Bayesian Mixture Models for Marker Dosage in Autopolyploids

Description

These functions provide tools for estimating marker dosage for dominant markers in regular autopolyploids via Bayesian mixture model. Wrappers are provided for generating MCMC samples using the JAGS software. Convergence diagnostics and posterior distribution densities are provided by the coda package.

Details

The simplest way to fit a model is to use runSegratioMM. Given segregation ratios and a ploidy level, a mixture model is constructed with default priors and initial values and JAGS run to produce an MCMC sample for statistical inference.

A standard model may be set up with setModel where two parameters are set, namely ploidy.level or the number of homologous chromosomes set either as a numeric or as a character string and also n.components or the number of components for mixture model (less than or equal to maximum number of possible dosages).

Vague or strong priors may be constructed automatically using setPriors. Plots of standard conjugate distributions may be obtained using DistributionPlotBinomial DistributionPlotGamma and DistributionPlotNorm.

If necessary, other operations like setting up initial values or the control files for JAGS may be set using setInits setControl dumpData dumpInits writeControlFile writeJagsFile. Once the BUGS files and JAGS control files are set up then JAGS may be run using runJags and results read using readJags.

Convergence diagnostics may be carried out using coda or the convenience wrapper diagnosticsJagsMix.

Dose allocation can be carried out using dosagesJagsMix.

Plots may be produced and objects printed and summarised using standard print and plot methods. Plots of theoretical binomial distributions with different ploidy levels and sample sizes may be obtained with plotFitted. In addition, plotFitted produces a lattice plot of the observed segregation ratios and fitted mixture model on the logit scale.

Author(s)

Peter Baker p.baker1@uq.edu.au

References

• Baker P, Jackson P, and Aitken K. (2010) Bayesian estimation of marker dosage in sugarcane and other autopolyploids. TAG Theoretical and Applied Genetics 120 (8): 1653-1672.

• J B S Haldane (1930) Theoretical genetics of autopolyploids. Journal of genetics 22 359–372

• Ripol, M I et al (1999) Statistical aspects of genetic mapping in autopolyploids. Gene 235 31–41

• JAGS http://mcmc-jags.sourceforge.net/

Examples

## simulate small autooctaploid data set of 100 markers for 50 individuals
## with %70 Single, %20 Double and %10 Triple Dose markers 
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=400,n.individuals=275)
##print(a1)
sr <-  segregationRatios(a1$markers)
x <- setModel(3,8)  # autooctapolid mode with 3 components

## Not run: 
## fit simple model in one hit with default priors, inits etc
## warning: this is too small an MCMC sample so should give inaccurate
## answers but it could still take quite a while
x.run <- runSegratioMM(sr, x, burn.in=2000, sample=5000)
print(x.run)

## plot observed segregation ratios, fitted model and expected distribution
plot(x.run, theoretical=TRUE)

## End(Not run)

Distribution Plot

Description

Plots probability density function given the parameters. May be useful when investigating parameter choice for prior distributions.

Usage

DistributionPlotBinomial(size = 200, prob = 0.5,
xlab = "Number of Successes", ylab = "Probability Mass", signif.digits = 3,
main = paste("Binomial Distribution: n =", size, "p =",
signif(prob, digits = signif.digits)))

DistributionPlotGamma(shape = 1, rate = 1, length = 100, xlab = "x",
ylab = "Density", main = bquote(paste("Gamma Distribution: ", alpha,
"=", .(signif(shape, digits = signif.digits)), ",", beta, "=",
.(signif(rate, digits = signif.digits)))), signif.digits = 3)

DistributionPlotNorm(mean = 0, sd = 1, length = 100, xlab = "x", ylab =
"Density", main = bquote(paste("Normal Distribution: ", mu, "=",
.(signif(mean, digits = signif.digits)), ",", sigma, "=", .(signif(sd,
digits = signif.digits)))), signif.digits = 3)

Arguments

Details

Based on functions in package Rcmdr

Value

None.

