Introduction to BFpack

Example analyses

Normal linear models (t-testing, linear regression, (M)AN(C)OVA)

For an object of class t_test (t testing), lm (linear regression), lml (multivariate linear regression), aov (AN(C)OVA), manova (MAN(C)OVA), the evidence and posterior probabilities are computed using the fractional Bayes factor (FBF) methodology O’Hagan (1995). Manual prior specification is avoided by using a minimal fraction of the data to construct a default (fractional) prior while the remaining fraction is used for hypothesis testing. For a simple t-test, there are 2 unknown parameters (the normal mean and the variance), and therefore at least 2 observations are required to identify the model, yielding a minimal fraction of 2/n, where n is the sample size. In this case, the default prior follows a Cauchy distribution. In the case of grouping variables, i.e., variables of class factor in R, it is recommended that the minimal fractions depend on the respective group sizes De Santis and Spezzaferri (2001). For instance, in case of an ANOVA with 2 groups (equivalent to a 2-sample t test with equal variances), there are 3 unknown parameters (the two normal means and the within-groups variance), and thus, at least 3 observations are required to identify the model. Therefore, the minimal fraction for the observations in group 1 and 2 are equal to 1.5/n_1 and 1.5/n_2, where n_1 and n_2 are the sample sizes in group 1 and 2, respectively. In the resulting BF object, the element fraction_groupID_observations contains the group IDs, which are based on all combinations of the levels of the factors in the linear model. If no grouping variables (factor) are included as predictors, the same minimal fraction is used for all observations. Moreover, besides regular fractional Bayes factors (which is the current default), adjusted fractional Bayes factors (AFBF) can also be computed by setting the argument BF.type = AFBF. In the AFBF, the default prior is shifted to the null value (Mulder, 2014; Mulder and Olsson-Collentine, 2019). The AFBF was specifically designed for testing hypotheses with only one-sided or order constraints. The general methodology for the multivariate linear model is discussed in Mulder et al. (2021) and Mulder and Gu (2021).

Univariate t testing

First a classical one sample t test is executed for the test value $\mu = 5$ on the therapeutic data

ttest1 <- bain::t_test(therapeutic, alternative = "greater", mu = 5)

The t_test function is part of the bain package. The function is equivalent to the standard t.test function with the addition that the returned object contains additional output than the standard t.test function.

To see which parameters can be tested on this object run

get_estimates(ttest1)

which shows that the only parameter that can be tested is the population mean which has name mu.

To perform an exploratory Bayesian t test of whether the population mean is equal to, smaller than, or larger than the null value (which is 5 here, as specified when defining the ttest1 object), one needs to run BF function on the object.

library(BFpack)
BF1 <- BF(ttest1)

This executes an exploratory (‘exhaustive’) test around the null value: H1: mu = 5 versus H2: mu < 5 versus H3: mu > 5 assuming equal prior probabilities for H1, H2, and H3 of 1/3. The output presents the posterior probabilities for the three hypotheses.

The same test would be executed when the same hypotheses are explicitly specified using the hypothesis argument.

hypothesis <- "mu = 5; mu < 5; mu > 5"
BF(ttest1, hypothesis = hypothesis)

In the above test the complement hypothesis is excluded automatically as the formualted hypothesis under the hypothesis argument cover the complete parameter space. Furthermore, when testing hypotheses via the hypothesis argument, the output also presents an Evidence matrix containing the Bayes factors between the hypotheses formulated in the hypothesis argument.

A standard two-sided test around the null value mu is executed by setting the hypothesis argument equal to the precise null hypothesis so that the complement hypothesis (which is included by default) corresponds to the hypothesis that assumes that the population mean is anything but the null value

hypothesis <- "mu = 5"
BF(ttest1, hypothesis = hypothesis)

The argument prior.hyp can be used to specify different prior probabilities for the hypotheses. For example, when the left one-tailed hypothesis is not possible based on prior considerations (e.g., see Mulder et al. (2021, Section 4.1)) while the precise (null) hypothesis and the right one-tailed hypothesis are equally likely, the argument prior.hyp should be a vector specifying the prior probabilities of the respective hypotheses

BF(ttest1, hypothesis = "mu = 5; mu < 5; mu > 5", prior.hyp = c(.5,0,.5))

For more information about the methodology, we refer the interested reader to Mulder et al. (2021) and Mulder and Gu (2021).

