README.md

In MarniTausen/BayzR: R package for bayz

Bayesian mixed models, shrinkage and interaction kernel regression

• How to install
• Short manual for using the bayz() main function
• Example

Here write down the applicability of the software. For more information access the website: http://www.bayz.biz/.

How to install

Most users would like to install our binary packages, that we have available for Windows 10 and MacOS in the bayz.biz website http://www.bayz.biz/.

If you need or want to use the source code for installation, we have two repositories on github: in ljanss/BayzR, and in MarniTausen/BayzR. Installation from the ljanss/BayzR is recommended as first option, this repository contains regularly ‘frozen’ version that are tested on different data sets and compile-tested on Windows and Mac-OS. The version in MarniTausen/BayzR is our development version and may only be of interest to those that want to co-develop and submit changes to our source code. You may run into bugs or compilation errors when using the MarniTausen/BayzR version because we may just be in the middle of developing / testing something.

To install from the github repositories, the following is needed:

1). download and install Rtools. This is not an R-package but a separate part of the R system and can be found on r-project.org.

2). install the devtools package.

install.packages("devtools")

3). Install dependencies lme4, coda and Rcpp.

install.packages("lme4")
install.packages("coda")
install.packages("Rcpp")

4). Download and install/compile the BayzR package using:

library(devtools)
devtools::install_github("ljanss/BayzR")

You may be asked to install several other packages. If you are sure you want the development-version, replace in step 3 to install from MarniTausen/BayzR. If the compilation succeeds, you are ready to use the main function bayz() after loading the package with library(BayzR).

Short Manual

The bayz function fits various mixed-linear and Bayesian shrinkage models with complex covariance structures using an extended R-formula syntax.

The model formula in bayz has the basic syntax of an R formula but with all explanatory (right-hand-side) terms wrapped by a function to specify how to fit the explanatory variables in the model. This may look like Yield \~ fx(Year) + rn(Variety) to fit Yield with Year as a fixed factor and Variety as a random factor. The equivalent lme4 model would be Yield \~ factor(Year) + (1\|Variety). The list of model functions currently available is: fx() : fixed factors (with interactions) rn() : random factors (with interactions) rg() : fixed regressions (with interactions or nested in a factor) rr() : random regressions The model functions allow to specify interactions of variables, hierarchies, use of matrices as input data, complex covariance structures, and prior distributions can be changed to modify standard mixed model in Bayesian shrinkage models.

Interactions between fators are specified using the colon, for instance by writing for a fixed Year-Location interaction fx(Year:Location). Bayz does NOT support automatic expansion with main effects by using the ‘star’ (Year*Location) or ‘forward slash’ (Year/Location) syntaxes, hence bayz requires to manually add the desired main effects in the model (but note that bayz uses the ‘forward slash’ to specify hierarchical models). Interactions between factors can be specified to any degree. The ‘star’ syntax can be used to indicate interaction between covariates, like rg(TempSum*Precip), where it is simply interpreted as multiplication. Interaction between a covariate and a factor is specified using the ‘pipe’ character as in lme4 models. It can be used to specify fixed nested regressions as rg(TempSum\|Year:Location) to specify regressions on TempSum within each Year-Location, or to speficy random slope models in rr(), for instance rr(TempSum\|Variety).

The bayz call has a data argument to specify an input data-frame (the “main data”) to contain model variables, but bayz will also search the R environment if variables are not found in the main data. Typically, input in the form of matrices such as large sets of covariates, proportional variance-covariance / correlation / kernel / similarity matrices used in variance models, as well as data for hierarchical models, are not in the main data. Matrices / kernels should be prepared with row-names to match a variable in the data. For matrices / kernels used in variance models, the link is straightforward, for instance in rn(Variety:Location, V=KG*KE), KG and KE can be a genetic and environmental kinship / kernel matrix, respectively, and KG should have row-names matching Variety levels, while KE should have row-names matching Location levels. To fit a large set of covariates, the match is specified using a hierachical specification rr(Variety/Metabolites), where Metabolites is then a matrix of covariates which must have row-names matching Variety levels. In both cases, such kernel or covariate matrices must have unique levels, but the main data may have repeated levels and in different order. If one has repeated metabolite data on each Variety, for instance at multiple time-points, then consider that Variety is not the appropriate link to the data, but Variety:Time is.

For random effects a variance-covariance structure can be specified using a V= option within the model-term function, for example rn(Variety, V=KG). When the fit is for an interaction of factors, the variance specification should expand to include one term for each variable, separated by stars (which should be read as Kronecker products). The variance structure is then built up from a combination of given (proportional) variance-covariance / correlation / kernel matrices and predefined acronyms IDEN, DIAG and VCOV that indicate parameterized matrices (with parameters to be estimated from the data). Alternatively, the variance structure can be specified as a linear model using V=\~, which is interpreted as a use of a log-linear model for the variances.

Examples

library(BayzR)
# A dummy test for bayz
y = rnorm(1000)
A = as.factor(rep(1:20,50))
B = as.factor(rep(1:10,each=100))
dat1 = data.frame(y,A,B)
library(BayzR)
fit1 = bayz(y~fx(A)+fx(B)+rn(A:B),data=dat1,chain=c(2000,100,20))
summary(fit1)

MarniTausen/BayzR documentation built on April 4, 2024, 9:30 a.m.