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

Rcmdr Binomial Normal GammaDist

Examples

## Binomial distribution
DistributionPlotBinomial()
DistributionPlotBinomial(size=20, prob=0.2)

## Gamma distribution
DistributionPlotGamma()

## Normal distribution
DistributionPlotNorm()

Compute DIC for fitted mixture model

Description

Computes and returns the Deviance Information Critereon (DIC) as suggested by Celeaux et al (2006) as their DIC$_4$ for Bayesian mixture models

Usage

calculateDIC(mcmc.mixture, model, priors, seg.ratios, chain=1, print.DIC=FALSE)

Arguments

Value

A scalar DIC is returned

Author(s)

Peter Baker p.baker1@uq.edu.au

References

• G Celeaux et. al. (2006) Deviance Information Criteria for Missing Data Models Bayesian Analysis 4 23pp

• D Spiegelhalter et. el. (2002) Bayesian measures of model complexity and fit JRSS B 64 583–640

See Also

dosagesMCMC readJags

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)

## compute segregation ratios
sr <-  segregationRatios(a1$markers)

## set up model, priors, inits etc and write files for JAGS
x <- setModel(3,8)
x2 <- setPriors(x)
dumpData(sr, x)
inits <- setInits(x,x2)
dumpInits(inits)
writeJagsFile(x, x2, stem="test")

## Not run: 
## run JAGS
small <- setControl(x, burn.in=200, sample=500)
writeControlFile(small)
rj <- runJags(small)  ## just run it
print(rj)

## read mcmc chains and print DIC
xj <- readJags(rj)
print(calculateDIC(xj, x, x2, sr))

## End(Not run)

MCMC diagnostics for polyploid segregation ratio mixture models

Description

Produce and/or plot various diagnostic measures from coda package for Bayesian mixture models for assessing marker dosage in autopolyploids

Usage

diagnosticsJagsMix(mcmc.mixture, diagnostics = TRUE, plots = FALSE,
 index = -c( grep("T\\[",varnames(mcmc.mixture$mcmc.list)),
             grep("b\\[",varnames(mcmc.mixture$mcmc.list)) ),
 trace.plots = FALSE, auto.corrs = FALSE, density.plots = FALSE,
 xy.plots = FALSE, hpd.intervals = FALSE, hdp.prob = 0.95,
 return.results = FALSE)

Arguments

Value

If return.results is TRUE then a list is returned with components depending on various settings of arguments

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

mcmc autocorr.diag raftery.diag geweke.diag gelman.diag trellisplots

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)
##print(a1)
sr <-  segregationRatios(a1$markers)
x <- setModel(3,8)

## Not run: 
## fit simple model in one hit

x.run <- runSegratioMM(sr, x, burn.in=200, sample=500)
print(x.run)
diagnosticsJagsMix(x.run)
diagnosticsJagsMix(x.run, plot=TRUE)

## End(Not run)

Compute dosages under specified Bayesian mixture model

Description

Computes and returns estimated dosages under specified model using posterior probabilities derived from mcmc chains by the proportion of samples in each dosage class.

Usage

dosagesJagsMix(mcmc.mixture, jags.control, seg.ratio, chain = 1,
max.post.prob = TRUE, thresholds = c(0.5, 0.6, 0.7, 0.8, 0.9, 0.95,
0.99), print = FALSE, print.warning = TRUE, index.sample = 20)

Arguments

Value

An object of class dosagesMCMC is returned with components:

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

dosagesMCMC readJags

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)

## compute segregation ratios
sr <-  segregationRatios(a1$markers)

## set up model, priors, inits etc and write files for JAGS
x <- setModel(3,8)
x2 <- setPriors(x)
dumpData(sr, x)
inits <- setInits(x,x2)
dumpInits(inits)
writeJagsFile(x, x2, stem="test")

## Not run: 
## run JAGS
small <- setControl(x, burn.in=200, sample=500)
writeControlFile(small)
rj <- runJags(small)  ## just run it
print(rj)

## read mcmc chains and produce dosage allocations
xj <- readJags(rj)
dd <- dosagesJagsMix(xj, small, sr)
print(dd)

## End(Not run)

Dumps segregation ratio data to file for subsequent JAGS run

Description

Given segregation ratio data provided as an object of class segRatio, data are dumped in R format for use by JAGS

Usage

dumpData(seg.ratio, model, stem = "test", fix.one = TRUE,
 data.file = paste(stem, "-data.R", sep = ""))

Arguments

Value

None.

