laplace: Laplace approximation and adaptive Gauss-Hermite quadrature

In nimble: MCMC, Particle Filtering, and Programmable Hierarchical Modeling

Laplace approximation and adaptive Gauss-Hermite quadrature

Description

Build a Laplace or AGHQ approximation algorithm for a given NIMBLE model.

Usage

buildLaplace(
  model,
  paramNodes,
  randomEffectsNodes,
  calcNodes,
  calcNodesOther,
  control = list()
)

buildAGHQ(
  model,
  nQuad = 1,
  paramNodes,
  randomEffectsNodes,
  calcNodes,
  calcNodesOther,
  control = list()
)

Arguments

model	a NIMBLE model object, such as returned by nimbleModel. The model must have automatic derivatives (AD) turned on, e.g. by using buildDerivs=TRUE in nimbleModel.
paramNodes	a character vector of names of parameter nodes in the model; defaults are provided by setupMargNodes. Alternatively, paramNodes can be a list in the format returned by setupMargNodes, in which case randomEffectsNodes, calcNodes, and calcNodesOther are not needed (and will be ignored).
randomEffectsNodes	a character vector of names of continuous unobserved (latent) nodes to marginalize (integrate) over using Laplace approximation; defaults are provided by setupMargNodes.
calcNodes	a character vector of names of nodes for calculating the integrand for Laplace approximation; defaults are provided by setupMargNodes. There may be deterministic nodes between paramNodes and calcNodes. These will be included in calculations automatically and thus do not need to be included in calcNodes (but there is no problem if they are).
calcNodesOther	a character vector of names of nodes for calculating terms in the log-likelihood that do not depend on any randomEffectsNodes, and thus are not part of the marginalization, but should be included for purposes of finding the MLE. This defaults to stochastic nodes that depend on paramNodes but are not part of and do not depend on randomEffectsNodes. There may be deterministic nodes between paramNodes and calcNodesOther. These will be included in calculations automatically and thus do not need to be included in calcNodesOther (but there is no problem if they are).
control	a named list for providing additional settings used in Laplace approximation. See control section below. Most of these can be updated later with the 'updateSettings' method.
nQuad	number of quadrature points for AGHQ (in one dimension). Laplace approximation is AGHQ with 'nQuad=1'. Only odd numbers of nodes really make sense. Often only one or a few nodes can achieve high accuracy. A maximum of 35 nodes is supported. Note that for multivariate quadratures, the number of nodes will be (number of dimensions)^nQuad.

buildLaplace

buildLaplace creates an object that can run Laplace approximation and for a given model or part of a model. buildAGHQ creates an object that can run adaptive Gauss-Hermite quadrature (AGHQ, sometimes called "adaptive Gaussian quadrature") for a given model or part of a model. Laplace approximation is AGHQ with one quadrature point, hence 'buildLaplace' simply calls 'buildAGHQ' with 'nQuad=1'. These methods approximate the integration over continuous random effects in a hierarchical model to calculate the (marginal) likelihood.

buildAGHQ and buildLaplace will by default (unless changed manually via 'control$split') determine from the model which random effects can be integrated over (marginalized) independently. For example, in a GLMM with a grouping factor and an independent random effect intercept for each group, the random effects can be marginalized as a set of univariate approximations rather than one multivariate approximation. On the other hand, correlated or nested random effects would require multivariate marginalization.

Maximum likelihood estimation is available for Laplace approximation ('nQuad=1') with univariate or multivariate integrations. With 'nQuad > 1', maximum likelihood estimation is available only if all integrations are univariate (e.g., a set of univariate random effects). If there are multivariate integrations, these can be calculated at chosen input parameters but not maximized over parameters. For example, one can find the MLE based on Laplace approximation and then increase 'nQuad' (using the 'updateSettings' method below) to check on accuracy of the marginal log likelihood at the MLE.

Beware that quadrature will use 'nQuad^k' quadrature points, where 'k' is the dimension of each integration. Therefore quadrature for 'k' greater that 2 or 3 can be slow. As just noted, 'buildAGHQ' will determine independent dimensions of quadrature, so it is fine to have a set of univariate random effects, as these will each have k=1. Multivariate quadrature (k>1) is only necessary for nested, correlated, or otherwise dependent random effects.

