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Commit ca2f26a5 authored by Maria Voigt's avatar Maria Voigt
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resolved conflicts in abundMod

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src/model_fitting/abundance_model.R
+ 3
− 1
View file @ ca2f26a5
...@@ -141,7 +141,9 @@ print(paste("this is ESW aerial:", ESW_aerial)) ...@@ -141,7 +141,9 @@ print(paste("this is ESW aerial:", ESW_aerial))
NCS <- 1.12 #nest construction rate from Spehar et al. 2010 NCS <- 1.12 #nest construction rate from Spehar et al. 2010
#NCS <- 1.18 # from Husson 2009 #NCS <- 1.18 # from Husson 2009
PNB <- 0.88 # proportion of nest builders from Spehar et al. 2010 PNB <- 0.88 # proportion of nest builders from Spehar et al. 2010
#PNB <- 0.89 #PNB <- 0.89 # from husson 2009
# there is also a NCS for lower Kinabatangan, but not sure, where this is exactly
options("scipen" = 100, "digits" = 4) options("scipen" = 100, "digits" = 4)
......
src/resource_use/preparing_grid_resource_use.py 0 → 100644
+ 289
− 0
View file @ ca2f26a5
# -*- coding: utf-8 -*-
"""
Spyder Editor
# script to prepare the grid with the resource use information
# in the output file we will have a
# a 1 at the first position if pixel has to be discarded
# a 2 at the first position if the pixel is considered
# a 1 at the second position for IDN
# a 2 at the second position for MYS
# a 0 - 5 at the 3rd position depending on the resource use category
# the grid_id from the 4rth to the 10th position (6 positions)
#
# we will now clip with populations and then also do this in the
# other resource use table, let's see how this looks like
"""
import numpy as np
import MacPyver as mp
import os
"""
# absence
os.system("gdal_rasterize \
-l absence_diss_exp \
-burn 1 \
-ot Float32 \
-te -1751798.359 1267454.241 -629798.359 2560454.241 -ts 1122 1293 \
/homes/mv39zilo/work/Borneo/data/response/cleaned_data/absence_shape/absence_shape_rep_expanded_22_08_17.shp \
/homes/mv39zilo/work/Borneo/data/response/cleaned_data/absence_shape/absence_shape_rep_expanded_22_08_17.tif")
os.system("gdal_rasterize \
-l absence_shape_rep_expanded_22_08_17 \
-burn 1 \
-ot Float32 \
-ts 1122 1293 \
/homes/mv39zilo/work/Borneo/data/response/cleaned_data/absence_shape/absence_shape_rep_expanded_22_08_17.shp \
/homes/mv39zilo/work/Borneo/data/response/cleaned_data/absence_shape/absence_shape_rep_expanded_22_08_17.tif")
os.system("gdalwarp \
-t_srs '+proj=aea +lat_1=7 +lat_2=-32 +lat_0=-15 +lon_0=125 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs' \
-ot Float64 -r bilinear \
-te -1751798.359 1267454.241 -629798.359 2560454.241 \
-ts 1122 1293 \
-overwrite \
/homes/mv39zilo/work/Borneo/data/response/cleaned_data/absence_shape/absence_shape_rep_expanded_22_08_17.tif \
/homes/mv39zilo/work/Borneo/data/response/cleaned_data/absence_shape/absence_shape_expanded_22_08_17_repro_res.tif"
)
os.system("gdalwarp \
-t_srs '+proj=aea +lat_1=7 +lat_2=-32 +lat_0=-15 +lon_0=125 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs' \
-ot Float64 -r near \
-te -1751798.359 1267454.241 -629798.359 2560454.241 \
-ts 1122 1293 \
-overwrite \
/homes/mv39zilo/work/Borneo/data/future/grid.tif \
/homes/mv39zilo/work/Borneo/data/future/grid_repro_res.tif"
)
os.system("gdalwarp \
-t_srs '+proj=aea +lat_1=7 +lat_2=-32 +lat_0=-15 +lon_0=125 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs' \
-ot Float64 -r near \
-te -1751798.359 1267454.241 -629798.359 2560454.241 \
-ts 1122 1293 \
-overwrite \
/homes/mv39zilo/work/Borneo/data/predictors/cleaned_data/cleaned_predictors/dem.tif \
/homes/mv39zilo/work/Borneo/data/predictors/cleaned_data/cleaned_predictors/dem_repro_res.tif"
)
"""
