# Using outercv with Bayesian shrinkage models

#### Athina Spiliopoulou, Myles Lewis

The two examples below implement Bayesian linear and logistic regression models using the horseshoe prior over parameters to encourage a sparse model. Models are fitted using the `hsstan` R package, which performs full Bayesian inference through a `Stan` implementation. In Bayesian inference model meta-parameters such as the amount of shrinkage are also given prior distributions and are thus directly learned from the data through sampling. This bypasses the need to cross-validate results over a grid of values for the meta-parameters, as would be required to find the optimal lambda in a lasso or elastic net model.

However, Bayesian inference is computationally intensive. In high-dimensional settings, with e.g. more than 10,000 biomarkers, pre-filtering of inputs based on univariate measures of association with the outcome may be beneficial. If pre-filtering of inputs is used then a cross-validation procedure is needed to ensure that the data points used for pre-filtering and model fitting differ from the data points used to quantify model performance. The `outercv()` function is used to perform univariate pre-filtering and cross-validate model performance in this setting.

CAUTION should be used when setting the number of cores available for parallelisation. The code will use the product of `cv.cores` and `mc.cores`. The former controls parallelisation over folds, while the latter controls parallelisation in the Bayesian inference procedure. The default setting for `hsstan` is to use 4 Markov chains for sampling. We recommend setting `options(mc.cores = 4)` which will parallelise computation over the chains. If we are also using a 10-fold cross-validation procedure, and set `cv.cores = 10` to parallelise computation over folds, then 40 cores will be used in total.

#### Linear regression with a Bayesian shrinkage model (continuous outcomes)

We use cross-validation and apply univariate filtering of predictors and model fitting in one part of the data (training fold), followed by evaluation of model performance on the left-out data (testing fold), repeated in each fold.

Only one cross-validation split is needed (function `outercv`) as the Bayesian model does not require cross-validation for meta-parameters.

Note, in the examples below length of sampling and number of chains is curtailed for speed. We recommend 4 chains, `warmup` 1000 and `iter` 2000 in practice.

    library(hsstan)

    # specify options for running the code
    nfolds <- 3

    # specify number of cores for parallelising computation
    # the product of cv.cores and mc.cores will be used in total
    # number of cores for parallelising over CV folds
    cv.cores <- 1
    # number of cores for parallelising hsstan sampling
    # to pass CRAN package checks we need to create object oldopt
    # in the end we reset options to the old configuration
    oldopt <- options(mc.cores = 2)

    # load iris dataset and simulate a continuous outcome
    data(iris)
    dt <- iris[, 1:4]
    colnames(dt) <- c("marker1", "marker2", "marker3", "marker4")
    dt <- as.data.frame(apply(dt, 2, scale))
    dt$outcome.cont <- -3 + 0.5 * dt$marker1 + 2 * dt$marker2 + rnorm(nrow(dt), 0, 2)

    # unpenalised covariates: always retain in the prediction model
    uvars <- "marker1"
    # penalised covariates: coefficients are drawn from hierarchical shrinkage prior
    pvars <- c("marker2", "marker3", "marker4") # penalised covariates
    # run cross-validation with univariate filter and hsstan
    res.cv.hsstan <- outercv(y = dt$outcome.cont, x = dt[, c(uvars, pvars)],
                             model = "model.hsstan",
                             filterFUN = lm_filter,
                             filter_options = list(force_vars = uvars,
                                                   nfilter = 2,
                                                   p_cutoff = NULL,
                                                   rsq_cutoff = 0.9),
                             n_outer_folds = nfolds, chains = 2,
                             cv.cores = cv.cores,
                             unpenalized = uvars, warmup = 100, iter = 200)

We can then view prediction performance based on the testing folds and examine the Bayesian model using the `hsstan` package.

