Package 'tmle3'

Description

all_ancestors returns a list of all_ancestors of the specified node. time_ordering attempts to find a time_ordering for the variables.

Usage

all_ancestors(node_name, npsem)

time_ordering(npsem)

Arguments

Description

Bound (Truncate) Likelihoods

Usage

bound(x, bounds)

Arguments

Description

Represents a counterfactual likelihood where one or more likelihood factors has been replaced with an intervention as specified by intervention_list. Inherits from Likelihood. Other factors (including their updates) are taken from an underlying observed_likelihood estimated from observed data.

Usage

make_CF_Likelihood(...)

Arguments

Format

R6Class object.

Value

Likelihood object

Constructor

make_CF_Likelihood(observed_likelihood, intervention_list, ...)

observed_likelihood

Likelihood obect specifying the relevant factors of the observed likelihood

intervention_list

A list of objects inheriting from LF_base, representing the intervention.

...

Not currently used.

Fields

observed_likelihood

Likelihood obect specifying the relevant factors of the observed likelihood

intervention_list

A list of objects inheriting from LF_base, representing the intervention.

See Also

Other Likelihood objects: LF_base, LF_derived, LF_emp, LF_fit, LF_known, LF_static, LF_targeted, Likelihood, Targeted_Likelihood, define_lf()

Description

Define a Likelihood Factor

Usage

define_lf(LF_class, ...)

Arguments

See Also

Other Likelihood objects: CF_Likelihood, LF_base, LF_derived, LF_emp, LF_fit, LF_known, LF_static, LF_targeted, Likelihood, Targeted_Likelihood

Description

Define a Parameter

Usage

define_param(Param_class, ...)

Arguments

See Also

Other Parameters: Param_ATC, Param_ATE, Param_ATT, Param_MSM, Param_TSM, Param_base, Param_delta, Param_mean, Param_stratified, Param_survival, tmle3_Fit

Description

PAR = Linear Contrast EY1-EY0

Usage

delta_param_ATE

Format

An object of class list of length 4.

Description

Odds Ratio odds(Y1)/odds(Y0)

Usage

delta_param_OR

Format

An object of class list of length 5.

Description

PAF = 1 - (1/RR(EY/E0))

Usage

delta_param_PAF

Format

An object of class list of length 5.

Description

PAR = Linear Contrast EY-EY0

Usage

delta_param_PAR

Format

An object of class list of length 4.

Description

Risk Ratio EY1/EY0

Usage

delta_param_RR

Format

An object of class list of length 5.

Description

Get and Plot Propensity Scores

Usage

density_formula(tmle_task, node = "A")

get_propensity_scores(likelihood, tmle_task, node = "A")

propensity_score_plot(likelihood, tmle_task, node = "A")

propensity_score_table(likelihood, tmle_task, node = "A")

Arguments

Description

Converts a data.table column from continuous to a discrete factor

Usage

discretize_variable(data, variable, num_cats, breakpoints = NULL)

Arguments

Value

the updated data.table, modified in place

Description

Get Empirical Mean of EIFs from Estimates

Usage

ED_from_estimates(estimates)

Arguments

Description

A Likelihood factor models a conditional density function. The conditioning set is defined as all parent nodes (defined in tmle3_Task). In the case of a continuous outcome variable, where a full density isn't needed, this can also model a conditional mean. This is the base class, which is intended to be abstract. See below for a list of possible likelihood factor classes.

Format

R6Class object.

Value

LF_base object

Constructor

define_lf(LF_base, name, ..., type = "density")

name

character, the name of the factor. Should match a node name in the nodes specified by tmle3_Task$npsem

...

Not currently used.

type

character, either "density", for conditional density or, "mean" for conditional mean

Methods

get_density(tmle_task)

Get conditional density values for for the observations in tmle_task.

• tmle_task: tmle3_Task to get likelihood values for

get_mean(tmle_task)

Get conditional mean values for for the observations in tmle_task.

• tmle_task: tmle3_Task to get likelihood values for

Fields

name

character, the name of the factor. Should match a node name in the nodes specified by tmle3_Task$npsem

type

character, either "density", for conditional density or, "mean" for conditional mean

variable_type

variable_type object, specifying the data type of the outcome variable. Only available after Likelihood training.

values

Possible values of the outcome variable, retrivied from the variable_type object. Only available after Likelihood training.