Multivariate t testing

Bayesian multivariate t tests can be executed by first fitting a multivariate (regression) model using the lm function, and subsequently, the means of the dependent variables (or other coefficients) in the model can be tested using the BF() function. Users have to be aware however that means are modeled using intercepts which are named (Intercept) by default by lm while the hypothesis argument in BF() does not allow effect names that include brackets (i.e., ( or )). To circumvent this, one can create a vector of 1s, with name (say) ones, to replace the intercept. For example, let us consider a multivariate normal model for the dependent variables Superficial, Middle, and Deep in the fmri data set:

fmri1 <- cbind(fmri,ones=1)
mlm1 <- lm(cbind(Superficial,Middle,Deep) ~ -1 + ones, data = fmri1)

Next, we can (for instance) test whether all means equal 0 (H1), whether all means are positive (H2), or none of these two hypotheses (complement):

BFmlm1 <- BF(mlm1, hypothesis="ones_on_Superficial=ones_on_Middle=ones_on_Deep=0;
                               (ones_on_Superficial,ones_on_Middle,ones_on_Deep)>0")

Analysis of variance

First an analysis of variance (ANOVA) model is fitted using the aov fuction in R.

aov1 <- aov(price ~ anchor * motivation, data = tvprices)

Next a Bayesian test can be performed on the fitted object. By default exploratory tests are executed of whether the individual main and interaction effects are zero or not (corresponding to the full model) (see Mulder et al. (2021, Section 4.2))

BF(aov1)

One can also test for specific equal/order hypotheses based on scientific expectations such as whether anchorrounded is positive, motivationlow is negative, and the interaction effect anchorrounded:motivationlow is negative (see Mulder et al. (2021, Section 4.2)) versus null hypothesis versus the complement hypothesis (which assumes that the constraints of neither two hypotheses hold). This test can be executed as follows:

constraints2 <- "anchorrounded > 0 & motivationlow < 0 &
  anchorrounded:motivationlow < 0; anchorrounded = 0 &
  motivationlow = 0 & anchorrounded:motivationlow = 0"
set.seed(1234)
BF(aov1, hypothesis = constraints2)

Univariate and multivariate multiple regression

For a univariate regression model, by default an exhaustive test is executed of whether an effect is zero, negative, or postive.

lm1 <- lm(Superficial ~ Face + Vehicle, data = fmri)
BF1 <- BF(lm1)
print(BF1)

Hypotheses can be tested with equality and/or order constraints on the effects of interest. If prefered the complement hypothesis can be omitted using the complement argument

BF2 <- BF(lm1, hypothesis = "Vehicle > 0 & Face < 0; Vehicle = Face = 0",
          complement = FALSE)
print(BF2)

In a multivariate regression model hypotheses can be tested on the effects on the same dependent variable, and on effects across different dependent variables. The name of an effect is constructed as the name of the predictor variable and the dependent variable separated by _on_. Testing hypotheses with both constraints within a dependent variable and across dependent variables makes use of a Monte Carlo estimate which may take a few seconds.

lm2 <- lm(cbind(Superficial, Middle, Deep) ~ Face + Vehicle,
              data = fmri)
constraint2 <- "Face_on_Deep = Face_on_Superficial = Face_on_Middle < 0 <
     Vehicle_on_Deep = Vehicle_on_Superficial = Vehicle_on_Middle;
     Face_on_Deep < Face_on_Superficial = Face_on_Middle < 0 < Vehicle_on_Deep =
     Vehicle_on_Superficial = Vehicle_on_Middle"
set.seed(123)
BF3 <- BF(lm2, hypothesis = constraint2)
summary(BF3)

For more information about the methodology, we refer the interested reader to Mulder and Olsson-Collentine (2019) and Mulder and Gu (2021)

Testing independent group variances

First a classical significance test is executed using the bartlett_test function, which is part of the BFpack package. The function is equivalent to the standard bartlett.test function with the addition that the returned object contains additional output needed for the test using the BF function.

bartlett1 <- bartlett_test(x = attention$accuracy, g = attention$group)

On an object of this class, by default BF executes an exploratory test of homogeneity (equality) of variances against an unconstrained (full) model

BF(bartlett1)

The group variances have names ADHD, Controls, and TS. This can be retrieved by running

get_estimates(bartlett1)

