blasso {monomvn} | R Documentation

## Bayesian Lasso/NG and Ridge Regression

### Description

Inference for ordinary least squares, lasso/NG and ridge regression models by (Gibbs) sampling from the Bayesian posterior distribution, augmented with Reversible Jump for model selection

### Usage

    bridge(X, y, T = 1000, thin = NULL, RJ = TRUE, M = NULL,
           beta = NULL, lambda2 = 1, s2 = var(y-mean(y)), mprior = 0,
           rd = NULL, ab = NULL, theta=0, rao.s2 = TRUE, icept = TRUE,
           normalize = TRUE, verb = 1)
    blasso(X, y, T = 1000, thin = NULL, RJ = TRUE, M = NULL,
           beta = NULL, lambda2 = 1, s2 = var(y-mean(y)),
           case = c("default", "ridge", "hs", "ng"), mprior = 0, rd = NULL,
           ab = NULL, theta=0, rao.s2 = TRUE, icept = TRUE,
           normalize = TRUE, verb = 1)

### Arguments

`X` | `data.frame`, `matrix`, or vector of inputs `X`
`y` | vector of output responses `y` of length equal to the leading dimension (rows) of `X`, i.e., `length(y) == nrow(X)`
`T` | total number of MCMC samples to be collected
`thin` | number of MCMC samples to skip before a sample is collected (via thinning). If `NULL` (default), then `thin` is determined based on the regression model implied by `RJ`, `lambda2`, and `ncol(X)`; and also on the errors model implied by `theta` and `nrow(X)`
`RJ` | if `TRUE` then model selection on the columns of the design matrix (and thus the parameter `beta` in the model) is performed by Reversible Jump (RJ) MCMC. The initial model is specified by the `beta` input, described below, and the maximal number of covariates in the model is specified by `M`
`M` | the maximal number of allowed covariates (columns of `X`) in the model. If input `lambda2 > 0` then any `M <= ncol(X)` is allowed. Otherwise it must be that `M <= min(ncol(X), length(y)-1)`, which is default value when a `NULL` argument is given
`beta` | initial setting of the regression coefficients. Any zero-components will imply that the corresponding covariate (column of `X`) is not in the initial model. When input `RJ = FALSE` (no RJ) and `lambda2 > 0` (use lasso) then no components are allowed to be exactly zero. The default setting is therefore contextual; see below for details
`lambda2` | square of the initial lasso penalty parameter. If zero, then least squares regressions are used
`s2` | initial variance parameter
`case` | specifies if ridge regression or the Normal-Gamma should be done instead of the lasso; only meaningful when `lambda2 > 0`
`mprior` | prior on the number of non-zero regression coefficients (and therefore covariates) `m` in the model. The default (`mprior = 0`) encodes the uniform prior on `0 <= m <= M`. A scalar value `0 < mprior < 1` implies a Binomial prior `Bin(m|n=M,p=mprior)`. A 2-vector `mprior=c(g,h)` of positive values `g` and `h` represents gives `Bin(m|n=M,p)` prior where `p~Beta(g,h)`
`rd` | `=c(r, delta)`, the alpha (shape) parameter and beta (rate) parameter to the gamma distribution prior `G(r,delta)` for the lambda2 parameter under the lasso model; or, the alpha (shape) parameter and beta (scale) parameter to the inverse-gamma distribution `IG(r/2, delta/2)` prior for the lambda2 parameter under the ridge regression model. A default of `NULL` generates appropriate non-informative values depending on the nature of the regression. See the details below for information on the special settings for ridge regression
`ab` | `=c(a, b)`, the alpha (shape) parameter and the beta (scale) parameter for the inverse-gamma distribution prior `IG(a,b)` for the variance parameter `s2`. A default of `NULL` generates appropriate non-informative values depending on the nature of the regression
`theta` | the rate parameter (`> 0`) to the exponential prior on the degrees of freedom paramter `nu` under a model with Student-t errors implemented by a scale-mixture prior. The default setting of `theta = 0` turns off this prior, defaulting to a normal errors prior
`rao.s2` | indicates whether Rao-Blackwellized samples for s^2 should be used (default `TRUE`); see below for more details
`icept` | if `TRUE`, an implicit intercept term is fit in the model, otherwise the the intercept is zero; default is `TRUE`
`normalize` | if `TRUE`, each variable is standardized to have unit L2-norm, otherwise it is left alone; default is `TRUE`
`verb` | verbosity level; currently only `verb = 0` and `verb = 1` are supported

