Package 'tramvs' reference manual

Title:	Optimal Subset Selection for Transformation Models
Description:	Greedy optimal subset selection for transformation models (Hothorn et al., 2018, <doi:10.1111/sjos.12291> ) based on the abess algorithm (Zhu et al., 2020, <doi:10.1073/pnas.2014241117> ). Applicable to models from packages 'tram' and 'cotram'.
Authors:	Lucas Kook [aut, cre], Sandra Siegfried [ctb], Torsten Hothorn [ctb]
Maintainer:	Lucas Kook <[email protected]>
License:	GPL-3
Version:	0.0-6
Built:	2024-12-04 07:33:31 UTC
Source:	CRAN

Optimal subset selection for multivariate transformation models

Description

Optimal subset selection for multivariate transformation models

Usage

abess_mmlt(
  mltargs,
  supp,
  k_max = supp,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  m0 = NULL,
  ...
)
abess_mmlt(
  mltargs,
  supp,
  k_max = supp,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  m0 = NULL,
  ...
)

Arguments

`mltargs`	Arguments passed to `mmlt`
`supp`	support size of the coefficient vector
`k_max`	maximum support size to consider during the splicing algorithm. Defaults to `supp`.
`thresh`	threshold when to stop splicing. Defaults to 0.01 * `supp` * p * log(log(n)) / n$, where p denotes the number of predictors and n the sample size.
`init`	initialize active set. Defaults to `TRUE` and initializes the active set with those covariates that are most correlated with score residuals of an unconditional `modFUN(update(formula, . ~ 1))`.
`m_max`	maximum number of iterating the splicing algorithm.
`m0`	Transformation model for initialization
`...`	Currently ignored

Value

List containing the fitted model via mmlt, active set A and inactive set I.

Optimal subset selection for transformation models

Description

Optimal subset selection for transformation models

Usage

abess_tram(
  formula,
  data,
  modFUN,
  supp,
  mandatory = NULL,
  k_max = supp,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  m0 = NULL,
  ...
)
abess_tram(
  formula,
  data,
  modFUN,
  supp,
  mandatory = NULL,
  k_max = supp,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  m0 = NULL,
  ...
)

Arguments

`formula`	object of class `"formula"`.
`data`	data frame containing the variables in the model.
`modFUN`	function for fitting a transformation model, e.g., `BoxCox()`.
`supp`	support size of the coefficient vector
`mandatory`	formula of mandatory covariates, which will always be included and estimated in the model. Note that this also changes the intialization of the active set. The active set is then computed with regards to the model residuals of `modFUN(mandatory, ...)` instead of the unconditional model.
`k_max`	maximum support size to consider during the splicing algorithm. Defaults to `supp`.
`thresh`	threshold when to stop splicing. Defaults to 0.01 * `supp` * p * log(log(n)) / n$, where p denotes the number of predictors and n the sample size.
`init`	initialize active set. Defaults to `TRUE` and initializes the active set with those covariates that are most correlated with score residuals of an unconditional `modFUN(update(formula, . ~ 1))`.
`m_max`	maximum number of iterating the splicing algorithm.
`m0`	Transformation model for initialization
`...`	additional arguments supplied to `modFUN`.

Value

List containing the fitted model via modFUN, active set A and inactive set I.

Examples

set.seed(24101968)
library(tramvs)

N <- 1e2
P <- 5
nz <- 3
beta <- rep(c(1, 0), c(nz, P - nz))
X <- matrix(rnorm(N * P), nrow = N, ncol = P)
Y <- 1 + X %*% beta + rnorm(N)

dat <- data.frame(y = Y, x = X)

abess_tram(y ~ ., dat, modFUN = Lm, supp = 3)

set.seed(24101968)
library(tramvs)

N <- 1e2
P <- 5
nz <- 3
beta <- rep(c(1, 0), c(nz, P - nz))
X <- matrix(rnorm(N * P), nrow = N, ncol = P)
Y <- 1 + X %*% beta + rnorm(N)

dat <- data.frame(y = Y, x = X)

abess_tram(y ~ ., dat, modFUN = Lm, supp = 3)

