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

#

#AUTHOR: Benoit Parmentier                                                                       

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

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("gwr_CAI") #other otpions to be added later

#interpolation_method<-c("kriging_CAI") 

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

#out_path<-"/home/parmentier/Data/IPLANT_project/Oregon_interpolation/Oregon_03142013/output_data"

out_path <- "/data/project/layers/commons/Oregon_interpolation/output_data"

seed_number<- 100  #if seed zero then no seed?     

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

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

seed_number_month <- 100

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

#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")

dates_selected<-"" # if empty string then predict for the full year specified earlier

screen_data_training<- FALSE #screen training data for NA and use same input training for all models fitted

use_clim_image <- TRUE # use predicted image as a base...rather than average Tmin at the station for delta

#Models to run...this can be changed for each run

#LC1: Evergreen/deciduous needleleaf trees

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

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

#                "y_var ~ s(lat,lon) + s(elev_s) + s(N_w)",

interp_method2 <- NULL #other options are "gwr" and "kriging"

#interp_method2 <- "gam" #other options are "gwr" and "kriging"

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