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

segRatio dump

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)

## compute segregation ratios
sr <-  segregationRatios(a1$markers)

## set up model for 3 components of autooctoploid
x <- setModel(3,8)

dumpData(sr, x)

Simulated autopolyploid dominant markers from 200 hexaploid individuals

Description

These data were simulated as 500 markers for 200 “auto–hexaploid individuals” exhibiting no overdispersion. The underlying percentages of single double and triple dose markers are 70%, 20% and 10%, respectively.

Usage

hexmarkers

Format

An object of S3 class sim.autoMarkers containing 500 simulated dominant markers for 200 auto–hexaploid individuals.

References

Haldane, J B S. 1930. Theoretical genetics of autopolyploids. Journal of Genetics 22: 359-372.

Baker P, Jackson P, and Aitken K. 2010. Bayesian estimation of marker dosage in sugarcane and other autopolyploids. TAG Theoretical and Applied Genetics 120 (8): 1653-1672.

Simulated overdispersed autopolyploid dominant markers from 200 hexaploid individuals

Description

These data are simulated as 500 markers for 200 “auto–hexaploid individuals” exhibiting overdispersion with the parameter shape1 = 25. The underlying percentages of single double and triple dose markers are 70%, 20% and 10%, respectively.

Usage

hexmarkers.overdisp

Format

An object of S3 class sim.autoMarkers containing 500 simulated dominant markers for 200 auto–hexaploid individuals.

References

Haldane, J B S. 1930. Theoretical genetics of autopolyploids. Journal of Genetics 22: 359-372.

Baker P, Jackson P, and Aitken K. 2010. Bayesian estimation of marker dosage in sugarcane and other autopolyploids. TAG Theoretical and Applied Genetics 120 (8): 1653-1672.

Results of MCMC estimation for simulated overdispersed markers

Description

MCMC was performed using the wrapper function runSegratioMM to run JAGS for a Bayesian mixture model on the segregation ratios obtained using the simulated data hexmarkers.overdisp. These data were simulated as 500 markers for 200 “auto–hexaploid individuals” exhibiting overdispersion with shape1=25. The underlying percentages of single double and triple dose markers are 70%, 20% and 10%, respectively.

Usage

mcmcHexRun

Format

An object of S3 class runJagsWrapper with various components including summaries and diagnostics.

References

Baker P, Jackson P, and Aitken K. 2010. Bayesian estimation of marker dosage in sugarcane and other autopolyploids. TAG Theoretical and Applied Genetics 120 (8): 1653-1672.

MCMC plots for segregation ratio mixture models

Description

Standard MCMC trace and density plots for specified mixure model parameters and posterior probability distributions for specified markers

Usage

## S3 method for class 'segratioMCMC'
plot(x, ..., row.index = c(1:10), var.index = c(1:6),
marker.index = c(1:8))

Arguments

Value

None.

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

dosagesMCMC readJags

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)
##print(a1)
sr <-  segregationRatios(a1$markers)
x <- setModel(3,8)

## Not run: 
## fit simple model in one hit and summarise

x.run <- runSegratioMM(sr, x, burn.in=200, sample=500)
plot(x.run$mcmc.mixture)

## End(Not run)

Plot observed segregation ratios and fitted and theoretical models

Description

Plots histogram of observed segregation ratios on logit scale along with scaled density of fitted components corresponding to dosage classes. Plots of expected theoretical distributions can be plotted with or without segregation ratio data.

Usage

## S3 method for class 'runJagsWrapper'
plot(x, theoretical=FALSE, ...)

plotFitted(seg.ratios, summary.mixture, add.random.effect=TRUE,
  theoretical=FALSE, model=NULL, theory.col="red",
  xaxis=c("logit","raw"), ylim=NULL, NCLASS=NULL, n.seq=100,
  xlab="logit(Segregation Ratio)", ylab="Density", density.plot=FALSE,
  fitted.lwd=2, fitted.col="blue", bar.col="lightgreen", cex=1,
  warnings = FALSE, main=NULL, ...)

plotTheoretical(ploidy.level=8, seg.ratios=NULL, n.components=NULL,
  expected.segratio=NULL, proportions=c(0.65,0.2,0.1,0.03,0.01,0.01, 0, 0),
  n.individuals=200, xaxis=c("raw","logit"),
  type.parents=c("heterogeneous","homozygous"), xlim=c(0,1),
  NCLASS=NULL, xlab="Segregation Ratio", ylab="Density",
  density.plot=FALSE, fitted.lwd=2, fitted.col="blue", cex=1,
  warnings = TRUE, main=NULL, ...)

