# Bayesian testing using conjugate priors and method of moments for single or multi value traits. Function to perform bayesian tests and ROPE comparisons using single or multi value traits with several distributions. ```r conjugate( s1 = NULL, s2 = NULL, method = c("t", "gaussian", "beta", "binomial", "lognormal", "lognormal2", "poisson", "negbin", "vonmises", "vonmises2", "uniform", "pareto", "gamma", "bernoulli", "exponential", "bivariate_uniform", "bivariate_gaussian", "bivariate_lognormal"), priors = NULL, plot = NULL, rope_range = NULL, rope_ci = 0.89, cred.int.level = 0.89, hypothesis = "equal", bayes_factor = NULL, support = NULL ) ``` ## Arguments - `s1`: A data.frame or matrix of multi value traits or a vector of single value traits. If a multi value trait is used then column names should include a number representing the "bin". Alternatively for distributions other than "binomial" (which requires list data with "successes" and "trials" as numeric vectors in the list, see examples) this can be a formula specifying `outcome ~ group` where group has exactly 2 levels. If using wide MV trait data then the formula should specify column positions ~ grouping such as `1:180 ~ group`. This sample is shown in red if plotted. - `s2`: An optional second sample, or if s1 is a formula then this should be a dataframe. This sample is shown in blue if plotted. - `method`: The distribution/method to use. Currently "t", "gaussian", "beta", "binomial", "lognormal", "lognormal2", "poisson", "negbin" (negative binomial), "uniform", "pareto", "gamma", "bernoulli", "exponential", "vonmises", and "vonmises2" are supported. The count (binomial, poisson and negative binomial), bernoulli, exponential, and pareto distributions are only implemented for single value traits due to their updating and/or the nature of the input data. The "t" and "gaussian" methods both use a T distribution with "t" testing for a difference of means and "gaussian" testing for a difference in the distributions (similar to a Z test). Both Von Mises options are for use with circular data (for instance hue values when the circular quality of the data is relevant). Note that non-circular distributions can be compared to each other. This should only be done with caution and may not be supported in all downstream functions. There are also 3 bivariate conjugate priors that are supported for use with single value data. Those are "bivariate_uniform", "bivariate_gaussian" and "bivariate_lognormal". - `priors`: Prior distributions described as a list of lists. If this is a single list then it will be duplicated for the second sample, which is generally a good idea if both samples use the same distribution (method). Elements in the inner lists should be named for the parameter they represent (see examples). These names vary by method (see details). By default this is NULL and weak priors (generally jeffrey's priors) are used. The `posterior` part of output can also be recycled as a new prior if Bayesian updating is appropriate for your use. - `plot`: deprecated, use `plot` method instead. - `rope_range`: Optional vector specifying a region of practical equivalence. This interval is considered practically equivalent to no effect. Kruschke (2018) suggests c(-0.1, 0.1) as a broadly reasonable ROPE for standardized parameters. That range could also be rescaled by a standard deviation/magnitude for non-standardized parameters, but ultimately this should be informed by your setting and scientific question. See Kruschke (2018) for details on ROPE and other Bayesian methods to aide decision-making tools:::Rd_expr_doi("10.1177/2515245918771304") and tools:::Rd_expr_doi("10.1037/a0029146") . - `rope_ci`: The credible interval probability to use for ROPE. Defaults to 0.89. - `cred.int.level`: The credible interval probability to use in computing HDI for samples, defaults to 0.89. - `hypothesis`: Direction of a hypothesis if two samples are provided. Options are "unequal", "equal", "greater", and "lesser", read as "sample1 greater than sample2". - `bayes_factor`: Optional point or interval to evaluate bayes factors on. Note that this generally only makes sense to use if you have informative priors where the change in odds between prior and posterior is meaningful about the data. If this is non-NULL then columns of bayes factors are added to the summary output. Note these are only implemented for univariate distributions. - `support`: Deprecated ## Returns A conjugate-class object with slots including: * summary : A data frame containing HDI/HDE values for each sample and the ROPE as well as posterior probability of the hypothesis and ROPE test (if specified). The HDE is the "Highest Density Estimate" of the posterior, that is the tallest part of the probability density function. The HDI is the Highest Density Interval, which is an interval that