See Also

Other Likelihood objects: CF_Likelihood, LF_derived, LF_emp, LF_fit, LF_known, LF_static, LF_targeted, Likelihood, Targeted_Likelihood, define_lf()

Description

Uses an sl3 learner to estimate a likelihood factor from data. Inherits from LF_base; see that page for documentation on likelihood factors in general.

Format

R6Class object.

Value

LF_base object

Constructor

define_lf(LF_fit, name, learner, ..., type = "density")

name

character, the name of the factor. Should match a node name in the nodes specified by tmle3_Task$npsem

learner

An sl3 learner to be used to estimate the factor

...

Not currently used.

type

character, either "density", for conditional density or, "mean" for conditional mean

Fields

learner

The learner or learner fit object

See Also

Other Likelihood objects: CF_Likelihood, LF_base, LF_emp, LF_fit, LF_known, LF_static, LF_targeted, Likelihood, Targeted_Likelihood, define_lf()

Description

Uses the empirical probability distribution (puts mass $1/n$ on each of the observations, or uses weights if specified) to estimate a marginal density. Inherits from LF_base; see that page for documentation on likelihood factors in general. Only compatible with marginal likelihoods (no parent nodes). Only compatible with densities (no conditional means). The type argument will be ignored if specified.

Format

R6Class object.

Value

LF_base object

Constructor

define_lf(LF_emp, name, ...)

name

character, the name of the factor. Should match a node name in the nodes specified by tmle3_Task$npsem

...

Not currently used.

See Also

Other Likelihood objects: CF_Likelihood, LF_base, LF_derived, LF_fit, LF_known, LF_static, LF_targeted, Likelihood, Targeted_Likelihood, define_lf()

Description

Uses an sl3 learner to estimate a likelihood factor from data. Inherits from LF_base; see that page for documentation on likelihood factors in general.

Format

R6Class object.

Value

LF_base object

Constructor

define_lf(LF_fit, name, learner, ..., type = "density")

name

character, the name of the factor. Should match a node name in the nodes specified by tmle3_Task$npsem

learner

An sl3 learner to be used to estimate the factor

...

Not currently used.

type

character, either "density", for conditional density or, "mean" for conditional mean

Fields

learner

The learner or learner fit object

See Also

Other Likelihood objects: CF_Likelihood, LF_base, LF_derived, LF_emp, LF_known, LF_static, LF_targeted, Likelihood, Targeted_Likelihood, define_lf()

Description

Incorporate existing knowledge about the likelihood Inherits from LF_base; see that page for documentation on likelihood factors in general.

Format

R6Class object.

Value

LF_base object

Constructor

define_lf(LF_fit, name, mean_fun, density_fun, ..., type = "density")

name

character, the name of the factor. Should match a node name in the nodes specified by tmle3_Task$npsem

mean_fun

A function that takes a sl3 regression task and returns true conditional means

density_fun

A function that takes a sl3 regression task and returns true conditional densities

...

Not currently used.

type

character, either "density", for conditional density or, "mean" for conditional mean

See Also

Other Likelihood objects: CF_Likelihood, LF_base, LF_derived, LF_emp, LF_fit, LF_static, LF_targeted, Likelihood, Targeted_Likelihood, define_lf()

Description

Likelihood factor for a variable that only has one value with probability 1. This is used for static interventions. Inherits from LF_base; see that page for documentation on likelihood factors in general.

Format

R6Class object.

Value

LF_base object

Constructor

define_lf(LF_static, name, type, value, ...)

name

character, the name of the factor. Should match a node name in the nodes specified by tmle3_Task$npsem

type

character, either "density", for conditional density or, "mean" for conditional mean

value

the static value

...

Not currently used.

Fields

value

the static value.

See Also

Other Likelihood objects: CF_Likelihood, LF_base, LF_derived, LF_emp, LF_fit, LF_known, LF_targeted, Likelihood, Targeted_Likelihood, define_lf()

Description

Uses an sl3 learner to estimate a likelihood factor from data. Inherits from LF_base; see that page for documentation on likelihood factors in general.

Format

R6Class object.

Value

LF_base object

Constructor

define_lf(LF_fit, name, learner, ..., type = "density")

name

character, the name of the factor. Should match a node name in the nodes specified by tmle3_Task$npsem

learner

An sl3 learner to be used to estimate the factor

...