absence_path = "/homes/mv39zilo/work/Borneo/data/response/cleaned_data/absence_shape/absence_shape_expanded_22_08_17_repro_res.tif"
absence = mp.tiff.read_tif(absence_path, 1)
grid_layer_path = "/homes/mv39zilo/work/Borneo/data/future/grid_repro_res.tif"
grid = mp.tiff.read_tif(grid_layer_path, 1)
grid = np.where(absence == 0, grid, 0)
np.unique(grid)
# now assigning number for each resource use
# 0 - absence
# 1 - plantation
# 2 - deforestation
# 3 - logging
# 4 - primary forest < 750m
# 5 - primary forest > 750
# 6 - regrowth
# 7 - plantations before 2000
# 8 - other
gaveau_defor_path = "/homes/mv39zilo/work/Borneo/data/predictors/raw_data/Remaining_forest_in_2015_and_deforestation_1973-2015/REGIONBorneo_FCDefDeg_1973to2015_CIFOR_repro_res.tif"
gaveau_defor_raw = mp.tiff.read_tif(gaveau_defor_path, 1)
gaveau_deforested = np.where((gaveau_defor_raw > 6 ) &
(gaveau_defor_raw < 10), 1, 0)
gaveau_logged = np.where(gaveau_defor_raw == 2, 1, 0)
gaveau_primary_forest = np.where(gaveau_defor_raw == 1, 1, 0)
gaveau_regrowth = np.where(gaveau_defor_raw == 4, 1, 0)
#plantation
plantation_path = "/homes/mv39zilo/work/Borneo/data/predictors/raw_data/Land_cover_trajectory_before_oil-palm_and_pulpwood_establishment/raster_plantation.tif"
plantations = mp.tiff.read_tif(plantation_path, 1)
# old plantations
old_plantations_path = "/homes/mv39zilo/work/Borneo/data/predictors/raw_data/Land_cover_trajectory_before_oil-palm_and_pulpwood_establishment/raster_plantation_old_repro_res.tif"
old_plantations = mp.tiff.read_tif(old_plantations_path, 1)
dem_path = "/homes/mv39zilo/work/Borneo/data/predictors/cleaned_data/cleaned_predictors/dem_repro_res.tif"
dem = mp.tiff.read_tif(dem_path, 1)
# elevation
# --> how much of primary forest in high elevation
# we can save some computing time by only considering
# the grid and not everything, because abundance will only be
# in rid
grid = np.where((plantations == 1) &
(grid == 1), 1 , grid)
grid = np.where((plantations == 0) &
(gaveau_deforested == 1)&
(grid == 1), 2 , grid)
# 3 logged
grid = np.where((plantations == 0) &
(gaveau_deforested == 0)&
(gaveau_logged == 1) &
(grid == 1), 3 , grid)
# 4 primary forest
grid = np.where((plantations == 0) &
(gaveau_deforested == 0)&
(gaveau_logged == 0) &
(gaveau_primary_forest == 1) &
(grid == 1), 4 , grid)
# add here primary forest at high altitudes
# because everywhere where we have primary forest
# we no longer have a 1 in grid but a six we do it differently
# 5 primary montane
grid = np.where((grid == 4) &
(dem > 750), 5, grid)
# 6 regrowth
grid = np.where((plantations == 0) &
(gaveau_deforested == 0)&
(gaveau_logged == 0) &
(gaveau_primary_forest == 0) &
(gaveau_regrowth ==1) &
(grid == 1), 6 , grid)
# 7 is old plantations
grid = np.where((plantations == 0) &
(gaveau_deforested == 0)&
(gaveau_logged == 0) &
(gaveau_primary_forest == 0) &
(gaveau_regrowth == 0) &
(old_plantations == 1) &
(grid == 1), 7 , grid)
# 8 is other
grid = np.where((plantations == 0) &
(gaveau_deforested == 0)&
(gaveau_logged == 0) &
(gaveau_primary_forest == 0) &
(gaveau_regrowth ==0) &
(grid == 1), 8, grid)
np.unique(grid)
mp.tiff.write_tif(file_with_srid = grid_layer_path,
full_output_name = "/homes/mv39zilo/work/Borneo/data/resource_use/resource_use_grid.tif",
data = grid,
dtype = 0)
# grid map with four categories
# 0 - absence - 0
# 1 - plantation - 1
# 2 - deforestation - 1
# 3 - logging - 1
# 4 - primary forest < 750m - 2
# 5 - primary forest > 750 - 2
# 6 - regrowth - 2
# 7 - plantations before 2000 - 3
# 8 - other - 3
grid_map = np.where((grid <= 3) &
(grid > 0), 1, 0)
grid_map = np.where((grid <= 6) &
(grid > 3), 2, grid_map)
grid_map = np.where((grid > 6), 3, grid_map)
# need to clip this with the area in which we have orangutans in 1999
year = 1999
