refine.SLik: Refine estimates iteratively.

View source: R/SLik.R

refineR Documentation

Refine estimates iteratively.


This is a generic function with currently methods for SLik, SLik_j and SLikp objects (as produced by MSL). Depending on the value of its newsimuls argument, and on whether the function used to generate empirical distributions can be called by R, it (1) defines new parameters points and/or (2) infers their summary likelihood or tail probabilities for each parameter point independently, adds the inferred values results as input for refined inference of likelihood or P-value response surface, and provides new point estimates and confidence intervals.


## S3 method for class 'SLik'
refine(object, method=NULL, ...)

## Default S3 method:
refine(object, surfaceData, Simulate =
            attr(surfaceData,"Simulate"), maxit = 1, n = NULL, 
            useEI = list(max=TRUE,profileCI=TRUE,rawCI=FALSE), 
            newsimuls = NULL, trypoints=NULL, useCI = TRUE, level = 0.95, 
            verbose = list(most=interactive(),final=NULL,movie=FALSE,proj=FALSE),
            precision = Infusion.getOption("precision"),
            nb_cores = NULL, packages=attr(object$logLs,"packages"), 
            env=attr(object$logLs,"env"), method, 
            eval_RMSEs=TRUE, update_projectors = FALSE,



an SLik or SLik_j object


A data.frame with attributes, usually taken from the object and thus not specified by user, usable as input for infer_surface.


Character string: name of the function used to simulate samples. The only meaningful non-default value is NULL, in which case refine may return (if newsimuls is also NULL) a data frame of parameter points on which to run a simulation function.


Maximum number of iterative refinements (see also precision argument)


A number of parameter points (excluding replicates and confidence interval points), whose likelihood should be computed (see n argument of sample_volume)


Cf this argument in rparam


For the SLik_j method, a matrix or data frame, with the same parameters and summary statistics as the data of the original infer_SLik_joint call.

For other methods, a list of simulation of distributions of summary statistics, in the same format as for link{add_simulation}. If no such list is provided (i.e., if newsimuls remains NULL), the attr(object$logLs,"Simulate") function is used (it is inherited from the Simulate argument of add_simulation through the initial sequence of calls of functions add_simulation, infer_logLs or infer_tailp, and infer_surface). If no such function is available, then this function returns parameters for which new distribution should be provided by the user.


A data frame of parameters on which the simulation function attr(object$logLs,"Simulate") should be called to extend the reference table. Only for programming by expert users, because poorly thought input trypoints could severely affect the inferences.


whether to include parameter points near the inferred confidence interval points in the set of points which likelihood should be computed


Intended coverage of confidence intervals


A list as shown by the default, or simply a vector of booleans. verbose$most controls whether to display information about progress and results, except plots; $final controls whether to plot() the final object to show the final likelihood surface. Default is to plot it only in an interactive session and if fewer than three parameters are estimated; $movie controls whether to plot() the updated object in each iteration; verbose$proj controls the verbose argument of project.character. If verbose is a vector of booleans, they are matched to as many elements from "most","movie","final","proj", in that order.


Requested local precision of surface estimation, in terms of prediction standard errors (RMSEs) of both the maximum summary log-likelihood and the likelihood ratio at any CI bound available. Iterations will stop when either maxit is reached, or if the RMSEs have been computed for the object (see eval_RMSEs argument) and this precision is reached for the RMSEs. A given precision on the CI bounds themselves might seem more interesting, but is not well specified by a single precision parameter if the parameters are on widely different scales.


Shortcut for cluster_args$spec for sample simulation.


A list of arguments for makeCluster, in addition to makeCluster's spec argument which is in most cases best specified by the nb_cores argument. Cluster arguments affect parallel computations in the functions to which they may be passed, i.e. for sample simulations by add_reftable, and for RMSE computations by MSL (alternatively add_simulation, infer_logLs and MSL are called in the primitive workflow). The default behaviour is determined by the previous nb_cores argument: nb_cores gives the number of parallel processes for sample simulation, with additional makeCluster arguments taken from cluster_args, but RMSE computations are performed serially. See Details for how to change this.


NULL or a list with possible elements add_simulation and logL_method, passed respectively as the packages arguments of add_simulation and infer_logLs, wherein they are the additional packages to be loaded on child processes. The default value keeps pre-refine values over iterations.


An environment, passed as the env argument to add_simulation. The default value keeps the pre-refine value over iterations.


(A vector of) suggested method(s) for estimation of smoothing parameters (see method argument of infer_surface). The ith element of the vector is used in the ith iteration, if available; otherwise the last element is used. This argument is not always heeded, in that REML may be used if the suggested method is GCV but it appears to perform poorly. The default for SLikp, SLik_j, and SLikp objects are "REML", "mixmodCluster", and "PQL", respectively.


passed to MSL


Boolean; whether to update the projectors at each iteration.


NULL or integer, passed to add_simulation. The default code uses an internal function, .update_seed, to update it from a previous iteration.


further arguments passed to or from other methods. refine passes these arguments to the plot method suitable for the object.


New parameter points are sampled as follows: the algorithm aims to sample uniformly the space of parameters contained in the confidence regions defined by the level argument, and to surround it by a region sampled proportionally to likelihood. In each iteration the algorithm aims to add as many points (say n) as computed in the first iteration, so that after k iterations of refine, there are n * (k+1) points in the simulation table. However, when not enough points satisfy certain criteria, only n/5 points may be added in an iteration, this being compensated in further iterations. For example, if n=600, the table may include only 720 points after the first refine, but 1800 after the second.

independent control of parallel computation for sample simulation and RMSE computations is possible:
control_args=list(RMSE=list(spec=<number of 'children'>)) can be used to force parallel computation of RMSEs;
control_args=list(spec=<.>, <other makeCluster arguments>)) would instead apply the same arguments to both reference table and RMSE computation, overcoming the default effect of nb_cores; finally
control_args=list(reftable=list(<makeCluster arguments>),RMSEs=list(<makeCluster arguments>)) allows full independent control of parallelisation for the two computations.


refine returns an updated SLik or SLik_j object.


See workflow examples in (by order of decreasing relevance) example_reftable, example_raw_proj and example_raw.


  ## see Note for links to examples.

Infusion documentation built on Sept. 29, 2022, 1:05 a.m.