AIC "tramvs"

Description

AIC "tramvs"

Usage

## S3 method for class 'tramvs'
AIC(object, ...)
## S3 method for class 'tramvs'
AIC(object, ...)

Arguments

`object`	object of class `"tramvs"`
`...`	additional arguments to `AIC()`

Value

Numeric vector containing AIC of best model

Optimal subset selection in a BoxCox-type transformation model

Description

Optimal subset selection in a BoxCox-type transformation model

Usage

BoxCoxVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)
BoxCoxVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)

Arguments

`formula`	object of class `"formula"`.
`data`	data frame containing the variables in the model.
`supp_max`	maximum support which to call `abess_tram` with.
`k_max`	maximum support size to consider during the splicing algorithm. Defaults to `supp`.
`thresh`	threshold when to stop splicing. Defaults to 0.01 * `supp` * p * log(log(n)) / n$, where p denotes the number of predictors and n the sample size.
`init`	initialize active set. Defaults to `TRUE` and initializes the active set with those covariates that are most correlated with score residuals of an unconditional `modFUN(update(formula, . ~ 1))`.
`m_max`	maximum number of iterating the splicing algorithm.
`parallel`	toggle for parallel computing via `future_lapply`
`future_args`	arguments passed to `plan`; defaults to a `"multisession"` with `supp_max` workers
`...`	Additional arguments supplied to `BoxCox`

Value

See tramvs

Coef "abess_tram"

Description

Coef "abess_tram"

Usage

## S3 method for class 'abess_tram'
coef(object, ...)
## S3 method for class 'abess_tram'
coef(object, ...)

Arguments

`object`	object of class `"tramvs"`
`...`	additional arguments to `coef()`

Value

Named numeric vector containing coefficient estimates see coef.tram

Coef "mmltvs"

Description

Coef "mmltvs"

Usage

## S3 method for class 'mmltvs'
coef(object, best_only = FALSE, ...)
## S3 method for class 'mmltvs'
coef(object, best_only = FALSE, ...)

Arguments

`object`	Object of class `"tramvs"`
`best_only`	Wether to return the coefficients of the best model only (default: FALSE)
`...`	additional arguments to `coef()`

Value

Vector (best_only = TRUE) or matrix (best_only = FALSE) of coefficients

Coef "tramvs"

Description

Coef "tramvs"

Usage

## S3 method for class 'tramvs'
coef(object, best_only = FALSE, ...)
## S3 method for class 'tramvs'
coef(object, best_only = FALSE, ...)

Arguments

`object`	Object of class `"tramvs"`
`best_only`	Wether to return the coefficients of the best model only (default: FALSE)
`...`	additional arguments to `coef()`

Value

Vector (best_only = TRUE) or matrix (best_only = FALSE) of coefficients

Optimal subset selection in a Colr-type transformation model

Description

Optimal subset selection in a Colr-type transformation model

Usage

ColrVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)
ColrVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)

Arguments

`formula`	object of class `"formula"`.
`data`	data frame containing the variables in the model.
`supp_max`	maximum support which to call `abess_tram` with.
`k_max`	maximum support size to consider during the splicing algorithm. Defaults to `supp`.
`thresh`	threshold when to stop splicing. Defaults to 0.01 * `supp` * p * log(log(n)) / n$, where p denotes the number of predictors and n the sample size.
`init`	initialize active set. Defaults to `TRUE` and initializes the active set with those covariates that are most correlated with score residuals of an unconditional `modFUN(update(formula, . ~ 1))`.
`m_max`	maximum number of iterating the splicing algorithm.
`parallel`	toggle for parallel computing via `future_lapply`
`future_args`	arguments passed to `plan`; defaults to a `"multisession"` with `supp_max` workers
`...`	Additional arguments supplied to `Colr`

Value

See tramvs

Compute correlation for initializing the active set

Description

Compute correlation for initializing the active set

Usage

cor_init(m0, mb)
cor_init(m0, mb)

Arguments

`m0`	`modFUN(formula, data)`
`mb`	`modFUN(mandatory, data)`

Value

Vector of correlations for initializing the active set, depends on type of model (see e.g. cor_init.default)

