Package 'mlt' reference manual

Title:	Most Likely Transformations
Description:	Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020) <DOI:10.18637/jss.v092.i01>. Shift-scale (Siegfried et al, 2023, <DOI:10.1080/00031305.2023.2203177>) and multivariate (Klein et al, 2022, <DOI:10.1111/sjos.12501>) transformation models are part of this package. A package vignette is available from <DOI:10.32614/CRAN.package.mlt.docreg> and more convenient user interfaces to many models from <DOI:10.32614/CRAN.package.tram>.
Authors:	Torsten Hothorn [aut, cre]
Maintainer:	Torsten Hothorn <[email protected]>
License:	GPL-2
Version:	1.6-1
Built:	2024-11-19 19:20:08 UTC
Source:	https://github.com/r-forge/ctm

General Information on the mlt Package

Description

The mlt package implements maximum likelihood estimation in conditional transformation models as introduced by Hothorn et al. (2020), Klein et al. (2022), and Siegfried et al. (2023).

An introduction to the package is available in the mlt package vignette from package mlt.docreg (Hothorn, 2020).

Novice users might find the high(er) level interfaces offered by package tram more convenient.

Author(s)

This package is authored by Torsten Hothorn <[email protected]>.

References

Torsten Hothorn, Lisa Moest, Peter Buehlmann (2018), Most Likely Transformations, Scandinavian Journal of Statistics, 45(1), 110–134, doi:10.1111/sjos.12291.

Torsten Hothorn (2020), Most Likely Transformations: The mlt Package, Journal of Statistical Software, 92(1), 1–68, doi:10.18637/jss.v092.i01

Nadja Klein, Torsten Hothorn, Luisa Barbanti, Thomas Kneib (2022), Multivariate Conditional Transformation Models. Scandinavian Journal of Statistics, 49, 116–142, doi:10.1111/sjos.12501.

Sandra Siegfried, Lucas Kook, Torsten Hothorn (2023), Distribution-Free Location-Scale Regression, The American Statistician, 77(4), 345–356, doi:10.1080/00031305.2023.2203177.

Torsten Hothorn (2024), On Nonparanormal Likelihoods. doi:10.48550/arXiv.2408.17346.

Confidence Bands

Description

Confidence bands for transformation, distribution, survivor or cumulative hazard functions

Usage

confband(object, newdata, level = 0.95, ...)
## S3 method for class 'mlt'
confband(object, newdata, level = 0.95, 
       type = c("trafo", "distribution", "survivor", "cumhazard"), 
       K = 20, cheat = K, ...)
confband(object, newdata, level = 0.95, ...)
## S3 method for class 'mlt'
confband(object, newdata, level = 0.95, 
       type = c("trafo", "distribution", "survivor", "cumhazard"), 
       K = 20, cheat = K, ...)

Arguments

`object`	an object of class `mlt`
`newdata`	a data frame of observations
`level`	the confidence level
`type`	the function to compute the confidence band for
`K`	number of grid points the function is evaluated at
`cheat`	number of grid points the function is evaluated at when using the quantile obtained for `K` grid points
`...`	additional arguments to `confint.glht`

Details

The function is evaluated at K grid points and simultaneous confidence intervals are then interpolated in order to construct the band.

A smoother band can be obtained by setting cheat to something larger than K: The quantile is obtained for K grid points but the number of evaluated grid points cheat can be much larger at no additional cost. Technically, the nominal level is not maintained in this case but the deviation will be small for reasonably large K.

Value

For each row in newdata the function and corresponding confidence band evaluated at the K (or cheat) grid points is returned.

Conditional Transformation Models

Description

Specification of conditional transformation models

Usage

ctm(response, interacting = NULL, shifting = NULL, scaling = NULL, 
    scale_shift = FALSE, data = NULL, 
    todistr = c("Normal", "Logistic", "MinExtrVal", "MaxExtrVal", 
                "Exponential", "Laplace", "Cauchy"), 
    sumconstr = inherits(interacting, c("formula", "formula_basis")), ...)
ctm(response, interacting = NULL, shifting = NULL, scaling = NULL, 
    scale_shift = FALSE, data = NULL, 
    todistr = c("Normal", "Logistic", "MinExtrVal", "MaxExtrVal", 
                "Exponential", "Laplace", "Cauchy"), 
    sumconstr = inherits(interacting, c("formula", "formula_basis")), ...)

Arguments

`response`	a basis function, ie, an object of class `basis`
`interacting`	a basis function, ie, an object of class `basis`
`shifting`	a basis function, ie, an object of class `basis`
`scaling`	a basis function, ie, an object of class `basis`
`scale_shift`	a logical choosing between two different model types in the presence of a `scaling` term
`data`	either a `data.frame` containing the model variables or a formal description of these variables in an object of class `vars`
`todistr`	a character vector describing the distribution to be transformed
`sumconstr`	a logical indicating if sum constraints shall be applied
`...`	arguments to `as.basis` when `shifting` is a formula

Details

This function only specifies the model which can then be fitted using mlt. The shift term is positive by default. All arguments except response can be missing (in this case an unconditional distribution is estimated). Hothorn et al. (2018) explain the model class.

Possible choices of the distributions the model transforms to (the inverse link functions $F_Z$ ) include the standard normal ("Normal"), the standard logistic ("Logistic"), the standard minimum extreme value ("MinExtrVal", also known as Gompertz distribution), and the standard maximum extreme value ("MaxExtrVal", also known as Gumbel distribution) distributions. The exponential distribution ("Exponential") can be used to fit Aalen additive hazard models. Laplace and Cauchy distributions are also available.

Shift-scale models (Siegfried et al., 2023) of the form

$P(Y \le y \mid X = x) = F_Z(\sqrt{\exp(s(x)^\top \gamma)} [(a(y) \otimes b(x))^\top \vartheta] + d(x)^\top \beta)$

(scale_shift = FALSE) or

$P(Y \le y \mid X = x) = F_Z(\sqrt{\exp(s(x)^\top \gamma)} [(a(y) \otimes b(x))^\top \vartheta + d(x)^\top \beta])$

(scale_shift = TRUE) with bases $a(y)$ (response), $b(x)$ (interacting), $d(x)$ (shifting), and $s(x)$ (scaling) can be specified as well.