contains X% of the posterior distribution, so `cred.int.level = 0.8` corresponds to an HDI that includes 80 percent of the posterior probability. Bayes factors are calculated as posterior/prior for each sample. * posterior : A list of updated parameters in the same format as the prior for the given method. If desired this does allow for Bayesian updating. * prior : The prior in a list with the same format as the posterior. * plot : A ggplot showing the distribution of samples and optionally the distribution of differences/ROPE. * plot_parameters: Parameters used in making a plot of the data. Contains support range and posterior recoded to use a density function. * data: Data from s1 and s2 arguments. * call: The function call. ## Details Prior distributions default to be weakly informative and in some cases you may wish to change them. * "t", "gaussian", and "lognormal": `priors = list(mu = 0, sd = 10)`, where mu is the mean and sd is standard deviation. For the lognormal method these describe the normal distribution of the mean parameter for lognormal data and are on the log scale. * "beta", "bernoulli", and "binomial": `priors = list(a = 0.5, b = 0.5)`, where a and b are shape parameters of the beta distribution. Note that for the binomial distribution this is used as the prior for success probability P, which is assumed to be beta distributed as in a beta-binomial distribution. * "lognormal2": `priors = list(a = 1, b = 1)`, where a and b are the shape and scale parameters of the gamma distribution of lognormal data's precision parameter (using the alternative mu, precision parameterization). * "gamma": `priors = list(shape = 0.5, scale = 0.5, known_shape = 1)`, where shape and scale are the respective parameters of the gamma distributed rate (inverse of scale) parameter of gamma distributed data. * "poisson" and "exponential": `priors = list(a = 0.5,b = 0.5)`, where a and b are shape parameters of the gamma distribution. * "negbin": `priors = list(r = 10, a = 0.5, b = 0.5)`, where r is the r parameter of the negative binomial distribution (representing the number of successes required) and where a and b are shape parameters of the beta distribution. Note that the r value is not updated. The conjugate beta prior is only valid when r is fixed and known, which is a limitation for this method. * "uniform": `list(scale = 0.5, location = 0.5)`, where scale is the scale parameter of the pareto distributed upper boundary and location is the location parameter of the pareto distributed upper boundary. Note that different sources will use different terminology for these parameters. These names were chosen for consistency with the `extraDistr` implementation of the pareto distribution. On Wikipedia the parameters are called shape and scale, corresponding to extraDistr's scale and location respectively, which can be confusing. Note that the lower boundary of the uniform is assumed to be 0. * "pareto": `list(a = 1, b = 1, known_location = min(data))`, where a and b are the shape and scale parameters of the gamma distribution of the pareto distribution's scale parameter. In this case location is assumed to be constant and known, which is less of a limitation than knowing r for the negative binomial method since location will generally be right around/just under the minimum of the sample data. Note that the pareto method is only implemented currently for single value traits since one of the statistics needed to update the gamma distribution here is the product of the data and we do not currently have a method to calculate a similar sufficient statistic from multi value traits. * "vonmises": `list(mu = 0, kappa = 0.5, boundary = c(-pi, pi), known_kappa = 1, n =1)`, where mu is the direction of the circular distribution (the mean), kappa is the precision of the mean, boundary is a vector including the two values that are the where the circular data "wraps" around the circle, known_kappa is the fixed value of precision for the total distribution, and n is the number of prior observations. This Von Mises option updates the conjugate prior for the mean direction, which is itself Von-Mises distributed. This in some ways is analogous to the T method, but assuming a fixed variance when the mean is updated. Note that due to how the rescaling works larger circular boundaries can be slow to plot. * "vonmises2": `priors = list(mu = 0, kappa = 0.5, boundary = c(-pi, pi), n = 1)`, where mu and kappa are mean direction and precision of the von mises distribution, boundary is a vector including the two values that are the where the circular data "wraps" around the circle, and n is the number of prior observations. This Von-Mises implementation does not assume constant variance and instead uses MLE to estimate kappa from the data and updates the kappa prior as a weighted average of the data and the prior. The