Let’s say we want to test whether a hypothesis (H1) which assumes that group variances of groups Controls and TS are equal and smaller than the group variance of the ADHD group, a hypothesis (H2) which assumes that the group variances of ADHD and TS are equal and larger than the Controls group, a hypothesis (H3) which assumes all group variances are equal, and a complement hypothesis (H4). To do this we run the following:

hypothesis <- "Controls = TS < ADHD; Controls < TS = ADHD; Controls = TS = ADHD"
set.seed(358)
BF_var <- BF(bartlett1, hypothesis)

A comprehensive output of this analysis can be obtained by running:

summary(BF_var)

which presents the results of an exploratory analysis and the results of a confirmatory analysis (based on the hypotheses formulated under the hypothesis argument). The exploratory analysis tests a hypothesis which assumes that the variances are equal across groups (homogeneity of variances) versus an alternative unrestricted hypothesis. The output shows that the posterior probabilities of these two hypotheses are approximately 0.803 and 0.197 (assuming equal priori probabilities). Note that the p value in the classical Bartlett test for these data equals 0.1638 which implies that the hypothesis of homogeneity of variances cannot be rejected using common significance levels, such as 0.05 or 0.01. Note however that this p value cannot be used as a measure for the evidence in the data in favor of homogeneity of group variances. This can be done using the proposed Bayes factor test which shows that the probability that the variances are equal is approximately 0.803. Also note that the exploratory test could equivalently tested via the hypothesis argument by running BF(bartlett1, "Controls = TS = ADHD").

The confirmatory test shows that H1 receives strongest support from the data, but H2 and H3 are viable competitors. It appears that even the complement H3 cannot be ruled out entirely given a posterior prob- ability of 0.058. To conclude, the results indicate that TS population are as heterogeneous in their attentional performances as the healthy control population in this specific task, but further research would be required to obtain more conclusive evidence.

For more information about the methodology, we refer the interested reader to Boing-Messing et al. (2017)

Testing correlations

BFpack can be used for testing overlapping, nonoverlapping, and independent correlations, between variables of different measurement levels (continuous, dichotomous, ordinal), possibly while correcting for certain covariates. Joint uniform priors are specified for the correlations in the full correlation matrices. This implies that all combinations of correlation values that result in positive definite correlation matrices are equally likely a priori.

By default BF performs exhaustive tests of whether the separate correlations are zero, negative, or positive.

set.seed(123)
cor1 <- cor_test(memory[,1:3])
BF1 <- BF(cor1)
print(BF1)

The names of the correlations is constructed using the names of the variables separated by _with_:

get_estimates(cor1)

Specific hypotheses based on prior/theoretical considerations can be tested using the hypothesis argument. As an example we show here how to test whether all correlations are equal and positive versus its complement.

BF2 <- BF(cor1, hypothesis = "Del_with_Im = Wmn_with_Im = Wmn_with_Del > 0")
print(BF2)

We can also test equality and order constraints on independent correlations across different groups. As the seventh column of the memory object is a group indicator, let us first create different objects for the two different groups, and perform Bayesian estimation on the correlation matrices of the two different groups

memoryHC <- subset(memory,Group=="HC")[,-(4:7)]
memorySZ <- subset(memory,Group=="SZ")[,-(4:7)]
set.seed(123)
cor1 <- cor_test(memoryHC,memorySZ)

In this case with multiple groups by default exploratory tests are executed of whether the correlations are zero, negative, or positive for each separate group (e.g., correlations in group gr1 are denoted by _in_gr1 at the end of the name)

get_estimates(cor1)

Next we test the one-sided hypothesis that the respective correlations in the first group (g1) are larger than the correlations in the second group (g2) via

set.seed(123)
BF6_cor <- BF(cor1, hypothesis =
  "Del_with_Im_in_g1 > Del_with_Im_in_g2 &
  Del_with_Wmn_in_g1 > Del_with_Wmn_in_g2 &
  Im_with_Wmn_in_g1 > Im_with_Wmn_in_g2")

By running print(BF6_cor), the output shows that the one-sided hypothesis received a posterior probability of 0.991 and the alternative received a posterior probability of .009 (assuming equal prior probabilities).

