include mkPriors/SimulateMixture from flowClust

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: flowStats
 Type: Package
 Title: Statistical methods for the analysis of flow cytometry data
-Version: 4.5.2
+Version: 4.5.3
 Author: Florian Hahne, Nishant Gopalakrishnan, Alireza Hadj Khodabakhshi,
         Chao-Jen Wong, Kyongryun Lee
 Maintainer: Greg Finak <[email protected]>, Mike Jiang <[email protected]>, Jake Wagner <[email protected]>
@@ -17,7 +17,8 @@ Suggests:
 Encoding: UTF-8
 Depends: R (>= 3.0.2)
 Imports: BiocGenerics, MASS, flowCore (>= 1.99.6), flowWorkspace, ncdfFlow(>= 2.19.5), flowViz, fda (>= 2.2.6), 
-   Biobase, methods, grDevices, graphics, stats, cluster, utils, KernSmooth, lattice, ks, RColorBrewer, rrcov
+   Biobase, methods, grDevices, graphics, stats, cluster, utils, KernSmooth, lattice, ks, RColorBrewer, rrcov, corpcor,
+    mnormt
 Enhances: RBGL,graph
 License: Artistic-2.0
 Lazyload: yes

NAMESPACE

@@ -10,8 +10,38 @@ importMethodsFrom(Biobase, channel)
 importMethodsFrom(BiocGenerics, cbind, colnames, lapply, mapply, order,
                   paste, pmax, pmin, rbind, rownames, sapply, setdiff,
                   table, unique)
-
-
+importFrom(corpcor,cov.shrink)
+
+importFrom(grDevices,cm.colors)
+importFrom(grDevices,gray)
+importFrom(grDevices,heat.colors)
+importFrom(grDevices,rainbow)
+importFrom(grDevices,terrain.colors)
+importFrom(grDevices,topo.colors)
+importFrom(graphics,abline)
+importFrom(graphics,contour)
+importFrom(graphics,curve)
+importFrom(graphics,hist)
+importFrom(graphics,image)
+importFrom(graphics,lines)
+importFrom(graphics,points)
+importFrom(graphics,stripchart)
+importFrom(graphics,title)
+importFrom(mnormt,rmt)
+importFrom(parallel,mclapply)
+importFrom(stats,BIC)
+importFrom(stats,cov)
+importFrom(stats,dist)
+importFrom(stats,kmeans)
+importFrom(stats,mahalanobis)
+importFrom(stats,na.omit)
+importFrom(stats,optim)
+importFrom(stats,optimize)
+importFrom(stats,qchisq)
+importFrom(stats,qf)
+importFrom(stats,qnorm)
+importFrom(stats,quantile)
+importFrom(stats,var)
 importFrom("grDevices", "colorRampPalette")
 importFrom("grDevices", "devAskNewPage")
 importFrom("utils", "read.csv")
@@ -27,6 +57,7 @@ importFrom("rrcov", "CovMcd")
 
 importFrom(MASS, huber, hubers, rlm, cov.rob)
 
+
 importFrom(ks, binning, hpi)
 
 importFrom(fda, Data2fd, create.bspline.basis, eval.fd, fdPar,fd,int2Lfd,vec2Lfd,norder,
@@ -83,6 +114,11 @@ export("lymphGate",
        "gate_tail",
        "tailgate"
        )
+export(SimulateMixture)
+export(flowClust2Prior)
+export(mkPrior)
+export(plotPrior)
+exportMethods(mkPrior)
 
 exportClasses("lymphFilter","rangeFilter","curv1Filter", 
               "curv2Filter", "norm2Filter")

R/SimulateMixture.R

@@ -0,0 +1,73 @@
+#' Random Generation from a t Mixture Model with Box-Cox Transformation
+#' 
+#' This function can be used to generate a sample from a multivariate \eqn{t}
+#' mixture model with Box-Cox transformation.
+#' 
+#' 
+#' @param N The number of observations.
+#' @param w A vector of length \eqn{K}, containing the \eqn{K} cluster
+#' proportions.
+#' @param mu A matrix of size \eqn{K \times P}{K x P}, where \eqn{K} is the
+#' number of clusters and \eqn{P} is the dimension, containing the \eqn{K} mean
+#' vectors.
+#' @param sigma An array of dimension \eqn{K \times P \times P}{K x P x P},
+#' containing the \eqn{K} covariance matrices.
+#' @param nu The degrees of freedom used for the \eqn{t} distribution.
+#' @param lambda The Box-Cox transformation parameter.  If missing, the
+#' conventional \eqn{t} distribution without transformation will be used.
+#' @return A matrix of size \eqn{N \times P}{N x P}.
+#' @author Raphael Gottardo <\email{raph@@stat.ubc.ca}>, Kenneth Lo
+#' <\email{c.lo@@stat.ubc.ca}>
+#' @seealso \code{\link{flowClust}}
+#' @keywords datagen
+#' @examples
+#' ### Number of components
+#' K <- 5
+#' ### Dimension
+#' p <- 2
+#' ### Number of observations
+#' n <- 200
+#' Mu <- matrix(runif(K*p, 0, 20), K, p)
+#' Sigma <- array(0, c(K, p, p))
+#' 
+#' for (k in 1:K)
+#' {
+#'     Sigma[k,,][outer(1:p, 1:p, ">")] <- runif(p*(p-1)/2,-.1,.1)
+#'     diag(Sigma[k,,]) <- runif(p,0,1)
+#'     ### Make sigma positive definite
+#'     Sigma[k,,] <- Sigma[k,,] %*% t(Sigma[k,,])
+#' }
+#' 
+#' ### Generate the weights
+#' w <- rgamma(K,10,1)
+#' w <- w/sum(w)
+#' 
+#' y <- SimulateMixture(n, w, Mu, Sigma, nu=4)
+#' @export 
+#' @importFrom mnormt rmt
+SimulateMixture <- function(N, w, mu, sigma, nu=4, lambda)
+{
+    # Number of clusters
+    K <- length(w)
+    if (K==1) {
+        mu <- matrix(mu, 1)
+        sigma <- array(sigma, c(1, ncol(mu), ncol(mu)))
+    } else if (length(mu)==K) {
+        mu <- matrix(mu, K, 1)
+        sigma <- array(sigma, c(K, 1, 1))
+    }
+	# dimension
+    py <- ncol(mu)
+	y <- matrix(0, N, py)
+    nu <- rep(nu,K)
+    if (!missing(lambda))
+        lambda <- rep(lambda,K)
+
+    label <- sample(1:K, N, replace=T, prob=w)
+    count <- table(c(label,1:K))-1
+    for (k in 1:K) if (count[k]>0) {    
+        y[label==k,] <- rmt(count[k], mu[k,], sigma[k,,], nu[k])
+        if (!missing(lambda)) y[label==k,] <- rbox(y[label==k,], lambda[k])    
+    }
+    y
+}

R/ellipse.R

@@ -0,0 +1,10 @@
+#' convert ellipse from cov/mu to points
+#' used to plot priors
+ellipse <- function (cov, centre, level = 0.95) 
+{
+  colnames(cov) <- c("x", "y")
+  eg <- ellipsoidGate(.gate = cov, mean = centre, distance = sqrt(qchisq(level, 2)))
+  pg <- as(eg, "polygonGate")
+  pg@boundaries
+
+  }