rate = 0.1, decay = 4e-4)
    m <- cotramNN(y ~ 0 + x, data = df, optimizer = optimizer, order = 6)
    library(cotram)
    fit(m, epochs = 800L, validation_split = 0)
    logLik(mm <- cotram(y ~ x, data = df, method = "logit")); logLik(m)
    coef(mm, with_baseline = TRUE); unlist(c(coef(m, which = "interacting"),
                                             coef(m, which = "shifting")))

}

Deep conditional transformation models with alternative formula interface

Description

Deep conditional transformation models with alternative formula interface

Usage

dctm(
  response,
  intercept = NULL,
  shift = NULL,
  shared = NULL,
  data,
  response_type = get_response_type(data[[all.vars(response)[1]]]),
  order = get_order(response_type, data[[all.vars(response)[1]]]),
  addconst_interaction = 0,
  latent_distr = "logistic",
  monitor_metrics = NULL,
  trafo_options = trafo_control(order_bsp = order, response_type = response_type),
  ...
)

Arguments

response

Formula for the response; e.g. ~ y

intercept

Formula for the intercept function; e.g., ~ x, for which interacting bases with the response will be set up

shift

Formula for the shift part of the model; e.g., ~ s(x)

shared

Formula for sharing weights between predictors in the intercept and shift part of the model

data

Named list or data.frame which may contain both structured and unstructured data.

response_type

Character; type of response. One of "continuous", "survival", "count", or "ordered". If not supplied manually it is determined by the first entry in data[[response]].

order

Integer; order of the response basis. Default 10 for Bernstein basis or number of levels minus one for ordinal responses.

addconst_interaction

latent_distr

A tfd_distribution or character; the base distribution for transformation models. If character, can be "normal", "logistic", "gumbel" or "gompertz".

monitor_metrics

See deepregression

trafo_options

Options for transformation models such as the basis function used, see trafo_control for more details.

...

Additional arguments passed to deepregression

Value

See return statement of deeptrafo

Examples

if (.Platform$OS.type != "windows" &&
    reticulate::py_available() &&
    reticulate::py_module_available("tensorflow") &&
    reticulate::py_module_available("keras") &&
    reticulate::py_module_available("tensorflow_probability")) {
   df <- data.frame(y = rnorm(50), x = rnorm(50))
   m <- dctm(response = ~ y, shift = ~ 0 + x, data = df)
   coef(m)
}

Deep Conditional Transformation Models

Description

Deep Conditional Transformation Models

Usage

deeptrafo(
  formula,
  data,
  response_type = get_response_type(data[[all.vars(fml)[1]]]),
  order = get_order(response_type, data[[all.vars(fml)[1]]]),
  addconst_interaction = 0,
  latent_distr = "logistic",
  loss = "nll",
  loss_args = NULL,
  monitor_metrics = NULL,
  trafo_options = trafo_control(order_bsp = order, response_type = response_type),
  return_data = FALSE,
  engine = "tf",
  ...
)

Arguments

formula

Formula specifying the response, interaction, shift terms as response | interacting ~ shifting. auto-regressive transformation models (ATMs).

data

Named list or data.frame which may contain both structured and unstructured data.

response_type

Character; type of response. One of "continuous", "survival", "count", or "ordered". If not supplied manually it is determined by the first entry in data[[response]].

order

Integer; order of the response basis. Default 10 for Bernstein basis or number of levels minus one for ordinal responses.

addconst_interaction

latent_distr

A tfd_distribution or character; the base distribution for transformation models. If character, can be "normal", "logistic", "gumbel" or "gompertz".

loss

Character; specifies the loss function used. The default is "nll", an internal function which takes latent_distr as an argument and returns a function with arguments y_true and y_pred to be given to the underlying 'keras' model. Custom loss functions can be supplied with the same structure, either as a character or function.

loss_args

Further additional arguments to loss.

monitor_metrics

See deepregression

trafo_options

Options for transformation models such as the basis function used, see trafo_control for more details.

return_data

Include full data in the returned object. Defaults to FALSE. Set to TRUE if inteded to use simulate afterwards.

engine

Ignored; for compatibility with package deepregression.

