Fit a conditional density f(A|W), where A can be continuous and multivariate and W is set of predictors. This estimator breaks up the support of a continuous A into discrete bins and fits the conditional hazard for each bin. By default the logistic regression will be used for fitting each bin hazard. Alternatively, arbitrary machine learning algorithms can be used via learners available in sl3 R package (see example below). Given several competing candidate density estimators, one can find the optimal convex combination these candidate estimators by using Super Learner [sl3]. For detailed description of the estimator implemented in this package see (Díaz Muñoz and van der Laan, 2011) and (Muñoz and van der Laan, 2012).

Authors: Oleg Sofrygin, Frank Blaauw, Antoine Chambaz, Mark van der Laan

To install the development version of condensier (requires the devtools package):

devtools::install_github('osofr/condensier', build_vignettes = FALSE)

Simulate some data with continuous outcome ("sA"):

library("simcausal")
D <- DAG.empty()
D <-
D + node("W1", distr = "rbern", prob = 0.5) +
  node("W2", distr = "rbern", prob = 0.3) +
  node("W3", distr = "rbern", prob = 0.3) +
  node("sA.mu", distr = "rconst", const = (0.98 * W1 + 0.58 * W2 + 0.33 * W3)) +
  node("sA", distr = "rnorm", mean = sA.mu, sd = 1)
D <- set.DAG(D, n.test = 10)
#> ...automatically assigning order attribute to some nodes...
#> node W1, order:1
#> node W2, order:2
#> node W3, order:3
#> node sA.mu, order:4
#> node sA, order:5
datO <- sim(D, n = 10000, rndseed = 12345)
#> simulating observed dataset from the DAG object

Fit conditional density using equal mass bins (same number of observations per bin):

library("condensier")
#> condensier
#> The condensier package is still in beta testing. Interpret results with caution.
dens_fit <- fit_density(
    X = c("W1", "W2", "W3"), 
    Y = "sA", 
    input_data = datO, 
    nbins = 20, 
    bin_method = "equal.mass",
    bin_estimator = speedglmR6$new())

Wrapper function to predict the conditional probability (likelihood) for new observations:

newdata <- datO[1:5, c("W1", "W2", "W3", "sA"), with = FALSE]
preds <- predict_probability(dens_fit, newdata)

Wrapper function to sample the values from the conditional density fit:

sampledY <- sample_value(dens_fit, newdata)

Fit conditional density using custom bin definitions (argument intrvls):

dens_fit <- fit_density(
    X = c("W1", "W2", "W3"),
    Y = "sA",
    input_data = datO,
    bin_estimator = speedglmR6$new(),
    intrvls = list(sA = seq(-4,4, by = 0.1)))

Fit conditional density using custom bin definitions and pool all bin indicators into a single long-format dataset. The pooling results in a single regression that is fit for all bin hazards, with a bin indicator added as an additional covariate.

dens_fit <- fit_density(
    X = c("W1", "W2", "W3"),
    Y = "sA",
    input_data = datO,
    bin_estimator = speedglmR6$new(),
    intrvls = list(sA = seq(-4,4, by = 0.1)),
    pool = TRUE)

Any binary-outcome regression learner available in sl3 package can be used as a "drop-in" learner for conditional bin hazard. Below, we use xgboost R package to define a new estimator of the bin hazard. Note that below, we are setting the tuning parameter pool to TRUE. This will have an effect of "pooling" all discrete bin indicators into a single dataset (with bin number added as a new covariate). This is followed by a single regression fit that is performed for all bins simultaneously (hence saving a lot of computation time and allowing the algorithm to perform smoothing over the bins).

library("sl3")
#> Error in library("sl3"): there is no package called 'sl3'

task <- sl3_Task$new(datO, covariates=c("W1", "W2", "W3"), outcome="sA")
#> Error in eval(expr, envir, enclos): object 'sl3_Task' not found
lrn <- Lrnr_condensier$new(nbins = 10, bin_method = "equal.len", pool = TRUE, 
  bin_estimator = Lrnr_xgboost$new(nrounds = 5, objective = "reg:logistic"))
#> Error in eval(expr, envir, enclos): object 'Lrnr_condensier' not found

trained_lrn = lrn$train(task)
#> Error in eval(expr, envir, enclos): object 'lrn' not found

newdata <- datO[1:5, c("W1", "W2", "W3", "sA")]
new_task <- sl3_Task$new(newdata, covariates=c("W1", "W2", "W3"),outcome="sA" )
#> Error in eval(expr, envir, enclos): object 'sl3_Task' not found
pred_probs = trained_lrn$predict(new_task)
#> Error in eval(expr, envir, enclos): object 'trained_lrn' not found
pred_probs
#> Error in eval(expr, envir, enclos): object 'pred_probs' not found