Value

An object of class ctm.

References

Torsten Hothorn, Lisa Moest, Peter Buehlmann (2018), Most Likely Transformations, Scandinavian Journal of Statistics, 45(1), 110–134, doi:10.1111/sjos.12291.

Sandra Siegfried, Lucas Kook, Torsten Hothorn (2023), Distribution-Free Location-Scale Regression, The American Statistician, 77(4), 345–356, doi:10.1080/00031305.2023.2203177.

Methods for ctm Objects

Description

Methods for objects of class ctm

Usage

## S3 method for class 'ctm'
variable.names(object, 
              which = c("all", "response", "interacting", 
                        "shifting", "scaling"), 
              ...)
## S3 method for class 'ctm'
coef(object, ...)
## S3 method for class 'ctm'
variable.names(object, 
              which = c("all", "response", "interacting", 
                        "shifting", "scaling"), 
              ...)
## S3 method for class 'ctm'
coef(object, ...)

Arguments

`object`	an unfitted conditional transformation model as returned by `ctm`
`which`	a character specifying which names shall be returned
`...`	additional arguments

Details

coef can be used to get and set model parameters.

Most Likely Transformations

Description

Likelihood-based model estimation in conditional transformation models

Usage

mlt(model, data, weights = NULL, offset = NULL, fixed = NULL, theta = NULL, 
    pstart = NULL, scale = FALSE, dofit = TRUE, optim = mltoptim())
mlt(model, data, weights = NULL, offset = NULL, fixed = NULL, theta = NULL, 
    pstart = NULL, scale = FALSE, dofit = TRUE, optim = mltoptim())

Arguments

`model`	a conditional transformation model as specified by `ctm`
`data`	a `data.frame` containing all variables specified in `model`
`weights`	an optional vector of case weights
`offset`	an optional vector of offset values; offsets are not added to an optional `scaling` term (see `link{ctm}`)
`fixed`	a named vector of fixed regression coefficients; the names need to correspond to column names of the design matrix
`theta`	optional starting values for the model parameters
`pstart`	optional starting values for the distribution function evaluated at the data
`scale`	a logical indicating if (internal) scaling shall be applied to the model coefficients
`dofit`	a logical indicating if the model shall be fitted to the data (`TRUE`) or not. If `theta` is given, a model of class `mlt` (a full "fitted" model) featuring these parameters is returned. Otherwise, an unfitted model of class `ctm` is returned
`optim`	a list of functions implementing suitable optimisers

Details

This function fits a conditional transformation model by searching for the most likely transformation as described in Hothorn et al. (2018) and Hothorn (2020).

Value

An object of class mlt with corresponding methods.

References

Torsten Hothorn, Lisa Moest, Peter Buehlmann (2018), Most Likely Transformations, Scandinavian Journal of Statistics, 45(1), 110–134, doi:10.1111/sjos.12291.

Torsten Hothorn (2020), Most Likely Transformations: The mlt Package, Journal of Statistical Software, 92(1), 1–68, doi:10.18637/jss.v092.i01

Sandra Siegfried, Lucas Kook, Torsten Hothorn (2023), Distribution-Free Location-Scale Regression, The American Statistician, 77(4), 345–356, doi:10.1080/00031305.2023.2203177.

Examples

 
  ### set-up conditional transformation model for conditional
  ### distribution of dist given speed
  dist <- numeric_var("dist", support = c(2.0, 100), bounds = c(0, Inf))
  speed <- numeric_var("speed", support = c(5.0, 23), bounds = c(0, Inf)) 
  ctmm <- ctm(response = Bernstein_basis(dist, order = 4, ui = "increasing"),
              interacting = Bernstein_basis(speed, order = 3))

  ### fit model
  mltm <- mlt(ctmm, data = cars)

  ### plot data
  plot(cars)
  ### predict quantiles and overlay data with model via a "quantile sheet"
  q <- predict(mltm, newdata = data.frame(speed = 0:24), type = "quantile", 
               p = 2:8 / 10, K = 500)
  tmp <- apply(q, 1, function(x) lines(0:24, x, type = "l"))

### set-up conditional transformation model for conditional
  ### distribution of dist given speed
  dist <- numeric_var("dist", support = c(2.0, 100), bounds = c(0, Inf))
  speed <- numeric_var("speed", support = c(5.0, 23), bounds = c(0, Inf)) 
  ctmm <- ctm(response = Bernstein_basis(dist, order = 4, ui = "increasing"),
              interacting = Bernstein_basis(speed, order = 3))

  ### fit model
  mltm <- mlt(ctmm, data = cars)

  ### plot data
  plot(cars)
  ### predict quantiles and overlay data with model via a "quantile sheet"
  q <- predict(mltm, newdata = data.frame(speed = 0:24), type = "quantile", 
               p = 2:8 / 10, K = 500)
  tmp <- apply(q, 1, function(x) lines(0:24, x, type = "l"))