Not currently used.

type

character, either "density", for conditional density or, "mean" for conditional mean

Fields

learner

The learner or learner fit object

See Also

Other Likelihood objects: CF_Likelihood, LF_base, LF_derived, LF_emp, LF_fit, LF_known, LF_static, Likelihood, Targeted_Likelihood, define_lf()

Description

This object represents an estimate of the relevant factors of the likelihood estimated from data, or based on a priori knowledge where appropriate. That is, it represents some subset of $P_n$. This object inherits from Lrnr_base, and so shares some properties with sl3 learners. Specifically, to fit a likelihood object to data, one calls likelihood$train(tmle3_task). Each likelihood factor is represented by an object inheriting from LF_base.

Usage

make_Likelihood(...)

Arguments

Format

R6Class object.

Value

Likelihood object

Constructor

make_Likelihood(factor_list, ...)

factor_list

A list of objects inheriting from LF_base, representing the individual relevant factors.

...

Not currently used.

Methods

validate_task(tmle_task)

Ensure that this likelihood is compatible with a particular tmle3_Task, in that the factor names must match the tmle_task$npsem names.

• tmle_task: the tmle3_Task to validate.

get_initial_likelihoods(tmle_task, nodes=NULL)

Gets initial (i.e. before any TMLE updates) likelihood values for the specified nodes (or all nodes if none are specified) for the observations in tmle_task.

• tmle_task: tmle3_Task to get likelihood values for

• nodes: character vectors, the list of nodes to get likelihood values for. If missing, values will be provided for all nodes.

get_likelihoods(tmle_task, nodes=NULL)

Gets updated (i.e. after all TMLE updates) likelihood values for the specified nodes (or all nodes if none are specified) for the observations in tmle_task.

• tmle_task: tmle3_Task to get likelihood values for

• nodes: character vectors, the list of nodes to get likelihood values for. If missing, values will be provided for all nodes.

get_possible_counterfactuals(nodes)

Gets all possible combination of counterfactual values for a set of nodes. This is useful for marginalizing over a node. Returns a data.frame with one row per possibility.

• nodes: character vectors, the list of nodes to get counterfactual values for. If missing, values will be provided for all nodes.

Fields

factor_list

The list of LF_base objects specifying the relevant likelihood factors

observed_values

The likelihood values for the observed data. These are cached, as they are used in many places in TMLE

update_list

A list of tmle_updates that have been calculated for this likelihood

See Also

Other Likelihood objects: CF_Likelihood, LF_base, LF_derived, LF_emp, LF_fit, LF_known, LF_static, LF_targeted, Targeted_Likelihood, define_lf()

Description

Cache Likelihood values, update those values

Description

Parameter definition for the Additive Effect of Treatment Among the Treated (ATT). Currently supports multiple static intervention nodes. Does yet not support dynamic rule or stochastic interventions.

Format

R6Class object.

Value

Param_base object

Current Issues

• clever covariates doesn't support updates; always uses initial (necessary for iterative TMLE, e.g. stochastic intervention)

• doesn't integrate over possible counterfactuals (necessary for stochastic intervention)

• clever covariate gets recalculated all the time (inefficient)

Constructor

define_param(Param_ATT, observed_likelihood, intervention_list, ..., outcome_node)

observed_likelihood

A Likelihood corresponding to the observed likelihood

intervention_list_treatment

A list of objects inheriting from LF_base, representing the treatment intervention.

intervention_list_control

A list of objects inheriting from LF_base, representing the control intervention.

...

Not currently used.

outcome_node

character, the name of the node that should be treated as the outcome

Fields

cf_likelihood_treatment

the counterfactual likelihood for the treatment

cf_likelihood_control

the counterfactual likelihood for the control

intervention_list_treatment

A list of objects inheriting from LF_base, representing the treatment intervention

intervention_list_control

A list of objects inheriting from LF_base, representing the control intervention

See Also

Other Parameters: Param_ATE, Param_ATT, Param_MSM, Param_TSM, Param_base, Param_delta, Param_mean, Param_stratified, Param_survival, define_param(), tmle3_Fit

Description

Parameter definition for the Average Treatment Effect (ATE).

Format

R6Class object.

Value

Param_base object

Constructor

define_param(Param_ATT, observed_likelihood, intervention_list, ..., outcome_node)

observed_likelihood

A Likelihood corresponding to the observed likelihood

intervention_list_treatment

A list of objects inheriting from LF_base, representing the treatment intervention.

intervention_list_control

A list of objects inheriting from LF_base, representing the control intervention.

...