Default method for computing correlation

Description

Default method for computing correlation

Usage

## Default S3 method:
cor_init(m0, mb)
## Default S3 method:
cor_init(m0, mb)

Arguments

`m0`	`modFUN(formula, data)`
`mb`	`modFUN(mandatory, data)`

Value

Vector of correlation for initializing the active set

Method for computing correlations in mmlts

Description

Method for computing correlations in mmlts

Usage

## S3 method for class 'mmlt'
cor_init(m0, mb)
## S3 method for class 'mmlt'
cor_init(m0, mb)

Arguments

`m0`	`modFUN(formula, data)`
`mb`	`modFUN(mandatory, data)`

Value

Vector of correlation for initializing the active set

Shit-scale tram method for computing correlation

Description

Shit-scale tram method for computing correlation

Usage

## S3 method for class 'stram'
cor_init(m0, mb)
## S3 method for class 'stram'
cor_init(m0, mb)

Arguments

`m0`	`modFUN(formula, data)`
`mb`	`modFUN(mandatory, data)`

Value

Vector of correlations for initializing the active set, includes both shift and scale residuals

Optimal subset selection in a cotram model

Description

Optimal subset selection in a cotram model

Usage

cotramVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)
cotramVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)

Arguments

`formula`	object of class `"formula"`.
`data`	data frame containing the variables in the model.
`supp_max`	maximum support which to call `abess_tram` with.
`k_max`	maximum support size to consider during the splicing algorithm. Defaults to `supp`.
`thresh`	threshold when to stop splicing. Defaults to 0.01 * `supp` * p * log(log(n)) / n$, where p denotes the number of predictors and n the sample size.
`init`	initialize active set. Defaults to `TRUE` and initializes the active set with those covariates that are most correlated with score residuals of an unconditional `modFUN(update(formula, . ~ 1))`.
`m_max`	maximum number of iterating the splicing algorithm.
`parallel`	toggle for parallel computing via `future_lapply`
`future_args`	arguments passed to `plan`; defaults to a `"multisession"` with `supp_max` workers
`...`	Additional arguments supplied to `cotram`

Value

See tramvs

Optimal subset selection in a Coxph-type transformation model

Description

Optimal subset selection in a Coxph-type transformation model

Usage

CoxphVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)
CoxphVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)

Arguments

`formula`	object of class `"formula"`.
`data`	data frame containing the variables in the model.
`supp_max`	maximum support which to call `abess_tram` with.
`k_max`	maximum support size to consider during the splicing algorithm. Defaults to `supp`.
`thresh`	threshold when to stop splicing. Defaults to 0.01 * `supp` * p * log(log(n)) / n$, where p denotes the number of predictors and n the sample size.
`init`	initialize active set. Defaults to `TRUE` and initializes the active set with those covariates that are most correlated with score residuals of an unconditional `modFUN(update(formula, . ~ 1))`.
`m_max`	maximum number of iterating the splicing algorithm.
`parallel`	toggle for parallel computing via `future_lapply`
`future_args`	arguments passed to `plan`; defaults to a `"multisession"` with `supp_max` workers
`...`	Additional arguments supplied to `Coxph`

Value

See tramvs

Optimal subset selection in a Lehmann-type transformation model

Description

Optimal subset selection in a Lehmann-type transformation model

Usage

LehmannVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)
LehmannVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)

Arguments

`formula`	object of class `"formula"`.
`data`	data frame containing the variables in the model.
`supp_max`	maximum support which to call `abess_tram` with.
`k_max`	maximum support size to consider during the splicing algorithm. Defaults to `supp`.
`thresh`	threshold when to stop splicing. Defaults to 0.01 * `supp` * p * log(log(n)) / n$, where p denotes the number of predictors and n the sample size.
`init`	initialize active set. Defaults to `TRUE` and initializes the active set with those covariates that are most correlated with score residuals of an unconditional `modFUN(update(formula, . ~ 1))`.
`m_max`	maximum number of iterating the splicing algorithm.
`parallel`	toggle for parallel computing via `future_lapply`
`future_args`	arguments passed to `plan`; defaults to a `"multisession"` with `supp_max` workers
`...`	Additional arguments supplied to `Lehmann`

