graper {graper} | R Documentation

## Fit a regression model with graper

### Description

Fit a regression model with graper given a matrix of predictors (`X`), a response vector (`y`) and a vector of group memberships for each predictor in `X` (`annot`). For each group a different strength of penalization is determined adaptively.

### Usage

    graper(X, y, annot, factoriseQ = TRUE, spikeslab = TRUE,
        intercept = TRUE, family = "gaussian", standardize = TRUE,
        n_rep = 1, max_iter = 3000, th = 0.01, d_tau = 0.001,
        r_tau = 0.001, d_gamma = 0.001, r_gamma = 0.001, r_pi = 1,
        d_pi = 1, calcELB = TRUE, verbose = TRUE, freqELB = 1,
        nogamma = FALSE, init_psi = 1)

### Arguments

`X` | design matrix of size n (samples) x p (features)
`y` | response vector of size n
`annot` | factor of length p indicating group membership of each feature (column) in X
`factoriseQ` | if set to TRUE, the variational distribution is assumed to fully factorize across features (faster, default). If FALSE, a multivariate variational distribution is used.
`spikeslab` | if set to TRUE, a spike and slab prior on the coefficients (default).
`intercept` | whether to include an intercept into the model
`family` | Likelihood model for the response, either "gaussian" for linear regression or "binomial" for logistic regression
`standardize` | whether to standardize the predictors to unit variance
`n_rep` | number of repetitions with different random initializations to be fit
`max_iter` | maximum number of iterations
`th` | convergence threshold for the evidence lower bound (ELB)
`d_tau` | hyper-parameters for prior of tau (noise precision)
`r_tau` | hyper-parameters for prior of tau (noise precision)
`d_gamma` | hyper-parameters for prior of gamma (coefficients' prior precision)
`r_gamma` | hyper-parameters for prior of gamma (coefficients' prior precision)
`r_pi` | hyper-parameters for Beta prior of the mixture probabilities in the spike and slab prior
`d_pi` | hyper-parameters for Beta prior of the mixture probabilities in the spike and slab prior
`calcELB` | whether to calculate the evidence lower bound (ELB)
`verbose` | whether to print out intermediate messages during fitting
`freqELB` | frequency at which the evidence lower bound (ELB) is to be calculated, i.e. each freqELB-th iteration
`nogamma` | if TRUE, the normal prior will have same variance for all groups (only relevant for spikeslab = TRUE)
`init_psi` | initial value for the spike variables

### Details

The function trains the graper model given a matrix of predictors (`X`), a response vector (`y`) and a vector of group memberships for each predictor in `X` (`annot`). For each feature group as specified in `annot` a penalty factor and sparsity level is learnt.

By default it uses a Spike-and-Slab prior on the coefficients and uses a fully factorized variational distribution in the inference. This provides a fast way to train the model. Using `spikeslab=FALSE` a ridge regression like model can be fitted using a normal instead of the spike and slab prior. Setting `factoriseQ = FALSE` gives a more exact inference scheme based on a multivariate variational distribution, but can be much slower.

As the optimization is non-convex is can be helpful to use multiple random initializations by setting `n_rep` to a value larger 1. The returned model is then chosen as the optimal fit with respect to the evidence lower bound (ELB).

Depending on the response vector a linear regression model (`family = "gaussian"`) or a logistic regression model (`family = "binomial"`) is fitted. Note, that the implementation of logistic regression is still experimental.

### Value

A graper object containing

EW_beta

  estimated model coefficientsin liner/logistic regression

EW_s

  estimated posterior-inclusion probabilities for each feature

intercept

  estimated intercept term

annot

  annotation vector of features to the groups as specified when calling `graper`

EW_gamma

  estimated penalty factor per group

EW_pi

  estimated sparsity level per group (from 1 (dense) to 0 (sparse))

EW_tau

  estimated noise precision

sigma2_tildebeta_s1, EW_tildebeta_s1, alpha_gamma, alpha_tau, beta_tau, Sigma_beta, alpha_pi, beta_pi

  parameters of the variational distributions of beta, gamma, tau and pi

ELB

  final value of the evidence lower bound

ELB_trace

  values of the evidence lower bound for all iterations

Options

  other options used when calling `graper`

### Examples

    # create data
    dat <- makeExampleData()

    # fit a sparse model with spike and slab prior
    fit <- graper(dat$X, dat$y, dat$annot)
    fit # print fitted object
    beta <- coef(fit, include_intercept = FALSE) # model coeffients
    pips <- getPIPs(fit) # posterior inclusion probabilities
    pf <- fit$EW_gamma # penalty factors per group
    sparsities <- fit$EW_pi # sparsity levels per group

    # fit a dense model without spike and slab prior
    fit <- graper(dat$X, dat$y, dat$annot, spikeslab = FALSE)

    # fit a dense model using a multivariate variational distribution
    fit <- graper(dat$X, dat$y, dat$annot, factoriseQ = TRUE, spikeslab = FALSE)

[Package graper version 1.0.0 Index]