...

Additional arguments passed to deepregression

Details

deeptrafo is the main function for setting up neural network transformation models and is called by all aliases for the more special cases (see e.g. ColrNN). The naming convention of the aliases follow the 'tram' package (see e.g. Colr) and add the suffix "NN" to the function name.

Value

An object of class c("deeptrafo", "deepregression")

References

Kook, L., Baumann, P. F., Dürr, O., Sick, B., & Rügamer, D. (2024). Estimating conditional distributions with neural networks using R package deeptrafo. Journal of Statistical Software. doi:10.18637/jss.v111.i10.

Examples

if (.Platform$OS.type != "windows" &&
  reticulate::py_available() &&
  reticulate::py_module_available("tensorflow") &&
  reticulate::py_module_available("keras") &&
  reticulate::py_module_available("tensorflow_probability")) {
  data("wine", package = "ordinal")
  wine$z <- rnorm(nrow(wine))
  wine$x <- rnorm(nrow(wine))

  nn <- \(x) x |>
    layer_dense(input_shape = 1L, units = 2L, activation = "relu") |>
    layer_dense(1L)

  fml <- rating ~ 0 + temp + contact + s(z, df = 3) + nn(x)

  m <- deeptrafo(fml, wine,
    latent_distr = "logistic", monitor_metric = NULL,
    return_data = TRUE, list_of_deep_models = list(nn = nn)
  )

  print(m)

  m %>% fit(epochs = 10, batch_size = nrow(wine))
  coef(m, which_param = "interacting")
  coef(m, which_param = "shifting")
  fitted(m)
  predict(m, type = "pdf")
  predict(m, type = "pdf", newdata = wine[, -2])
  logLik(m)
  logLik(m, newdata = wine[1:10, ])
  plot(m)
  mcv <- cv(m, cv_folds = 3)
  ens <- ensemble(m, n_ensemble = 3)
  coef(ens)
}

Deep ensembling for neural network transformation models

Description

Deep ensembling for neural network transformation models

Usage

## S3 method for class 'deeptrafo'
ensemble(
  x,
  n_ensemble = 5,
  reinitialize = TRUE,
  mylapply = lapply,
  verbose = FALSE,
  patience = 20,
  plot = TRUE,
  print_members = TRUE,
  stop_if_nan = TRUE,
  save_weights = TRUE,
  callbacks = list(),
  save_fun = NULL,
  seed = seq_len(n_ensemble),
  ...
)

Arguments

x

Object of class "deeptrafo".

n_ensemble

Numeric; number of ensemble members to fit.

reinitialize

Logical; if TRUE (default), model weights are initialized randomly prior to fitting each member. Fixed weights are not affected.

mylapply

Function; lapply function to be used; defaults to lapply

verbose

Logical; whether to print training in each fold.

patience

Integer; number of patience for early stopping.

plot

Logical; whether to plot the resulting losses in each fold.

print_members

Logical; print results for each member.

stop_if_nan

Logical; whether to stop ensembling if NaN values occur

save_weights

Logical; whether to save the ensemble weights.

callbacks

List; callbacks used for fitting.

save_fun

Function; function to be applied to each member to be stored in the final result.

seed

Numeric vector of length n_ensemble; seeds for model initialization.

...

Further arguments passed to object$fit_fun.

Value

Ensemble of "deeptrafo" models with list of training histories and fitted weights included in ensemble_results. For details see the return statment in ensemble.

Define Predictor of Transformation Model

Description

Define Predictor of Transformation Model

Usage

from_preds_to_trafo(
  atm_toplayer = function(x) layer_dense(x, units = 1L, name = "atm_toplayer"),
  const_ia = NULL,
  ...
)

Arguments

atm_toplayer

Function to be applied on top of the transformed lags.

const_ia

See addconst_interaction in deeptrafo or deepregression.

...

For compatibility with 'deepregression'

Details

Not intended to be used directly by the end user.

