#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_07052013.R")) #Include GAM_day

source(file.path(script_path,"GAM_fusion_function_multisampling_validation_metrics_09012013.R"))

#met_stations_outfiles_obj_file<-"met_stations_outfiles_obj_gam_CAI__365d_gam_CAI_lst_comb3_08252013.RData"

var<-"TMAX" # variable being interpolated

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_CAI") #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("gam_daily") #other otpions to be added later

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

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

step_month <-0.1         

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

#dates_selected<-c("20100101","20100102","20100103","20100901") # Note that the dates set must have a specific format: yyymmdd

#dates_selected<-c("20100101","20100102","20100301","20100302","20100501","20100502","20100701","20100702","20100901","20100902","20101101","20101102")

#LC1: Evergreen/deciduous needleleaf trees

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

#Combination 4: for paper baseline=s(lat,lon)

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

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

#Default name of LST avg to be matched               

lst_avg<-c("mm_01","mm_02","mm_03","mm_04","mm_05","mm_06","mm_07","mm_08","mm_09","mm_10","mm_11","mm_12")  

                                "list_models","lst_avg","out_path","script_path","use_clim_image","join_daily",

                                "interpolation_method")

raster_prediction_obj <-raster_prediction_fun(list_param_raster_prediction)

############## STAGE 5: OUTPUT ANALYSES ##################