Arguments

Details

plotFitted plot histogram of observed segregation ratios on logit scale along with scaled density of fitted components corresponding to dosage classes using trellis

plotTheoretical plot expected distribution of autopolyploid dominant markers on probability (0,1) scale. Segregation ratios may also be plotted

plot.runJagsWrapper plots the fitted values of object of class runJagsWrapper which has been produced by using runSegratioMM to set up and fit mixture model

Note that since trellis graphics are employed, plots may need to be printed in order to see them

Value

None.

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

summary.mcmc mcmc segratioMCMC readJags diagnosticsJagsMix runSegratioMM

Examples

## simulate small autooctaploid data set
plotTheoretical(8, proportion=c(0.7,0.2,0.1),n.individuals=50)
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)
##print(a1)
sr <-  segregationRatios(a1$markers)
x <- setModel(3,8)

## fit simple model in one hit and summarise
## Not run: 
x.run <- runSegratioMM(sr, x, burn.in=200, sample=500)
print(x.run)

## plot fitted model using 'plotFitted'
plotFitted(sr, x.run$summary)
a.plot <- plotFitted(sr, x.run$summary, density.plot=TRUE)
print(a.plot)
## or the easier way
plot(x.run, theoretical=TRUE)

## End(Not run)

Doses from Bayesian mixture model

Description

Prints objects of S3 class dosagesMCMC or segratioMCMC

Usage

## S3 method for class 'dosagesMCMC'
print(x, ..., index.sample = 20)

## S3 method for class 'segratioMCMC'
print(x, ..., row.index = c(1:10), var.index = c(1:6), marker.index
 = c(1:8), chain = 1)

Arguments

Value

None.

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

dosagesMCMC readJags

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)
##print(a1)
sr <-  segregationRatios(a1$markers)
x <- setModel(3,8)

## fit simple model in one hit

## Not run: 
x.run <- runSegratioMM(sr, x, burn.in=200, sample=500)

print(x.run$doses)

## End(Not run)

Running JAGS

Description

Print details and timing of JAGS run and summaries of results

Usage

## S3 method for class 'runJags'
print(x, ...)
## S3 method for class 'runJagsWrapper'
print(x, ...)

Arguments

Details

print.runJags can be employed when runJags is called directly and reports timimgs and dates while print.runJagsWrapper provides summary statistics when runSegratioMM is used.

Value

None.

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

runJags runSegratioMM

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)
##print(a1)
sr <-  segregationRatios(a1$markers)
x <- setModel(3,8)

## Not run: 
## fit simple model in one hit

x.run <- runSegratioMM(sr, x, burn.in=200, sample=500)
print(x.run)

## End(Not run)

Read MCMC sample(s) from a JAGS run

Description

wrapper to read.openbugs which returns object of class mcmc.list and so can be used to specify the start and end iterations for the MCMC sample(s) and also specify thinning

Usage

readJags(run.jags, quiet = TRUE, ...)

Arguments

Value

Returns object of class segratioMCMC with components

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

mcmc.list setPriors setInits expected.segRatio segRatio setControl dumpData dumpInits or for an easier way to run a segregation ratio mixture model see runSegratioMM

Examples

library(polySegratio)

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)
##print(a1)
sr <-  segregationRatios(a1$markers)
x <- setModel(3,8)
x2 <- setPriors(x)
cat(x$bugs.code,x2$bugs.code,sep="\n")


x3 <- setModel(3,8, random.effect = TRUE)
x4 <- setPriors(x3, type="strong")

dumpData(sr, x3)
inits <- setInits(x,x2)
dumpInits(inits)
##x.priors <- setPriors(x, "vague")
writeJagsFile(x, x2, stem="test")

small <- setControl(x, burn.in=20, sample=50)
writeControlFile(small)
## Not run: 
rj <- runJags(small)  ## just run it

xj <- readJags(rj)
print(xj)

## End(Not run)

Run JAGS to create MCMC sample for segregation ratio mixture model

Description

Runs external program JAGS and returns MCMC list for processing by coda.