Not currently used.

outcome_node

character, the name of the node that should be treated as the outcome

Fields

cf_likelihood_treatment

the counterfactual likelihood for the treatment

cf_likelihood_control

the counterfactual likelihood for the control

intervention_list_treatment

A list of objects inheriting from LF_base, representing the treatment intervention

intervention_list_control

A list of objects inheriting from LF_base, representing the control intervention

See Also

Other Parameters: Param_ATC, Param_ATT, Param_MSM, Param_TSM, Param_base, Param_delta, Param_mean, Param_stratified, Param_survival, define_param(), tmle3_Fit

Description

Parameter definition for the Additive Effect of Treatment Among the Treated (ATT). Currently supports multiple static intervention nodes. Does yet not support dynamic rule or stochastic interventions.

Format

R6Class object.

Value

Param_base object

Current Issues

• clever covariates doesn't support updates; always uses initial (necessary for iterative TMLE, e.g. stochastic intervention)

• doesn't integrate over possible counterfactuals (necessary for stochastic intervention)

• clever covariate gets recalculated all the time (inefficient)

Constructor

define_param(Param_ATT, observed_likelihood, intervention_list, ..., outcome_node)

observed_likelihood

A Likelihood corresponding to the observed likelihood

intervention_list_treatment

A list of objects inheriting from LF_base, representing the treatment intervention.

intervention_list_control

A list of objects inheriting from LF_base, representing the control intervention.

...

Not currently used.

outcome_node

character, the name of the node that should be treated as the outcome

Fields

cf_likelihood_treatment

the counterfactual likelihood for the treatment

cf_likelihood_control

the counterfactual likelihood for the control

intervention_list_treatment

A list of objects inheriting from LF_base, representing the treatment intervention

intervention_list_control

A list of objects inheriting from LF_base, representing the control intervention

See Also

Other Parameters: Param_ATC, Param_ATE, Param_MSM, Param_TSM, Param_base, Param_delta, Param_mean, Param_stratified, Param_survival, define_param(), tmle3_Fit

Description

A parameter is a function of the likelihood. Once given a Likelihood object, a parameter will a value. These objects also contain information about the efficient influence function (EIF) of a parameter, as well as its clever covariate(s).

Format

R6Class object.

Value

Param_base object

Constructor

define_param(Param_base, observed_likelihood, ..., outcome_node)

observed_likelihood

A Likelihood corresponding to the observed likelihood

...

Not currently used.

outcome_node

character, the name of the node that should be treated as the outcome

Methods

clever_covariates(tmle_task = NULL)

Get the clever covariates for an TMLE update step.

• tmle_task: tmle3_Task to get clever covariate values for. If NULL, the tmle_task used to train the observed likelihood will be used

estimates(tmle_task = NULL)

Get the parameter estimates and influence curve values.

• tmle_task: tmle3_Task to get clever covariate values for. If NULL, the tmle_task used to train the observed likelihood will be used

Fields

observed_likelihood

the observed likelihood

outcome_node

character, the name of the outcome node

See Also

Other Parameters: Param_ATC, Param_ATE, Param_ATT, Param_MSM, Param_TSM, Param_delta, Param_mean, Param_stratified, Param_survival, define_param(), tmle3_Fit

Description

These parameters are smooth functionals of one or more other params They are not fit directly with tmle, but are estimated using the delta method todo: better docs They do not return have clever covariates

See Also

Other Parameters: Param_ATC, Param_ATE, Param_ATT, Param_MSM, Param_TSM, Param_base, Param_mean, Param_stratified, Param_survival, define_param(), tmle3_Fit

Description

Parameter for marginal mean of Y: $\Psi=E[Y]$. No TMLE update needed, but can be used in delta method calculations. Useful for example, in calculating attributable risks.

Format

R6Class object.

Value

Param_base object

Constructor

define_param(Param_TSM, observed_likelihood, intervention_list, ..., outcome_node)

observed_likelihood

A Likelihood corresponding to the observed likelihood

...

Not currently used.

outcome_node

character, the name of the node that should be treated as the outcome

Fields

cf_likelihood

the counterfactual likelihood for this treatment

intervention_list

A list of objects inheriting from LF_base, representing the intervention

See Also

Other Parameters: Param_ATC, Param_ATE, Param_ATT, Param_MSM, Param_TSM, Param_base, Param_delta, Param_stratified, Param_survival, define_param(), tmle3_Fit

Description

Stratified Parameter Estimates via MSM

Format

R6Class object.