Methods for mlt Objects

Description

Methods for objects of class mlt

Usage

## S3 method for class 'mlt'
coef(object, fixed = TRUE, ...)
coef(object) <- value
## S3 method for class 'mlt'
weights(object, ...)
## S3 method for class 'mlt'
logLik(object, parm = coef(object, fixed = FALSE), w = NULL, newdata, ...)
## S3 method for class 'mlt'
vcov(object, parm = coef(object, fixed = FALSE), complete = FALSE, ...)
Hessian(object, ...)
## S3 method for class 'mlt'
Hessian(object, parm = coef(object, fixed = FALSE), ...)
Gradient(object, ...)
## S3 method for class 'mlt'
Gradient(object, parm = coef(object, fixed = FALSE), ...)
## S3 method for class 'mlt'
estfun(x, parm = coef(x, fixed = FALSE),
       w = NULL, newdata, ...)
## S3 method for class 'mlt'
residuals(object, parm = coef(object, fixed = FALSE), 
       w = NULL, newdata, what = c("shifting", "scaling"), ...)
## S3 method for class 'mlt'
mkgrid(object, n, ...)
## S3 method for class 'mlt'
bounds(object)
## S3 method for class 'mlt'
variable.names(object, ...)
## S3 method for class 'mlt_fit'
update(object, weights = stats::weights(object), 
       subset = NULL, offset = object$offset, theta = coef(object, fixed = FALSE), 
       fixed = NULL, ...)
## S3 method for class 'mlt'
as.mlt(object)
## S3 method for class 'mlt'
coef(object, fixed = TRUE, ...)
coef(object) <- value
## S3 method for class 'mlt'
weights(object, ...)
## S3 method for class 'mlt'
logLik(object, parm = coef(object, fixed = FALSE), w = NULL, newdata, ...)
## S3 method for class 'mlt'
vcov(object, parm = coef(object, fixed = FALSE), complete = FALSE, ...)
Hessian(object, ...)
## S3 method for class 'mlt'
Hessian(object, parm = coef(object, fixed = FALSE), ...)
Gradient(object, ...)
## S3 method for class 'mlt'
Gradient(object, parm = coef(object, fixed = FALSE), ...)
## S3 method for class 'mlt'
estfun(x, parm = coef(x, fixed = FALSE),
       w = NULL, newdata, ...)
## S3 method for class 'mlt'
residuals(object, parm = coef(object, fixed = FALSE), 
       w = NULL, newdata, what = c("shifting", "scaling"), ...)
## S3 method for class 'mlt'
mkgrid(object, n, ...)
## S3 method for class 'mlt'
bounds(object)
## S3 method for class 'mlt'
variable.names(object, ...)
## S3 method for class 'mlt_fit'
update(object, weights = stats::weights(object), 
       subset = NULL, offset = object$offset, theta = coef(object, fixed = FALSE), 
       fixed = NULL, ...)
## S3 method for class 'mlt'
as.mlt(object)

Arguments

`object`, `x`	a fitted conditional transformation model as returned by `mlt`
`fixed`	a logical indicating if only estimated coefficients (`fixed = FALSE`) should be returned OR (for `update`) a named vector of fixed regression coefficients; the names need to correspond to column names of the design matrix
`value`	coefficients to be assigned to the model
`parm`	model parameters
`w`	model weights
`what`	type of residual: `shifting` means score with respect to a constant intercept for the shift term and `scaling` means score with respect to a constant intercept in the scaling term. This works whether or not such terms are actually present in the model
`weights`	model weights
`newdata`	an optional data frame of new observations. Allows evaluation of the log-likelihood for a given model `object` on these new observations. The parameters `parm` and `w` are ignored in this situation.
`n`	number of grid points
`subset`	an optional integer vector indicating the subset of observations to be used for fitting.
`offset`	an optional vector of offset values
`theta`	optional starting values for the model parameters
`complete`	currently ignored
`...`	additional arguments

Details

coef can be used to get and set model parameters, weights and logLik extract weights and evaluate the log-likelihood (also for parameters other than the maximum likelihood estimate). Hessian returns the Hessian (of the negative log-likelihood) and vcov the inverse thereof. Gradient gives the negative gradient (minus sum of the score contributions) and estfun the negative score contribution by each observation. mkgrid generates a grid of all variables (as returned by variable.names) in the model. update allows refitting the model with alternative weights and potentially different starting values. bounds gets bounds for bounded variables in the model.

Control Optimisation

Description

Define optimisers and their control parameters

Usage

mltoptim(auglag = list(maxtry = 5, kkt2.check = FALSE), 
         spg = list(maxit = 10000, quiet = TRUE, checkGrad = FALSE), 
         nloptr = list(algorithm = "NLOPT_LD_MMA", xtol_rel = 1.0e-8, maxeval = 1000L), 
         trace = FALSE)
mltoptim(auglag = list(maxtry = 5, kkt2.check = FALSE), 
         spg = list(maxit = 10000, quiet = TRUE, checkGrad = FALSE), 
         nloptr = list(algorithm = "NLOPT_LD_MMA", xtol_rel = 1.0e-8, maxeval = 1000L), 
         trace = FALSE)

Arguments

`auglag`	A list with control parameters for the `auglag` optimiser. `maxtry` is the number of times the algorithm is started on random starting values in case it failed with the precomputed ones.
`spg`	A list with control parameters for the `BBoptim` optimiser (calling `spg` internally).
`nloptr`	A list with control parameters for the `nloptr` family of optimisers.
`trace`	A logical switching trace reports by the optimisers off.

Details

This function sets-up functions to be called in mlt internally.

Value

A list of functions with arguments theta (starting values), f (log-likelihood), g (scores), ui and ci (linear inequality constraints). Adding further such functions is a way to add more optimisers to mlt. The first one in this list converging defines the resulting model.

Examples


  ### set-up linear transformation model for conditional
  ### distribution of dist given speed
  dist <- numeric_var("dist", support = c(2.0, 100), bounds = c(0, Inf))
  ctmm <- ctm(response = Bernstein_basis(dist, order = 4, ui = "increasing"),
              shifting = ~ speed, data = cars)

  ### use auglag with kkt2.check = TRUE => the numerically determined
  ### hessian is returned as "optim_hessian" slot
  op <- mltoptim(auglag = list(maxtry = 5, kkt2.check = TRUE))[1]
  mltm <- mlt(ctmm, data = cars, scale = FALSE, optim = op)

  ### compare analytical and numerical hessian
  all.equal(c(Hessian(mltm)), c(mltm$optim_hessian), tol = 1e-4)

### set-up linear transformation model for conditional
  ### distribution of dist given speed
  dist <- numeric_var("dist", support = c(2.0, 100), bounds = c(0, Inf))
  ctmm <- ctm(response = Bernstein_basis(dist, order = 4, ui = "increasing"),
              shifting = ~ speed, data = cars)

  ### use auglag with kkt2.check = TRUE => the numerically determined
  ### hessian is returned as "optim_hessian" slot
  op <- mltoptim(auglag = list(maxtry = 5, kkt2.check = TRUE))[1]
  mltm <- mlt(ctmm, data = cars, scale = FALSE, optim = op)