Value

See tramvs

Optimal subset selection in an Lm-type transformation model

Description

Optimal subset selection in an Lm-type transformation model

Usage

LmVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)
LmVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)

Arguments

`formula`	object of class `"formula"`.
`data`	data frame containing the variables in the model.
`supp_max`	maximum support which to call `abess_tram` with.
`k_max`	maximum support size to consider during the splicing algorithm. Defaults to `supp`.
`thresh`	threshold when to stop splicing. Defaults to 0.01 * `supp` * p * log(log(n)) / n$, where p denotes the number of predictors and n the sample size.
`init`	initialize active set. Defaults to `TRUE` and initializes the active set with those covariates that are most correlated with score residuals of an unconditional `modFUN(update(formula, . ~ 1))`.
`m_max`	maximum number of iterating the splicing algorithm.
`parallel`	toggle for parallel computing via `future_lapply`
`future_args`	arguments passed to `plan`; defaults to a `"multisession"` with `supp_max` workers
`...`	Additional arguments supplied to `Lm`

Value

See tramvs

logLik "tramvs"

Description

logLik "tramvs"

Usage

## S3 method for class 'tramvs'
logLik(object, ...)
## S3 method for class 'tramvs'
logLik(object, ...)

Arguments

`object`	object of class `"tramvs"`
`...`	additional arguments to `logLik()`

Value

Numeric vector containing log-likelihood of best model, see logLik.tram

Select optimal subset based on high dimensional BIC in mmlts

Description

Select optimal subset based on high dimensional BIC in mmlts

Usage

mmltVS(
  mltargs,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  verbose = TRUE,
  parallel = FALSE,
  m0 = NULL,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)
mmltVS(
  mltargs,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  verbose = TRUE,
  parallel = FALSE,
  m0 = NULL,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)

Arguments

`mltargs`	Arguments passed to `mmlt`
`supp_max`	maximum support which to call `abess_tram` with.
`k_max`	maximum support size to consider during the splicing algorithm. Defaults to `supp`.
`thresh`	threshold when to stop splicing. Defaults to 0.01 * `supp` * p * log(log(n)) / n$, where p denotes the number of predictors and n the sample size.
`init`	initialize active set. Defaults to `TRUE` and initializes the active set with those covariates that are most correlated with score residuals of an unconditional `modFUN(update(formula, . ~ 1))`.
`m_max`	maximum number of iterating the splicing algorithm.
`verbose`	show progress bar (default: `TRUE`)
`parallel`	toggle for parallel computing via `future_lapply`
`m0`	Transformation model for initialization
`future_args`	arguments passed to `plan`; defaults to a `"multisession"` with `supp_max` workers
`...`	Arguments passed on to `abess_mmlt` `supp` support size of the coefficient vector

Details

L0-penalized (i.e., best subset selection) multivariate transformation models using the abess algorithm.

Value

object of class "mltvs", containing the regularization path (information criterion SIC and coefficients coefs), the best fit (best_fit) and all other models (all_fits)

Plot "tramvs" object

Description

Plot "tramvs" object

Usage

## S3 method for class 'tramvs'
plot(x, which = c("tune", "path"), ...)
## S3 method for class 'tramvs'
plot(x, which = c("tune", "path"), ...)

Arguments

`x`	object of class `"tramvs"`
`which`	plotting either the regularization path (`"path"`) or the information criterion against the support size (`"tune"`, default)
`...`	additional arguments to `plot()`

Value

Returns invisible(NULL)

Optimal subset selection in a Polr-type transformation model

Description

Optimal subset selection in a Polr-type transformation model

Usage

PolrVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)
PolrVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)

