Test predictions with new column names

DevelopingModels/abundance-gam.R

@@ -1,4 +1,4 @@
-abundance-gam=function(level,date,forecastmonth,forecastyear,NewMoonNumber,CI_level) {
+abundance-gam=function(level,date,forecastmonth,forecastyear,newmoonnumber,CI_level) {
 
 mc.cores = 8 # How many species to run in parallel
 new_yday = yday(Sys.Date()) # Replace Sys.Date with the next Portal sampling date
@@ -324,7 +324,7 @@ CIs = function(sp, new_date, n_samples = 10000){
 
 ci_predictions = lapply(species, CIs, new_date=Sys.Date())
 
-return(bind_rows(date,forecastmonth,forecastyear,NewMoonNumber,"abundance","GAM",level,ci_predictions) %>%
+return(bind_rows(date,forecastmonth,forecastyear,newmoonnumber,"abundance","GAM",level,ci_predictions) %>%
   rename(LowerPI = X1, UpperPI = X2))
 
 }

forecast_tools.R

@@ -123,7 +123,7 @@ plot_data = function(data) {
 #'
 #' Will only return values where there are matching comparison columns (currency, level, species)
 #'
-#' @param observations dataframe Has the columns NewMoonNumber, currency, level, species, actual
+#' @param observations dataframe Has the columns newmoonnumber, currency, level, species, actual
 #' @param forecasts dataframe passes the forecast validity check. Must have matching values in
 #'                  the comparison columns
 #' @param error_metric chr either 'mse' for mean squared error or 'likelihood' for the likelihood of the observation

models/model_functions.R

@@ -15,7 +15,7 @@ get_moon_data <- function(){
 }
 
 get_rodent_data <- function(moons, forecast_date, filename_suffix){
-  #Period 203/NewMoonNumber 217 will be when the training data timeseries
+  #Period 203/newmoonnumber 217 will be when the training data timeseries
   #begins. Corresponding to Jan 1995
   historic_start_period=203
   historic_start_newmoon=217
@@ -27,7 +27,7 @@ get_rodent_data <- function(moons, forecast_date, filename_suffix){
   #Control plots
   #The total rodent count in each treatment
   controls$total = rowSums(controls[,-(1:2)])
-  #Drop non-control treatments and add in NewMoonNumber
+  #Drop non-control treatments and add in newmoonnumber
   controls = controls %>%
     filter(treatment == 'control') %>%
     select(-treatment) %>%
@@ -55,7 +55,7 @@ get_weather_data <- function(moons, all, first_forecast_newmoon, last_forecast_n
     ungroup() %>%
     left_join(moons, by=c('year','month'))
 
-  #Offset the NewMoonNumber to create a 6 month lag between
+  #Offset the newmoonnumber to create a 6 month lag between
   #rodent observations and weather
   weather_data$NewMoonNumber_with_lag = weather_data$newmoonnumber + 6
 
@@ -73,8 +73,8 @@ get_weather_data <- function(moons, all, first_forecast_newmoon, last_forecast_n
   weather_forecast_months = moons %>%
     filter(newmoonnumber >= first_forecast_newmoon-5, newmoonnumber <= last_forecast_newmoon-5)
 
-  #  x=subset(weather,NewMoonNumber>=first_forecast_newmoon-5) %>%
-  #  subset(NewMoonNumber<=last_forecast_newmoon-5)
+  #  x=subset(weather,newmoonnumber>=first_forecast_newmoon-5) %>%
+  #  subset(newmoonnumber<=last_forecast_newmoon-5)
 
   weathermeans=weather_data[dim(weather_data)[1]-36:dim(weather_data)[1],] %>%
     group_by(month) %>%