  ### compare analytical and numerical hessian
  all.equal(c(Hessian(mltm)), c(mltm$optim_hessian), tol = 1e-4)

Multivariate Conditional Transformation Models

Description

Conditional transformation models for multivariate continuous, discrete, or a mix of continuous and discrete outcomes

Usage

mmlt(..., formula = ~ 1, data, conditional = FALSE, theta = NULL, fixed = NULL,
     scale = FALSE, optim = mltoptim(auglag = list(maxtry = 5)), 
     args = list(seed = 1, M = 1000), dofit = TRUE, domargins = TRUE)
## S3 method for class 'cmmlt'
coef(object, newdata, 
     type = c("all", "conditional", "Lambdapar", "Lambda", "Lambdainv", 
              "Precision", "PartialCorr", "Sigma", "Corr", 
              "Spearman", "Kendall"), fixed = TRUE, 
     ...)
## S3 method for class 'mmmlt'
coef(object, newdata, 
     type = c("all", "marginal", "Lambdapar", "Lambda", "Lambdainv", 
              "Precision", "PartialCorr", "Sigma", "Corr", 
              "Spearman", "Kendall"), fixed = TRUE,
     ...)
## S3 method for class 'mmlt'
predict(object, newdata, margins = 1:J, 
        type = c("trafo", "distribution", "survivor", "density", "hazard"), 
                 log = FALSE, args = object$args, ...)
## S3 method for class 'mmlt'
simulate(object, nsim = 1L, seed = NULL, newdata, K = 50, ...)
mmlt(..., formula = ~ 1, data, conditional = FALSE, theta = NULL, fixed = NULL,
     scale = FALSE, optim = mltoptim(auglag = list(maxtry = 5)), 
     args = list(seed = 1, M = 1000), dofit = TRUE, domargins = TRUE)
## S3 method for class 'cmmlt'
coef(object, newdata, 
     type = c("all", "conditional", "Lambdapar", "Lambda", "Lambdainv", 
              "Precision", "PartialCorr", "Sigma", "Corr", 
              "Spearman", "Kendall"), fixed = TRUE, 
     ...)
## S3 method for class 'mmmlt'
coef(object, newdata, 
     type = c("all", "marginal", "Lambdapar", "Lambda", "Lambdainv", 
              "Precision", "PartialCorr", "Sigma", "Corr", 
              "Spearman", "Kendall"), fixed = TRUE,
     ...)
## S3 method for class 'mmlt'
predict(object, newdata, margins = 1:J, 
        type = c("trafo", "distribution", "survivor", "density", "hazard"), 
                 log = FALSE, args = object$args, ...)
## S3 method for class 'mmlt'
simulate(object, nsim = 1L, seed = NULL, newdata, K = 50, ...)

Arguments

`...`	marginal transformation models, one for each response, for `mmlt`. Additional arguments for the methods.
`formula`	a model formula describing a model for the dependency structure via the lambda parameters. The default is set to `~ 1` for constant lambdas.
`data`	a data.frame.
`conditional`	logical; parameters are defined conditionally (only possible when all models are probit models). This is the default as described by Klein et al. (2022). If `FALSE`, parameters can be directly interpreted marginally, this is explained in Section 2.6 by Klein et al. (2022). Using `conditional = FALSE` with probit-only models gives the same likelihood but different parameter estimates.
`theta`	an optional vector of starting values.
`fixed`	an optional named numeric vector of predefined parameter values or a logical (for `coef`) indicating to also return fixed parameters (only when `type = "all"`).
`scale`	a logical indicating if (internal) scaling shall be applied to the model coefficients.
`optim`	a list of optimisers as returned by `mltoptim`
`args`	a list of arguments for `lpmvnorm`.
`dofit`	logical; parameters are fitted by default, otherwise a list with log-likelihood and score function is returned.
`domargins`	logical; all model parameters are fitted by default, including the parameters of marginal models.
`object`	an object of class `mmlt`.
`newdata`	an optional data.frame coefficients and predictions shall be computed for.
`type`	type of coefficient or prediction to be returned.
`margins`	indices defining marginal models to be evaluated. Can be single integers giving the marginal distribution of the corresponding variable, or multiple integers (currently only `1:j` implemented).
`log`	logical; return log-probabilities or log-densities if `TRUE`.
`nsim`	number of samples to generate.
`seed`	optional seed for the random number generator.
`K`	number of grid points to generate.

Details

The function implements core functionality for fitting multivariate conditional transformation models as described by Klein et al (2020).

Value

An object of class mmlt with coef and predict methods.

References

Nadja Klein, Torsten Hothorn, Luisa Barbanti, Thomas Kneib (2022), Multivariate Conditional Transformation Models. Scandinavian Journal of Statistics, 49, 116–142, doi:10.1111/sjos.12501.

Torsten Hothorn (2024), On Nonparanormal Likelihoods. doi:10.48550/arXiv.2408.17346.

Methods for mmlt Objects

Description

Methods for objects of class mmlt

Usage

## S3 method for class 'mmlt'
weights(object, ...)
## S3 method for class 'mmlt'
logLik(object, parm = coef(object, fixed = FALSE), w = NULL, newdata = NULL, ...)
## S3 method for class 'mmlt'
vcov(object, parm = coef(object, fixed = FALSE), complete = FALSE, ...)
## S3 method for class 'mmlt'
Hessian(object, parm = coef(object, fixed = FALSE), ...)
## S3 method for class 'mmlt'
Gradient(object, parm = coef(object, fixed = FALSE), ...)
## S3 method for class 'mmlt'
estfun(x, parm = coef(x, fixed = FALSE),
       w = NULL, newdata = NULL, ...)
## S3 method for class 'mmlt'
mkgrid(object, ...)
## S3 method for class 'mmlt'
variable.names(object, response_only = FALSE, ...)
## S3 method for class 'mmlt'
weights(object, ...)
## S3 method for class 'mmlt'
logLik(object, parm = coef(object, fixed = FALSE), w = NULL, newdata = NULL, ...)
## S3 method for class 'mmlt'
vcov(object, parm = coef(object, fixed = FALSE), complete = FALSE, ...)
## S3 method for class 'mmlt'
Hessian(object, parm = coef(object, fixed = FALSE), ...)
## S3 method for class 'mmlt'
Gradient(object, parm = coef(object, fixed = FALSE), ...)
## S3 method for class 'mmlt'
estfun(x, parm = coef(x, fixed = FALSE),
       w = NULL, newdata = NULL, ...)
## S3 method for class 'mmlt'
mkgrid(object, ...)
## S3 method for class 'mmlt'
variable.names(object, response_only = FALSE, ...)

