Project

General

Profile

« Previous | Next » 

Revision c5d0da95

Added by Benoit Parmentier about 11 years ago

running kriging fusion with 20 to 30% monthly holdout for multitimescale paper, OR

View differences:

climate/research/oregon/interpolation/master_script_temp.R
10 10 
#STAGE 5: Output analyses: assessment of results for specific dates...
11 11 
#
12 12 
#AUTHOR: Benoit Parmentier
13 
#DATE: 09/12/2013
13 
#DATE: 09/13/2013
14 14 

  		




  15
  15
  
    
#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#363, TASK$568--   


  		




  16
  16
  
    

  		




  ......




  80
  80
  
    
#met_stations_outfiles_obj_file<-"met_stations_outfiles_obj_gam_CAI__365d_gam_CAI_lst_comb3_08252013.RData"


  		




  81
  81
  
    

  		




  82
  82
  
    
var<-"TMAX" # variable being interpolated


  		




  83
  
  
    
out_prefix<-"_365d_kriging_fus_lst_comb3_09122013"                #User defined output prefix


  		




  84
  
  
    
out_suffix<-"_OR_09122013"                                       #Regional suffix


  		




  
  83
  
    
out_prefix<-"_365d_kriging_fus_lst_comb3_09132013"                #User defined output prefix


  		




  
  84
  
    
out_suffix<-"_OR_09132013"                                       #Regional suffix


  		




  85
  85
  
    
out_suffix_modis <-"_05302013"                       #pattern to find tiles produced previously     


  		




  86
  86
  
    

  		




  87
  87
  
    
#interpolation_method<-c("gam_fusion","gam_CAI","gam_daily") #other otpions to be added later


  		




  ......




  254
  254
  
    
nb_sample_month <-1           #number of time random sampling must be repeated for every hold out proportion


  		




  255
  255
  
    
step_month <-0.1         


  		




  256
  256
  
    
constant_month <-0             #if value 1 then use the same samples as date one for the all set of dates


  		




  257
  
  
    
prop_minmax_month <-c(0,0.1)  #if prop_min=prop_max and step=0 then predictions are done for the number of dates...


  		




  
  257
  
    
prop_minmax_month <-c(0.2,0.3)  #if prop_min=prop_max and step=0 then predictions are done for the number of dates...


  		




  258
  258
  
    

  		




  259
  259
  
    
#dates_selected<-c("20100101","20100102","20100103","20100901") # Note that the dates set must have a specific format: yyymmdd


  		




  260
  260
  
    
#dates_selected<-c("20100101","20100102","20100301","20100302","20100501","20100502","20100701","20100702","20100901","20100902","20101101","20101102")


  		




  261
  261
  
    
dates_selected<-"" # if empty string then predict for the full year specified earlier


  		




  262
  
  
    
screen_data_training<-FALSE #screen training data for NA and use same input training for all models fitted


  		




  
  262
  
    
screen_data_training<- FALSE #screen training data for NA and use same input training for all models fitted


  		




  263
  263
  
    
use_clim_image <- TRUE # use predicted image as a base...rather than average Tmin at the station for delta


  		




  264
  264
  
    
join_daily <- FALSE # join monthly and daily station before calucating delta


  		




  265
  265
  
    

  		












Also available in:  Unified diff