### Details

The Bayesian lasso model and Gibbs Sampling algorithm is described in detail in Park & Casella (2008). The algorithm implemented by this function is identical to that described therein, with the exception of an added “option” to use a Rao-Blackwellized sample of s^2 (with beta integrated out) for improved mixing, and the model selections by RJ described below. When input argument `lambda2 = 0` is supplied, the model is a simple hierarchical linear model where (beta,s2) is given a Jeffrey's prior

Specifying `RJ = TRUE` causes Bayesian model selection and averaging to commence for choosing which of the columns of the design matrix `X` (and thus parameters `beta`) should be included in the model. The zero-components of the `beta` input specify which columns are in the initial model, and `M` specifies the maximal number of columns.

The RJ mechanism implemented here for the Bayesian lasso model selection differs from the one described by Hans (2008), which is based on an idea from Geweke (1996). Those methods require departing from the Park & Casella (2008) latent-variable model and requires sampling from each conditional beta[i] | beta[-i], ... for all i, since a mixture prior with a point-mass at zero is placed on each beta[i]. Out implementation here requires no such special prior and retains the joint sampling from the full beta vector of non-zero entries, which we believe yields better mixing in the Markov chain. RJ proposals to increase/decrease the number of non-zero entries does proceed component-wise, but the acceptance rates are high due due to marginalized between-model moves (Troughton & Godsill, 1997).

When the lasso prior or RJ is used, the automatic thinning level (unless `thin != NULL`) is determined by the number of columns of `X` since this many latent variables are introduced

Bayesian ridge regression is implemented as a special case via the `bridge` function. This essentially calls `blasso` with `case = "ridge"`. A default setting of `rd = c(0,0)` is implied by `rd = NULL`, giving the Jeffery's prior for the penalty parameter lambda^2 unless `ncol(X) >= length(y)` in which case the proper specification of `rd = c(5,10)` is used instead.

The Normal–Gamma prior (Griffin & Brown, 2009) is implemented as an extension to the Bayesian lasso with `case = "ng"`

When `theta > 0` then the Student-t errors via scale mixtures (and thereby extra latent variables `omega2`) of Geweke (1993) is applied as an extension to the Bayesian lasso/ridge model. If Student-t errors are used the automatic thinning level is augmented (unless `thin != NULL`) by the number of rows in `X` since this many latent variables are introduced

### Value

`blasso` returns an object of class `"blasso"`, which is a `list` containing a copy of all of the input arguments as well as of the components listed below.

`call` | a copy of the function call as used
`mu` | a vector of `T` samples of the (un-penalized) “intercept” parameter
`beta` | a `T*ncol(X)` `matrix` of `T` samples from the (penalized) regression coefficients
`m` | the number of non-zero entries in each vector of `T` samples of `beta`
`s2` | a vector of `T` samples of the variance parameter
`lambda2` | a vector of `T` samples of the penalty parameter
`gamma` | a vector of `T` with the gamma parameter when `case = "ng"`
`tau2i` | a `T*ncol(X)` `matrix` of `T` samples from the (latent) inverse diagonal of the prior covariance matrix for `beta`, obtained for Lasso regressions
`omega2` | a `T*nrow(X)` `matrix` of `T` samples from the (latent) diagonal of the covariance matrix of the response providing a scale-mixture implementation of Student-t errors with degrees of freedom `nu` when active (input `theta > 0`)
`nu` | a vector of `T` samples of the degrees of freedom parameter to the Student-t errors mode when active (input `theta > 0`)
`pi` | a vector of `T` samples of the Binomial proportion `p` that was given a Beta prior, as described above for the 2-vector version of the `mprior` input
`lpost` | the log posterior probability of each (saved) sample of the joint parameters
`llik` | the log likelihood of each (saved) sample of the parameters
`llik.norm` | the log likelihood of each (saved) sample of the parameters under the Normal errors model when sampling under the Student-t model; i.e., it is not present unless `theta > 0`

### Note

Whenever `ncol(X) >= nrow(X)` it must be that either `RJ = TRUE` with `M <= nrow(X)-1` (the default) or that the lasso is turned on with `lambda2 > 0`. Otherwise the regression problem is ill-posed.

