Manage merge conflict in appveyor

Merge branch 'master' of https://github.com/BiologicalRecordsCentre/sparta

# Conflicts:
#	appveyor.yml

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: sparta
 Type: Package
 Title: Trend Analysis for Unstructured Data
-Version: 0.2.14
-Date: 2020-02-15
+Version: 0.2.19
+Date: 2020-05-26
 Authors@R: c(person("Tom", "August", role = c("aut", "cre"), email = "[email protected]"),
              person("Gary", "Powney", role = c("aut")),
              person("Charlie", "Outhwaite", role = c("aut")),

R/dataGaps.r

@@ -4,6 +4,10 @@ dataGaps<-function(years, minmod, maxmod, mindat, maxdat){
      actualyears <- (years-1) + minmod
      middleyears <- subset(actualyears,actualyears %in% seq(mindat,maxdat,1))
      middleyears <- sort(unique(middleyears))
-     gap_middle <- max(diff(middleyears)-1)
+     if(length(middleyears)>1){
+        gap_middle <- max(diff(middleyears)-1)
+     } else {
+        gap_middle <- NA
+     }
      return(list(gap_start=gap_start,gap_end=gap_end,gap_middle=gap_middle))
-   }
+   }

R/occDetModel.r

@@ -10,6 +10,15 @@
 #' @param site A character vector of site names, as long as the number of observations.
 #' @param survey A  vector as long as the number of observations. 
 #'        This must be a Date if includeJDay = \code{TRUE}
+#' @param replicate An optional vector to identify replicate samples (visits) per survey. Need not be globally unique (e.g can be 1, 2, .. n within surveys) 
+#' @param closure_period An optional vector of integers specifying the closure period. 
+#'        If \code{FALSE} then closure_period will be extracted as the year from the survey.
+#' @param criterion Determines whether the model should be run. If an integer then this defines the threshold number of records (50 in Outhwaite et al 2019).
+#' Other options are `EqualWt` or `HighSpec`, which define the application of "rules of thumb" defined in Pocock et al 2019. 
+#' Defaults to 1, in which case the model is applied for so long there is a single record of the focal species.
+#' @param provenance An optional text string allowing the user to identify the dataset.
+#' @param rem_aggs_with_missing_regions An option which if TRUE will remove all aggregates which contain at least one region with no data.
+#' @param allowSitesMultiRegions An option that permits sites to be included in more than one region. If `FALSE` then these sites are dropped.
 #' @param species_list A character vector of taxa names for which models should be run. This is
 #'        optional and by default models will be run for all taxa
 #' @param write_results logical, should results be saved to \code{output_dir}. This is
@@ -168,11 +177,16 @@
 #'                        region_aggs = list(agg1 = c('region1', 'region2')))       
 #' }
 #' @export
-#' @references Roy, H.E., Adriaens, T., Isaac, N.J.B. et al. (2012) Invasive alien predator
-#'             causes rapid declines of native European ladybirds. Diversity & Distributions,
-#'             18, 717-725.
+#' @references Isaac, N.J.B., van Strien, A.J., August, T.A., de Zeeuw, M.P. and Roy, D.B. (2014).
+#'             Statistics for citizen science: extracting signals of change from noisy ecological data.
+#'             \emph{Methods in Ecology and Evolution}, 5: 1052-1060.
+#' @references Outhwaite, C.L., Chandler, R.E., Powney, G.D., Collen, B., Gregory, R.D. & Isaac, N.J.B. (2018).
+#'             Prior specification in Bayesian occupancy modelling improves analysis of species occurrence data. 
+#'             \emph{Ecological Indicators}, 93: 333-343.
 
-occDetModel <- function(taxa, site, survey,
+occDetModel <- function(taxa, site, survey, 
+                        replicate = NULL, closure_period = NULL, criterion = 1, provenance = NULL,
+                        rem_aggs_with_missing_regions = TRUE, allowSitesMultiRegions = FALSE,
                         species_list = unique(taxa), write_results = TRUE,
                         output_dir = getwd(), nyr = 2, n_iterations = 5000,
                         burnin = 1500, thinning = 3, n_chains = 3, 
@@ -202,6 +216,8 @@ occDetModel <- function(taxa, site, survey,
   visitData <- formatOccData(taxa = taxa,
                              site = site,
                              survey = survey,
+                             replicate = replicate, 
+                             closure_period = replicate,
                              includeJDay = includeJDay)
 
   ### loop through the species list running the Bayesian occupancy model function ###
@@ -228,7 +244,11 @@ occDetModel <- function(taxa, site, survey,
                                       additional.parameters = additional.parameters,
                                       additional.BUGS.elements = additional.BUGS.elements,
                                       additional.init.values = additional.init.values,
-                                      return_data = return_data)
+                                      provenance = provenance,
+                                      criterion = criterion,
+                                      return_data = return_data,
+                                      rem_aggs_with_missing_regions = rem_aggs_with_missing_regions,
+                                      allowSitesMultiRegions = allowSitesMultiRegions)
   }
 
   class(output) <- 'occDetList'