Value

Param_base object

Current Issues

• clever covariates doesn't support updates; always uses initial (necessary for iterative TMLE, e.g. stochastic intervention)

• clever covariate gets recalculated all the time (inefficient)

Constructor

define_param(Param_MSM, observed_likelihood, strata_variable, ...)

observed_likelihood

A Likelihood corresponding to the observed likelihood

msm

form of the MSM. Default is "A + V", consistent with the default of treatment_node and strata_name.

weight

"Cond.Prob.", "Unif." or custom input function. Note that custom function should support vector input. Default is "Cond.Prob.".

...

Not currently used.

covariate_node

character, the name of the node that should be treated as the covariate

treatment_node

character, the name of the node that should be treated as the treatment

outcome_node

character, the name of the node that should be treated as the outcome

Fields

cf_likelihood

the counterfactual likelihood for this treatment

See Also

Other Parameters: Param_ATC, Param_ATE, Param_ATT, Param_TSM, Param_base, Param_delta, Param_mean, Param_stratified, Param_survival, define_param(), tmle3_Fit

Description

Stratified Parameter Estimates

Format

R6Class object.

Value

Param_base object

Current Issues

• clever covariates doesn't support updates; always uses initial (necessary for iterative TMLE, e.g. stochastic intervention)

• doesn't integrate over possible counterfactuals (necessary for stochastic intervention)

• clever covariate gets recalculated all the time (inefficient)

Constructor

define_param(Param_TSM, observed_likelihood, intervention_list, ..., outcome_node)

observed_likelihood

A Likelihood corresponding to the observed likelihood

intervention_list

A list of objects inheriting from LF_base, representing the intervention.

...

Not currently used.

outcome_node

character, the name of the node that should be treated as the outcome

Fields

cf_likelihood

the counterfactual likelihood for this treatment

intervention_list

A list of objects inheriting from LF_base, representing the intervention

See Also

Other Parameters: Param_ATC, Param_ATE, Param_ATT, Param_MSM, Param_TSM, Param_base, Param_delta, Param_mean, Param_survival, define_param(), tmle3_Fit

Description

Survival Curve

Format

R6Class object.

Value

Param_base object

Constructor

define_param(Param_survival, observed_likelihood, intervention_list, ..., outcome_node)

observed_likelihood

A Likelihood corresponding to the observed likelihood

intervention_list

A list of objects inheriting from LF_base, representing the intervention.

...

Not currently used.

outcome_node

character, the name of the node that should be treated as the outcome

Fields

cf_likelihood

the counterfactual likelihood for this treatment

intervention_list

A list of objects inheriting from LF_base, representing the intervention

See Also

Other Parameters: Param_ATC, Param_ATE, Param_ATT, Param_MSM, Param_TSM, Param_base, Param_delta, Param_mean, Param_stratified, define_param(), tmle3_Fit

Description

Parameter definition for the Treatment Specific Mean (TSM): $E_W[E_Y|A(Y|A=a|W)|$. Currently supports multiple static intervention nodes. Does yet not support dynamic rule or stochastic interventions.

Format

R6Class object.

Value

Param_base object

Current Issues

• clever covariates doesn't support updates; always uses initial (necessary for iterative TMLE, e.g. stochastic intervention)

• doesn't integrate over possible counterfactuals (necessary for stochastic intervention)

• clever covariate gets recalculated all the time (inefficient)

Constructor

define_param(Param_TSM, observed_likelihood, intervention_list, ..., outcome_node)

observed_likelihood

A Likelihood corresponding to the observed likelihood

intervention_list

A list of objects inheriting from LF_base, representing the intervention.

...

Not currently used.

outcome_node

character, the name of the node that should be treated as the outcome

Fields

cf_likelihood

the counterfactual likelihood for this treatment

intervention_list

A list of objects inheriting from LF_base, representing the intervention

See Also

Other Parameters: Param_ATC, Param_ATE, Param_ATT, Param_MSM, Param_base, Param_delta, Param_mean, Param_stratified, Param_survival, define_param(), tmle3_Fit

Description

Plot results of variable importance analysis

Usage

plot_vim(vim_results)

Arguments

Description

Handles the common W (covariates), A (treatment/intervention), Y (outcome) data structure

Usage

point_tx_npsem(node_list, variable_types = NULL)

point_tx_task(data, node_list, variable_types = NULL, ...)

point_tx_likelihood(tmle_task, learner_list)

Arguments

Description

Process data to account for missingness in preparation for TMLE

Usage

process_missing(
  data,
  node_list,
  complete_nodes = c("A", "Y"),
  impute_nodes = NULL,
  max_p_missing = 0.5
)

Arguments

Details

Rows where there is missingness in any of the complete_nodes will be dropped. Then, missingness will be median-imputed for the variables in the impute_nodes. Indicator variables of missingness will be generated for these nodes.

