Merge pull request #61 from annecori/new-version

New version

.Rbuildignore

@@ -1,4 +1,5 @@
 fitRotavirus.R
 ^.*\.Rproj$
 ^\.Rproj\.user$
-^\.travis.yml
+^\.travis\.yml$
+^codecov\.yml$

.travis.yml

@@ -1,2 +1,35 @@
 language: r
-cache: packages
+cache: packages
+
+matrix:
+  include:
+    - os: linux
+      r: release
+      env:
+        - R_CODECOV=true
+    - os: linux
+      r: devel
+    - os: linux
+      r: oldrel
+    - os: osx
+      osx_image: xcode8.3
+
+warnings_are_errors: true
+
+notifications:
+  email:
+    on_success: change
+    on_failure: change
+
+after_success:
+  - if [[ "${R_CODECOV}" ]]; then Rscript -e 'covr::codecov()'; fi
+
+env:
+  global:
+    - NOT_CRAN: true
+    - R_BUILD_ARGS="--resave-data --compact-vignettes=gs+qpdf"
+    - R_CHECK_ARGS="--as-cran --timings"
+    - R_CHECK_TIME="TRUE"
+    - R_CHECK_TESTS="TRUE"
+    - _R_CHECK_TIMINGS_="0"
+    - _R_CHECK_SYSTEM_CLOCK_="0"

DESCRIPTION

@@ -1,6 +1,5 @@
 Package: EpiEstim
-Version: 2.0-0
-Date: 2012/01/17
+Version: 2.1-0
 Title: EpiEstim: a package to estimate time varying reproduction
     numbers from epidemic curves
 Author: Anne Cori <[email protected]>
@@ -24,12 +23,12 @@ Imports:
     scales,
     grDevices
 Remotes:
-    reconhub/incidence,
-    nickreich/coarseDataTools@hackout3
+    nickreich/coarseDataTools
 Suggests:
     testthat,
     compare,
-    utils
+    utils,
+    covr
 License: GPL (>=2)
 LazyLoad: yes
 Packaged: 2012-05-30 10:35:03 UTC; acori

NAMESPACE

@@ -1,16 +1,18 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(plot,estimate_R)
 export(DiscrSI)
 export(EstimateR)
 export(OverallInfectivity)
 export(WT)
 export(check_cdt_samples_convergence)
 export(coarse2estim)
 export(discr_si)
-export(estimate_r)
+export(estimate_R)
 export(init_mcmc_params)
+export(make_config)
+export(make_mcmc_control)
 export(overall_infectivity)
-export(plots)
 export(wallinga_teunis)
 import(grid)
 import(gridExtra)

R/check_cdt_samples_convergence.R

@@ -1,66 +1,70 @@
-#######################################################################################################################
-# check_cdt_samples_convergence runs a Gelman Rubin test to check convergence of the MCMC chain in coarseDataTools #
-#######################################################################################################################
+################################################################################
+# check_cdt_samples_convergence runs a Gelman Rubin test to check convergence of
+# the MCMC chain in coarseDataTools #
+################################################################################
 