For more information about the methodology, we refer the interested reader to Mulder (2016) and Mulder and Gelissen (2019)

Meta-analyses

BFpack supports testing the mean effect in a fixed effects meta-analysis model, a random effects model, and a (hybrid) marginalized random effects meta-analysis (marema) model. Depending on the nature of the parameter, different default priors are readily implemented. For testing a standardized effect (set BF.type = "stand.effect"), a normal prior with mean 0 and standard deviation 0.5 is specified which implies that, on average, medium (0.5) standardized effect sizes are expected (if the null is false). For testing a log odds (set BF.type = "log.odds"), a Student prior with mean 0, scale 2.36, and 13.1 degrees of freedom is used, which corresponds to success probabilities following uniform priors in the interval (0,1). For testing correlations (set BF.type = "correlation"), a logistic prior with scale 0.5 is used for the Fisher transformed correlation, which corresponds to a uniform prior for the correlation in the interval (-1,1). To specify a unit-information prior (set BF.type = "unit.info"), the total sample size sum(ni) is used to construct a minimally informative normal unit-information prior using the sample sizes ni of the individual studies from the rma.uni object. For a manually specified prior, the argument BF.type needs to be an object of class prior from the metaBMA package. Note that in order for the Bayes factors to be meaningful, the prior should reflect a plausible range of values given the available context. Arbitrarily vague priors (which can be used for estimation) should not be used for Bayes factor testing.

For illustrative purposes, we use hypothetical simulated data for standardized effect sizes:

set.seed(123)
tau2 <- 0.05
vi <- runif(10, min = 0.01, max = 0.2)
yi <- rnorm(10, mean = 0, sd = sqrt(vi+tau2))

where tau2 denotes the true between-study heterogeneity, vi is a vector containing the squared standard errors of 10 studies, and yi is a vector containing the estimated effects sizes in the 10 studies. To test the overall effect size and the between-study heterogeneity using BFpack, an initial meta-analysis needs to be executed using the metafor package. A random effects meta-analysis model is considered:

res1 <- metafor::rma(yi = yi, vi = vi)

Subsequently, the output is plugged into the BF function using the default prior for a standardized effect (normal prior with mean of 0 and a sd of 0.5). For a random effects model (res1$method does not equal "EE" or "FE"), BFpack computes Bayes factors and posterior probabilities of a zero, negative, and positive mean effect:

BFmeta1 <- BF(res1, BF.type = "stand.effect")

The summary output gives the posterior probabilities for a zero, negative, and positive mean effect size mu assuming equal prior probabilities:

summary(BFmeta1)

The results indicate support for a zero effect with a posterior probability of approximately 0.75 under both the random effects model and the marema model.

Under the random effects model and the marema model, the posterior mean and median, the lower and upper bound of the 95% Bayesian credible intervals, and the posterior probability that the parameters are positive can be obtained by calling:

BFmeta1$estimates

Note that under the marema model, the between-study heterogeneity tau2 can attain negative values (indicating support for a fixed effects model). Therefore, the posterior probability that tau2 is positive under the marema model gives an indication of the posterior support of a random effects model given the available data. In this case, the posterior probability that tau2 is positive is about 0.95, which indicates clear support for a random effects model.

In order to test the mean effect under a fixed effects meta-analysis model, this needs to be specified when fitting the initial meta-analytic model:

res2 <- metafor::rma(yi = yi, vi = vi, method = "EE")
BFmeta2 <- BF(res2, BF.type = "stand.effect")

For more information about the methodology, we refer the interested reader to Van Aert and Mulder (2021) and Mulder and van Aert (in prep.).

Exponential random graph models

…

Network autocorrelation models

…

Hypothesis testing using normal approximations

Running `BF` on a named vector

The input for the BF function can also be a named vector containing the estimates of the parameters of interest. In this case the error covariance matrix of the estimates is also needed via the Sigma argument, as well as the sample size that was used for obtaining the estimates via the n argument. Bayes factors are then computed using Gaussian approximations of the likelihood (and posterior), similar as in classical Wald test. Furthermore, a minimally informative default prior is automatically constructed using a minimal fraction of the data, similar as the adjusted fractional Bayes factor. The minimal fraction is constructed by dividing the number of parameters that are tested in the hypothesis by the total sample size n. Moreover, the prior is shifted to the null value, resulting in an approximate adjusted fractional Bayes factor. This methodology is used by default in case the input object x has class glm, coxph, survreg, polr, and zeroinfl. Below we first show how the function can be used for testing coefficients in a logistic regression model, and then we show how the function can be used for testing coefficients in a poisson regression model and as well an equivalent analysis when x is a named vector using the MLEs, error covariance matrix Sigma, and sample size n.

Logistic regression

An example hypothesis test is considered under a logistic regression model. First a logistic regression model is fitted using the glm function

fit_glm <- glm(sent ~ ztrust + zfWHR + zAfro + glasses + attract + maturity +
               tattoos, family = binomial(), data = wilson)

By default exploratory exhaustive tests are executed of whether the separate regression coefficients are zero, negative, or positive:

BF(fit_glm)

The names of the regression coefficients on which constrained hypotheses can be formualted can be extracted using the get_estimates function.

get_estimates(fit_glm)

Two different hypotheses are formulated with competing equality and/or order constraints on the regression coefficients of interest Mulder et al. (2021, Section 4.4)

BF_glm <- BF(fit_glm, hypothesis = "ztrust > (zfWHR, zAfro) > 0;
             ztrust > zfWHR = zAfro = 0")
summary(BF_glm)

By calling the summary function on the output object of class BF, the results of the exploratory tests are presented of whether each separate parameter is zero, negative, or positive, and the results of the confirmatory test of the hypotheses under the hypothesis argument are presented. When the hypotheses do not cover the complete parameter space, by default the complement hypothesis is added which covers the remaining parameter space that is not covered by the constraints under the hypotheses of interest. In the above example, the complement hypothesis covers the parameter space where neither "ztrust > (zfWHR, zAfro) > 0" holds, nor where "ztrust > zfWHR = zAfro = 0" holds.

For more information about the methodology, we refer the interested reader to Gu et al. (2018) and Mulder et al. (2021)

Poisson regression

We illustrate this for a Poisson regression model

poisson1 <- glm(formula = breaks ~ wool + tension, data = datasets::warpbreaks,
             family = poisson)

The estimates, the error covariance matrix, and the sample size are extracted from the fitted model

estimates <- poisson1$coefficients
covmatrix <- vcov(poisson1)
samplesize <- nobs(poisson1)

Constrained hypotheses on the parameters names(estimates) can then be tested as follows

BF1 <- BF(estimates, Sigma = covmatrix, n = samplesize, hypothesis = 
  "woolB > tensionM > tensionH; woolB = tensionM = tensionH")

Note that the same hypothesis test would be executed when calling

BF2 <- BF(poisson1, hypothesis = "woolB > tensionM > tensionH;
          woolB = tensionM = tensionH")

because the same Bayes factor is used when running BF on an object of class glm (see Method: Bayes factor using Gaussian approximations when calling print(BF1) and print(BF2)).

For more information about the methodology, we refer the interested reader to Gu et al. (2018)

Introduction to BFpack

Mulder, J., Williams, D. R., Gu, X., Tomarken, A., Boeing-Messing, F., Olsson-Collentine, A., Meijerink, M., Menke, J., van Aert, R., Fox, J.-P., Hoijtink, H., Rosseel, Y., Wagenmakers, E.-J., and van Lissa, C.

2024-12-02

Introduction

Reference

Usage

Arguments

Output

Example analyses

Normal linear models (t-testing, linear regression, (M)AN(C)OVA)

Univariate t testing

Multivariate t testing

Analysis of variance

Univariate and multivariate multiple regression

Testing independent group variances

Testing correlations

Meta-analyses

Exponential random graph models

Network autocorrelation models

Hypothesis testing using normal approximations

Running `BF` on a named vector

Logistic regression

Poisson regression

Introduction to BFpack

Mulder, J., Williams, D. R., Gu, X., Tomarken, A., Boeing-Messing, F., Olsson-Collentine, A., Meijerink, M., Menke, J., van Aert, R., Fox, J.-P., Hoijtink, H., Rosseel, Y., Wagenmakers, E.-J., and van Lissa, C.

2024-12-02

Introduction

Reference

Usage

Arguments

Output

Example analyses

Normal linear models (t-testing, linear regression, (M)AN(C)OVA)

Univariate t testing

Multivariate t testing

Analysis of variance

Univariate and multivariate multiple regression

Testing independent group variances

Testing correlations

Meta-analyses

Exponential random graph models

Network autocorrelation models

Hypothesis testing using normal approximations

Running BF on a named vector

Logistic regression

Poisson regression

Running `BF` on a named vector