Usage

runJags(jags.control, jags = "jags", quiet = FALSE,
 cmd.file = paste(jags.control$stem, ".cmd", sep = ""), timing = TRUE)

Arguments

Value

Returns object of class runJAGS with components

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

setPriors setInits expected.segRatio segRatio setControl dumpData dumpInits or for an easier way to run a segregation ratio mixture model see runSegratioMM

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)
sr <-  segregationRatios(a1$markers)

## set up model with 3 components
x <- setModel(3,8)
x2 <- setPriors(x)
dumpData(sr, x)
inits <- setInits(x,x2)
dumpInits(inits)
##x.priors <- setPriors(x, "vague")
writeJagsFile(x, x2, stem="test")

## Not run: 
small <- setControl(x, burn.in=20, sample=50)
writeControlFile(small)
rj <- runJags(small)  ## just run it
print(rj)

## End(Not run)

Run a Bayesian mixture model for marker dosage with minimal effort

Description

Given segregation ratios and a ploidy level, a mixture model is constructed with default priors and initial values and JAGS run to produce an MCMC sample for statistical inference. Returns an object of S3 class runJagsWrapper

Usage

runSegratioMM(seg.ratios, model, priors = setPriors(model),
 inits = setInits(model, priors), jags.control =
 setControl(model, stem, burn.in = burn.in, sample = sample, thin = thin),
 burn.in = 2000, sample = 5000, thin = 1, stem = "test", fix.one = TRUE,
 print = TRUE, plots = TRUE, print.diagnostics = TRUE,
 plot.diagnostics = TRUE, run.diagnostics.later=FALSE )

Arguments

Value

Returns object of class runJagsWrapper with components

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

setPriors setInits expected.segRatio segRatio setControl dumpData dumpInits and diagnosticsJagsMix

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)
##print(a1)
sr <-  segregationRatios(a1$markers)
x <- setModel(3,8)

## Not run: 
## fit simple model in one hit

x.run <- runSegratioMM(sr, x, burn.in=200, sample=500)
print(x.run)

## End(Not run)

Set up controls for a JAGS segregation ratio model run

Description

Sets up directives for running JAGS which are subsequently put into a .cmd file. MCMC attributes such as the size of burn in, length of MCMC and thinning may be specified

Usage

setControl(model, stem = "test", burn.in = 2000, sample = 5000, thin = 1,
 bugs.file = paste(stem, ".bug", sep = ""),
 data.file = paste(stem, "-data.R", sep = ""),
 inits.file = paste(stem, "-inits.R", sep = ""),
 monitor.var = model$monitor.var, seed=1)

Arguments

Value

Returns an object of class jagsControl which is a list with components

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

setModel setInits expected.segRatio segRatio setControl dumpData dumpInits or for an easier way to run a segregation ratio mixture model see runSegratioMM

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)

## set up model with 3 components
x <- setModel(3,8)
x2 <- setPriors(x)

jc <- setControl(x)
print(jc)

Set up and dump initial values given the model and prior

Description

Given a model of class modelSegratioMM and priors of class priorsSegratioMM, initial values are computed using approximate expected values by setInits and then written to file by dumpInits

Usage

setInits(model, priors, seed = 1)

dumpInits(inits, stem = "test", inits.file = paste(stem, "-inits.R",
  sep = ""))

Arguments

Value

Returns a list with the following initial values:

Note

Warning: If a number of chains are to be produced then several seeds may be specified. Currently, this is largely untested and so it is highly unlikely that this will actually work for all functions in this package.