Arguments

`object`, `x`	a fitted multivariate transformation model as returned by `mmlt`
`fixed`	a logical indicating if only estimated coefficients (`fixed = FALSE`) should be returned OR (for `update`) a named vector of fixed regression coefficients; the names need to correspond to column names of the design matrix
`parm`	model parameters
`w`	model weights
`weights`	model weights
`newdata`	an optional data frame of new observations. Allows evaluation of the log-likelihood for a given model `object` on these new observations. The parameters `parm` and `w` are ignored in this situation.
`response_only`	only return the names of the response variables
`complete`	currently ignored
`...`	additional arguments

Details

Plots, Predictions and Samples from mlt Objects

Description

Plot, predict and sample from objects of class mlt

Usage

## S3 method for class 'ctm'
plot(x, newdata, type = c(
         "distribution", "logdistribution", 
         "survivor", "logsurvivor", 
         "density", "logdensity", 
         "hazard", "loghazard", 
         "cumhazard", "logcumhazard", 
         "odds", "logodds", 
         "quantile", "trafo"),
     q = NULL, prob = 1:(K - 1) / K, K = 50, col = rgb(.1, .1, .1, .1), lty = 1, 
     add = FALSE, ...)
## S3 method for class 'mlt'
plot(x, ...)
## S3 method for class 'ctm'
predict(object, newdata, type = c("trafo", 
         "distribution", "logdistribution", 
         "survivor", "logsurvivor", 
         "density", "logdensity", 
         "hazard", "loghazard", 
         "cumhazard", "logcumhazard", 
         "odds", "logodds", 
         "quantile"), 
         terms = c("bresponse", "binteracting", "bshifting"), 
         q = NULL, prob = NULL, K = 50, interpolate = FALSE, ...)
## S3 method for class 'mlt'
predict(object, newdata = object$data, ...)
## S3 method for class 'ctm'
simulate(object, nsim = 1, seed = NULL, newdata, K = 50, q = NULL,
         interpolate = FALSE, bysim = TRUE, ...)
## S3 method for class 'mlt'
simulate(object, nsim = 1, seed = NULL, newdata = object$data, bysim = TRUE, ...)
## S3 method for class 'ctm'
plot(x, newdata, type = c(
         "distribution", "logdistribution", 
         "survivor", "logsurvivor", 
         "density", "logdensity", 
         "hazard", "loghazard", 
         "cumhazard", "logcumhazard", 
         "odds", "logodds", 
         "quantile", "trafo"),
     q = NULL, prob = 1:(K - 1) / K, K = 50, col = rgb(.1, .1, .1, .1), lty = 1, 
     add = FALSE, ...)
## S3 method for class 'mlt'
plot(x, ...)
## S3 method for class 'ctm'
predict(object, newdata, type = c("trafo", 
         "distribution", "logdistribution", 
         "survivor", "logsurvivor", 
         "density", "logdensity", 
         "hazard", "loghazard", 
         "cumhazard", "logcumhazard", 
         "odds", "logodds", 
         "quantile"), 
         terms = c("bresponse", "binteracting", "bshifting"), 
         q = NULL, prob = NULL, K = 50, interpolate = FALSE, ...)
## S3 method for class 'mlt'
predict(object, newdata = object$data, ...)
## S3 method for class 'ctm'
simulate(object, nsim = 1, seed = NULL, newdata, K = 50, q = NULL,
         interpolate = FALSE, bysim = TRUE, ...)
## S3 method for class 'mlt'
simulate(object, nsim = 1, seed = NULL, newdata = object$data, bysim = TRUE, ...)

Arguments

`object`	a fitted conditional transformation model as returned by `mlt` or an unfitted conditional transformation model as returned by `ctm`
`x`	a fitted conditional transformation model as returned by `mlt`
`newdata`	an optional data frame of observations
`type`	type of prediction or plot to generate
`q`	quantiles at which to evaluate the model
`prob`	probabilities for the evaluation of the quantile function (`type = "quantile"`)
`terms`	terms to evaluate for the predictions, corresponds to the argument `response`, `interacting` and `shifting` in `ctm`
`K`	number of grid points to generate (in the absence of `q`)
`col`	color for the lines to plot
`lty`	line type for the lines to plot
`add`	logical indicating if a new plot shall be generated (the default)
`interpolate`	logical indicating if quantiles shall be interpolated linearily. This unnecessary option is no longer implemented (starting with 1.2-1).
`nsim`	number of samples to generate
`seed`	optional seed for the random number generator
`bysim`	logical, if `TRUE` a list with `nsim` elements is returned, each element is of length `nrow(newdata)` and contains one sample from the conditional distribution for each row of `newdata`. If `FALSE`, a list of length `nrow(newdata)` is returned, its ith element of length `nsim` contains `nsim` samples from the conditional distribution given `newdata[i,]`.
`...`	additional arguments

Details

plot evaluates the transformation function over a grid of q values for all observations in newdata and plots these functions (according to type). predict evaluates the transformation function over a grid of q values for all observations in newdata and returns the result as a matrix (where _columns_ correspond to _rows_ in newdata, see examples). Lack of type = "mean" is a feature and not a bug.

Argument type defines the scale of the plots or predictions: type = "distribution" means the cumulative distribution function, type = "survivor" is the survivor function (one minus distribution function), type = "density" the absolute continuous or discrete density (depending on the response), type = "hazard", type = "cumhazard", and type = "odds" refers to the hazard (absolute continuous or discrete), cumulative hazard (defined as minus log-survivor function in both the absolute continuous and discrete cases), and odds (distribution divided by survivor) functions. The quantile function can be evaluated for probabilities prob by type = "quantile".

