Revision a5ab37e3
Added by Benoit Parmentier over 11 years ago
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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: 07/18/2013
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#DATE: 07/21/2013
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#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#363, TASK$568-- |
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#stages_to_run<-c(1,2,3,4,5) #May decide on antoher strategy later on... |
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#stages_to_run<-c(0,2,3,4,5) #May decide on antoher strategy later on... |
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stages_to_run<-c(0,0,0,4,5) #MRun only raster fitting, prediction and assessemnt (providing lst averages, covar brick and met stations)
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stages_to_run<-c(0,2,3,4,5) #MRun only raster fitting, prediction and assessemnt (providing lst averages, covar brick and met stations)
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#If stage 2 is skipped then use previous covar object |
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covar_obj_file<-"/data/project/layers/commons/data_workflow/output_data_365d_gam_fus_lst_test_run_07172013/covar_obj__365d_gam_fus_lst_test_run_07172013.RData" |
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#If stage 3 is skipped then use previous met_stations object |
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var<-"TMAX" # variable being interpolated |
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out_prefix<-"_365d_gam_fus_lst_test_run_07182013" #User defined output prefix
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out_suffix<-"_OR_07182013" #Regional suffix
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out_prefix<-"_365d_gam_day_mult_lst_comb3_07202013" #User defined output prefix
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out_suffix<-"_OR_07202013" #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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#interpolation_method<-c("gam_CAI") #other otpions to be added later
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interpolation_method<-c("gam_fusion") #other otpions to be added later
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#interpolation_method<-c("gam_daily") #other otpions to be added later
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#interpolation_method<-c("gam_fusion") #other otpions to be added later
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interpolation_method<-c("gam_daily") #other otpions to be added later
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#interpolation_method<-c("kriging_daily") #other otpions to be added later
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#interpolation_method<-c("gwr_daily") #other otpions to be added later
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#out_path <- paste("/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/output_data",
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# out_prefix,"/",sep="") |
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out_path<-"/data/project/layers/commons/data_workflow/output_data" |
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out_path<-"/home/parmentier/Data/IPLANT_project/Oregon_interpolation/Oregon_03142013/output_data"
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#out_path<-"/data/project/layers/commons/data_workflow/output_data"
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out_path <-paste(out_path,out_prefix,sep="") |
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if (!file.exists(out_path)){
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#Set additional parameters |
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#Input for sampling function... |
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seed_number<- 100 #if seed zero then no seed? |
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nb_sample<-1 #number of time random sampling must be repeated for every hold out proportion |
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step<-0 |
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nb_sample<-15 #number of time random sampling must be repeated for every hold out proportion
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step<-0.1
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constant<-0 #if value 1 then use the same samples as date one for the all set of dates |
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prop_minmax<-c(0.3,0.3) #if prop_min=prop_max and step=0 then predicitons are done for the number of dates...
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prop_minmax<-c(0.1,0.7) #if prop_min=prop_max and step=0 then predicitons 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<-"" # if empty string then predict for the full year specified earlier |
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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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#Models to run...this can be changed for each run |
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#LC1: Evergreen/deciduous needleleaf trees |
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#Combination for test run: |
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list_models<-c("y_var ~ s(elev_s)",
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"y_var ~ s(LST)", |
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"y_var ~ s(lat,lon)+ s(elev_s)", |
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"y_var ~ te(lat,lon,elev_s)", |
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"y_var ~ s(lat,lon) + s(elev_s) + s(N_w,E_w) + s(LST)", |
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"y_var ~ s(lat,lon) + s(elev_s) + s(N_w,E_w) + s(LST) + s(LC1)") |
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#Combination 3: for paper baseline=s(lat,lon)+s(elev) |
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list_models<-c("y_var ~ s(lat,lon) + s(elev_s)",
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"y_var ~ s(lat,lon) + s(elev_s) + s(N_w)", |
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"y_var ~ s(lat,lon) + s(elev_s) + s(E_w)", |
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"y_var ~ s(lat,lon) + s(elev_s) + s(LST)", |
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"y_var ~ s(lat,lon) + s(elev_s) + s(DISTOC)", |
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"y_var ~ s(lat,lon) + s(elev_s) + s(LC1)", |
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"y_var ~ s(lat,lon) + s(elev_s) + s(CANHGHT)", |
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"y_var ~ s(lat,lon) + s(elev_s) + s(LST) + ti(LST,LC1)", |
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"y_var ~ s(lat,lon) + s(elev_s) + s(LST) + ti(LST,CANHGHT)") |
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#Default name of LST avg to be matched |
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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")
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Also available in: Unified diff
master script, multisampling first predictions with new code using prop hold out 10% to 70%