implemented_model_classes {tramicp}R Documentation

Aliases for implemented model classes

Description

ICP for Box-Cox-type transformed normal regression, parametric and semiparametric survival models, continuous outcome logistic regression, linear regression, cumulative ordered regression, generalized linear models; and nonparametric ICP via ranger. While TRAMICP based on quantile and survival random forests is also supported, for these methods it comes without theoretical guarantees as of yet.

Usage

BoxCoxICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

SurvregICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

survregICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

coxphICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

ColrICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

CoxphICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

LehmannICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

LmICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

lmICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

PolrICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

polrICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

glmICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

cotramICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

rangerICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

survforestICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

qrfICP(
  formula,
  data,
  env,
  verbose = TRUE,
  type = "residual",
  test = "gcm.test",
  controls = NULL,
  alpha = 0.05,
  baseline_fixed = TRUE,
  greedy = FALSE,
  max_size = NULL,
  mandatory = NULL,
  ...
)

Arguments

formula

A formula including response and covariate terms.

data

A data.frame containing response and explanatory variables.

env

A formula specifying the environment variables (see details).

verbose

Logical, whether output should be verbose (default TRUE).

type

Character, type of invariance ("residual" or "wald"); see Details.

test

Character, specifies the invariance test to be used when type = "residual". The default is "gcm.test". Other implemented tests are "HSIC", "t.test", "var.test", and "combined". Alternatively, a custom function for testing invariance of the form \(r, e, controls) {...} can be supplied, which outputs a list with entry "p.value".

controls

Controls for the used tests and the overall procedure, see dicp_controls.

alpha

Level of invariance test, default 0.05.

baseline_fixed

Fixed baseline transformation, see dicp_controls.

greedy

Logical, whether to perform a greedy version of ICP (default is FALSE).

max_size

Numeric; maximum support size.

mandatory

A formula containing mandatory covariates, i.e., covariates which by domain knowledge are believed to be parents of the response or are in another way required for the environment or model to be valid (for instance, conditionally valid environments or random effects in a mixed model).

...

Further arguments passed to modFUN.

Value

Object of type "dICP". See dicp

Examples

set.seed(123)
d <- dgp_dicp(mod = "boxcox", n = 300)
BoxCoxICP(Y ~ X2, data = d, env = ~ E, type = "wald")


set.seed(123)
d <- dgp_dicp(mod = "weibull", n = 300)
SurvregICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
### or
library("survival")
d$Y <- Surv(d$Y)
survregICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
CoxphICP(Y ~ X2, data = d, env = ~ E)
coxphICP(Y ~ X2, data = d, env = ~ E)



set.seed(123)
d <- dgp_dicp(mod = "colr", n = 300)
ColrICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)



set.seed(123)
d <- dgp_dicp(mod = "coxph", n = 300)
LehmannICP(Y ~ X2, data = d, env = ~ E)



set.seed(123)
d <- dgp_dicp(mod = "lm", n = 300)
LmICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
### or
lmICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)



set.seed(123)
d <- dgp_dicp(mod = "polr", n = 300)
PolrICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
### or
PolrICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)



set.seed(123)
d <- dgp_dicp(mod = "binary", n = 300)
glmICP(Y ~ X1 + X2 + X3, data = d, env = ~ E, family = "binomial")



set.seed(123)
d <- dgp_dicp(mod = "cotram", n = 300)
cotramICP(Y ~ X2, data = d, env = ~ E)



set.seed(123)
d <- dgp_dicp(mod = "binary", n = 300)
rangerICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)



set.seed(12)
d <- dgp_dicp(mod = "coxph", n = 3e2)
d$Y <- survival::Surv(d$Y, sample(0:1, 3e2, TRUE, prob = c(0.1, 0.9)))
survforestICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)



set.seed(12)
d <- dgp_dicp(mod = "boxcox", n = 3e2)
qrfICP(Y ~ X1 + X2 + X3, data = d, env = ~ E)
[Package tramicp version 0.0-2 Index]