Note that the predict method for ctm objects requires all model coefficients to be specified in this unfitted model. simulate draws samples from object by numerical inversion of the quantile function.

Note that offsets are ALWAYS IGNORED when computing predictions. If you want the methods to pay attention to offsets, specify them as a variable in the model with fixed regression coefficient using the fixed argument in mlt.

More examples can be found in Hothorn (2018).

References

Torsten Hothorn (2020), Most Likely Transformations: The mlt Package, Journal of Statistical Software, 92(1), 1–68, doi:10.18637/jss.v092.i01

Examples


  library("survival")
  op <- options(digits = 2)

  ### GBSG2 dataset
  data("GBSG2", package = "TH.data")

  ### right-censored response
  GBSG2$y <- with(GBSG2, Surv(time, cens))

  ### define Bernstein(log(time)) parameterisation
  ### of transformation function. The response
  ### is bounded (log(0) doesn't work, so we use log(1))
  ### support defines the support of the Bernstein polynomial
  ### and add can be used to make the grid wider (see below)
  rvar <- numeric_var("y", bounds = c(0, Inf), 
                      support = c(100, 2000))
  rb <- Bernstein_basis(rvar, order = 6, ui = "increasing")
  ### dummy coding of menopausal status
  hb <- as.basis(~ 0 + menostat, data = GBSG2)
  ### treatment contrast of hormonal treatment
  xb <- as.basis(~ horTh, data = GBSG2, remove_intercept = TRUE)

  ### set-up and fit Cox model, stratified by menopausal status
  m <- ctm(rb, interacting = hb, shifting = xb, todistr = "MinExtrVal")
  fm <- mlt(m, data = GBSG2)

  ### generate grid for all three variables
  ### note that the response grid ranges between 1 (bounds[1])
  ### and 2000 (support[2])
  (d <- mkgrid(m, n = 10))
  ### data.frame of menopausal status and treatment
  nd <- do.call("expand.grid", d[-1])

  ### plot model on different scales, for all four combinations
  ### of menopausal status and hormonal treatment
  typ <- c("distribution", "survivor", "density", "hazard", 
           "cumhazard", "odds")
  layout(matrix(1:6, nrow = 2))
  nl <- sapply(typ, function(tp) 
      ### K = 500 makes densities and hazards smooth
      plot(fm, newdata = nd, type = tp, col = 1:nrow(nd), K = 500))
  legend("topleft", lty = 1, col = 1:nrow(nd), 
         legend = do.call("paste", nd), bty = "n")

  ### plot calls predict, which generates a grid with K = 50
  ### response values
  ### note that a K x nrow(newdata) matrix is returned
  ### (for reasons explained in the next example)
  predict(fm, newdata = nd, type = "survivor")

  ### newdata can take a list, and evaluates the survivor
  ### function on the grid defined by newdata 
  ### using a linear array model formulation and is 
  ### extremely efficient (wrt computing time and memory)
  ### d[1] (the response grid) varies fastest
  ### => the first dimension of predict() is always the response,
  ### not the dimension of the predictor variables (like one 
  ### might expect)
  predict(fm, newdata = d, type = "survivor")

  ### owing to this structure, the result can be quickly stored in 
  ### a data frame as follows
  cd <- do.call("expand.grid", d)
  cd$surv <- c(S <- predict(fm, newdata = d, type = "survivor"))

  ### works for distribution functions
  all.equal(1 - S, predict(fm, newdata = d, type = "distribution"))
  ### cumulative hazard functions
  all.equal(-log(S), predict(fm, newdata = d, type = "cumhazard"))
  ### log-cumulative hazard functions (= trafo, for Cox models)
  all.equal(log(-log(S)), predict(fm, newdata = d, type = "logcumhazard"))
  all.equal(log(-log(S)), predict(fm, newdata = d, type = "trafo"))
  ### densities, hazards, or odds functions
  predict(fm, newdata = d, type = "density")
  predict(fm, newdata = d, type = "hazard")
  predict(fm, newdata = d, type = "odds")
  ### and quantiles (10 and 20%)
  predict(fm, newdata = d[-1], type = "quantile", prob = 1:2 / 10)

  ### note that some quantiles are only defined as intervals
  ### (> 2000, in this case). Intervals are returned as an "response" 
  ### object, see ?R. Unfortunately, these can't be stored as array, so
  ### a data.frame is returned where the quantile varies first
  p <- c(list(prob = 1:9/10), d[-1])
  np <- do.call("expand.grid", p)
  (Q <- predict(fm, newdata = d[-1], type = "quantile", prob = 1:9 / 10))
  np$Q <- Q
  np

  ### simulating from the model works by inverting the distribution 
  ### function; some obs are right-censored at 2000
  (s <- simulate(fm, newdata = nd, nsim = 3))
  ### convert to Surv
  sapply(s, as.Surv)

  ### generate 3 parametric bootstrap samples from the model
  tmp <- GBSG2[, c("menostat", "horTh")]
  s <- simulate(fm, newdata = tmp, nsim = 3)
  ### refit the model using the simulated response
  lapply(s, function(y) {
    tmp$y <- y
    coef(mlt(m, data = tmp))
  })

  options(op)

library("survival")
  op <- options(digits = 2)

  ### GBSG2 dataset
  data("GBSG2", package = "TH.data")

  ### right-censored response
  GBSG2$y <- with(GBSG2, Surv(time, cens))

  ### define Bernstein(log(time)) parameterisation
  ### of transformation function. The response
  ### is bounded (log(0) doesn't work, so we use log(1))
  ### support defines the support of the Bernstein polynomial
  ### and add can be used to make the grid wider (see below)
  rvar <- numeric_var("y", bounds = c(0, Inf), 
                      support = c(100, 2000))
  rb <- Bernstein_basis(rvar, order = 6, ui = "increasing")
  ### dummy coding of menopausal status
  hb <- as.basis(~ 0 + menostat, data = GBSG2)
  ### treatment contrast of hormonal treatment
  xb <- as.basis(~ horTh, data = GBSG2, remove_intercept = TRUE)

