#STAGE 5: Output analyses: assessment of results for specific dates...

#

#AUTHOR: Benoit Parmentier                                                                       

#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#363, TASK$568--   

#source(file.path(script_path,"download_and_produce_MODIS_LST_climatology_06112013.R"))

source(file.path(script_path,"covariates_production_temperatures_08052013.R"))

source(file.path(script_path,"Database_stations_covariates_processing_function_06112013.R"))

source(file.path(script_path,"results_interpolation_date_output_analyses_08052013.R"))

#source(file.path(script_path,"results_covariates_database_stations_output_analyses_04012013.R")) #to be completed

#FUNCTIONS CALLED FROM GAM ANALYSIS RASTER PREDICTION ARE FOUND IN...

source(file.path(script_path,"sampling_script_functions_08252013.R"))

source(file.path(script_path,"interpolation_method_day_function_multisampling_10112013.R")) #Include GAM_day

source(file.path(script_path,"GAM_fusion_function_multisampling_validation_metrics_10102013.R"))

var<-"TMAX" # variable being interpolated

#out_prefix<-"_365d_gam_cai_lst_comb3_10102013"                #User defined output prefix

out_suffix_modis <-"_05302013"                       #pattern to find tiles produced previously     

#interpolation_method<-c("gam_fusion","gam_CAI","gam_daily") #other otpions to be added later

#interpolation_method<-c("gam_fusion") #other otpions to be added later

#interpolation_method<-c("kriging_fusion") #other otpions to be added later

#interpolation_method<-c("gwr_fusion") #other otpions to be added later

#interpolation_method<-c("gwr_CAI") #other otpions to be added later

#interpolation_method<-c("kriging_CAI") 

#interpolation_method<-c("kriging_daily") #other otpions to be added later

#interpolation_method<-c("gwr_daily") #other otpions to be added later

seed_number_month <- 100

nb_sample_month <-1           #number of time random sampling must be repeated for every hold out proportion

constant_month <- 0             #if value 1 then use the same samples as date one for the all set of dates

prop_minmax_month <-c(0,0)  #if prop_min=prop_max and step=0 then predictions are done for the number of dates...

               "y_var ~ s(lat,lon) + s(elev_s) + s(LST)",

               "y_var ~ s(lat,lon) + s(elev_s) + s(LST) + ti(LST,LC1)")

#Combination 3: for paper baseline=s(lat,lon)+s(elev)

#list_models<-c("y_var ~ s(lat,lon) + s(elev_s)",