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Revision c5d0da95

Added by Benoit Parmentier over 11 years ago

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

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climate/research/oregon/interpolation/master_script_temp.R
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#STAGE 5: Output analyses: assessment of results for specific dates...
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#
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#AUTHOR: Benoit Parmentier
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#DATE: 09/12/2013
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#DATE: 09/13/2013
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#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#363, TASK$568--   


  		




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#met_stations_outfiles_obj_file<-"met_stations_outfiles_obj_gam_CAI__365d_gam_CAI_lst_comb3_08252013.RData"


  		




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var<-"TMAX" # variable being interpolated


  		




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out_prefix<-"_365d_kriging_fus_lst_comb3_09122013"                #User defined output prefix


  		




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out_suffix<-"_OR_09122013"                                       #Regional suffix


  		




  
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out_prefix<-"_365d_kriging_fus_lst_comb3_09132013"                #User defined output prefix


  		




  
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out_suffix<-"_OR_09132013"                                       #Regional suffix


  		




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


  		




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


  		




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


  		




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step_month <-0.1         


  		




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


  		




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


  		




  
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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...


  		




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


  		




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


  		




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


  		




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


  		




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


  		




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


  		




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


  		




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