    summary(res.cv.hsstan)
    #> Single cross-validation to measure performance
    #> Outer loop:  3-fold CV
    #> No inner loop
    #> 150 observations, 4 predictors
    #>
    #> Model:  model.hsstan
    #> Filter:  lm_filter
    #>         n.filter
    #> Fold 1         3
    #> Fold 2         3
    #> Fold 3         3
    #>
    #> Final fit:             mean   sd  2.5% 97.5% n_eff Rhat
    #> (Intercept) -3.17 0.14 -3.40 -2.89   221 0.99
    #> marker1      0.37 0.32 -0.36  0.90   209 1.00
    #> marker2      2.07 0.22  1.70  2.47   196 1.00
    #> marker3      0.11 0.31 -0.43  0.92   104 1.00
    #>
    #> Result:
    #>     RMSE   Rsquared        MAE
    #>   2.1226     0.4772     1.6971

    sampler.stats(res.cv.hsstan$final_fit)
    #>         accept.stat stepsize divergences treedepth gradients warmup sample
    #> chain:1      0.9843   0.0241           0         8     14844   0.25   0.12
    #> chain:2      0.9722   0.0316           0         8     11356   0.12   0.09
    #> all          0.9783   0.0279           0         8     26200   0.37   0.21
    print(projsel(res.cv.hsstan$final_fit), digits = 4)  # adding marker2
    #>                                                      Model        KL        ELPD
    #>                                             Intercept only   0.32508  -374.99055
    #>                                           Initial submodel   0.32031  -374.72416
    #>                                                    marker2   0.00151  -325.82289
    #>                                                    marker3   0.00000  -325.88824
    #>                var       kl rel.kl.null rel.kl   elpd delta.elpd
    #> 1   Intercept only 0.325077     0.00000     NA -375.0  -49.10230
    #> 2 Initial submodel 0.320306     0.01468 0.0000 -374.7  -48.83591
    #> 3          marker2 0.001506     0.99537 0.9953 -325.8    0.06535
    #> 4          marker3 0.000000     1.00000 1.0000 -325.9    0.00000
    options(oldopt)  # reset options

Here adding `marker2` improves the model fit: substantial decrease of KL-divergence from the full model to the submodel. Adding `marker3` does not improve the model fit: no decrease of KL-divergence from the full model to the submodel.

#### Logistic regression with a Bayesian shrinkage model (continuous outcomes)

We use cross-validation and apply univariate filtering of predictors and model fitting in one part of the data (training fold), followed by evaluation of model performance on the left-out data (testing fold), repeated in each fold.

Only one cross-validation split is needed (function `outercv`) as the Bayesian model does not require cross-validation for meta-parameters.

    # sigmoid function
    sigmoid <- function(x) {1 / (1 + exp(-x))}

    # specify options for running the code
    nfolds <- 3
    cv.cores <- 1
    oldopt <- options(mc.cores = 2)

    # load iris dataset and create a binary outcome
    set.seed(267)
    data(iris)
    dt <- iris[, 1:4]
    colnames(dt) <- c("marker1", "marker2", "marker3", "marker4")
    dt <- as.data.frame(apply(dt, 2, scale))
    rownames(dt) <- paste0("sample", c(1:nrow(dt)))
    dt$outcome.bin <- sigmoid(0.5 * dt$marker1 + 2 * dt$marker2) > runif(nrow(dt))
    dt$outcome.bin <- factor(dt$outcome.bin)

    # unpenalised covariates: always retain in the prediction model
    uvars <- "marker1"
    # penalised covariates: coefficients are drawn from hierarchical shrinkage prior
    pvars <- c("marker2", "marker3", "marker4") # penalised covariates
    # run cross-validation with univariate filter and hsstan
    res.cv.hsstan <- outercv(y = dt$outcome.bin,
                             x = as.matrix(dt[, c(uvars, pvars)]),
                             model = "model.hsstan",
                             filterFUN = ttest_filter,
                             filter_options = list(force_vars = uvars,
                                                   nfilter = 2,
                                                   p_cutoff = NULL,
                                                   rsq_cutoff = 0.9),
                             n_outer_folds = nfolds, chains = 2,
                             cv.cores = cv.cores,
                             unpenalized = uvars, warmup = 100, iter = 200)