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

setModel setPriors setControl dumpInits

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)

## set up model, priors, inits etc and write files for JAGS
x <- setModel(3,8)
x2 <- setPriors(x)
inits <- setInits(x,x2)
dumpInits(inits)

Set characteristics of the Bayesian mixture model for dosages

Description

Used to automatically set up Bayesian finite mixture models for dosage allocation of dominant markers in autopolyploids given the number of components and ploidy level

Usage

setModel(n.components, ploidy.level, random.effect = FALSE, seg.ratios =NULL,
 ploidy.name = NULL, equal.variances=TRUE, 
 type.parents = c("heterogeneous", "homozygous"))

Arguments

Value

Returns object of class modelSegratioMM with components

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

setPriors setInits expected.segRatio segRatio setControl dumpData dumpInits or for an easier way to run a segregation ratio mixture model see runSegratioMM

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)

## set up model with 3 components
x <- setModel(3,8)
print(x)

Set prior distributions for parameters of Bayesian mixture model for dosages

Description

May be used to automatically set up vague or strong priors or explicitly set them for Bayesian finite mixture model specified as an object of class modelSegratioMM using setModel

Usage

setPriors(model, type.prior = c("strong",
              "vague", "strong.tau","strong.s", "specified"),
mean.vague = 0.1, prec.vague = 0.1, A.vague = 0.1, B.vague = 0.1,
prec.strong=400, n.individuals=200, reffect.A = 44, reffect.B = 0.8,
M.sd = 0.025, STRONG.PREC=c(0.025, 0.975), UPPER = 0.995,  PREC.INT=0.2,
params = NULL, segRatio = NULL)

Arguments

Value

Returns an object of class priorsSegratioMM which is a list with components

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

setModel setInits expected.segRatio segRatio setControl dumpData dumpInits or for an easier way to run a segregation ratio mixture model see runSegratioMM

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)

## set up model with 3 components
x <- setModel(3,8)
x2 <- setPriors(x)
print(x2)

x2b <- setPriors(x, "strong")
print(x2b)

Summary statistics for an segratioMCMC object

Description

Wrapper for summary.mcmc processing only mixture model parameters although markers may also easily be summarised. The mean, standard deviation, naive standard error of the mean (ignoring autocorrelation of the chain) and time-series standard error based on an estimate of the spectral density at 0. For details see summary.mcmc

Usage

## S3 method for class 'segratioMCMC'
summary(object, ..., row.index = c(1:10),
 var.index = NULL,
 marker.index = c(1:8))

Arguments

Value

An object of class summarySegratioMCMC is returned which contains summary statistics for parameters and some markers. For details see summary.mcmc

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

summary.mcmc mcmc segratioMCMC readJags diagnosticsJagsMix

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)
##print(a1)
sr <-  segregationRatios(a1$markers)
x <- setModel(3,8)

## Not run: 
## fit simple model in one hit and summarise

x.run <- runSegratioMM(sr, x, burn.in=200, sample=500)
print(summary(x.run$mcmc.mixture))
print(summary(x.run$mcmc.mixture, var.index=c(1:3), marker.index=c(1:4)))

## End(Not run)

Write JAGS .cmd file for running JAGS

Description

Write JAGS .cmd file to disk

Usage

writeControlFile(jags.control,
     file = paste(jags.control$stem, ".cmd", sep = ""))

Arguments

Value

None.

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

setControl runJags

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)
sr <-  segregationRatios(a1$markers)

## set up model with 3 components
x <- setModel(3,8)
x2 <- setPriors(x)
dumpData(sr, x)
inits <- setInits(x,x2)
dumpInits(inits)
##x.priors <- setPriors(x, "vague")
writeJagsFile(x, x2, stem="test")

small <- setControl(x, burn.in=20, sample=50)
writeControlFile(small)

Writes BUGS file for processing by JAGS

Description

Given the model and priors a file is written to disk for subsequent JAGS run. BUGS code contained in the model and priors objects is combined and alterered if necessary

Usage

writeJagsFile(model, priors, stem = "test")

Arguments

Value

None.

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

segRatio dump

Examples

## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)

## compute segregation ratios
sr <-  segregationRatios(a1$markers)

## set up model for 3 components of autooctoploid
x <- setModel(3,8)
x2 <- setPriors(x)

dumpData(sr, x)
inits <- setInits(x,x2)
dumpInits(inits)
##x.priors <- setPriors(x, "vague")
writeJagsFile(x, x2, stem="test")