abundance_path = "/homes/mv39zilo/work/Borneo/analysis/model_prep_and_running/results/abundMod/testing_ae_and_absence/pipeline_results/ppln_ae75m_50-2017-02-28T18-00-52/prediction_map_" + str(year) + "_2017-02-28_repro_res.tif"
abundance = mp.tiff.read_tif(abundance_path, 1)
grid_map = np.where(abundance > 0.1, grid_map, 0)
np.unique(grid_map)
mp.tiff.write_tif(file_with_srid = grid_layer_path,
full_output_name = "/homes/mv39zilo/work/Borneo/data/resource_use/resource_use_grid_map.tif",
data = grid_map,
dtype = 0)
"""
# test
indir = "/homes/mv39zilo/work/Borneo/analysis/model_prep_and_running/results/resource_use"
abundance_layer_path = indir + "/abundance_pred_2015_no_absence.tif"
# read the data
abundance_data = mp.tiff.read_tif(abundance_layer_path, 1)
test = np.where((grid == 0) &
(abundance_data > 0), abundance_data, 0)
np.sum(test)
mp.tiff.write_tif(file_with_srid = grid_layer_path,
full_output_name = "/homes/mv39zilo/work/Borneo/data/bootstrap/test.tif",
data = test,
dtype = 4)
"""
# rasterize borneo with info on country or province
"""
os.system("gdal_rasterize \
-a ID_1 \
-l Borneo \
-ot Float32 \
-te -1751798.359 1267454.241 -629798.359 2560454.241 -ts 1122 1293 \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo.shp \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo_province.tif")
os.system("gdal_rasterize \
-a ID_1 \
-l Borneo \
-ot Float32 \
-ts 1122 1293 \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo.shp \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo_province.tif")
os.system("gdalwarp \
-r near -t_srs '+proj=aea +lat_1=7 +lat_2=-32 +lat_0=-15 +lon_0=125 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs' \
-te -1751798.359 1267454.241 -629798.359 2560454.241 -ts 1122 1293 \
-overwrite \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo_province.tif \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo__province_repro_res.tif")
os.system("gdal_rasterize \
-a ID_0 \
-l Borneo \
-ot Float32 \
-te -1751798.359 1267454.241 -629798.359 2560454.241 -ts 1122 1293 \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo.shp \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo_country.tif")
os.system("gdal_rasterize \
-a ID_0 \
-l Borneo \
-ot Float32 \
-ts 1122 1293 \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo.shp \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo_country.tif")
os.system("gdalwarp \
-r near -t_srs '+proj=aea +lat_1=7 +lat_2=-32 +lat_0=-15 +lon_0=125 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs' \
-te -1751798.359 1267454.241 -629798.359 2560454.241 -ts 1122 1293 \
-overwrite \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo_country.tif \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo_country_repro_res.tif")
"""
src/validation/grid_mapping_for_resource_use.py 0 → 100644
+ 192
− 0
View file @ ca2f26a5
# -*- coding: utf-8 -*-
"""
Spyder Editor
# script to prepare the grid with the resource use information
# in the output file we will have a
# a 1 at the first position if pixel has to be discarded
# a 2 at the first position if the pixel is considered
# a 1 at the second position for IDN
# a 2 at the second position for MYS
# a 0 - 5 at the 3rd position depending on the resource use category
# the grid_id from the 4rth to the 10th position (6 positions)
#
# now assigning number for each resource use
# now assigning number for each resource use
# 0 - absence
# 1 - plantation
# 2 - deforestation
# 3 - landcover change
# 4 - logging
# 5 - primary forest < 750m
# 6 - primary forest > 750
# 7 - regrowth
# 8 - plantations before 2000
# 9 - other
"""
import numpy as np
import MacPyver as mp
import os
"""
# absence
os.system("gdal_rasterize \
-l absence_shape_rep \
-burn 1 \
-ot Float32 \
-te -1751798.359 1267454.241 -629798.359 2560454.241 -ts 1122 1293 \