The recommended way to find the maximum likelihood estimate and associated outputs is by calling runLaplace or runAGHQ. The input should be the compiled Laplace or AGHQ algorithm object. This would be produced by running compileNimble with input that is the result of buildLaplace or buildAGHQ.

For more granular control, see below for methods findMLE and summary. See function summaryLaplace for an easier way to call the summary method and obtain results that include node names. These steps are all done within runLaplace and runAGHQ.

The NIMBLE User Manual at r-nimble.org also contains an example of Laplace approximation.

How input nodes are processed

buildLaplace and buildAGHQ make good tries at deciding what to do with the input model and any (optional) of the node arguments. However, random effects (over which approximate integration will be done) can be written in models in multiple equivalent ways, and customized use cases may call for integrating over chosen parts of a model. Hence, one can take full charge of how different parts of the model will be used.

Any of the input node vectors, when provided, will be processed using nodes <- model$expandNodeNames(nodes), where nodes may be paramNodes, randomEffectsNodes, and so on. This step allows any of the inputs to include node-name-like syntax that might contain multiple nodes. For example, paramNodes = 'beta[1:10]' can be provided if there are actually 10 scalar parameters, 'beta[1]' through 'beta[10]'. The actual node names in the model will be determined by the exapndNodeNames step.

In many (but not all) cases, one only needs to provide a NIMBLE model object and then the function will construct reasonable defaults necessary for Laplace approximation to marginalize over all continuous latent states (aka random effects) in a model. The default values for the four groups of nodes are obtained by calling setupMargNodes, whose arguments match those here (except for a few arguments which are taken from control list elements here).

setupMargNodes tries to give sensible defaults from any combination of paramNodes, randomEffectsNodes, calcNodes, and calcNodesOther that are provided. For example, if you provide only randomEffectsNodes (perhaps you want to marginalize over only some of the random effects in your model), setupMargNodes will try to determine appropriate choices for the others.

setupMargNodes also determines which integration dimensions are conditionally independent, i.e., which can be done separately from each other. For example, when possible, 10 univariate random effects will be split into 10 univariate integration problems rather than one 10-dimensional integration problem.

The defaults make general assumptions such as that randomEffectsNodes have paramNodes as parents. However, The steps for determining defaults are not simple, and it is possible that they will be refined in the future. It is also possible that they simply don't give what you want for a particular model. One example where they will not give desired results can occur when random effects have no prior parameters, such as 'N(0,1)' nodes that will be multiplied by a scale factor (e.g. sigma) and added to other explanatory terms in a model. Such nodes look like top-level parameters in terms of model structure, so you must provide a randomEffectsNodes argument to indicate which they are.

It can be helpful to call setupMargNodes directly to see exactly how nodes will be arranged for Laplace approximation. For example, you may want to verify the choice of randomEffectsNodes or get the order of parameters it has established to use for making sense of the MLE and results from the summary method. One can also call setupMargNodes, customize the returned list, and then provide that to buildLaplace as paramNodes. In that case, setupMargNodes will not be called (again) by buildLaplace.

If setupMargNodes is emitting an unnecessary warning, simply use control=list(check=FALSE).

Managing parameter transformations that may be used internally

If any paramNodes (parameters) or randomEffectsNodes (random effects / latent states) have constraints on the range of valid values (because of the distribution they follow), they will be used on a transformed scale determined by parameterTransform. This means the Laplace approximation itself will be done on the transformed scale for random effects and finding the MLE will be done on the transformed scale for parameters. For parameters, prior distributions are not included in calculations, but they are used to determine valid parameter ranges and hence to set up any transformations. For example, if sigma is a standard deviation, you can declare it with a prior such as sigma ~ dhalfflat() to indicate that it must be greater than 0.

For default determination of when transformations are needed, all parameters must have a prior distribution simply to indicate the range of valid values. For a param p that has no constraint, a simple choice is p ~ dflat().