  ### set-up and fit Cox model, stratified by menopausal status
  m <- ctm(rb, interacting = hb, shifting = xb, todistr = "MinExtrVal")
  fm <- mlt(m, data = GBSG2)

  ### generate grid for all three variables
  ### note that the response grid ranges between 1 (bounds[1])
  ### and 2000 (support[2])
  (d <- mkgrid(m, n = 10))
  ### data.frame of menopausal status and treatment
  nd <- do.call("expand.grid", d[-1])

  ### plot model on different scales, for all four combinations
  ### of menopausal status and hormonal treatment
  typ <- c("distribution", "survivor", "density", "hazard", 
           "cumhazard", "odds")
  layout(matrix(1:6, nrow = 2))
  nl <- sapply(typ, function(tp) 
      ### K = 500 makes densities and hazards smooth
      plot(fm, newdata = nd, type = tp, col = 1:nrow(nd), K = 500))
  legend("topleft", lty = 1, col = 1:nrow(nd), 
         legend = do.call("paste", nd), bty = "n")

  ### plot calls predict, which generates a grid with K = 50
  ### response values
  ### note that a K x nrow(newdata) matrix is returned
  ### (for reasons explained in the next example)
  predict(fm, newdata = nd, type = "survivor")

  ### newdata can take a list, and evaluates the survivor
  ### function on the grid defined by newdata 
  ### using a linear array model formulation and is 
  ### extremely efficient (wrt computing time and memory)
  ### d[1] (the response grid) varies fastest
  ### => the first dimension of predict() is always the response,
  ### not the dimension of the predictor variables (like one 
  ### might expect)
  predict(fm, newdata = d, type = "survivor")

  ### owing to this structure, the result can be quickly stored in 
  ### a data frame as follows
  cd <- do.call("expand.grid", d)
  cd$surv <- c(S <- predict(fm, newdata = d, type = "survivor"))

  ### works for distribution functions
  all.equal(1 - S, predict(fm, newdata = d, type = "distribution"))
  ### cumulative hazard functions
  all.equal(-log(S), predict(fm, newdata = d, type = "cumhazard"))
  ### log-cumulative hazard functions (= trafo, for Cox models)
  all.equal(log(-log(S)), predict(fm, newdata = d, type = "logcumhazard"))
  all.equal(log(-log(S)), predict(fm, newdata = d, type = "trafo"))
  ### densities, hazards, or odds functions
  predict(fm, newdata = d, type = "density")
  predict(fm, newdata = d, type = "hazard")
  predict(fm, newdata = d, type = "odds")
  ### and quantiles (10 and 20%)
  predict(fm, newdata = d[-1], type = "quantile", prob = 1:2 / 10)

  ### note that some quantiles are only defined as intervals
  ### (> 2000, in this case). Intervals are returned as an "response" 
  ### object, see ?R. Unfortunately, these can't be stored as array, so
  ### a data.frame is returned where the quantile varies first
  p <- c(list(prob = 1:9/10), d[-1])
  np <- do.call("expand.grid", p)
  (Q <- predict(fm, newdata = d[-1], type = "quantile", prob = 1:9 / 10))
  np$Q <- Q
  np

  ### simulating from the model works by inverting the distribution 
  ### function; some obs are right-censored at 2000
  (s <- simulate(fm, newdata = nd, nsim = 3))
  ### convert to Surv
  sapply(s, as.Surv)

  ### generate 3 parametric bootstrap samples from the model
  tmp <- GBSG2[, c("menostat", "horTh")]
  s <- simulate(fm, newdata = tmp, nsim = 3)
  ### refit the model using the simulated response
  lapply(s, function(y) {
    tmp$y <- y
    coef(mlt(m, data = tmp))
  })

  options(op)

Response Variables

Description

Represent a possibly censored or truncated response variable

Usage

R(object, ...)
## S3 method for class 'numeric'
R(object = NA, cleft = NA, cright = NA,
   tleft = NA, tright = NA, tol = sqrt(.Machine$double.eps), 
   as.R.ordered = FALSE, as.R.interval = FALSE, ...)
## S3 method for class 'ordered'
R(object, cleft = NA, cright = NA, ...)
## S3 method for class 'integer'
R(object, cleft = NA, cright = NA, bounds = c(min(object), Inf), ...)
## S3 method for class 'factor'
R(object, ...)
## S3 method for class 'Surv'
R(object, as.R.ordered = FALSE, as.R.interval = FALSE, ...)
as.Surv(object)
## S3 method for class 'response'
as.Surv(object)
## S3 method for class 'response'
as.double(x, ...)
R(object, ...)
## S3 method for class 'numeric'
R(object = NA, cleft = NA, cright = NA,
   tleft = NA, tright = NA, tol = sqrt(.Machine$double.eps), 
   as.R.ordered = FALSE, as.R.interval = FALSE, ...)
## S3 method for class 'ordered'
R(object, cleft = NA, cright = NA, ...)
## S3 method for class 'integer'
R(object, cleft = NA, cright = NA, bounds = c(min(object), Inf), ...)
## S3 method for class 'factor'
R(object, ...)
## S3 method for class 'Surv'
R(object, as.R.ordered = FALSE, as.R.interval = FALSE, ...)
as.Surv(object)
## S3 method for class 'response'
as.Surv(object)
## S3 method for class 'response'
as.double(x, ...)