Value

A function of list_pred_param returning a list of output tensors that is passed to model_fun of deepregression

Initializes the Processed Additive Predictor for TM's Interaction

Description

Initializes the Processed Additive Predictor for TM's Interaction

Usage

h1_init(yterms, h1pred, add_const_positiv = 0)

Arguments

yterms

Terms for the response

h1pred

Interacting predictor

add_const_positiv

Shift basis for the predictors to be strictly positive

Value

returns a subnetwork_init function with pre-defined arguments

Generic negative log-likelihood for transformation models

Description

Generic negative log-likelihood for transformation models

Usage

nll(latent_distr)

Arguments

latent_distr

Target distribution, character or tfd_distribution. If character, can be either "logistic", "normal", "gumbel", "gompertz".

Value

A function for computing the negative log-likelihood of a neural network transformation model with generic response.

Ordinal neural network transformation models

Description

Ordinal neural network transformation models

Usage

ontram(
  response,
  intercept = NULL,
  shift = NULL,
  shared = NULL,
  data,
  response_type = "ordered",
  order = get_order(response_type, data[[all.vars(response)[1]]]),
  addconst_interaction = 0,
  latent_distr = "logistic",
  monitor_metrics = NULL,
  trafo_options = trafo_control(order_bsp = order, response_type = response_type),
  ...
)

Arguments

response

Formula for the response; e.g., ~ y

intercept

Formula for the intercept function; e.g., ~ x, for which interacting bases with the response will be set up

shift

Formula for the shift part of the model; e.g., ~ s(x)

shared

Formula for sharing weights between predictors in the intercept and shift part of the model

data

Named list or data.frame which may contain both structured and unstructured data.

response_type

Character; type of response. One of "continuous", "survival", "count", or "ordered". If not supplied manually it is determined by the first entry in data[[response]].

order

Integer; order of the response basis. Default 10 for Bernstein basis or number of levels minus one for ordinal responses.

addconst_interaction

latent_distr

A tfd_distribution or character; the base distribution for transformation models. If character, can be "normal", "logistic", "gumbel" or "gompertz".

monitor_metrics

See deepregression

trafo_options

Options for transformation models such as the basis function used, see trafo_control for more details.

...

Additional arguments passed to deepregression

Value

See return statement of deeptrafo

References

Kook, L. & Herzog, L., Hothorn, T., Dürr, O., & Sick, B. (2022). Deep and interpretable regression models for ordinal outcomes. Pattern Recognition, 122, 108263. DOI 10.1016/j.patcog.2021.108263

Examples

if (.Platform$OS.type != "windows" &&
    reticulate::py_available() &&
    reticulate::py_module_available("tensorflow") &&
    reticulate::py_module_available("keras") &&
    reticulate::py_module_available("tensorflow_probability")) {
    df <- data.frame(y = ordered(sample.int(6, 50, TRUE)), x = rnorm(50))
    m <- ontram(response = ~ y, shift = ~ x, data = df)
    coef(m)
}

Plot method for deep conditional transformation models

Description

Plot method for deep conditional transformation models

Usage

## S3 method for class 'deeptrafo'
plot(
  x,
  which = NULL,
  type = c("smooth", "trafo", "pdf", "cdf"),
  newdata = NULL,
  which_param = c("shifting", "interacting"),
  only_data = FALSE,
  K = 40,
  q = NULL,
  ...
)

Arguments

x

Object of class "deeptrafo".

which

Which effect to plot, default selects all smooth effects in the shift term.

type

Character; One of "smooth", "trafo", "pdf", or "cdf".

newdata

Optional new data (list or data.frame) to evaluate predictions at. If the response is missing, plots are generated on a grid of length K

which_param

Character; either "interacting" or "shifting".

only_data

Logical, if TRUE, only the data for plotting is returned.

K

Integer; If type == "smooth" the length of an equidistant grid at which a two-dimensional function is evaluated for plotting. Otherwise, length of the grid to evaluate predictions at, see newdata.

q

Vector of response values to compute predictions at, see newdata

...