Then covariates will be processed as follows:

1. any covariate with more than max_p_missing missingness will be dropped

2. indicators of missingness will be generated

3. missing values will be median-imputed

Value

list containing the following elements:

• data, the updated dataset

• node_list, the updated list of nodes

• n_dropped, the number of observations dropped

• dropped_cols, the variables dropped due to excessive missingness

Description

Logistic Submodel Fluctuation

Usage

submodel_logit(eps, X, offset)

Arguments

Description

Generates a data.table summarizing results with inference

Usage

summary_from_estimates(
  task,
  estimates,
  param_types = NULL,
  param_names = NULL,
  init_psi = NULL,
  simultaneous_ci = FALSE
)

Arguments

Value

data.table summarizing results

Description

Handles the W (covariates), A (treatment/intervention), T_tilde (time-to-event), Delta (censoring indicator), t_max (the maximum time to estimate) survival data structure

Usage

survival_tx_npsem(node_list, variable_types = NULL)

survival_tx_task(data, node_list, variable_types = NULL, ...)

survival_tx_likelihood(tmle_task, learner_list)

Arguments

Description

Represents a likelihood where one or more likelihood factors has been updated to target a set of parameter(s)

Format

R6Class object.

Value

Likelihood object

Constructor

make_Likelihood(factor_list, ...)

factor_list

A list of objects inheriting from LF_base, representing the individual relevant factors.

...

Not currently used.

Methods

validate_task(tmle_task)

Ensure that this likelihood is compatible with a particular tmle3_Task, in that the factor names must match the tmle_task$npsem names.

• tmle_task: the tmle3_Task to validate.

get_initial_likelihoods(tmle_task, nodes=NULL)

Gets initial (i.e. before any TMLE updates) likelihood values for the specified nodes (or all nodes if none are specified) for the observations in tmle_task.

• tmle_task: tmle3_Task to get likelihood values for

• nodes: character vectors, the list of nodes to get likelihood values for. If missing, values will be provided for all nodes.

get_likelihoods(tmle_task, nodes=NULL)

Gets updated (i.e. after all TMLE updates) likelihood values for the specified nodes (or all nodes if none are specified) for the observations in tmle_task.

• tmle_task: tmle3_Task to get likelihood values for

• nodes: character vectors, the list of nodes to get likelihood values for. If missing, values will be provided for all nodes.

get_possible_counterfactuals(nodes)

Gets all possible combination of counterfactual values for a set of nodes. This is useful for marginalizing over a node. Returns a data.frame with one row per possibility.

• nodes: character vectors, the list of nodes to get counterfactual values for. If missing, values will be provided for all nodes.

Fields

factor_list

The list of LF_base objects specifying the relevant likelihood factors

observed_values

The likelihood values for the observed data. These are cached, as they are used in many places in TMLE

update_list

A list of tmle_updates that have been calculated for this likelihood

See Also

Other Likelihood objects: CF_Likelihood, LF_base, LF_derived, LF_emp, LF_fit, LF_known, LF_static, LF_targeted, Likelihood, define_lf()

Description

O=(W,A,Y) W=Covariates A=Treatment (binary or categorical) Y=Outcome (binary or bounded continuous)

Usage

tmle_ATC(treatment_level, control_level)

Arguments

Description

O=(W,A,Y) W=Covariates A=Treatment (binary or categorical) Y=Outcome (binary or bounded continuous)

Usage

tmle_ATE(treatment_level, control_level)

Arguments

Description

O=(W,A,Y) W=Covariates A=Treatment (binary or categorical) Y=Outcome (binary or bounded continuous)

Usage

tmle_ATT(treatment_level, control_level)

Arguments

Description

O=(W,A,Y) W=Covariates A=Treatment (binary or categorical) Y=Outcome (binary or bounded continuous)

Usage

tmle_MSM(weight = "Cond.Prob.", n_samples = 30)

Arguments

Description

O = (W, A, Y) W = Covariates A = Treatment (binary or categorical) Y = Outcome (binary or bounded continuous)

Usage

tmle_OR(baseline_level, contrast_level)

Arguments

Description

O=(W,A,Y) W=Covariates A=Treatment (binary or categorical) Y=Outcome (binary or bounded continuous)

Usage

tmle_PAR(baseline_level)

Arguments

Description

O = (W, A, Y) W = Covariates A = Treatment (binary or categorical) Y = Outcome (binary or bounded continuous)

Usage

tmle_RR(baseline_level, contrast_level)

Arguments

Description

O=(W,A,Y) W=Covariates A=Treatment (binary or categorical) Y=Outcome (binary or bounded continuous)

Usage

tmle_stratified(base_spec, base_estimate = TRUE)

Arguments

Description

See the associated handbook chapter

Usage

tmle_survival(treatment_level, control_level, target_times = NULL, ...)

