groupBayesianCNVs {CNVrd2} | R Documentation

## groupBayesianCNVs

### Description

Cluster segmentation scores into different groups by using prior information from one population.

### Usage

    groupBayesianCNVs(xData, nGroups, lambda0, sd0, alpha0, distanceBetweenGroups, inits = NULL,
    		precisionOfGroupMeans = 3000, sdOftau = NULL, n.adapt = 100,
    		nUpdate = 1000, n.iter = 20000, thin = 5, n.chains = 1,
    		heidel.diag = FALSE, leftLimit = NULL, rightLimit = NULL)

### Arguments

`xData` | a numeric vector of observations (segmentation scores).
`nGroups` | an integer indicating a number of groups.
`lambda0` | Prior means of groups.
`sd0` | Prior standard deviations of groups.
`alpha0` | Prior parameters for mixing proportions.
`distanceBetweenGroups` | Prior value for the distance between groups.
`inits` | A list of initial values of parameters.
`precisionOfGroupMeans` | Prior parameter of group means (default = 3000).
`sdOftau` | Prior parameter of the standard deviations of group precisions.
`n.adapt` | the number of iterations for adaptation (`rjags`'s parameter)
`nUpdate` | the number of iterations for burn-in process.
`n.iter` | the number of iterations for sampling (`rjags`'s parameter)
`thin` | thinning interval for monitors (`rjags`'s parameter)
`n.chains` | the number of parallel chains for the model (`rjags`'s parameter, default = 1)
`heidel.diag` | If heidel.diag = TRUE then Heidelberger and Welch's convergence diagnostic is used.
`leftLimit` | Values which are less than this value will be allocated to the smallest group.
`rightLimit` | Values which are larger than this value will be allocated to the largest group.

### Details

This function assumes that users already know the information of groups' means, standard deviations; the distances between groups.

### Value

`mcmcChains` | A list of mcarray objects for means, standard deviations, proportions
`m1` | Means of groups
`s1` | Standard deviations of groups
`p1` | Proportions of groups
`allGroups` | A data.frame includes samples and their corresponding groups
`hTest` | Results of Heidelberger and Welch's convergence diagnostic

### Note

#####

### Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. hoangtannguyenvn@gmail.com

### References

Martyn Plummer (2013). rjags: Bayesian graphical models using MCMC. R package version 3-10. http://CRAN.R-project.org/package=rjags.

Lunn, David J., et al. WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 10.4 (2000): 325-337.

### See Also

groupCNVs

### Examples

    ## Not run:
    data(ccl3l1data)

    xyEuro <- ccl3l1data[grep("CEU|TSI|IBS|GBR|FIN", ccl3l1data[, 2]), ]

    names(yEuro) <- rownames(xyEuro)

    ##Clustering European segmentation scores into group: 5 groups were chosen

    objectClusterEuroCCL3L1 <- new("clusteringCNVs", x = yEuro, k = 5)

    europeanCCL3L1Groups <- groupCNVs(Object = objectClusterEuroCCL3L1)

    ##Obtain prior information
    #Means
    lambda0 <- as.numeric(europeanCCL3L1Groups$m)
    #SD
    sdEM <- as.numeric(europeanCCL3L1Groups$sigma)
    #Proportions
    pEM <- as.numeric(europeanCCL3L1Groups$p)

    ###Calculate the distances between groups
    for (ii in 2:5){print(lambda0[ii] - lambda0[ii-1])}

    ###All segmentation scores
    ccl3l1X <- ccl3l1data$SS
    names(ccl3l1X) <- as.character(ccl3l1data$Name)
    range(ccl3l1X)

    ##Set prior information:
    #prior for the sd of the means of groups:
    #5 was set for the third group = 2 CN
    sd <- c(1, 1, 5, 1, 1)
    ccl3l1X <- sort(ccl3l1X)
    ###Data
    xData <- ccl3l1X
    ###Number of groups
    nGroups <- 10
    ###prior for means of groups
    lambda0 <- lambda0
    ###Prior for mixing proportions
    alpha0 <-  c(3, 29, 44, 18, 7,  5, rep(2, nGroups -length(pEM) -1))
    ##Prior for the distances between groups
    distanceBetweenGroups = 0.485

    sdEM = sdEM

    ##Adjust standard deviation for the fifth group
    sdEM[5] <- sdEM[4]

     set.seed(123)
     groupCCL3L1allPops <- groupBayesianCNVs(xData = xData, nGroups = nGroups,
                                             lambda0 = lambda0,
                                             sd0 = sdEM, alpha0 = alpha0,
                                             distanceBetweenGroups = distanceBetweenGroups,
                                             sdOftau = sd,
                                            rightLimit = 4)

    ## End(Not run)

[Package CNVrd2 version 1.22.0 Index]