/homes/mv39zilo/work/Borneo/data/response/cleaned_data/absence_shape/absence_shape_rep.shp \
/homes/mv39zilo/work/Borneo/data/response/cleaned_data/absence_shape/absence_shape_rep.tif")
os.system("gdal_rasterize \
-l absence_shape_rep \
-burn 1 \
-ot Float32 \
-ts 1122 1293 \
/homes/mv39zilo/work/Borneo/data/response/cleaned_data/absence_shape/absence_shape_rep.shp \
/homes/mv39zilo/work/Borneo/data/response/cleaned_data/absence_shape/absence_shape_rep.tif")
os.system("gdalwarp \
-t_srs '+proj=aea +lat_1=7 +lat_2=-32 +lat_0=-15 +lon_0=125 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs' \
-ot Float64 -r near \
-te -1751798.359 1267454.241 -629798.359 2560454.241 \
-ts 1122 1293 \
-overwrite \
/homes/mv39zilo/work/Borneo/data/response/cleaned_data/absence_shape/absence_shape_rep.tif \
/homes/mv39zilo/work/Borneo/data/response/cleaned_data/absence_shape/absence_shape_repro_res.tif"
)
os.system("gdalwarp \
-t_srs '+proj=aea +lat_1=7 +lat_2=-32 +lat_0=-15 +lon_0=125 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs' \
-ot Float64 -r near \
-te -1751798.359 1267454.241 -629798.359 2560454.241 \
-ts 1122 1293 \
-overwrite \
/homes/mv39zilo/work/Borneo/data/future/grid.tif \
/homes/mv39zilo/work/Borneo/data/future/grid_repro_res.tif"
)
"""
absence_path = "/homes/mv39zilo/work/Borneo/data/response/cleaned_data/absence_shape/absence_shape_expanded_22_08_17_repro_res.tif"
absence = mp.tiff.read_tif(absence_path, 1)
# input grid with grid_ids as values
grid = mp.tiff.read_tif("/homes/mv39zilo/work/Borneo/analysis/model_prep_and_running/results/future/grid_with_id_repro_res.tif", 1)
np.unique(grid)
resource_use = mp.tiff.read_tif("/homes/mv39zilo/work/Borneo/data/resource_use/resource_use_grid.tif", 1)
np.unique(resource_use)
country_layer_path = "/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo_country_repro_res.tif"
borneo = mp.tiff.read_tif(country_layer_path, 1)
"""
# format
1 0 0 0 0 0 000 000
(1 / 2) 0 (3 / 6) 0 (1-9) 0 700 000
out/in, MYS/IDN, category,grid_id
"""
# pixel is out / out
resource_grid_1 = np.where(absence == 1, 100000000000, 200000000000)
# country
resource_grid_2 = np.where(borneo == 136, (resource_grid_1 + 3 * 1000000000),
resource_grid_1)
resource_grid_3 = np.where(borneo == 106, (resource_grid_2 + 6 * 1000000000),
resource_grid_2)
resource_grid_4 = np.where(resource_use > 0, (resource_grid_3 + resource_use * 10000000),
resource_grid_3)
np.unique(resource_grid_4 )
# now assigning number for each resource use
resource_grid_5 = np.where(grid > 0, (resource_grid_4 + grid),
resource_grid_4 )
np.unique(resource_grid_5)
mp.tiff.write_tif(file_with_srid = "/homes/mv39zilo/work/Borneo/analysis/model_prep_and_running/results/future/grid_with_id_repro_res.tif",
full_output_name = "/homes/mv39zilo/work/Borneo/analysis/model_prep_and_running/results/resource_use/resource_grid_absence_country_category_id.tif",
data = resource_grid_5,
dtype = 5)
# use this in R script "prepare_boot_grid.R"
"""
# test
indir = "/homes/mv39zilo/work/Borneo/analysis/model_prep_and_running/results/resource_use"
abundance_layer_path = indir + "/abundance_pred_2015_no_absence.tif"
# read the data
abundance_data = mp.tiff.read_tif(abundance_layer_path, 1)
test = np.where((grid == 0) &
(abundance_data > 0), abundance_data, 0)
np.sum(test)
mp.tiff.write_tif(file_with_srid = grid_layer_path,
full_output_name = "/homes/mv39zilo/work/Borneo/data/bootstrap/test.tif",
data = test,
dtype = 4)
"""
# rasterize borneo with info on country or province
"""
os.system("gdal_rasterize \
-a ID_1 \
-l Borneo \
-ot Float32 \
-te -1751798.359 1267454.241 -629798.359 2560454.241 -ts 1122 1293 \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo.shp \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo_province.tif")