 #' Checking convergence of an MCMC chain by using the Gelman-Rubin algorithm
-#' 
-#' \code{check_cdt_samples_convergence} Checking convergence of an MCMC chain by using the Gelman-Rubin algorithm
-#' 
-#' @param cdt_samples the \code{@sample} slot of a \code{cd.fit.mcmc} S4 object (see package \code{coarseDataTools})
-#' @return TRUE if the Gelman Rubin test for convergence was successful, FALSE otherwise
+#'
+#' \code{check_cdt_samples_convergence} Checking convergence of an MCMC chain by
+#'  using the Gelman-Rubin algorithm
+#'
+#' @param cdt_samples the \code{@sample} slot of a \code{cd.fit.mcmc} S4 object 
+#' (see package \code{coarseDataTools})
+#' @return TRUE if the Gelman Rubin test for convergence was successful, FALSE 
+#' otherwise
 #' @details{
-#' This function splits an MCMC chain in two halfs and uses the Gelman-Rubin algorithm to assess convergence of the chain by comparing its two halves.
+#' This function splits an MCMC chain in two halfs and uses the Gelman-Rubin 
+#' algorithm to assess convergence of the chain by comparing its two halves.
 #' }
-#' @seealso \code{\link{estimate_r}}
+#' @seealso \code{\link{estimate_R}}
 #' @author Anne Cori
 #' @importFrom coda gelman.diag
 #' @export
 #' @examples
 #' \dontrun{
-#' ## Note the following examples use an MCMC routine 
-#' ## to estimate the serial interval distribution from data, 
+#' ## Note the following examples use an MCMC routine
+#' ## to estimate the serial interval distribution from data,
 #' ## so they may take a few minutes to run
-#' 
+#'
 #' ## load data on rotavirus
 #' data("MockRotavirus")
-#' 
-#' ## estimate the serial interval from data 
-#' SI_fit <- coarseDataTools::dic.fit.mcmc(dat = MockRotavirus$si_data, 
-#'                              dist="G", 
-#'                              init_pars=init_mcmc_params(MockRotavirus$si_data, "G"),
-#'                              burnin = 1000, 
-#'                              n.samples = 5000)
-#'                              
+#'
+#' ## estimate the serial interval from data
+#' SI_fit <- coarseDataTools::dic.fit.mcmc(dat = MockRotavirus$si_data,
+#'                      dist="G",
+#'                      init_pars=init_mcmc_params(MockRotavirus$si_data, "G"),
+#'                      burnin = 1000,
+#'                      n.samples = 5000)
+#'
 #' ## use check_cdt_samples_convergence to check convergence
 #' converg_diag <- check_cdt_samples_convergence(SI_fit@samples)
 #' converg_diag
-#' 
+#'
 #' }
-#' 
-check_cdt_samples_convergence <- function(cdt_samples)
-{
-
-  #############################################################################################################################
-  # checking convergence of the MCMC by using the Gelman-Rubin algorithm between the first and second half of the MCMC sample
-  spl1 <- cdt_samples[1:floor(nrow(cdt_samples)/2),]
-  spl2 <- cdt_samples[(ceiling(nrow(cdt_samples)/2)+1):nrow(cdt_samples),]
+#'
+check_cdt_samples_convergence <- function(cdt_samples) {
+  ## checking convergence of the MCMC by using the Gelman-Rubin algorithm 
+  ## between the first and second half of the MCMC sample
+  spl1 <- cdt_samples[seq_len(floor(nrow(cdt_samples) / 2)), ]
+  spl2 <- cdt_samples[seq(ceiling(nrow(cdt_samples) / 2) + 1, nrow(cdt_samples)), ]
   GRD <- gelman.diag(as.mcmc.list(list(as.mcmc(spl1), as.mcmc(spl2))))
-  # Is any of the potential scale reduction factors >1.1 (looking at the upper CI)? 
-  # If so this would suggest that the MCMC has not converged well. 
-  if(any(GRD$psrf[,"Upper C.I."]>1.1))
-  {
-    warning("The Gelman-Rubin algorithm suggests the MCMC may not have converged within the number of iterations (MCMC.burnin + n1) specified. 
+  # Is any of the potential scale reduction factors >1.1 
+  # (looking at the upper CI)?
+  # If so this would suggest that the MCMC has not converged well.
+  if (any(GRD$psrf[, "Upper C.I."] > 1.1)) {
+    warning("The Gelman-Rubin algorithm suggests the MCMC may not have converged
+within the number of iterations (MCMC.burnin + n1) specified.
             You can visualise the full MCMC chain using: \n
             > par(mfrow=c(2,1))
-            > plot(res$SI.Moments[,'Mean'], type='l', xlab='Iterations', ylab='Mean SI') 
-            > plot(res$SI.Moments[,'Std'], type='l', xlab='Iterations', ylab='Std SI'),
-            where res is the output of estimate_r
+            > plot(res$SI.Moments[,'Mean'], type='l', xlab='Iterations', 
+ylab='Mean SI')
+            > plot(res$SI.Moments[,'Std'], type='l', xlab='Iterations', 
+ylab='Std SI'),
+            where res is the output of estimate_R
             and decide whether to rerun for longer.")
     return(FALSE)
-  }else
-  {
+  } else {
     cat("\nGelman-Rubin MCMC convergence diagnostic was successful.")
     return(TRUE)
   }
-
 }