BF.default | R Documentation |
The BF
function can be used for hypothesis testing and
model
selection using the Bayes factor. By default exploratory hypothesis tests are
performed of whether each model parameter equals zero, is negative, or is
positive.
Confirmatory hypothesis tests can be executed by specifying hypotheses with
equality and/or order constraints on the parameters of interest.
## Default S3 method:
BF(x, hypothesis = NULL, prior.hyp = NULL, complement = TRUE, Sigma, n, ...)
## S3 method for class 'lm'
BF(x, hypothesis = NULL, prior.hyp = NULL, complement = TRUE, BF.type = 2, ...)
## S3 method for class 't_test'
BF(x, hypothesis = NULL, prior.hyp = NULL, complement = TRUE, BF.type = 2, ...)
x |
An R object containing the outcome of a statistical analysis.
An R object containing the outcome of a statistical analysis. Currently, the
following objects can be processed: t_test(), bartlett_test(), lm(), aov(),
manova(), cor_test(), lmer() (only for testing random intercep variances),
glm(), coxph(), survreg(), polr(), zeroinfl(), rma(), ergm(), or named vector objects.
In the case |
hypothesis |
A character string containing the constrained (informative) hypotheses to
evaluate in a confirmatory test. The default is NULL, which will result in standard exploratory testing
under the model |
prior.hyp |
A vector specifying the prior probabilities of the hypotheses. The default is NULL which will specify equal prior probabilities. |
complement |
a logical specifying whether the complement should be added
to the tested hypothesis under |
Sigma |
An approximate posterior covariance matrix (e.g,. error covariance
matrix) of the parameters of interest. This argument is only required when |
n |
The (effective) sample size that was used to acquire the estimates in the named vector
|
... |
Parameters passed to and from other functions. |
BF.type |
An integer that specified the type of Bayes factor (or prior) that is used for the test.
Currently, this argument is only used for models of class 'lm' and 't_test',
where |
The function requires a fitted modeling object. Current analyses
that are supported: t_test
,
bartlett_test
,
aov
, manova
,
lm
, mlm
,
glm
, hetcor
,
lmer
, coxph
,
survreg
, ergm
,
bergm
,
zeroinfl
, rma
and polr
.
For testing parameters from the results of t_test(), lm(), aov(),
manova(), and bartlett_test(), hypothesis testing is done using
adjusted fractional Bayes factors are computed (using minimal fractions).
For testing measures of association (e.g., correlations) via cor_test()
,
Bayes factors are computed using joint uniform priors under the correlation
matrices. For testing intraclass correlations (random intercept variances) via
lmer()
, Bayes factors are computed using uniform priors for the intraclass
correlations. For all other tests, approximate adjusted fractional Bayes factors
(with minimal fractions) are computed using Gaussian approximations, similar as
a classical Wald test.
The output is an object of class BF
. The object has elements:
BFtu_exploratory: The Bayes factors of the constrained hypotheses against the unconstrained hypothesis in the exploratory test.
PHP_exploratory: The posterior probabilities of the constrained hypotheses in the exploratory test.
BFtu_confirmatory: The Bayes factors of the constrained hypotheses against
the unconstrained hypothesis in the confirmatory test using the hypothesis
argument.
PHP_confirmatory: The posterior probabilities of the constrained hypotheses
in the confirmatory test using the hypothesis
argument.
BFmatrix_confirmatory: The evidence matrix which contains the Bayes factors between all possible pairs of hypotheses in the confirmatory test.
BFtable_confirmatory: The Specification table
(output when printing the
summary
of a BF
for a confirmatory test) which contains the different
elements of the extended Savage Dickey density ratio where
The first column 'complex=
' quantifies the relative complexity of the
equality constraints of a hypothesis (the prior density at the equality constraints in the
extended Savage Dickey density ratio).
The second column 'complex>
' quantifies the relative complexity of the
order constraints of a hypothesis (the prior probability of the order constraints in the extended
Savage Dickey density ratio).
The third column 'fit=
' quantifies the relative fit of the equality
constraints of a hypothesis (the posterior density at the equality constraints in the extended
Savage Dickey density ratio).
The fourth column 'fit>
' quantifies the relative fit of the order
constraints of a hypothesis (the posterior probability of the order constraints in the extended
Savage Dickey density ratio)
The fifth column 'BF=
' contains the Bayes factor of the equality constraints
against the unconstrained hypothesis.
The sixth column 'BF>
' contains the Bayes factor of the order constraints
against the unconstrained hypothesis.
The seventh column 'BF
' contains the Bayes factor of the constrained hypothesis
against the unconstrained hypothesis.
The eighth column 'BF=
' contains the posterior probabilities of the
constrained hypotheses.
prior: The prior probabilities of the constrained hypotheses in a confirmatory test.
hypotheses: The tested constrained hypotheses in a confirmatory test.
estimates: The unconstrained estimates.
model: The input model x
.
call: The call of the BF
function.
BF(default)
: S3 method for a named vector 'x'
BF(lm)
: S3 method for an object of class 'lm'
BF(t_test)
: BF S3 method for an object of class 't_test'
Mulder, J., D.R. Williams, Gu, X., A. Tomarken, F. Böing-Messing, J.A.O.C. Olsson-Collentine, Marlyne Meyerink, J. Menke, J.-P. Fox, Y. Rosseel, E.J. Wagenmakers, H. Hoijtink., and van Lissa, C. (2021). BFpack: Flexible Bayes Factor Testing of Scientific Theories in R. Journal of Statistical Software. <DOI:10.18637/jss.v100.i18>
# EXAMPLE 1. One-sample t test
ttest1 <- t_test(therapeutic, mu = 5)
print(ttest1)
# confirmatory Bayesian one sample t test
BF1 <- BF(ttest1, hypothesis = "mu = 5")
summary(BF1)
# exploratory Bayesian one sample t test
BF(ttest1)
# EXAMPLE 2. ANOVA
aov1 <- aov(price ~ anchor * motivation,data = tvprices)
BF1 <- BF(aov1, hypothesis = "anchorrounded = motivationlow;
anchorrounded < motivationlow")
summary(BF1)
# EXAMPLE 3. linear regression
lm1 <- lm(mpg ~ cyl + hp + wt, data = mtcars)
BF(lm1, hypothesis = "wt < cyl < hp = 0")
# EXAMPLE 4. Logistic regression
fit <- glm(sent ~ ztrust + zfWHR + zAfro + glasses + attract + maturity +
tattoos, family = binomial(), data = wilson)
BF1 <- BF(fit, hypothesis = "ztrust > zfWHR > 0;
ztrust > 0 & zfWHR = 0")
summary(BF1)
# EXAMPLE 5. Correlation analysis
set.seed(123)
cor1 <- cor_test(memory[1:20,1:3])
BF1 <- BF(cor1)
summary(BF1)
BF2 <- BF(cor1, hypothesis = "Wmn_with_Im > Wmn_with_Del > 0;
Wmn_with_Im = Wmn_with_Del = 0")
summary(BF2)
# EXAMPLE 6. Bayes factor testing on a named vector
# A Poisson regression model is used to illustrate the computation
# of Bayes factors with a named vector as input
poisson1 <- glm(formula = breaks ~ wool + tension,
data = datasets::warpbreaks, family = poisson)
# extract estimates, error covariance matrix, and sample size:
estimates <- poisson1$coefficients
covmatrix <- vcov(poisson1)
samplesize <- nobs(poisson1)
# compute Bayes factors on equal/order constrained hypotheses on coefficients
BF1 <- BF(estimates, Sigma = covmatrix, n = samplesize, hypothesis =
"woolB > tensionM > tensionH; woolB = tensionM = tensionH")
summary(BF1)
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