R/plot_DetectionOverTime.R

@@ -21,8 +21,18 @@
 #' @export
 
 
-plot_DetectionOverTime <- function(model, spname = NULL, min.yr = NULL){
+plot_DetectionOverTime <- function(model, spname = NULL, min.yr = NULL, CI=95){
 
+
+  # convert the CI into quantiles
+  # first check that CI is sensible
+  if((CI > 100) | (CI <= 0)) stop("Credible intervals must be between 0 and 100")
+  CI2q <- function(CI) {
+    q <- (1 - CI/100)/2
+    return(c(q, 1-q))
+  }
+  quant <- CI2q(CI)
+
   sims_list <- model$BUGSoutput$sims.list
 
   # the base: alpha.p is common to all models: 
@@ -47,11 +57,14 @@ plot_DetectionOverTime <- function(model, spname = NULL, min.yr = NULL){
     # the model was fitted with categorical list length
     pDet2 <- pDet1 + sims_list$dtype2.p[,1]
     pDet4 <- pDet1 + sims_list$dtype3.p[,1]
-  } 
-  # there is also an option to ignore list length, 
-  # in which case the probability of detection is assumed to be constant across surveys
-  # i.e. if the survey was systematic
-
+  } else {
+    # there is also an option to ignore list length, 
+    # in which case the probability of detection is assumed to be constant across surveys
+    # i.e. if the survey was systematic
+    # in this case the there are no estimates for the other pDet values.
+    pDet2 <- pDet4 <- NA
+  }
+
   pDet <- melt(list(pDet1, pDet2, pDet4))
   names(pDet) <- c("it", "year", "lgt_pDet", "ListLength")
   pDet$ListLength[pDet$ListLength==3] <- 4 # the "third" category is for a list of length 4
@@ -61,9 +74,9 @@ plot_DetectionOverTime <- function(model, spname = NULL, min.yr = NULL){
   # now summarize these posterior distributions
   pDet_summary <-ddply(
         pDet, .(year, ListLength), summarise, 
-        mean_pDet = mean(pDet),
-        lower95CI = quantile(pDet, 0.025),
-        upper95CI = quantile(pDet, 0.975))
+        mean_pDet = mean(pDet, na.rm=T),
+        lower95CI = quantile(pDet, quant[1], na.rm=T),
+        upper95CI = quantile(pDet, quant[2], na.rm=T))
 
   # if the user has supplied a year then switch the x axis to start at that minimum
   if(!is.null(min.yr)) pDet_summary$year <- pDet_summary$year + min.yr - 1
@@ -75,7 +88,7 @@ plot_DetectionOverTime <- function(model, spname = NULL, min.yr = NULL){
     ylab("Detection probability") +
     ggtitle(spname) +
     theme_bw()
-  gp 
+  gp
 
 }
 

appveyor.yml

@@ -24,18 +24,19 @@ environment:
 # Adapt as necessary starting from here
 
   matrix:
-  - R_VERSION: devel
-    NOT_CRAN: true
-    R_CHECK_ARGS: "--no-manual"
-    USE_RTOOLS: true
-    WARNINGS_AS_ERRORS: OFF
-
-  - R_VERSION: devel
-    NOT_CRAN: true
-    R_CHECK_ARGS: "--no-manual"
-    USE_RTOOLS: true
-    WARNINGS_AS_ERRORS: OFF
 
+  # - R_VERSION: devel
+  #   NOT_CRAN: true
+  #   R_CHECK_ARGS: "--no-manual"
+  #   USE_RTOOLS: true
+  #   WARNINGS_AS_ERRORS: OFF
+
+  # - R_VERSION: devel
+  #   NOT_CRAN: true
+  #   R_CHECK_ARGS: "--no-manual"
+  #   USE_RTOOLS: true
+  #   WARNINGS_AS_ERRORS: OFF
+
   - R_VERSION: stable
     NOT_CRAN: false
     USE_RTOOLS: true

tests/testthat/testoccDetFunc.r

@@ -536,7 +536,7 @@ test_that("Test occDetFunc using regions and region aggregates", {
                                      modeltype = c("ranwalk", "halfcauchy"),
                                      regional_codes = regionsmulti,
                                      region_aggs = list(agg1 = c('region1', 'region2'))),
-               'sites are not assigned to a region in regional_codes and will be removed')
+               '1 sites are not assigned to a region in regional_codes and will be removed')
 
   # test for sites in occurence data but not regions
   regionsmissing <- regionsDF[2:50,]
@@ -551,7 +551,7 @@ test_that("Test occDetFunc using regions and region aggregates", {
                                      modeltype = c("ranwalk", "halfcauchy"),
                                      regional_codes = regionsmissing,
                                      region_aggs = list(agg1 = c('region1', 'region2'))),
-               '1 sites are in occurrence data but not in regional data and will be removed')
+               '1 sites are in occurrence data but not in regional_codes and will be removed')
 
   # test for regional aggregate containing unknown region
   expect_error(results <- occDetFunc(taxa_name = 'a',