Arguments

`formula`	object of class `"formula"`.
`data`	data frame containing the variables in the model.
`supp_max`	maximum support which to call `abess_tram` with.
`k_max`	maximum support size to consider during the splicing algorithm. Defaults to `supp`.
`thresh`	threshold when to stop splicing. Defaults to 0.01 * `supp` * p * log(log(n)) / n$, where p denotes the number of predictors and n the sample size.
`init`	initialize active set. Defaults to `TRUE` and initializes the active set with those covariates that are most correlated with score residuals of an unconditional `modFUN(update(formula, . ~ 1))`.
`m_max`	maximum number of iterating the splicing algorithm.
`parallel`	toggle for parallel computing via `future_lapply`
`future_args`	arguments passed to `plan`; defaults to a `"multisession"` with `supp_max` workers
`...`	Additional arguments supplied to `Polr`

Value

See tramvs

Predict "tramvs"

Description

Predict "tramvs"

Usage

## S3 method for class 'tramvs'
predict(object, ...)
## S3 method for class 'tramvs'
predict(object, ...)

Arguments

`object`	object of class `"tramvs"`
`...`	additional arguments to `predict.tram()`

Value

See predict.tram

Print "tramvs"

Description

Print "tramvs"

Usage

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

Arguments

`x`	object of class `"tramvs"`
`...`	ignored

Value

"tramvs" object is returned invisibly

Residuals "tramvs"

Description

Residuals "tramvs"

Usage

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

Arguments

`object`	object of class `"tramvs"`
`...`	additional arguments to `residuals()`

Value

Numeric vector containing residuals of best model, see residuals.tram

SIC generic

Description

SIC generic

Usage

SIC(object, ...)
SIC(object, ...)

Arguments

`object`	Model to compute SIC from
`...`	for methods compatibility only

Value

Numeric vector (best_only = TRUE) or data.frame with SIC values

SIC "tramvs"

Description

SIC "tramvs"

Usage

## S3 method for class 'tramvs'
SIC(object, best_only = FALSE, ...)
## S3 method for class 'tramvs'
SIC(object, best_only = FALSE, ...)

Arguments

`object`	object of class `"tramvs"`
`best_only`	Wether to return the coefficients of the best model only (default: FALSE)
`...`	for methods compatibility only

Value

Numeric vector (best_only = TRUE) or data.frame with SIC values

Simulate "tramvs"

Description

Simulate "tramvs"

Usage

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

Arguments

`object`	object of class `"tramvs"`
`nsim`	number of simulations
`seed`	random seed for simulation
`...`	additional arguments to `simulate()`

Value

See simulate.mlt

Summary "tramvs"

Description

Summary "tramvs"

Usage

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

Arguments

`object`	object of class `"tramvs"`
`...`	ignored

Value

"tramvs" object is returned invisibly

Support "tramvs"

Description

Support "tramvs"

Usage

## S3 method for class 'tramvs'
support(object, ...)
## S3 method for class 'tramvs'
support(object, ...)

Arguments

`object`	object of class `"tramvs"`
`...`	ignored

Value

Character vector containing active set of best fit

Optimal subset selection in a Survreg model

Description

Optimal subset selection in a Survreg model

Usage

SurvregVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)
SurvregVS(
  formula,
  data,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)

Arguments

`formula`	object of class `"formula"`.
`data`	data frame containing the variables in the model.
`supp_max`	maximum support which to call `abess_tram` with.
`k_max`	maximum support size to consider during the splicing algorithm. Defaults to `supp`.
`thresh`	threshold when to stop splicing. Defaults to 0.01 * `supp` * p * log(log(n)) / n$, where p denotes the number of predictors and n the sample size.
`init`	initialize active set. Defaults to `TRUE` and initializes the active set with those covariates that are most correlated with score residuals of an unconditional `modFUN(update(formula, . ~ 1))`.
`m_max`	maximum number of iterating the splicing algorithm.
`parallel`	toggle for parallel computing via `future_lapply`
`future_args`	arguments passed to `plan`; defaults to a `"multisession"` with `supp_max` workers
`...`	Additional arguments supplied to `Survreg`

