MCMCglmm: MCMC Generalised Linear Mixed Models

> library(MCMCglmm)

Loading required package: coda

Attaching package: 'coda'

The following object is masked from 'package:VGAM':

    nvar

Loading required package: ape

Attaching package: 'ape'

The following objects are masked from 'package:igraph':

    edges, mst, ring


Attaching package: 'MCMCglmm'

The following objects are masked from 'package:igraph':

    path, sir

The following object is masked from 'package:nadiv':

    prunePed

> data("BTdata")
> data("BTped")
> data("PlodiaPO")

バージョン: 2.22

関数名 概略
BTdata Blue Tit Data for a Quantitative Genetic Experiment
BTped Blue Tit Pedigree
Ddivergence d-divergence
Dexpressions List of unevaluated expressions for (mixed) partial derivatives of fitness with respect to linear predictors.
Dtensor Tensor of (mixed) partial derivatives
KPPM Kronecker Product Permutation Matrix
MCMCglmm Multivariate Generalised Linear Mixed Models
MCMCglmm-package Multivariate Generalised Linear Mixed Models
PlodiaPO Phenoloxidase measures on caterpillars of the Indian meal moth.
PlodiaR Resistance of Indian meal moth caterpillars to the granulosis virus PiGV.
PlodiaRB Resistance (as a binary trait) of Indian meal moth caterpillars to the granulosis virus PiGV.
Ptensor Tensor of Sample (Mixed) Central Moments
SShorns Horn type and genders of Soay Sheep
Tri2M Lower/Upper Triangle Elements of a Matrix
at.level Incidence Matrix of Levels within a Factor
at.set Incidence Matrix of Combined Levels within a Factor
buildV Forms expected (co)variances for GLMMs fitted with MCMCglmm
commutation Commutation Matrix
dcmvnorm Density of a (conditional) multivariate normal variate
evalDtensor Evaluates a list of (mixed) partial derivatives
gelman.prior Prior Covariance Matrix for Fixed Effects.
inverseA Inverse Relatedness Matrix and Phylogenetic Covariance Matrix
knorm (Mixed) Central Moments of a Multivariate Normal Distribution
krzanowski.test Krzanowski's Comparison of Subspaces
kunif Central Moments of a Uniform Distribution
list2bdiag Forms the direct sum from a list of matrices
mult.memb Design Matrices for Multiple Membership Models
path Design Matrix for Path Analyses
plot.MCMCglmm Plots MCMC chains from MCMCglmm using plot.mcmc
plotsubspace Plots covariance matrices
posterior.ante Posterior distribution of ante-dependence parameters
posterior.cor Transforms posterior distribution of covariances into correlations
posterior.evals Posterior distribution of eigenvalues
posterior.inverse Posterior distribution of matrix inverse
posterior.mode Estimates the marginal parameter modes using kernel density estimation
predict.MCMCglmm Predict method for GLMMs fitted with MCMCglmm
prunePed Pedigree pruning
rIW Random Generation from the Conditional Inverse Wishart Distribution
rbv Random Generation of MVN Breeding Values and Phylogenetic Effects
residuals.MCMCglmm Residuals form a GLMM fitted with MCMCglmm
rtcmvnorm Random Generation from a Truncated Conditional Normal Distribution
rtnorm Random Generation from a Truncated Normal Distribution
simulate.MCMCglmm Simulate method for GLMMs fitted with MCMCglmm
sir Design Matrix for Simultaneous and Recursive Relationships between Responses
sm2asreml Converts sparseMatrix to asreml's giv format
spl Orthogonal Spline Design Matrix
summary.MCMCglmm Summarising GLMM Fits from MCMCglmm

MCMCglmm

Arguments

  • fixed
  • random
  • rcov
  • family
  • mev
  • prior
  • tune
  • pedigree
  • nodes
  • scale
  • nitt
  • thin
  • burnin
  • pr
  • pl
  • verbose
  • DIC
  • singular.ok
  • saveX
  • saveZ
  • saveXL
  • slice
  • ginverse
> data("PlodiaPO")
> model1 <- PlodiaPO %>% MCMCglmm(PO ~ 1, random = ~FSfamily, data = ., verbose = FALSE)
> summary(model1)


 Iterations = 3001:12991
 Thinning interval  = 10
 Sample size  = 1000 

 DIC: -240.0398 

 G-structure:  ~FSfamily

         post.mean l-95% CI u-95% CI eff.samp
FSfamily   0.01001  0.00515  0.01575     1320

 R-structure:  ~units

      post.mean l-95% CI u-95% CI eff.samp
units   0.03408  0.03021  0.03872     1000

 Location effects: PO ~ 1 

            post.mean l-95% CI u-95% CI eff.samp  pMCMC
(Intercept)     1.163    1.128    1.193     1000 <0.001

PlodiaPO

> data("PlodiaPO")
> PlodiaPO %>% {
+   print(class(.))
+   print(dplyr::glimpse(.))
+ }

[1] "data.frame"
Observations: 511
Variables: 3
$ FSfamily (fctr) F1, F1, F1, F1, F1, F1, F1, F1, F1, F1, F1, F2, F2, ...
$ PO       (dbl) 0.6685459, 0.8024980, 0.8233658, 0.7696421, 0.9437393...
$ plate    (int) 1, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1,...
NULL