Importance: Variable Importance


The Importance function considers variable importance (or predictor importance) to be the effect that the variable has on replicates y^rep (or Y^rep) when the variable is removed from the model by setting it equal to zero. Here, variable importance is considered in terms of the comparison of posterior predictive checks. This may be considered to be a form of sensitivity analysis, and can be useful for model revision, variable selection, and model interpretation.

Currently, this function only tests the variable importance of design matrix X.


Importance(object, Model, Data, Categorical=FALSE, Discrep, d=0, CPUs=1,



An object of class demonoid, iterquad, laplace, pmc, or vb is required.


The model specification function is required.


A data set in a list is required. The dependent variable is required to be named either y or Y. The Importance function will sequentially remove each column vector in X, so X is required to be in data set Data.


Logical. If TRUE, then y and yhat are considered to be categorical (such as y=0 or y=1), rather than continuous. This defaults to FALSE.


This optional argument allows a discrepancy statistic to be included. For more information on discrepancy statistics, see summary.demonoid.ppc.


This is an optional integer to be used with the Discrep argument above, and it defaults to d=0. For more information on discrepancy, see summary.demonoid.ppc.


This argument accepts an integer that specifies the number of central processing units (CPUs) of the multicore computer or computer cluster. This argument defaults to CPUs=1, in which parallel processing does not occur.


This argument specifies the type of parallel processing to perform, accepting either Type="PSOCK" or Type="MPI".


Variable importance is defined here as the impact of each variable (predictor, or column vector) in design matrix X on y^rep (or Y^rep), when the variable is removed.

First, the full model is predicted with the predict.demonoid, predict.iterquad, predict.laplace, predict.pmc, or predict.vb function, and summarized with the summary.demonoid.ppc, summary.iterquad.ppc, summary.laplace.ppc, summary.pmc.ppc, or summary.vb.ppc function, respectively. The results are stored in the first row of the output. Each successive row in the output corresponds to the application of predict and summary functions, but with each variable in design matrix X being set to zero and effectively removed. The results show the impact of sequentially removing each predictor.

The criterion for variable importance may differ from model to model. As a default, BPIC is recommended. The Bayesian Predictive Information Criterion (BPIC) was introduced by Ando (2007). BPIC is a variation of the Deviance Information Criterion (DIC) that has been modified for predictive distributions. For more information on DIC (Spiegelhalter et al., 2002), see the accompanying vignette entitled "Bayesian Inference". BPIC = Dbar + 2pD.

With BPIC, variable importance has a positive relationship, such that larger values indicate a more important variable, because removing that variable resulted in a worse fit to the data. The best model has the lowest BPIC.

In a model in which the dependent variable is not categorical, it is also recommended to consider the L-criterion (Laud and Ibrahim, 1995), provided that sample size is small enough that it does not result in Inf. For more information on the L-criterion, see the accompanying vignette entitled "Bayesian Inference".

With the L-criterion, variable importance has a positive relationship, such that larger values indicate a more important variable, because removing that variable resulted in a worse fit to the data. Ibrahim (1995) recommended considering the model with the lowest L-criterion, say as L[1], and the model with the closest L-criterion, say as L[2], and creating a comparison score as phi = (L[2]-L[1])/S[L], where S.L is from the L[1] model. If the comparison score, phi is less than 2, then L[2] is within 2 standard deviations of L[1], and is the recommended cut-off for model choice.

The Importance function may suggest that a model fits the data better with a variable removed. In which case, the user may choose to leave the variable in the model (perhaps the model is misspecified without the variable), investigate and possibly re-specify the relationship between the independent and dependent variable(s), or remove the variable and update the model again.

In contrast to variable importance, the PosteriorChecks function calculates parameter importance, which is the probability that each parameter's marginal posterior distribution is greater than zero, where an important parameter does not include zero in its probability interval (see p.interval). Parameter importance and variable importance may disagree, and both should be studied.

The Importance function tends to indicate that a model fits the data better when variables are removed that have parameters with marginal posterior distributions that include 0 in the 95% probability interval (variables associated with lower parameter importance).

Often, in complicated models, it is difficult to assess variable importance by examining the marginal posterior distribution of the associated parameter(s). Consider polynomial regression, in which each variable may have multiple parameters.

The information provided by the Importance function may be used for model revision, or reporting the relative importance of variables.

The plot.importance function is available to plot the output of the Importance function according to BPIC, predictive concordance (Gelfand, 1996), the selected discrepancy statistic (Gelman et al., 1996), or the L-criterion.

Parallel processing may be performed when the user specifies CPUs to be greater than one, implying that the specified number of CPUs exists and is available. Parallelization may be performed on a multicore computer or a computer cluster. Either a Simple Network of Workstations (SNOW) or Message Passing Interface is used (MPI). With small data sets and few samples, parallel processing may be slower, due to computer network communication. With larger data sets and more samples, the user should experience a faster run-time.


Importance returns an object of class importance, which is a matrix with a number of rows equal to the number of columns in design matrix X + 1 (including the full model), and 4 columns, which are BPIC, Concordance (or Mean.Lift if categorical), Discrep, and L-criterion. Each row represents a model with a predictor in X removed (except for the first row, which is the full model), and the resulting posterior predictive checks. For non-categorical dependent variables, an attribute is returned with the object, and the attribute is a vector of S.L, the calibration number of the L-criterion.


Statisticat, LLC.


Ando, T. (2007). "Bayesian Predictive Information Criterion for the Evaluation of Hierarchical Bayesian and Empirical Bayes Models". Biometrika, 94(2), p. 443–458.

Gelfand, A. (1996). "Model Determination Using Sampling Based Methods". In Gilks, W., Richardson, S., Spiegehalter, D., Chapter 9 in Markov Chain Monte Carlo in Practice. Chapman and Hall: Boca Raton, FL.

Laud, P.W. and Ibrahim, J.G. (1995). "Predictive Model Selection". Journal of the Royal Statistical Society, B 57, p. 247–262.

Spiegelhalter, D.J., Best, N.G., Carlin, B.P., and van der Linde, A. (2002). "Bayesian Measures of Model Complexity and Fit (with Discussion)". Journal of the Royal Statistical Society, B 64, p. 583–639.

See Also

is.importance, IterativeQuadrature, LaplaceApproximation, LaplacesDemon, PMC, plot.importance, PosteriorChecks, p.interval, predict.demonoid, predict.iterquad, predict.laplace, predict.pmc, predict.vb, summary.demonoid.ppc, summary.iterquad.ppc, summary.laplace.ppc, summary.pmc.ppc, summary.vb.ppc, and VariationalBayes.


#First, update the model with the LaplacesDemon function, such as
#the example with linear regression, creating an object called Fit.
#Importance(Fit, Model, MyData, Discrep="Chi-Square", CPUs=1)

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