Understanding inner and outer optimizations

Note that there are two numerical optimizations when finding maximum likelihood estimates with a Laplace or (1D) AGHQ algorithm: (1) maximizing the joint log-likelihood of random effects and data given a parameter value to construct the approximation to the marginal log-likelihood at the given parameter value; (2) maximizing the approximation to the marginal log-likelihood over the parameters. In what follows, the prefix 'inner' refers to optimization (1) and 'outer' refers to optimization (2). Currently both optimizations default to using method "BFGS". However, one can use other optimizers or simply run optimization (2) manually from R; see the example below. In some problems, choice of inner and/or outer optimizer can make a big difference for obtaining accurate results, especially for standard errors. Hence it is worth experimenting if one is in doubt.

control list arguments

The control list allows additional settings to be made using named elements of the list. Most (or all) of these can be updated later using the 'updateSettings' method. Supported elements include:

• split. If TRUE (default), randomEffectsNodes will be split into conditionally independent sets if possible. This facilitates more efficient Laplace or AGHQ approximation because each conditionally independent set can be marginalized independently. If FALSE, randomEffectsNodes will be handled as one multivariate block, with one multivariate approximation. If split is a numeric vector, randomEffectsNodes will be split by calling split(randomEffectsNodes, control$split). The last option allows arbitrary control over how randomEffectsNodes are blocked.

• check. If TRUE (default), a warning is issued if paramNodes, randomEffectsNodes and/or calcNodes are provided but seem to have missing or unnecessary elements based on some default inspections of the model. If unnecessary warnings are emitted, simply set check=FALSE.

• innerOptimControl. A list (either an R list or a 'optimControlNimbleList') of control parameters for the inner optimization of Laplace approximation using nimOptim. See 'Details' of nimOptim for further information. Default is 'nimOptimDefaultControl()'.

• innerOptimMethod. Optimization method to be used in nimOptim for the inner optimization. See 'Details' of nimOptim. Currently nimOptim in NIMBLE supports: "Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "nlminb", and user-provided optimizers. By default, method "BFGS" is used for both univariate and multivariate cases. For "nlminb" or user-provided optimizers, only a subset of elements of the innerOptimControlList are supported. (Note that control over the outer optimization method is available as an argument to 'findMLE'). Choice of optimizers can be important and so can be worth exploring.

• innerOptimStart. Method for determining starting values for the inner optimization. Options are:

  • "zero" (default): use all zeros;

  • "last": use the result of the last inner optimization;

  • "last.best": use the result of the best inner optimization so far for each conditionally independent part of the approximation;

  • "constant": always use the same values, determined by innerOptimStartValues;

  • "random": randomly draw new starting values from the model (i.e., from the prior);

  • "model": use values for random effects stored in the model, which are determined from the first call.

  Note that "model" and "zero" are shorthand for "constant" with particular choices of innerOptimStartValues. Note that "last" and "last.best" require a choice for the very first values, which will come from innerOptimStartValues. The default is innerOptimStart="zero" and may change in the future.

• innerOptimStartValues. Values for some of innerOptimStart approaches. If a scalar is provided, that value is used for all elements of random effects for each conditionally independent set. If a vector is provided, it must be the length of *all* random effects. If these are named (by node names), the names will be used to split them correctly among each conditionally independent set of random effects. If they are not named, it is not always obvious what the order should be because it may depend on the conditionally independent sets of random effects. It should match the order of names returned as part of 'summaryLaplace'.

• innerOptimWarning. If FALSE (default), do not emit warnings from the inner optimization. Optimization methods may sometimes emit a warning such as for bad parameter values encountered during the optimization search. Often, a method can recover and still find the optimum. In the approximations here, sometimes the inner optimization search can fail entirely, yet the outer optimization see this as one failed parameter value and can recover. Hence, it is often desirable to silence warnings from the inner optimizer, and this is done by default. Set innerOptimWarning=TRUE to see all warnings.

• useInnerCache. If TRUE (default), use caching system for efficiency of inner optimizations. The caching system records one set of previous parameters and uses the corresponding results if those parameters are used again (e.g., in a gradient call). This should generally not be modified.

• outerOptimControl. A list of control parameters for maximizing the Laplace log-likelihood using nimOptim. See 'Details' of nimOptim for further information.

• computeMethod. There are three approaches available for internal details of how the approximations, and specifically derivatives involved in their calculation, are handled. These are labeled simply 1, 2, and 3, and the default is 2. The relative performance of the methods will depend on the specific model. Users wanting to explore efficiency can try switching from method 2 (default) to methods 1 or 3 and comparing performance. The first Laplace approximation with each method will be (much) slower than subsequent Laplace approximations. Further details are not provided at this time.