Now that we have defined the candidate bin hazard estimator, it is time to train the model and obtained predictions (likelihood) based on new observations

trained_lrn = lrn$train(task)
#> Error in eval(expr, envir, enclos): object 'lrn' not found

newdata <- datO[1:5, c("W1", "W2", "W3", "sA")]
new_task <- sl3_Task$new(newdata, covariates=c("W1", "W2", "W3"),outcome="sA" )
#> Error in eval(expr, envir, enclos): object 'sl3_Task' not found
pred_probs = trained_lrn$predict(new_task)
#> Error in eval(expr, envir, enclos): object 'trained_lrn' not found
pred_probs
#> Error in eval(expr, envir, enclos): object 'pred_probs' not found

Finally, multiple candidate density estimators can be optimally stacked or combined with a Super Learner. The convex combination of the candidates is found by minimizing the cross-validated negative loglikelihood loss function. In this example we define 3 candidate density learners:

lrn1 <- Lrnr_condensier$new(nbins = 25, bin_method = "equal.len", pool = TRUE, 
  bin_estimator = Lrnr_glm_fast$new(family = "binomial"))
lrn2 <- Lrnr_condensier$new(nbins = 20, bin_method = "equal.mass", pool = TRUE,
  bin_estimator = Lrnr_xgboost$new(nrounds = 50, objective = "reg:logistic"))
lrn3 <- Lrnr_condensier$new(nbins = 35, bin_method = "equal.len", pool = TRUE,
  bin_estimator = Lrnr_xgboost$new(nrounds = 50, objective = "reg:logistic"))

We proceed by training the Super Learner (with 10 fold cross-validation) and then finding the optimal convex combination of the candidate densities with the meta-learner Lrnr_solnp_density:

sl <- Lrnr_sl$new(learners = list(lrn1, lrn2, lrn3),
                  metalearner = Lrnr_solnp_density$new())
sl_fit <- sl$train(task)

To predict for new data, wrap the desired dataset into an sl3-task object and call predict on above sl_fit object:

newdata <- datO[1:5, c("W1", "W2", "W3", "sA")]
new_task <- sl3_Task$new(newdata, covariates=c("W1", "W2", "W3"),outcome="sA" )
sl_fit$predict(new_task)

Note that bin_estimator can be also a Super-Learner object from sl3. In this case the bin hazard will be estimated by stacking several candidate estimators. For example, below, we define a single density learner lrn, with the hazard estimator defined by the Super-Learner that stacks two candidates (GLM and xgboost GBM). Note that in contrast to the above example, this Super-Learner fit will be optimized for the logistic regression problem (estimating pooled bin hazards), but still using internal 10-fold cross-validation.

library("sl3")
lrn <- Lrnr_condensier$new(nbins = 35, bin_method = "equal.len", pool = TRUE, bin_estimator = 
  Lrnr_sl$new(
    learners = list(
      Lrnr_glm_fast$new(family = "binomial"),
      Lrnr_xgboost$new(nrounds = 50, objective = "reg:logistic")
      ),
    metalearner = Lrnr_glm$new()
    ))
binSL_fit <- lrn$train(task)

In prinicple, one can nest the two of the above described types of Super Learners: the Super Learner that fits the bin hazard of each candidate density and the Super Learner that finds the optimal combination of the candidate densities. However, due to potential performance constraints, we currently advise against that.

One can build a custom version of their own Super Learner by using the stacking and cross-validation procedures availabe in sl3. Here we define a stack of 3 learners, then train all 3 and predict for new data (likelihood):

learner_stack <- Stack$new(lrn1, lrn2, lrn3)
stack_fit <- learner_stack$train(task)
preds <- stack_fit$predict(new_task)

Here we cross-validate all 3 learners in the stack, using the default 10-fold CV:

cv_stack <- Lrnr_cv$new(learner_stack)
cv_fit <- cv_stack$train(task)

The development of this package was funded through an NIH grant (R01 AI074345-07).

The contents of this repository are distributed under the MIT license.

The MIT License (MIT)

Copyright (c) 2017 Oleg Sofrygin 

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[1] I. Díaz Muñoz and M. J. van der Laan. "Super learner based conditional density estimation with application to marginal structural models". In: The international journal of biostatistics 7.1 (2011), pp. 1-20.

[2] I. D. Muñoz and M. van der Laan. "Population intervention causal effects based on stochastic interventions". In: Biometrics 68.2 (2012), pp. 541-549.