Arguments

Description

O=(W,A,Y) W=Covariates A=Treatment (binary or categorical) Y=Outcome (binary or bounded continuous)

Usage

tmle_TSM_all()

Description

Using a tmle3_Spec object, fit a TMLE

Usage

tmle3(tmle_spec, data, node_list, learner_list = NULL)

Arguments

Value

A tmle3_Fit object

Description

A tmle_fit object, containing initial and updated estimates, as well as data about the fitting procedure. TMLE updates are calculated when the object is constructed.

Usage

fit_tmle3(...)

Arguments

Format

R6Class object.

Value

Param_base object

Constructor

fit_tmle3(tmle_task, likelihood, tmle_params, updater, max_it=100, ...)

tmle_task

A tmle3_Task object defining the data and NP-SEM

likelihood

A Likelihood object defining the factorized likelihood

tmle_params

A list of parameters inheriting from Param_base defining the parameter(s) of interest

updater

A tmle3_Update object defining the update procedure, including submodel and loss function

maxit

integer, maximum number of TMLE iterations

...

Not currently used.

Methods

set_timings(start_time, task_time, likelihood_time, params_time, fit_time)

Provide the timings for the different steps of the TMLE procedure, for later reporting to the user

• tmle_task: tmle3_Task to get clever covariate values for. If NULL, the tmle_task used to train the observed likelihood will be used

estimates(tmle_task = NULL)

Get the parameter estimates and influence curve values.

• tmle_task: tmle3_Task to get clever covariate values for. If NULL, the tmle_task used to train the observed likelihood will be used

Fields

tmle_task

A tmle3_Task object defining the data and NP-SEM

likelihood

A Likelihood object defining the factorized likelihood

tmle_params

A list of parameters inheriting from Param_base defining the parameter(s) of interest

tmle_names

A list of parameter names, obtained by calling param$name on each parameter

updater

A tmle3_Update object defining the update procedure, including submodel and loss function

steps

integer, he number of steps until TMLE converged

ED

vector, the mean of the EIF for all the parameters

initial_psi

vector, the initial parameter estimates

estimates

list, final parameter estimates and ICs

summary

data.table, summary of results

timings

data.frame, timings for each step (provided by tmle3_Fit$set_timings)

See Also

Other Parameters: Param_ATC, Param_ATE, Param_ATT, Param_MSM, Param_TSM, Param_base, Param_delta, Param_mean, Param_stratified, Param_survival, define_param()

Description

This class defines a node in an NPSEM

Usage

define_node(...)

Arguments

Format

R6Class object.

Value

tmle3_Node object

Constructor

make_tmle3_task(name, variables, parents = c(), variable_type = NULL)

name

character, the name of node

variables

character vector, the names of the variables that comprise the node

parents

character vector, the names of the parent nodes. If censoring, node is assumed to have no parents.

variable_type

variable_type object, specifying the data type of this variable. If censoring, variable_type will be guessed later from the data.

Methods

guess_variable_type(variable_data)

Guesses the variable_type from the provided data. This will be called by the tmle3_Task constructor if no variable_type was provided.

• variable_data: the observed variable data.

Fields

name

character, the name of node

variables

character vector, the names of the variables that comprise the node

parents

character vector, the names of the parent nodes. If censoring, node is assumed to have no parents.

variable_type

variable_type object, specifying the data type of this variable.

Description

Current limitations: pretty much tailored to Param_TSM

Description

Defines a TML Estimator (except for the data)

Description

Defines a TML Estimator (except for the data)

Description

Defines a TML Estimator (except for the data)

Description

Defines a Stratified TML Estimator with MSM (except for the data)

Description

Current limitations: pretty much tailored to Param_TSM see TODOs for places generalization can be added

Description

Current limitations: pretty much tailored to Param_TSM see TODOs for places generalization can be added

Description

Current limitations: pretty much tailored to Param_TSM see TODOs for places generalization can be added

Description

Defines a Stratified TML Estimator (except for the data)

Description

Defines a TML Estimator (except for the data)

Description

Current limitations: pretty much tailored to Param_TSM See TODOs for places generalization can be added

Description

This class inherits from sl3_Task. In addition to all the methods supported by sl3_Task, it supports the following.