os.system("gdal_rasterize \
-a ID_1 \
-l Borneo \
-ot Float32 \
-ts 1122 1293 \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo.shp \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo_province.tif")
os.system("gdalwarp \
-r near -t_srs '+proj=aea +lat_1=7 +lat_2=-32 +lat_0=-15 +lon_0=125 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs' \
-te -1751798.359 1267454.241 -629798.359 2560454.241 -ts 1122 1293 \
-overwrite \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo_province.tif \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo__province_repro_res.tif")
os.system("gdal_rasterize \
-a ID_0 \
-l Borneo \
-ot Float32 \
-te -1751798.359 1267454.241 -629798.359 2560454.241 -ts 1122 1293 \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo.shp \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo_country.tif")
os.system("gdal_rasterize \
-a ID_0 \
-l Borneo \
-ot Float32 \
-ts 1122 1293 \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo.shp \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo_country.tif")
os.system("gdalwarp \
-r near -t_srs '+proj=aea +lat_1=7 +lat_2=-32 +lat_0=-15 +lon_0=125 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs' \
-te -1751798.359 1267454.241 -629798.359 2560454.241 -ts 1122 1293 \
-overwrite \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo_country.tif \
/homes/mv39zilo/work/Borneo/data/auxiliary_additional_data/Borneo_shape/cleaned_data/Borneo_country_repro_res.tif")
"""
src/validation/marias_boot_correct.R
+ 2
− 3
View file @ ca2f26a5
...@@ -89,7 +89,7 @@ options("scipen" = 100, "digits" = 4) ...@@ -89,7 +89,7 @@ options("scipen" = 100, "digits" = 4)
# load("/homes/mv39zilo/work/Borneo/outreach/Correspondance/November_2016/Roger/images/abundance_model_fitting_2016-12-02.RData") # load("/homes/mv39zilo/work/Borneo/outreach/Correspondance/November_2016/Roger/images/abundance_model_fitting_2016-12-02.RData")
#indir <- "/homes/mv39zilo/work/Borneo/analysis/model_prep_and_running/results/abundMod/testing_ae_and_absence/pipeline_results/ppln_ae75m_50-2017-02-28T18-00-52" #indir <- "/homes/mv39zilo/work/Borneo/analysis/model_prep_and_running/results/abundMod/testing_ae_and_absence/pipeline_results/absence_density/ppln_ae75m_50-2017-02-28T18-00-52"
# include abundMod_results # include abundMod_results
#oreductirs_obs und m_terms #oreductirs_obs und m_terms
abundMod_results_path <- path.to.current(indir, "abundMod_results", "rds") abundMod_results_path <- path.to.current(indir, "abundMod_results", "rds")
...@@ -104,14 +104,13 @@ m_terms_path <- path.to.current(indir, "m_terms", "rds") ...@@ -104,14 +104,13 @@ m_terms_path <- path.to.current(indir, "m_terms", "rds")
if(is_verbose){print(paste("m_terms_path", m_terms_path))} if(is_verbose){print(paste("m_terms_path", m_terms_path))}
m_terms <- readRDS(m_terms_path) m_terms <- readRDS(m_terms_path)
ests=apply(abundMod_results [, grepl(x=colnames(abundMod_results ), pattern="coeff")], 2, function(x){ ests=apply(abundMod_results [, grepl(x=colnames(abundMod_results ), pattern="coeff")], 2, function(x){
x[is.na(x)]=0 x[is.na(x)]=0
sum(x*abundMod_results$w_aic) sum(x*abundMod_results$w_aic)
}) })
SEs=apply(abundMod_results [, grepl(x=colnames(abundMod_results ), pattern="SE")], 2, function(x){ SEs=apply(abundMod_results [, grepl(x=colnames(abundMod_results ), pattern="SE")], 2, function(x){
x[is.na(x)]=0 x[is.na(x)]=0
sum(x*abundMod_results$w_aic) sum(x*abundMod_results$w_ai)c
}) })
SEs=SEs[-length(SEs)] SEs=SEs[-length(SEs)]
names(ests)=gsub(x=names(ests), pattern="coeff_", replacement="", fixed=T) names(ests)=gsub(x=names(ests), pattern="coeff_", replacement="", fixed=T)
......
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