Arguments

`object`	A vector of (conceptually) exact measurements or an object of class `response` (for `as.Surv`) or a list.
`x`	same as `object`.
`cleft`	A vector of left borders of censored measurements
`cright`	A vector of right borders of censored measurements
`tleft`	A vector of left truncations
`tright`	A vector of right truncations
`tol`	Tolerance for checking if `cleft` < `cright`
`bounds`	Range of possible values for integers
`as.R.ordered`	logical, should numeric responses or right-censored (and possible left-truncated survival) times be coded as ordered factor? This leads to a parameterisation allowing to maximise the nonparametric maximum likelihood
`as.R.interval`	logical, should numeric responses be coded for the nonparametric maximum likelihood
`...`	other arguments, ignored except for `tleft` and `tright` to `R.ordered` and `R.integer`

Details

R is basically an extention of Surv for the representation of arbitrarily censored or truncated measurements at any scale. The storage.mode of object determines if models are fitted by the discrete likelihood (integers or factors) or the continuous likelihood (log-density for numeric objects). Interval-censoring is given by intervals (cleft, cright], also for integers and factors (see example below). Left- and right-truncation can be defined by the tleft and tright arguments. Existing Surv objects can be converted using R(Surv(...))$ and, in some cases, an as.Surv() method exists for representing response objects as Surv objects.

R applied to a list calls R for each of the list elements and returns a joint object.

More examples can be found in Hothorn (2018) and in vignette("tram", package = "tram").

References

Torsten Hothorn (2020), Most Likely Transformations: The mlt Package, Journal of Statistical Software, 92(1), 1–68, doi:10.18637/jss.v092.i01

Examples


 library("survival")
 
 ### randomly right-censored continuous observations
 time <- as.double(1:9)
 event <- rep(c(FALSE, TRUE), length = length(time))

 Surv(time, event)
 R(Surv(time, event))

 ### right-censoring, left-truncation
 ltm <- 1:9 / 10
 Surv(ltm, time, event)
 R(Surv(ltm, time, event))

 ### interval-censoring
 Surv(ltm, time, type = "interval2")
 R(Surv(ltm, time, type = "interval2"))

 ### interval-censoring, left/right-truncation
 lc <- as.double(1:4)
 lt <- c(NA, NA, 7, 8)
 rt <- c(NA, 9, NA, 10)
 x <- c(3, NA, NA, NA)
 rc <- as.double(11:14)
 R(x, cleft = lt, cright = rt)
 as.Surv(R(x, cleft = lt, cright = rt))
 R(x, tleft = 1, cleft = lt, cright = rt)
 R(x, tleft = 1, cleft = lt, cright = rt, tright = 15)
 R(x, tleft = lc, cleft = lt, cright = rt, tright = rc)

 ### discrete observations: counts
 x <- 0:9
 R(x)
 ### partially interval-censored counts
 rx <- c(rep(NA, 6), rep(15L, 4))
 R(x, cright = rx)

 ### ordered factor
 x <- gl(5, 2, labels = LETTERS[1:5], ordered = TRUE)
 R(x)
 ### interval-censoring (ie, observations can have multiple levels)
 lx <- ordered(c("A", "A", "B", "C", "D", "E"), 
               levels = LETTERS[1:5], labels = LETTERS[1:5])
 rx <- ordered(c("B", "D", "E", "D", "D", "E"), 
               levels = LETTERS[1:5], labels = LETTERS[1:5])
 R(rx, cleft = lx, cright = rx)

 ### facilitate nonparametric maximum likelihood
 (y <- round(runif(10), 1))
 R(y, as.R.ordered = TRUE)

 R(Surv(time, event), as.R.ordered = TRUE)
 R(Surv(ltm, time, event), as.R.ordered = TRUE)

library("survival")
 
 ### randomly right-censored continuous observations
 time <- as.double(1:9)
 event <- rep(c(FALSE, TRUE), length = length(time))

 Surv(time, event)
 R(Surv(time, event))

 ### right-censoring, left-truncation
 ltm <- 1:9 / 10
 Surv(ltm, time, event)
 R(Surv(ltm, time, event))

 ### interval-censoring
 Surv(ltm, time, type = "interval2")
 R(Surv(ltm, time, type = "interval2"))

 ### interval-censoring, left/right-truncation
 lc <- as.double(1:4)
 lt <- c(NA, NA, 7, 8)
 rt <- c(NA, 9, NA, 10)
 x <- c(3, NA, NA, NA)
 rc <- as.double(11:14)
 R(x, cleft = lt, cright = rt)
 as.Surv(R(x, cleft = lt, cright = rt))
 R(x, tleft = 1, cleft = lt, cright = rt)
 R(x, tleft = 1, cleft = lt, cright = rt, tright = 15)
 R(x, tleft = lc, cleft = lt, cright = rt, tright = rc)

 ### discrete observations: counts
 x <- 0:9
 R(x)
 ### partially interval-censored counts
 rx <- c(rep(NA, 6), rep(15L, 4))
 R(x, cright = rx)

 ### ordered factor
 x <- gl(5, 2, labels = LETTERS[1:5], ordered = TRUE)
 R(x)
 ### interval-censoring (ie, observations can have multiple levels)
 lx <- ordered(c("A", "A", "B", "C", "D", "E"), 
               levels = LETTERS[1:5], labels = LETTERS[1:5])
 rx <- ordered(c("B", "D", "E", "D", "D", "E"), 
               levels = LETTERS[1:5], labels = LETTERS[1:5])
 R(rx, cleft = lx, cright = rx)

 ### facilitate nonparametric maximum likelihood
 (y <- round(runif(10), 1))
 R(y, as.R.ordered = TRUE)

 R(Surv(time, event), as.R.ordered = TRUE)
 R(Surv(ltm, time, event), as.R.ordered = TRUE)

Package 'mlt'

Help Index

General Information on the mlt Package

Description

Author(s)

References

Confidence Bands

Description

Usage

Arguments

Details

Value

Conditional Transformation Models

Description

Usage

Arguments

Details

Value

References

Methods for ctm Objects

Description

Usage

Arguments

Details

Most Likely Transformations

Description

Usage

Arguments

Details

Value

References

Examples

Methods for mlt Objects

Description

Usage

Arguments

Details

Control Optimisation

Description

Usage

Arguments

Details

Value

Examples

Multivariate Conditional Transformation Models

Description

Usage

Arguments

Details

Value

References

Methods for mmlt Objects

Description

Usage

Arguments

Details

Plots, Predictions and Samples from mlt Objects

Description

Usage

Arguments

Details

References

Examples

Response Variables

Description

Usage

Arguments

Details

References

Examples