• gridType (relevant only nQuad>1). For multivariate AGHQ, a grid must be constructed based on the Hessian at the inner mode. Options include "cholesky" (default) and "spectral" (i.e., eigenvectors and eigenvalues) for the corresponding matrix decompositions on which the grid can be based.

# end itemize

Available methods

The object returned by buildLaplace is a nimbleFunction object with numerous methods (functions). Here these are described in three tiers of user relevance.

Most useful methods

The most relevant methods to a user are:

• calcLogLik(p, trans=FALSE). Calculate the approximation to the marginal log-likelihood function at parameter value p, which (if trans is FALSE) should match the order of paramNodes. For any non-scalar nodes in paramNodes, the order within the node is column-major. The order of names can be obtained from method getNodeNamesVec(TRUE). Return value is the scalar (approximate, marginal) log likelihood.

  If trans is TRUE, then p is the vector of parameters on the transformed scale, if any, described above. In this case, the parameters on the original scale (as the model was written) will be determined by calling the method pInverseTransform(p). Note that the length of the parameter vector on the transformed scale might not be the same as on the original scale (because some constraints of non-scalar parameters result in fewer free transformed parameters than original parameters).

• calcLaplace(p, trans). This is the same as calcLogLik but requires that the approximation be Laplace (i.e nQuad is 1), and results in an error otherwise.

• findMLE(pStart, method, hessian). Find the maximum likelihood estimates of parameters using the approximated marginal likelihood. This can be used if nQuad is 1 (Laplace case) or if nQuad>1 and all marginalizations involve only univariate random effects. Arguments include pStart: initial parameter values (defaults to parameter values currently in the model); method: (outer) optimization method to use in nimOptim (defaults to "BFGS", although some problems may benefit from other choices); and hessian: whether to calculate and return the Hessian matrix (defaults to TRUE, which is required for subsequent use of 'summary' method). Second derivatives in the Hessian are determined by finite differences of the gradients obtained by automatic differentiation (AD). Return value is a nimbleList of type optimResultNimbleList, similar to what is returned by R's optim. See help(nimOptim). Note that parameters ('par') are returned for the natural parameters, i.e. how they are defined in the model. But the 'hessian', if requested, is computed for the parameters as transformed for optimization if necessary. Hence one must be careful interpreting 'hessian' if any parameters have constraints, and the safest next step is to use the 'summary' method or 'summaryLaplace' function.

• summary(MLEoutput, originalScale, randomEffectsStdError, jointCovariance). Summarize the maximum likelihood estimation results, given object MLEoutput that was returned by findMLE. The summary can include a covariance matrix for the parameters, the random effects, or both), and these can be returned on the original parameter scale or on the (potentially) transformed scale(s) used in estimation. It is often preferred instead to call function (not method) 'summaryLaplace' because this will attach parameter and random effects names (i.e., node names) to the results.

  In more detail, summary accepts the following optional arguments:

  • originalScale. Logical. If TRUE, the function returns results on the original scale(s) of parameters and random effects; otherwise, it returns results on the transformed scale(s). If there are no constraints, the two scales are identical. Defaults to TRUE.

  • randomEffectsStdError. Logical. If TRUE, standard errors of random effects will be calculated. Defaults to FALSE.

  • jointCovariance. Logical. If TRUE, the joint variance-covariance matrix of the parameters and the random effects will be returned. If FALSE, the variance-covariance matrix of the parameters will be returned. Defaults to FALSE.

  The object returned by summary is an AGHQuad_summary nimbleList with elements:

  • params. A nimbleList that contains estimates and standard errors of parameters (on the original or transformed scale, as chosen by originalScale).

  • randomEffects. A nimbleList that contains estimates of random effects and, if requested (randomEffectsStdError=TRUE) their standard errors, on original or transformed scale. Standard errors are calculated following the generalized delta method of Kass and Steffey (1989).

  • vcov. If requested (i.e. jointCovariance=TRUE), the joint variance-covariance matrix of the parameters and random effects, on original or transformed scale. If jointCovariance=FALSE, the covariance matrix of the parameters, on original or transformed scale.