predictions/2017-08-17Allforecasts_model_aic.csv

@@ -0,0 +1,89 @@
+"date","currency","model","level","species","aic","fit_start_newmoon","fit_end_newmoon","initial_newmoon"
+2017-08-17,"abundance","Forecast","All","total",3034.60905229841,217,495,495
+2017-08-17,"abundance","AutoArima","All","total",91.6324260132344,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","BA",384.428584411101,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","DM",1619.66569476751,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","DO",1406.53591968049,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","DS",210.824782951823,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","NA",819.825386119389,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","OL",752.414537331397,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","OT",1357.92431694639,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","PB",1647.6533009071,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","PE",1101.22977607059,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","PF",979.150769576377,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","PH",69.7529763394115,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","PL",330.5131889748,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","PM",644.09415520891,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","PP",2123.32674855875,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","RF",54.4160054117943,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","RM",1017.66468317416,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","RO",264.191906955607,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","SF",443.915119000338,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","SH",512.612175218924,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","SO",25.9847290527406,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","total",2432.51309905006,217,495,495
+2017-08-17,"abundance","Poisson Env","All","BA",630.520320082472,217,495,495
+2017-08-17,"abundance","Poisson Env","All","DM",1648.64210410767,217,495,495
+2017-08-17,"abundance","Poisson Env","All","DO",1461.0569689118,217,495,495
+2017-08-17,"abundance","Poisson Env","All","DS",301.196540529903,217,495,495
+2017-08-17,"abundance","Poisson Env","All","NA",816.878722711045,217,495,495
+2017-08-17,"abundance","Poisson Env","All","OL",805.512059636401,217,495,495
+2017-08-17,"abundance","Poisson Env","All","OT",1455.22472687595,217,495,495
+2017-08-17,"abundance","Poisson Env","All","PB",1999.45148560436,217,495,495
+2017-08-17,"abundance","Poisson Env","All","PE",1151.45011622835,217,495,495
+2017-08-17,"abundance","Poisson Env","All","PF",1186.77733831323,217,495,495
+2017-08-17,"abundance","Poisson Env","All","PH",65.0195026688012,217,495,495
+2017-08-17,"abundance","Poisson Env","All","PL",403.105046547161,217,495,495
+2017-08-17,"abundance","Poisson Env","All","PM",753.062701323725,217,495,495
+2017-08-17,"abundance","Poisson Env","All","PP",4630.90697419721,217,495,495
+2017-08-17,"abundance","Poisson Env","All","RF",49.5879839469814,217,495,495
+2017-08-17,"abundance","Poisson Env","All","RM",1239.11456255679,217,495,495
+2017-08-17,"abundance","Poisson Env","All","RO",299.458133928879,217,495,495
+2017-08-17,"abundance","Poisson Env","All","SF",598.736678051361,217,495,495
+2017-08-17,"abundance","Poisson Env","All","SH",598.017529551761,217,495,495
+2017-08-17,"abundance","Poisson Env","All","SO",16.9928952446082,217,495,495
+2017-08-17,"abundance","Poisson Env","All","total",3887.79500372605,217,495,495
+2017-08-17,"abundance","Forecast","All","total",3034.60905229841,217,495,495
+2017-08-17,"abundance","AutoArima","All","total",91.6324260132344,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","BA",384.428584411101,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","DM",1619.66569476751,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","DO",1406.53591968049,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","DS",210.824782951823,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","NA",819.825386119389,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","OL",752.414537331397,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","OT",1357.92431694639,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","PB",1647.6533009071,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","PE",1101.22977607059,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","PF",979.150769576377,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","PH",69.7529763394115,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","PL",330.5131889748,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","PM",644.09415520891,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","PP",2123.32674855875,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","RF",54.4160054117943,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","RM",1017.66468317416,217,495,495
+2017-08-17,"abundance","NegBinom Time Series","All","RO",264.191906955607,217,495,495
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+2017-08-17,"abundance","NegBinom Time Series","All","total",2432.51309905006,217,495,495
+2017-08-17,"abundance","Poisson Env","All","BA",630.520320082472,217,495,495
+2017-08-17,"abundance","Poisson Env","All","DM",1648.64210410767,217,495,495
+2017-08-17,"abundance","Poisson Env","All","DO",1461.0569689118,217,495,495
+2017-08-17,"abundance","Poisson Env","All","DS",301.196540529903,217,495,495
+2017-08-17,"abundance","Poisson Env","All","NA",816.878722711045,217,495,495
+2017-08-17,"abundance","Poisson Env","All","OL",805.512059636401,217,495,495
+2017-08-17,"abundance","Poisson Env","All","OT",1455.22472687595,217,495,495
+2017-08-17,"abundance","Poisson Env","All","PB",1999.45148560436,217,495,495
+2017-08-17,"abundance","Poisson Env","All","PE",1151.45011622835,217,495,495
+2017-08-17,"abundance","Poisson Env","All","PF",1186.77733831323,217,495,495
+2017-08-17,"abundance","Poisson Env","All","PH",65.0195026688012,217,495,495
+2017-08-17,"abundance","Poisson Env","All","PL",403.105046547161,217,495,495
+2017-08-17,"abundance","Poisson Env","All","PM",753.062701323725,217,495,495
+2017-08-17,"abundance","Poisson Env","All","PP",4630.90697419721,217,495,495
+2017-08-17,"abundance","Poisson Env","All","RF",49.5879839469814,217,495,495
+2017-08-17,"abundance","Poisson Env","All","RM",1239.11456255679,217,495,495
+2017-08-17,"abundance","Poisson Env","All","RO",299.458133928879,217,495,495
+2017-08-17,"abundance","Poisson Env","All","SF",598.736678051361,217,495,495
+2017-08-17,"abundance","Poisson Env","All","SH",598.017529551761,217,495,495
+2017-08-17,"abundance","Poisson Env","All","SO",16.9928952446082,217,495,495
+2017-08-17,"abundance","Poisson Env","All","total",3887.79500372605,217,495,495