mu parameter is then updated per Von-Mises conjugacy. * "bivariate_uniform": `list(location_l = 1, location_u = 2, scale = 1)`, where scale is the shared scale parameter of the pareto distributed upper and lower boundaries and location l and u are the location parameters for the Lower (l) and Upper (u) boundaries of the uniform distribution. Note this uses the same terminology for the pareto distribution's parameters as the "uniform" method. * "bivariate_gaussian" and "bivariate_lognormal": `list(mu = 0, sd = 10, a = 1, b = 1)`, where mu and sd are the mean and standard deviation of the Normal distribution of the data's mean and a and b are the shape and scale of the gamma distribution on precision. Note that internally this uses the Mu and Precision parameterization of the normal distribution and those are the parameters shown in the plot and tested, but priors use Mu and SD for the normal distribution of the mean. ## Examples ```r mv_ln <- mvSim( dists = list( rlnorm = list(meanlog = log(130), sdlog = log(1.2)), rlnorm = list(meanlog = log(100), sdlog = log(1.3)) ), n_samples = 30 ) # lognormal mv ln_mv_ex <- conjugate( s1 = mv_ln[1:30, -1], s2 = mv_ln[31:60, -1], method = "lognormal", priors = list(mu = 5, sd = 2), rope_range = c(-40, 40), rope_ci = 0.89, cred.int.level = 0.89, hypothesis = "equal", support = NULL ) # lognormal sv ln_sv_ex <- conjugate( s1 = rlnorm(100, log(130), log(1.3)), s2 = rlnorm(100, log(100), log(1.6)), method = "lognormal", priors = list(mu = 5, sd = 2), rope_range = NULL, rope_ci = 0.89, cred.int.level = 0.89, hypothesis = "equal", support = NULL ) # Z test mv example mv_gauss <- mvSim( dists = list( rnorm = list(mean = 50, sd = 10), rnorm = list(mean = 60, sd = 12) ), n_samples = 30 ) gauss_mv_ex <- conjugate( s1 = mv_gauss[1:30, -1], s2 = mv_gauss[31:60, -1], method = "gaussian", priors = list(mu = 30, sd = 10), rope_range = c(-25, 25), rope_ci = 0.89, cred.int.level = 0.89, hypothesis = "equal", support = NULL ) # T test sv example with two different priors gaussianMeans_sv_ex <- conjugate( s1 = rnorm(10, 50, 10), s2 = rnorm(10, 60, 12), method = "t", priors = list(list(mu = 40, sd = 10), list(mu = 45, sd = 8)), rope_range = c(-5, 8), rope_ci = 0.89, cred.int.level = 0.89, hypothesis = "equal", support = NULL ) # beta mv example set.seed(123) mv_beta <- mvSim( dists = list( rbeta = list(shape1 = 5, shape2 = 8), rbeta = list(shape1 = 10, shape2 = 10) ), n_samples = c(30, 20) ) beta_mv_ex <- conjugate( s1 = mv_beta[1:30, -1], s2 = mv_beta[31:50, -1], method = "beta", priors = list(a = 0.5, b = 0.5), rope_range = c(-0.1, 0.1), rope_ci = 0.89, cred.int.level = 0.89, hypothesis = "equal", bayes_factor = 0.5 # note this may not be reasonable with these priors ) # beta sv example beta_sv_ex <- conjugate( s1 = rbeta(20, 5, 5), s2 = rbeta(20, 8, 5), method = "beta", priors = list(a = 0.5, b = 0.5), rope_range = c(-0.1, 0.1), rope_ci = 0.89, cred.int.level = 0.89, hypothesis = "equal", bayes_factor = c(0.5, 0.75) # note this may not be reasonable with these priors ) # binomial sv example # note that specifying trials = 20 would also work # and the number of trials will be recycled to the length of successes binomial_sv_ex <- conjugate( s1 = list(successes = c(15, 14, 16, 11), trials = c(20, 20, 20, 20)), s2 = list(successes = c(7, 8, 10, 5), trials = c(20, 20, 20, 20)), method = "binomial", priors = list(a = 0.5, b = 0.5), rope_range = c(-0.1, 0.1), rope_ci = 0.89, cred.int.level = 0.89, hypothesis = "equal" ) # poisson sv example poisson_sv_ex <- conjugate( s1 = rpois(20, 10), s2 = rpois(20, 8), method = "poisson", priors = list(a = 0.5, b = 0.5), rope_range = c(-1, 1), rope_ci = 0.89, cred.int.level = 0.89, hypothesis = "equal" ) # negative binomial sv example # knowing r (required number of successes) is an important caveat for this method. # in the current implementation we suggest using the poisson method for data such as leaf counts negbin_sv_ex <- conjugate( s1 = rnbinom(20, 10, 0.5), s2 = rnbinom(20, 10, 0.25), method = "negbin", priors = list(r = 10, a = 0.5, b = 0.5), rope_range = c(-1, 1), rope_ci = 0.89, cred.int.level = 0.89, hypothesis = "equal" ) # von mises mv example mv_gauss <- mvSim( dists = list( rnorm = list(mean = 50, sd = 10), rnorm = list(mean = 60, sd = 12) ), n_samples = c(30, 40) ) vm1_ex <- conjugate( s1 = mv_gauss[1:30, -1], s2 = mv_gauss[31:70, -1], method = "vonmises", priors = list(mu = 45, kappa = 1, boundary = c(0, 180), known_kappa = 1, n = 1), rope_range = c(-1, 1), rope_ci = 0.89, cred.int.level = 0.89, hypothesis = "equal" ) # von mises 2 sv example vm2_ex <- conjugate( s1 = brms::rvon_mises(10, 2, 2), s2 = brms::rvon_mises(15, 3, 3), method = "vonmises2", priors = list(mu = 0, kappa = 0.5, boundary = c(-pi, pi), n = 1), cred.int.level = 0.95 ) ``` ## See Also [barg() for additional reporting]

conjugate function