  • scale. "original" or "transformed", the scale on which results were requested.

Methods for more advanced uses

Additional methods to access or control more details of the Laplace approximation include:

• updateSettings. This provides a single function through which many of the settings described above (mostly for the control list) can be later changed. Options that can be changed include: innerOptimMethod, innerOptimStart, innerOptimStartValues, useInnerCache, nQuad, gridType, innerOptimControl, outerOptimControl, and computeMethod. For innerOptimStart, method "zero" cannot be specified but can be achieved by choosing method "constant" with innerOptimStartValues=0. Only provided options will be modified. The exceptions are innerOptimControl, outerOptimControl, which are replaced only replace_innerOptimControl=TRUE or replace_outerOptimControl=TRUE, respectively.

• getNodeNamesVec(returnParams). Return a vector (>1) of names of parameters/random effects nodes, according to returnParams = TRUE/FALSE. Use this if there is more than one node.

• getNodeNameSingle(returnParams). Return the name of a single parameter/random effect node, according to returnParams = TRUE/FALSE. Use this if there is only one node.

• checkInnerConvergence(message). Checks whether all internal optimizers converged. Returns a zero if everything converged and one otherwise. If message = TRUE, it will print more details about convergence for each conditionally independent set.

• gr_logLik(p, trans). Gradient of the (approximated) marginal log-likelihood at parameter value p. Argument trans is similar to that in calcLaplace. If there are multiple parameters, the vector p is given in the order of parameter names returned by getNodeNamesVec(returnParams=TRUE).

• gr_Laplace(p, trans). This is the same as gr_logLik.

• otherLogLik(p). Calculate the calcNodesOther nodes, which returns the log-likelihood of the parts of the model that are not included in the Laplace or AGHQ approximation.

• gr_otherLogLik(p). Gradient (vector of derivatives with respect to each parameter) of otherLogLik(p). Results should match gr_otherLogLik_internal(p) but may be more efficient after the first call.

Internal or development methods

Some methods are included for calculating the (approximate) marginal log posterior density by including the prior distribution of the parameters. This is useful for finding the maximum a posteriori probability (MAP) estimate. Currently these are provided for point calculations without estimation methods.

• calcPrior_p(p). Log density of prior distribution.

• calcPrior_pTransformed(pTransform). Log density of prior distribution on transformed scale, includes the Jacobian.

• calcPostLogDens(p). Marginal log posterior density in terms of the parameter p.

• calcPostLogDens_pTransformed (pTransform). Marginal log posterior density in terms of the transformed parameter, which includes the Jacobian transformation.

• gr_postLogDens_pTransformed(pTransform). Graident of marginal log posterior density on the transformed scale. Other available options that are used in the derivative for more flexible include logDetJacobian(pTransform) and gr_logDeJacobian(pTransform), as well as gr_prior(p).

Finally, methods that are primarily for internal use by other methods include:

• gr_logLik_pTransformed. Gradient of the Laplace approximation (calcLogLik_pTransformed(pTransform)) at transformed (unconstrained) parameter value pTransform.

• pInverseTransform(pTransform). Back-transform the transformed parameter value pTransform to original scale.

• derivs_pInverseTransform(pTransform, order). Derivatives of the back-transformation (i.e. inverse of parameter transformation) with respect to transformed parameters at pTransform. Derivative order is given by order (any of 0, 1, and/or 2).

• reInverseTransform(reTrans). Back-transform the transformed random effects value reTrans to original scale.

• derivs_reInverseTransform(reTrans, order). Derivatives of the back-transformation (i.e. inverse of random effects transformation) with respect to transformed random effects at reTrans. Derivative order is given by order (any of 0, 1, and/or 2).

• optimRandomEffects(pTransform). Calculate the optimized random effects given transformed parameter value pTransform. The optimized random effects are the mode of the conditional distribution of random effects given data at parameters pTransform, i.e. the calculation of calcNodes.

• inverse_negHess(p, reTransform). Calculate the inverse of the negative Hessian matrix of the joint (parameters and random effects) log-likelihood with respect to transformed random effects, evaluated at parameter value p and transformed random effects reTransform.