We view prediction performance based on testing folds and examine the model.

    summary(res.cv.hsstan)
    #> Single cross-validation to measure performance
    #> Outer loop:  3-fold CV
    #> No inner loop
    #> 150 observations, 4 predictors
    #> FALSE  TRUE
    #>    78    72
    #>
    #> Model:  model.hsstan
    #> Filter:  ttest_filter
    #>         n.filter
    #> Fold 1         3
    #> Fold 2         3
    #> Fold 3         3
    #>
    #> Final fit:             mean   sd  2.5% 97.5% n_eff Rhat
    #> (Intercept) -0.12 0.23 -0.56  0.27   200    1
    #> marker1      0.50 0.30 -0.10  1.16   208    1
    #> marker2      1.91 0.36  1.26  2.66   263    1
    #> marker3      0.01 0.24 -0.55  0.69   171    1
    #>
    #> Result:
    #>          Reference
    #> Predicted FALSE TRUE
    #>     FALSE    56   23
    #>     TRUE     22   49
    #>
    #>               AUC            Accuracy   Balanced accuracy
    #>            0.8284              0.7000              0.6993

    # examine the Bayesian model
    sampler.stats(res.cv.hsstan$final_fit)
    #>         accept.stat stepsize divergences treedepth gradients warmup sample
    #> chain:1      0.9826   0.0611           0         7      6792   0.08   0.07
    #> chain:2      0.9797   0.0651           0         7      5620   0.13   0.06
    #> all          0.9811   0.0631           0         7     12412   0.21   0.13
    print(projsel(res.cv.hsstan$final_fit), digits = 4)  # adding marker2
    #>                                                      Model        KL        ELPD
    #>                                             Intercept only   0.20643  -104.26957
    #>                                           Initial submodel   0.20118  -104.17411
    #>                                                    marker2   0.00060  -73.84232
    #>                                                    marker3   0.00000  -73.93210
    #>                var        kl rel.kl.null rel.kl    elpd delta.elpd
    #> 1   Intercept only 2.064e-01     0.00000     NA -104.27  -30.33748
    #> 2 Initial submodel 2.012e-01     0.02543  0.000 -104.17  -30.24201
    #> 3          marker2 5.964e-04     0.99711  0.997  -73.84    0.08977
    #> 4          marker3 9.133e-18     1.00000  1.000  -73.93    0.00000
    options(oldopt)  # reset options

Here adding `marker2` improves the model fit: substantial decrease of KL-divergence from the full model to the submodel. Adding `marker3` does not improve the model fit: no decrease of KL-divergence from the full model to the submodel.

# Parallelisation

Note at time of writing, there appears to be a bug in `rstan` (used by `hsstan`) leading to it ignoring the argument `cores` and instead spawning multiple processes as specified by the `chain` argument. This behaviour can be limited by setting `options(mc.cores = ..)`.

# Troubleshooting

A key problem with parallelisation in R is that errors, warnings and user input have to be suppressed during multicore processing. If a `nestedcv` call is not working, we recommend that you try it with `cv.cores=1` first to check it starts up without error messages.

# Citation

If you use this package, please cite as:

Lewis MJ, Spiliopoulou A, Goldmann K, Pitzalis C, McKeigue P, Barnes MR (2023). nestedcv: an R package for fast implementation of nested cross-validation with embedded feature selection designed for transcriptomics and high dimensional data. Bioinformatics Advances. https://doi.org/10.1093/bioadv/vbad048

# References

Carpenter, B., et al. Stan: A Probabilistic Programming Language. Journal of Statistical Software 2017;76(1):1-32.

Piironen, J. and Vehtari, A. Sparsity information and regularization in the horseshoe and other shrinkage priors. Electronic Journal of Statistics 2017;11(2):5018-5051, 5034.