Since the starting values are considered to be first sample (of `T`), the total number of (new) samples obtained by Gibbs Sampling will be `T-1`

### Author(s)

Robert B. Gramacy bobby@statslab.cam.ac.uk

### References

Park, T., Casella, G. (2008). The Bayesian Lasso.
Journal of the American Statistical Association, Volume 103, Number 482, June 2008 , pp. 681-686(6)
http://www.stat.ufl.edu/~casella/Papers/Lasso.pdf

Griffin, J.E. and Brown, P.J. (2009). Inference with Normal–Gamma prior distributions in regression problems. Tech. rep., University of Kent.
http://www.kent.ac.uk/ims/personal/jeg28/paper_ba10_final.pdf

Chris Hans. (2008). Bayesian Lasso regression. Technical Report No. 810, Department of Statistics, The Ohio State University, Columbus, OH 43210.
http://www.stat.osu.edu/~hans/Papers/blasso.pdf

Geweke, J. (1996). Variable selection and model comparison in regression. In Bayesian Statistics 5. Editors: J.M. Bernardo, J.O. Berger, A.P. Dawid and A.F.M. Smith, 609-620. Oxford Press.

Paul T. Troughton and Simon J. Godsill (1997). A reversible jump sampler for autoregressive time series, employing full conditionals to achieve efficient model space moves. Technical Report CUED/F-INFENG/TR.304, Cambridge University Engineering Department.

Geweke, J. (1993) Bayesian treatment of the independent Student-t linear model. Journal of Applied Econometrics, Vol. 8, S19-S40

http://www.statslab.cam.ac.uk/~bobby/monomvn.html

### See Also

`lm` , `lars` in the lars package, `regress`, `lm.ridge` in the MASS package

### Examples

    ## following the lars diabetes example
    data(diabetes)
    attach(diabetes)

    ## Ordinary Least Squares regression
    reg.ols <- regress(x, y)

    ## Lasso regression
    reg.las <- regress(x, y, method="lasso")

    ## Bayesian Lasso regression
    reg.blas <- blasso(x, y)

    ## summarize the beta (regression coefficients) estimates
    plot(reg.blas, burnin=200)
    points(drop(reg.las$b), col=2, pch=20)
    points(drop(reg.ols$b), col=3, pch=18)
    legend("topleft", c("blasso-map", "lasso", "lsr"),
           col=c(2,2,3), pch=c(21,20,18))

    ## plot the size of different models visited
    plot(reg.blas, burnin=200, which="m")

    ## get the summary
    s <- summary(reg.blas, burnin=200)

    ## calculate the probability that each beta coef != zero
    s$bn0

    ## summarize s2
    plot(reg.blas, burnin=200, which="s2")
    s$s2

    ## summarize lambda2
    plot(reg.blas, burnin=200, which="lambda2")
    s$lambda2

    ## fit with Student-t errors
    ## (~400-times slower due to automatic thinning level)
    regt.blas <- blasso(x, y, theta=0.1)

    ## plotting some information about nu, and quantiles
    plot(regt.blas, "nu", burnin=200)
    quantile(regt.blas$nu[-(1:200)], c(0.05, 0.95))

    ## Bayes Factor shows strong evidence for Student-t model
    mean(exp(regt.blas$llik[-(1:200)] - regt.blas$llik.norm[-(1:200)]))

    ## clean up
    detach(diabetes)

    ##
    ## a big-p small-n example
    ##

    n <- 25; m <- 51
    xmuS <- randmvn(n, m)
    X <- xmuS$x[,1:(m-1)]
    Y <- drop(xmuS$x[,m])
    obl <- blasso(X, Y, verb=0)

    ## plot summary of the model order
    plot(obl, burnin=10, which="m")

    ## fit a standard lasso model
    oml <- regress(X, Y, method="lasso")

    ## compare via RMSE, most often blasso will win
    beta <- xmuS$S[m,-m] %*% solve(xmuS$S[-m,-m])
    sqrt(mean((apply(obl$beta, 2, mean) - beta)^2))
    sqrt(mean((oml$b[-1] - beta)^2))

    ## now try both Bayesian & ML ridge regression
    obr <- bridge(X, Y, verb=0)
    omr <- regress(X, Y, method="ridge")
    sqrt(mean((apply(obr$beta[-c(1:200),], 2, mean) - beta)^2))
    sqrt(mean((omr$b[-1] - beta)^2))

[Package monomvn version 1.8 Index]