• hess_logLik_wrt_p_wrt_re(p, reTransform). Calculate the Hessian matrix of the joint log-likelihood with respect to parameters and transformed random effects, evaluated at parameter value p and transformed random effects reTransform.

• one_time_fixes(). Users never need to run this. Is is called when necessary internally to fix dimensionality issues if there is only one parameter in the model.

• calcLogLik_pTransformed(pTransform). Laplace approximation at transformed (unconstrained) parameter value pTransform. To make maximizing the Laplace likelihood unconstrained, an automated transformation via parameterTransform is performed on any parameters with constraints indicated by their priors (even though the prior probabilities are not used).

• gr_otherLogLik_internal(p). Gradient (vector of derivatives with respect to each parameter) of otherLogLik(p). This is obtained using automatic differentiation (AD) with single-taping. First call will always be slower than later calls.

• cache_outer_logLik(logLikVal). Save the marginal log likelihood value to the inner Laplace mariginlization functions to track the outer maximum internally.

• reset_outer_inner_logLik(). Reset the internal saved maximum marginal log likelihood.

• get_inner_cholesky(atOuterMode = integer(0, default = 0)). Returns the cholesky of the negative Hessian with respect to the random effects. If atOuterMode = 1 then returns the value at the overall best marginal likelihood value, otherwise atOuterMode = 0 returns the last.

• get_inner_mode(atOuterMode = integer(0, default = 0)). Returns the mode of the random effects for either the last call to the innner quadrature functions (atOuterMode = 0 ), or the last best value for the marginal log likelihood, atOuterMode = 1.

Author(s)

Wei Zhang, Perry de Valpine, Paul van Dam-Bates

References

Kass, R. and Steffey, D. (1989). Approximate Bayesian inference in conditionally independent hierarchical models (parametric empirical Bayes models). Journal of the American Statistical Association, 84(407), 717-726.

Liu, Q. and Pierce, D. A. (1994). A Note on Gauss-Hermite Quadrature. Biometrika, 81(3) 624-629.

Jackel, P. (2005). A note on multivariate Gauss-Hermite quadrature. London: ABN-Amro. Re.

Skaug, H. and Fournier, D. (2006). Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models. Computational Statistics & Data Analysis, 56, 699-709.

Examples

pumpCode <- nimbleCode({ 
  for (i in 1:N){
    theta[i] ~ dgamma(alpha, beta)
    lambda[i] <- theta[i] * t[i]
    x[i] ~ dpois(lambda[i])
  }
  alpha ~ dexp(1.0)
  beta ~ dgamma(0.1, 1.0)
})
pumpConsts <- list(N = 10, t = c(94.3, 15.7, 62.9, 126, 5.24, 31.4, 1.05, 1.05, 2.1, 10.5))
pumpData <- list(x = c(5, 1, 5, 14, 3, 19, 1, 1, 4, 22))
pumpInits <- list(alpha = 0.1, beta = 0.1, theta = rep(0.1, pumpConsts$N))
pump <- nimbleModel(code = pumpCode, name = "pump", constants = pumpConsts, 
                    data = pumpData, inits = pumpInits, buildDerivs = TRUE)
                    
# Build Laplace approximation
pumpLaplace <- buildLaplace(pump)

## Not run: 
# Compile the model
Cpump <- compileNimble(pump)
CpumpLaplace <- compileNimble(pumpLaplace, project = pump)
# Calculate MLEs of parameters
MLEres <- CpumpLaplace$findMLE()
# Calculate estimates and standard errors for parameters and random effects on original scale
allres <- CpumpLaplace$summary(MLEres, randomEffectsStdError = TRUE)

# Change the settings and also illustrate runLaplace
CpumpLaplace$updateSettings(innerOptimMethod = "nlminb", outerOptimMethod = "nlminb")
newres <- runLaplace(CpumpLaplace)

# Illustrate use of the component log likelihood and gradient functions to
# run an optimizer manually from R.
# Use nlminb to find MLEs
MLEres.manual <- nlminb(c(0.1, 0.1),
                        function(x) -CpumpLaplace$calcLogLik(x),
                        function(x) -CpumpLaplace$gr_Laplace(x))

## End(Not run)

nimble documentation built on June 22, 2024, 9:49 a.m.