| ... | ... |
@@ -1,7 +1,7 @@ |
| 1 | 1 |
Package: COMPASS |
| 2 | 2 |
Type: Package |
| 3 | 3 |
Title: Combinatorial Polyfunctionality Analysis of Single Cells |
| 4 |
-Version: 1.27.1 |
|
| 4 |
+Version: 1.27.2 |
|
| 5 | 5 |
Date: 2014-07-11 |
| 6 | 6 |
Author: Lynn Lin, Kevin Ushey, Greg Finak, Ravio Kolde (pheatmap) |
| 7 | 7 |
Authors@R: c( person("Lynn", "Lin", role="aut", email="[email protected]"),
|
| ... | ... |
@@ -62,4 +62,4 @@ LazyLoad: yes |
| 62 | 62 |
LazyData: yes |
| 63 | 63 |
biocViews: ImmunoOncology, FlowCytometry |
| 64 | 64 |
Encoding: UTF-8 |
| 65 |
-RoxygenNote: 7.1.0 |
|
| 65 |
+RoxygenNote: 7.1.1 |
| ... | ... |
@@ -154,3 +154,25 @@ checkCOMPASSConvergence<-function(mlist,ncores=1){
|
| 154 | 154 |
} |
| 155 | 155 |
} |
| 156 | 156 |
|
| 157 |
+#'@title Diagnostic of a set of COMPASS Models. |
|
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+#' @param x a list of compass model fits of the same data with the same number of iterations, different seeds. |
|
| 159 |
+#' Run some mcmc diagnostics on a series of COMPASS model fits. |
|
| 160 |
+#' Assuming the input is a list of model fits for the same data with the same number of iterations and different seeds. |
|
| 161 |
+#' Run Gelman's Rhat diagnostics on the alpha_s and alpha_u hyperparameter chains, treating each model as an independent chain. |
|
| 162 |
+#' Rhat should be near 1 but rarely are in practice. Very large values may be a concern. |
|
| 163 |
+#' The method returns an average model, by averaging the mean_gamma matrices (equally weighted since each input has the same number of iterations). |
|
| 164 |
+#' This mean model should be better then any of the individual models. |
|
| 165 |
+#' It can be plotted via "plot(result$mean_model)". |
|
| 166 |
+#' @export |
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| 167 |
+COMPASSMCMCDiagnosis<-function(x){
|
|
| 168 |
+ require(coda) |
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| 169 |
+ require(abind) |
|
| 170 |
+ diag<-list() |
|
| 171 |
+ diag$alpha_s<-coda::gelman.diag(Map(function(x)coda::as.mcmc(x$fit$alpha_s),x)) |
|
| 172 |
+ diag$alpha_u<-coda::gelman.diag(Map(function(x)coda::as.mcmc(x$fit$alpha_u),x)) |
|
| 173 |
+ mean_gamma <- apply(Map(function(x) abind(x, along = 3), Map(function(x) x$fit$mean_gamma, x))[[1]], 1:2, mean) |
|
| 174 |
+ mean_model <- x[[1]] |
|
| 175 |
+ mean_model$fit$mean_gamma <- mean_gamma |
|
| 176 |
+ return(list(diag=diag,mean_model=mean_model)) |
|
| 177 |
+} |
|
| 178 |
+ |
| 157 | 179 |
new file mode 100644 |
| ... | ... |
@@ -0,0 +1,21 @@ |
| 1 |
+% Generated by roxygen2: do not edit by hand |
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| 2 |
+% Please edit documentation in R/Rhat.R |
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+\name{COMPASSMCMCDiagnosis}
|
|
| 4 |
+\alias{COMPASSMCMCDiagnosis}
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|
| 5 |
+\title{Diagnostic of a set of COMPASS Models.}
|
|
| 6 |
+\usage{
|
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| 7 |
+COMPASSMCMCDiagnosis(x) |
|
| 8 |
+} |
|
| 9 |
+\arguments{
|
|
| 10 |
+\item{x}{a list of compass model fits of the same data with the same number of iterations, different seeds.
|
|
| 11 |
+Run some mcmc diagnostics on a series of COMPASS model fits. |
|
| 12 |
+Assuming the input is a list of model fits for the same data with the same number of iterations and different seeds. |
|
| 13 |
+Run Gelman's Rhat diagnostics on the alpha_s and alpha_u hyperparameter chains, treating each model as an independent chain. |
|
| 14 |
+Rhat should be near 1 but rarely are in practice. Very large values may be a concern. |
|
| 15 |
+The method returns an average model, by averaging the mean_gamma matrices (equally weighted since each input has the same number of iterations). |
|
| 16 |
+This mean model should be better then any of the individual models. |
|
| 17 |
+It can be plotted via "plot(result$mean_model)".} |
|
| 18 |
+} |
|
| 19 |
+\description{
|
|
| 20 |
+Diagnostic of a set of COMPASS Models. |
|
| 21 |
+} |
