getVerifiedPredictions: Verified Predictions

In leppott/CASTfxn: Functions for Causal Assessment Screening Tool (CAST)

Description

Get verified predictions.

Usage

getVerifiedPredictions(TargetSiteID, data.SampSummary, data.bio.taxa.raw,
  data.chem.info, data.SSTV.totabund, data.MT.bio, matchedData, ref.sites,
  BioIndex_Val = "IBI", BioIndex_Nar = "NarRat",
  BioIndex_Nar_Deg = "Violates", dir_results = file.path(getwd(),
  "Results"), dir_sub = "VerifiedPredictions", biocomm = "bmi")

Arguments

TargetSiteID	Site ID
data.SampSummary	x
data.bio.taxa.raw	x
data.chem.info	x
data.SSTV.totabund	x
data.MT.bio	Master Taxa list for biological data
matchedData	matched biological and chemical stressor data.
ref.sites	Vector of reference sites IDs.
BioIndex_Val	Column name for biological index value; list.MatchBioData$site.b.rsp
BioIndex_Nar	Column name for biological index narrative rating; list.MatchBioData$site.b.rsp
BioIndex_Nar_Deg	Biological index degraded narrative text; list.MatchBioData$site.b.rsp
dir_results	Directory to save plots. Default = working directory and Results.
dir_sub	Subdirectory for outputs from this function. Default = "VerifiedPredictions"
biocomm	Biological community; algae or BMI. Default = "BMI".

Details

Required objects:

* data.SampSummary; StationID_Master, CollDate, ChemSampleID, PhabSampID, BMI.Metrics.SampID, Algae.Metrics.SampID

* data.bio.taxa.raw; BMI.Metrics.SampID

* data.chem.info; SSTV, Analyte, SSTV, SensMin, SensMax, TolMin, TolMax

* data.SSTV.totabund; BMI.Metrics.SampID, StationID_Master, ChemSampleID, SSTV.analyte , SensRelAbund, TolRelAbund, SensTaxa, SampleAbundance, TolTaxa

* TargetSiteID

Value

Results text file and jpeg files to "Results" "VerifiedPredictions" folder in working directory of box plots and a single PDF of all plots.

Examples

TargetSiteID <- "SRCKN001.61"
dir_results  <- file.path(getwd(), "Results")

# Data getSiteInfo
# data, example included with package
data.Stations.Info <- data_Sites          # need for getSiteInfo and getChemDataSubsets
data.SampSummary   <- data_SampSummary
data.303d.ComID    <- data_303d
data.bmi.metrics   <- data_BMIMetrics
data.algae.metrics <- data_AlgMetrics
data.mod           <- data_ReachMod
data.MT.bio        <- data_BMIMasterTaxa

# Cluster based on elevation category  # need for getSiteInfo and getChemDataSubsets
elev_cat <- toupper(data.Stations.Info[data.Stations.Info[,"StationID_Master"]==TargetSiteID
                    , "ElevCategory"])
if(elev_cat=="HI"){
   data.cluster <- data_Cluster_Hi
} else if(elev_cat=="LO") {
   data.cluster <- data_Cluster_Lo
}

# Map data
# San Diego
#flowline <- rgdal::readOGR(dsn = "data_gis/NHDv2_Flowline_Ecoreg85", layer = "NHDv2_eco85_Project")
#outline <- rgdal::readOGR(dsn = "data_gis/Eco85", layer = "Ecoregion85")
# AZ
map_flowline  <- data_GIS_Flow_HI
map_flowline2 <- data_GIS_Flow_LO
if(elev_cat=="HI"){
   map_flowline <- data_GIS_Flow_HI
} else if(elev_cat=="LO") {
   map_flowline <- data_GIS_Flow_LO
}
map_outline   <- data_GIS_AZ_Outline
# Project site data to USGS Albers Equal Area
usgs.aea <- "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23
              +lon_0=-96 +x_0=0 +y_0=0 +datum=NAD83
              +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0"
# projection for outline
my.aea <- "+proj=aea +lat_1=20 +lat_2=60 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 
           +datum=NAD83 +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0"
map_proj <- my.aea
# 
dir_sub <- "SiteInfo"

# Run getSiteInfo
list.SiteSummary <- getSiteInfo(TargetSiteID, dir_results, data.Stations.Info
                                , data.SampSummary, data.303d.ComID
                                , data.bmi.metrics, data.algae.metrics
                                , data.cluster, data.mod
                                , map_proj, map_outline, map_flowline
                                , dir_sub=dir_sub)

# Data getChemDataSubsets
# data, example included with package
data.chem.raw  <- data_Chem
data.chem.info <- data_ChemInfo
site.COMID     <- list.SiteSummary$COMID
site.Clusters  <- list.SiteSummary$ClustIDs

# Run getChemDataSubsets
list.data <- getChemDataSubsets(TargetSiteID, comid=site.COMID, cluster=site.Clusters
                                , data.cluster=data.cluster, data.Stations.Info=data.Stations.Info
                                , data.chem.raw=data.chem.raw, data.chem.info=data.chem.info)

# Data getStressorList
chem.info     <- list.data$chem.info
cluster.chem  <- list.data$cluster.chem
cluster.samps <- list.data$cluster.samps
ref.sites     <- list.data$ref.sites
site.chem     <- list.data$site.chem
dir_sub <- "CandidateCauses"

# set cutoff for possible stressor identification
probsLow  <- 0.10
probsHigh <- 0.90 
biocomm <- "bmi"

# Run getStressorList
list.stressors <- getStressorList(TargetSiteID, site.Clusters, chem.info, cluster.chem
                                 , cluster.samps, ref.sites, site.chem
                                 , probsHigh, probsLow, biocomm, dir_results
                                 , dir_sub)
                                 
# Data getBMIMatches
## remove "none"
stressors <- list.stressors$stressors[list.stressors$stressors != "none"]
stressors_logtransf <- list.stressors$stressors_LogTransf[list.stressors$stressors != "none"]

# Run getBioMatches
biocomm <- "BMI"
data.bio.metrics <- data_BMIMetrics
list.MatchBioData<- getBioMatches(stressors, list.data, list.SiteSummary, data.SampSummary
                                  , data.chem.raw, data.bio.metrics, biocomm)
  
# Data getVerifiedPredictions
# data import, example
# data.bio.taxa.raw  <- read.delim(paste(myDir.Data,"data.bmi.taxa.raw.tab",sep=""))
# data.SSTV.totabund <- read.delim(paste(myDir.Data,"data.totabund.bySample.tab",sep=""))
#
# data, example included with package
data.bio.taxa.raw  <- data_BMIcounts
data.SSTV.totabund <- data_BMIRelAbund
BioIndex_Val       <- "IBI"
BioIndex_Nar       <- "NarRat"
BioIndex_Nar_Deg   <- "Violates"
dir_sub            <- "VerifiedPredictions"
biocomm <- "bmi"

# Run getVerifiedPredictions
getVerifiedPredictions(TargetSiteID
                       , data.SampSummary
                       , data.bio.taxa.raw
                       , data.chem.info
                       , data.SSTV.totabund
                       , data.MT.bio
                       , list.MatchBioData
                       , ref.sites
                       , BioIndex_Val
                       , BioIndex_Nar
                       , BioIndex_Nar_Deg
                       , dir_results
                       , dir_sub)

leppott/CASTfxn documentation built on Sept. 6, 2019, 11:04 p.m.