Usage

make_tmle3_Task(...)

Arguments

Format

R6Class object.

Value

tmle3_Task object

Constructor

make_tmle3_task(data, npsem, ...)

data

A data.frame or data.table containing the underlying data

npsem

A list of tmle3_Node objects, where each is created using define_node. These specify the NPSEM. See examples.

...

Other arguments passed to the constructor of sl3_Task. NB: Support for these is currently limited.

Methods

get_tmle_node(node_name, bound = FALSE)

Gets the data associated with a tmle_node. Bounds the data if requested.

• node_name: character, the name of the node to get.

• bound: logical, if true the data is transformed to be in (0,1) based on pre-specified bounds.

get_regression_task(target_node, bound = FALSE)

Gets a sl3_Task suitable for fitting the conditional likelihood factor with the target_node as the outcome.

• target_node: character, the name of the node to get.

generate_counterfacutal_task(uuid, new_data)

Generates a new tmle_Task where some nodes are overridden to have counterfactual values.

• uuid: A unique identifier for the counterfactual task, as generated by UUIDgenerate

  new_data: A data.frame or data.table with the counterfactual values. Column names must refer to node names in the npsem for this task.

Fields

npsem

The list of tmle3_Node objects specifying the NPSEM

Description

Current Limitations: loss function and submodel are hard-coded (need to accept arguments for these)

Constructor

define_param(maxit, cvtmle, one_dimensional, constrain_step, delta_epsilon, verbose)

maxit

The maximum number of update iterations

cvtmle

If TRUE, use CV-likelihood values when calculating updates.

one_dimensional

If TRUE, collapse clever covariates into a one-dimensional clever covariate scaled by the mean of their EIFs.

constrain_step

If TRUE, step size is at most delta_epsilon (it can be smaller if a smaller step decreases the loss more).

delta_epsilon

The maximum step size allowed if constrain_step is TRUE.

convergence_type

The convergence criterion to use: (1) "scaled_var" corresponds to sqrt(Var(D)/n)/logn (the default) while (2) "sample_size" corresponds to 1/n.

fluctuation_type

Whether to include the auxiliary covariate for the fluctuation model as a covariate or to treat it as a weight. Note that the option "weighted" is incompatible with a multi-epsilon submodel (one_dimensional = FALSE).

use_best

If TRUE, the final updated likelihood is set to the likelihood that minimizes the ED instead of the likelihood at the last update step.

verbose

If TRUE, diagnostic output is generated about the updating procedure.

Description

Current Limitations: loss function and submodel are hard-coded (need to accept arguments for these)

Constructor

define_param(maxit, cvtmle, one_dimensional, constrain_step, delta_epsilon, verbose)

maxit

The maximum number of update iterations

cvtmle

If TRUE, use CV-likelihood values when calculating updates.

one_dimensional

If TRUE, collapse clever covariates into a one-dimensional clever covariate scaled by the mean of their EIFs.

constrain_step

If TRUE, step size is at most delta_epsilon (it can be smaller if a smaller step decreases the loss more).

delta_epsilon

The maximum step size allowed if constrain_step is TRUE.

convergence_type

The convergence criterion to use: (1) "scaled_var" corresponds to sqrt(Var(D)/n)/logn (the default) while (2) "sample_size" corresponds to 1/n.

fluctuation_type

Whether to include the auxiliary covariate for the fluctuation model as a covariate or to treat it as a weight. Note that the option "weighted" is incompatible with a multi-epsilon submodel (one_dimensional = FALSE).

verbose

If TRUE, diagnostic output is generated about the updating procedure.

Description

Compute Variable Importance Measures (VIM) with any given parameter

Usage

tmle3_vim(
  tmle_spec,
  data,
  node_list,
  learner_list = NULL,
  adjust_for_other_A = TRUE
)

Arguments

Description

Manually Train Likelihood Factor The internal training process for likelihood factors is somewhat obtuse, so this function does the steps to manually train one, which is helpful if you want to use a likelihood factor independently of a likelihood object

Usage

train_lf(lf, tmle_task)

Arguments