Further arguments, passed to fit, plot or predict function

Options for transformation models

Description

Options for transformation models

Usage

trafo_control(
  order_bsp = 10L,
  support = function(y) range(y),
  y_basis_fun = NULL,
  y_basis_fun_lower = NULL,
  y_basis_fun_prime = NULL,
  penalize_bsp = 0,
  order_bsp_penalty = 2,
  tf_bsps = FALSE,
  response_type = c("continuous", "ordered", "survival", "count"),
  atm_toplayer = function(x) {
     layer_dense(x, units = 1L, name = "atm_toplayer",
    use_bias = FALSE)
 },
  basis = c("bernstein", "ordered", "shiftscale")
)

Arguments

order_bsp

The order of Bernstein polynomials in case y_basis_fun is a Bernstein polynomial defined by eval_bsp or (one less than) the number of classes of an ordinal outcome.

support

A function returning a vector with two elements, namely the support for the basis of y.

y_basis_fun

Function; basis function for Y

y_basis_fun_lower

Function; basis function for lower bound of interval censored response

y_basis_fun_prime

Function; basis function derivative

penalize_bsp

Scalar value > 0; controls amount of penalization of Bernstein polynomials.

order_bsp_penalty

Integer; order of Bernstein polynomial penalty. 0 results in a penalty based on integrated squared second order derivatives, values >= 1 in difference penalties.

tf_bsps

Logical; whether to use a TensorFlow implementation of the Bernstein polynomial functions.

response_type

Character; type of response can be continuous, ordered, survival, or count.

atm_toplayer

Function; a function specifying the layer on top of ATM lags.

basis

Character or function; implemented options are "bernstein" (a Bernstein polynomial basis), "ordered" (for ordinal responses), or "shiftscale" for (log-) linear bases

Value

Returns a named list with all options, basis functions, support, and penalties.

Transformation ensembles

Description

Transformation ensembles

Usage

trafoensemble(
  formula,
  data,
  n_ensemble = 5,
  verbose = FALSE,
  print_members = TRUE,
  stop_if_nan = TRUE,
  save_weights = TRUE,
  callbacks = list(),
  save_fun = NULL,
  seed = seq_len(n_ensemble),
  tf_seeds = seq_len(n_ensemble),
  ...
)

Arguments

formula

Formula specifying the response, interaction, shift terms as response | interacting ~ shifting. auto-regressive transformation models (ATMs).

data

Named list or data.frame which may contain both structured and unstructured data.

n_ensemble

Numeric; number of ensemble members to fit.

verbose

Logical; whether to print training in each fold.

print_members

Logical; print results for each member.

stop_if_nan

Logical; whether to stop ensembling if NaN values occur

save_weights

Logical; whether to save the ensemble weights.

callbacks

List; callbacks used for fitting.

save_fun

Function; function to be applied to each member to be stored in the final result.

seed

Numeric vector of length n_ensemble; seeds for model re-initialization. Changing these seeds does not change the parameters of the interacting predictor coef(obj, which_param = "interacting"), change tf_seeds to adapt those coefficients.

tf_seeds

Numeric vector of length n_ensemble; explicit seed for changing the parameters of the interacting predictor. Distinct from seed which is used for weight re-initialization of the rest of the model (i.e., the shifting predictor and potential neural network components in the interacting component).

...

Further arguments passed to deeptrafo and fit.

Value

Ensemble of "deeptrafo" models with list of training histories and fitted weights included in ensemble_results. For details see the return statment in ensemble.

Tune and evaluate weighted transformation ensembles

Description

Tune and evaluate weighted transformation ensembles

Usage

weighted_logLik(
  object,
  weights = NULL,
  newdata = NULL,
  convert_fun = function(x, ...) mean(x, ...),
  batch_size = NULL,
  ...
)

Arguments

object

Object of class "dtEnsemble"

weights

Numeric; weight-vector of length n_ensemble, if NULL the weights are tuned on newdata

newdata

List or data.frame; new data to evaluate or tune the weights on

convert_fun

Function; applied to the log-likelihood values of all observations.

batch_size

Integer; optional, useful if data is too large.

...

Further arguments supplied to print.deeptrafo

Value

Returns list of ensemble members, average, and ensemble log-likelihood converted by convert_fun