blme (version 1.0-3)

blme: Fit Bayesian Linear and Generalized Linear Mixed-Effects Models

Description

Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting. Built off of lmer.

Usage

blmer(formula, data, REML = TRUE,
      control = lmerControl(), start = NULL, verbose = 0L,
      subset, weights, na.action, offset, contrasts = NULL,
      devFunOnly = FALSE, cov.prior = wishart,
      fixef.prior = NULL, resid.prior = NULL, ...)
bglmer(formula, data, family = gaussian,
       control = glmerControl(), start = NULL, verbose = 0L,
       nAGQ = 1L, subset, weights, na.action, offset,
       contrasts = NULL, mustart, etastart,
       devFunOnly = FALSE, cov.prior = wishart,
       fixef.prior = NULL, ...)

Arguments

cov.prior

a BLME prior or list of priors with allowable distributions: wishart, invwishart, gamma, invgamma, or NULL. Imposes a prior over the

fixef.prior

a BLME prior of family normal, t, or NULL. Imposes a prior over the fixed effects/modeled coefficients. Default is NULL.

resid.prior

a BLME prior of family gamma, invamma, point or NULL. Imposes a prior over the noise/residual variance, also known as common scale parameter or the conditional variance given the random effects.

start

like the start arguments for lmer and glmer a numeric vector or named list. Unlike the aforementioned, list members of fixef

formula, data, REML, family, control, verbose, nAGQ, mustart, etastart, devFunOnly, ...

model specification arguments as in lmer and glmer; see there for details.

subset, weights, na.action, offset, contrasts

further model specification arguments as in lm; see there for details.

Value

• An object of class "bmerMod", for which many methods are available. See there for details.

concept

• GLMM
• NLMM

Details

The bulk of the usage for blmer and bglmer closely follows the functions lmer and glmer. Those help pages provide a good overview of fitting linear and generalized linear mixed models. The primary distinction is that blmer and bglmer allow the user to do Bayesian inference or penalized maximum likelihood, with priors imposed on the different model components. For the specifics of any distribution listed below, see the distributions page. Covariance Prior The cov.prior argument applies a prior over the covariance matrix of the random effects/modeled coefficients. As there is one covariance matrix for every named grouping factor - that is every element that appears to the right of a vertical bar ("|") in the model formula - it is possible to apply as many different priors as there are said factors. The general formats of an argument to blmer or bglmer for such a prior are of the form:

• cov.prior = factor.name ~ covariance.distribution(option1 = value1, ...)
• cov.prior = list(fc.nm ~ dist1, fc.nm ~ dist2, ..., default.distribution)

If the factor.name ~ construct is ommitted, the prior is interpretted as a default and applied to all factors that lack specific priors of their own. Options are not required, but permit fine-tuning of the model.

Supported distributions are gamma, invgamma, wishart, invwishart, NULL, and custom.

The common.scale option, a logical, determines whether or not the prior applies to in the absolute-real world sense (value = FALSE), or if the prior is applied to the random effect covariance divided by the estimated residual variance (TRUE). As a practical matter, when false computation can be slower as the profiled common scale may no longer have a closed-form solution. As such, the default for all cases is TRUE.

Other options are specified along with the specific distributions and defaults are explained in the blme distributions page.

Fixed Effects Prior Priors on the fixed effects, or unmodeled coefficients, are specified in a fashion similar to that of covariance priors. The general format is

• fixef.prior = multivariate.distribution(options1 = value1, ...)

At present, the implemented multivariate distributions are normal, t, and NULL. t priors cannot be used when REML is TRUE, as that integral does not have a closed form solution.

Residual Variance Prior

The general format for a residual variance prior is the same as for a fixed effect prior. The supported distributions are point, gamma, invgamma.

See Also

lmer, glmer, merMod class, and lm.

Examples

##  covariance prior
data("sleepstudy", package = "lme4")
(fm1 <- blmer(Reaction ~ Days + (0 + Days|Subject), sleepstudy,
              cov.prior = gamma))
(fm2 <- blmer(Reaction ~ Days + (0 + Days|Subject), sleepstudy,
              cov.prior = gamma(shape = 2, rate = 0.5, posterior.scale = 'sd')))
(fm3 <- blmer(Reaction ~ Days + (1 + Days|Subject), sleepstudy,
              cov.prior = wishart))
(fm4 <- blmer(Reaction ~ Days + (1 + Days|Subject), sleepstudy,
              cov.prior = invwishart(df = 5, scale = diag(0.5, 2))))

##  custom prior
penaltyFn <- function(sigma)
  dcauchy(sigma, 0, 10, log = TRUE)
(fm5 <- blmer(Reaction ~ Days + (0 + Days|Subject), sleepstudy,
              cov.prior = custom(penaltyFn, chol = TRUE, scale = "log")))


##  fixed effect prior
(fm6 <- blmer(Reaction ~ Days + (1 + Days|Subject), sleepstudy,
              cov.prior = NULL,
              fixef.prior = normal))
(fm7 <- blmer(Reaction ~ Days + (1 + Days|Subject), sleepstudy,
              cov.prior = NULL,
              fixef.prior = normal(cov = diag(0.5, 2), common.scale = FALSE)))

##  residual variance prior
##  eight schools example
y <- c(28, 8, -3, 7, -1, 1, 18, 12);
sigma <- c(15, 10, 16, 11, 9, 11, 10, 18);
g <- 1:8;
  
(schools <- blmer(y ~ 1 + (1 | g), resid.prior = point,
                  cov.prior = NULL, REML = FALSE,
                  weights = 1 / sigma^2));