Value

See tramvs

Select optimal subset based on high dimensional BIC

Description

Select optimal subset based on high dimensional BIC

Usage

tramvs(
  formula,
  data,
  modFUN,
  mandatory = NULL,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  m0 = NULL,
  verbose = TRUE,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)
tramvs(
  formula,
  data,
  modFUN,
  mandatory = NULL,
  supp_max = NULL,
  k_max = NULL,
  thresh = NULL,
  init = TRUE,
  m_max = 10,
  m0 = NULL,
  verbose = TRUE,
  parallel = FALSE,
  future_args = list(strategy = "multisession", workers = supp_max),
  ...
)

Arguments

`formula`	object of class `"formula"`.
`data`	data frame containing the variables in the model.
`modFUN`	function for fitting a transformation model, e.g., `BoxCox()`.
`mandatory`	formula of mandatory covariates, which will always be included and estimated in the model. Note that this also changes the intialization of the active set. The active set is then computed with regards to the model residuals of `modFUN(mandatory, ...)` instead of the unconditional model.
`supp_max`	maximum support which to call `abess_tram` with.
`k_max`	maximum support size to consider during the splicing algorithm. Defaults to `supp`.
`thresh`	threshold when to stop splicing. Defaults to 0.01 * `supp` * p * log(log(n)) / n$, where p denotes the number of predictors and n the sample size.
`init`	initialize active set. Defaults to `TRUE` and initializes the active set with those covariates that are most correlated with score residuals of an unconditional `modFUN(update(formula, . ~ 1))`.
`m_max`	maximum number of iterating the splicing algorithm.
`m0`	Transformation model for initialization
`verbose`	show progress bar (default: `TRUE`)
`parallel`	toggle for parallel computing via `future_lapply`
`future_args`	arguments passed to `plan`; defaults to a `"multisession"` with `supp_max` workers
`...`	Arguments passed on to `abess_tram` `supp` support size of the coefficient vector

Details

L0-penalized (i.e., best subset selection) transformation models using the abess algorithm.

Value

object of class "tramvs", containing the regularization path (information criterion SIC and coefficients coefs), the best fit (best_fit) and all other models (all_fits)

Examples

set.seed(24101968)
library("tramvs")

N <- 1e2
P <- 5
nz <- 3
beta <- rep(c(1, 0), c(nz, P - nz))
X <- matrix(rnorm(N * P), nrow = N, ncol = P)
Y <- 1 + X %*% beta + rnorm(N)

dat <- data.frame(y = Y, x = X)
res <- tramvs(y ~ ., data = dat, modFUN = Lm)
plot(res, type = "b")
plot(res, which = "path")

set.seed(24101968)
library("tramvs")

N <- 1e2
P <- 5
nz <- 3
beta <- rep(c(1, 0), c(nz, P - nz))
X <- matrix(rnorm(N * P), nrow = N, ncol = P)
Y <- 1 + X %*% beta + rnorm(N)

dat <- data.frame(y = Y, x = X)
res <- tramvs(y ~ ., data = dat, modFUN = Lm)
plot(res, type = "b")
plot(res, which = "path")

Package 'tramvs'

Help Index

Optimal subset selection for multivariate transformation models

Description

Usage

Arguments

Value

Optimal subset selection for transformation models

Description

Usage

Arguments

Value

Examples

AIC "tramvs"

Description

Usage

Arguments

Value

Optimal subset selection in a BoxCox-type transformation model

Description

Usage

Arguments

Value

Coef "abess_tram"

Description

Usage

Arguments

Value

Coef "mmltvs"

Description

Usage

Arguments

Value

Coef "tramvs"

Description

Usage

Arguments

Value

Optimal subset selection in a Colr-type transformation model

Description

Usage

Arguments

Value

Compute correlation for initializing the active set

Description

Usage

Arguments

Value

Default method for computing correlation

Description

Usage

Arguments

Value

Method for computing correlations in mmlts

Description

Usage

Arguments

Value

Shit-scale tram method for computing correlation

Description

Usage

Arguments

Value

Optimal subset selection in a cotram model

Description

Usage

Arguments

Value

Optimal subset selection in a Coxph-type transformation model

Description

Usage

Arguments

Value

Optimal subset selection in a Lehmann-type transformation model

Description

Usage

Arguments

Value

Optimal subset selection in an Lm-type transformation model

Description