In [1]:

    

import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels
from statsmodels.sandbox.stats.multicomp import multipletests

import matplotlib.pyplot as plt
% matplotlib inline


    

In [2]:

    

# check FDR threshold
multipletests(np.repeat(0.002, 4), method = 'fdr_bh')[1]


    

    
Out[2]:





array([ 0.002,  0.002,  0.002,  0.002])

In [3]:

    

alpha = pd.DataFrame({'type': np.tile(['Independent', 'Cumulative'], 4), 
                      'covariate': np.repeat(['study_id', 'empo_3', 'host_scientific_name', 'envo_biome_3'], 2),
                     'value_ES': [0.847, 0.847, 0.8, 0.024, 0.839, 0.0024, 0.817, 0.0007]})


    

In [4]:

    

alpha


    

    
Out[4]:






  

    

      

      
covariate
      
type
      
value_ES
    
  
  

    

      
0
      
study_id
      
Independent
      
0.8470
    
    

      
1
      
study_id
      
Cumulative
      
0.8470
    
    

      
2
      
empo_3
      
Independent
      
0.8000
    
    

      
3
      
empo_3
      
Cumulative
      
0.0240
    
    

      
4
      
host_scientific_name
      
Independent
      
0.8390
    
    

      
5
      
host_scientific_name
      
Cumulative
      
0.0024
    
    

      
6
      
envo_biome_3
      
Independent
      
0.8170
    
    

      
7
      
envo_biome_3
      
Cumulative
      
0.0007
    
  

In [5]:

    

sns.set_style("ticks")
fig, ax = plt.subplots()
fig = sns.barplot(x='covariate', y='value_ES', hue='type', data=alpha)
fig.set_xticklabels(ax.get_xticklabels(), rotation=45)
fig.set(xlabel='RDA selected covariates', ylabel='effect size')
sns.plt.legend(loc='center left',bbox_to_anchor=(1,0.5))
sns.despine()
fig = fig.get_figure()
fig.tight_layout()
fig.savefig('alpha_ef.pdf')


    

In [6]:

    

######## beta_unweighted ##########


    

In [7]:

    

# check FDR threshold
multipletests(np.hstack([np.repeat(0.002, 4), 0.008]), method = 'fdr_bh')[1]


    

    
Out[7]:





array([ 0.0025,  0.0025,  0.0025,  0.0025,  0.008 ])

In [8]:

    

beta_uw = pd.DataFrame({'type': np.tile(['Independent', 'Cumulative'], 5), 
                      'covariate': np.repeat(['study_id', 'empo_3', 'host_scientific_name', 'envo_biome_3', 'longitude_deg'], 2),
                     'value_ES': [0.544, 0.544, 0.442, 0.151, 0.511, 0.003, 0.403, 0.001, 0.237, 0.0002]})


    

In [9]:

    

beta_uw


    

    
Out[9]:






  

    

      

      
covariate
      
type
      
value_ES
    
  
  

    

      
0
      
study_id
      
Independent
      
0.5440
    
    

      
1
      
study_id
      
Cumulative
      
0.5440
    
    

      
2
      
empo_3
      
Independent
      
0.4420
    
    

      
3
      
empo_3
      
Cumulative
      
0.1510
    
    

      
4
      
host_scientific_name
      
Independent
      
0.5110
    
    

      
5
      
host_scientific_name
      
Cumulative
      
0.0030
    
    

      
6
      
envo_biome_3
      
Independent
      
0.4030
    
    

      
7
      
envo_biome_3
      
Cumulative
      
0.0010
    
    

      
8
      
longitude_deg
      
Independent
      
0.2370
    
    

      
9
      
longitude_deg
      
Cumulative
      
0.0002
    
  

In [10]:

    

sns.set_style("ticks")
fig, ax = plt.subplots()
fig = sns.barplot(x='covariate', y='value_ES', hue='type', data=beta_uw)
fig.set_xticklabels(ax.get_xticklabels(), rotation=45)
fig.set(xlabel='RDA selected covariates', ylabel='effect size')
sns.plt.legend(loc='center left',bbox_to_anchor=(1,0.5))
sns.despine()
fig = fig.get_figure()
fig.tight_layout()
fig.savefig('beta_uw_ef.pdf')


    

In [11]:

    

######## beta_weighted ##########


    

In [12]:

    

# check FDR threshold
multipletests(np.repeat(0.002, 5), method = 'fdr_bh')[1]


    

    
Out[12]:





array([ 0.002,  0.002,  0.002,  0.002,  0.002])

In [13]:

    

beta_w = pd.DataFrame({'type': np.tile(['Independent', 'Cumulative'], 5), 
                      'covariate': np.repeat(['study_id', 'empo_3', 'host_scientific_name', 'envo_biome_3', 'longitude_deg'], 2),
                     'value_ES': [0.427, 0.427, 0.401, 0.185, 0.371, 0.004, 0.143, 0.002, 0.272, 0.0004]})


    

In [14]:

    

beta_w


    

    
Out[14]:






  

    

      

      
covariate
      
type
      
value_ES
    
  
  

    

      
0
      
study_id
      
Independent
      
0.4270
    
    

      
1
      
study_id
      
Cumulative
      
0.4270
    
    

      
2
      
empo_3
      
Independent
      
0.4010
    
    

      
3
      
empo_3
      
Cumulative
      
0.1850
    
    

      
4
      
host_scientific_name
      
Independent
      
0.3710
    
    

      
5
      
host_scientific_name
      
Cumulative
      
0.0040
    
    

      
6
      
envo_biome_3
      
Independent
      
0.1430
    
    

      
7
      
envo_biome_3
      
Cumulative
      
0.0020
    
    

      
8
      
longitude_deg
      
Independent
      
0.2720
    
    

      
9
      
longitude_deg
      
Cumulative
      
0.0004
    
  

In [15]:

    

sns.set_style("ticks")
fig, ax = plt.subplots()
fig = sns.barplot(x='covariate', y='value_ES', hue='type', data=beta_w)
fig.set_xticklabels(ax.get_xticklabels(), rotation=45)
fig.set(xlabel='RDA selected covariates', ylabel='effect size')
sns.plt.legend(loc='center left',bbox_to_anchor=(1,0.5))
sns.despine()
fig = fig.get_figure()
fig.tight_layout()
fig.savefig('beta_w_ef.pdf')


    

In [ ]: