import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
import pingouin as pg
import forestplot as fp
pd.set_option("display.unicode.east_asian_width", True) # 输出列名对齐
pd.set_option("display.max_columns", None) # 修改最大列数，防止列显示不全
pd.set_option('display.expand_frame_repr', False) # 设置不折叠数据
sleep = pd.read_csv('../Data/sleep75.csv', index_col=0)
"""
age black case clerical construc educ earns74 gdhlth inlf leis1 leis2 leis3 smsa lhrwage lothinc male marr prot rlxall selfe sleep slpnaps south spsepay spwrk75 totwrk union worknrm workscnd exper yngkid yrsmarr hrwage agesq
rownames
1 32 0 1 0.000000 0.000000 12 0 0 1 3529 3479 3479 0 1.955861 10.075380 1 1 1 3163 0 3113 3163 0 0 0 3438 0 3438 0 14 0 13 7.070004 1024
2 31 0 2 0.000000 0.000000 14 9500 1 1 2140 2140 2140 0 0.357674 0.000000 1 0 1 2920 1 2920 2920 1 0 0 5020 0 5020 0 11 0 0 1.429999 961
3 44 0 3 0.000000 0.000000 17 42500 1 1 4595 4505 4227 1 3.021887 0.000000 1 1 0 3038 1 2670 2760 0 20000 1 2815 0 2815 0 21 0 0 20.529997 1936
4 30 0 4 0.000000 0.000000 12 42500 1 1 3211 3211 3211 0 2.263844 0.000000 0 1 1 3083 1 3083 3083 0 5000 1 3786 0 3786 0 12 0 12 9.619998 900
5 64 0 5 0.000000 0.000000 14 2500 1 1 4052 4007 4007 0 1.011601 9.328213 1 1 1 3493 0 3448 3493 0 2400 1 2580 0 2580 0 44 0 33 2.750000 4096
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
702 45 0 702 0.182331 0.030075 12 5500 1 0 5069 4669 4644 1 NaN 9.664660 0 1 1 3410 0 2985 3385 0 16000 1 2026 0 2026 0 27 0 18 NaN 2025
703 34 0 703 0.182331 0.030075 10 2500 0 0 5885 5870 5870 0 NaN 0.000000 1 1 0 3535 0 3520 3535 0 0 0 675 1 465 210 18 0 4 NaN 1156
704 37 0 704 0.182331 0.030075 12 3500 1 0 4719 4719 4584 0 NaN 9.235130 0 1 1 3645 0 3510 3510 0 12000 1 1851 0 1851 0 19 0 17 NaN 1369
705 54 0 705 0.182331 0.030075 17 32500 1 0 5149 5119 5031 0 NaN 9.210441 0 1 1 3088 1 2970 3000 1 35000 1 1961 1 1481 480 31 0 22 NaN 2916
706 30 0 706 0.182331 0.030075 16 6750 1 0 4747 4302 4302 0 NaN 9.940350 0 1 0 3415 0 2970 3415 1 0 0 2363 0 2363 0 8 1 9 NaN 900
[706 rows x 34 columns]
"""
sleep_label = pd.read_csv('../Data/sleep75-des.csv').rename(columns={'des': 'label'})
"""
var label group
0 age in years age
1 black =1 if black other factors
2 case identifier other factors
3 clerical =1 if clerical worker occupation
4 construc =1 if construction worker occupation
5 educ years of schooling labor factors
6 earns74 total earnings, 1974 labor factors
7 gdhlth =1 if in good or excel. health health factors
8 inlf =1 if in labor force labor factors
9 leis1 sleep - totwrk other factors
10 leis2 slpnaps - totwrk other factors
11 leis3 rlxall - totwrk other factors
12 smsa =1 if live in smsa area of residence
13 lhrwage log hourly wage labor factors
14 lothinc log othinc, unless othinc < 0 labor factors
15 male =1 if male other factors
16 marr =1 if married family factors
17 prot =1 if Protestant other factors
18 rlxall slpnaps + personal activs other sleep factors
19 selfe =1 if self employed labor factors
20 sleep mins sleep at night, per wk other factors
21 slpnaps minutes sleep, inc. naps other sleep factors
22 south =1 if live in south area of residence
23 spsepay spousal wage income other factors
24 spwrk75 =1 if spouse works other factors
25 totwrk mins worked per week labor factors
26 union =1 if belong to union labor factors
27 worknrm mins work main job labor factors
28 workscnd mins work second job labor factors
29 exper age - educ - 6 labor factors
30 yngkid =1 if children < 3 present family factors
31 yrsmarr years married family factors
32 hrwage hourly wage labor factors
33 agesq age^2 age
"""
# 删除无关变量
sleep.drop(columns=['case', 'leis1', 'leis2', 'leis3'], inplace=True)
"""
age black clerical construc educ earns74 gdhlth inlf smsa lhrwage lothinc male marr prot rlxall selfe sleep slpnaps south spsepay spwrk75 totwrk union worknrm workscnd exper yngkid yrsmarr hrwage agesq
rownames
1 32 0 0.000000 0.000000 12 0 0 1 0 1.955861 10.075380 1 1 1 3163 0 3113 3163 0 0 0 3438 0 3438 0 14 0 13 7.070004 1024
2 31 0 0.000000 0.000000 14 9500 1 1 0 0.357674 0.000000 1 0 1 2920 1 2920 2920 1 0 0 5020 0 5020 0 11 0 0 1.429999 961
3 44 0 0.000000 0.000000 17 42500 1 1 1 3.021887 0.000000 1 1 0 3038 1 2670 2760 0 20000 1 2815 0 2815 0 21 0 0 20.529997 1936
4 30 0 0.000000 0.000000 12 42500 1 1 0 2.263844 0.000000 0 1 1 3083 1 3083 3083 0 5000 1 3786 0 3786 0 12 0 12 9.619998 900
5 64 0 0.000000 0.000000 14 2500 1 1 0 1.011601 9.328213 1 1 1 3493 0 3448 3493 0 2400 1 2580 0 2580 0 44 0 33 2.750000 4096
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
702 45 0 0.182331 0.030075 12 5500 1 0 1 NaN 9.664660 0 1 1 3410 0 2985 3385 0 16000 1 2026 0 2026 0 27 0 18 NaN 2025
703 34 0 0.182331 0.030075 10 2500 0 0 0 NaN 0.000000 1 1 0 3535 0 3520 3535 0 0 0 675 1 465 210 18 0 4 NaN 1156
704 37 0 0.182331 0.030075 12 3500 1 0 0 NaN 9.235130 0 1 1 3645 0 3510 3510 0 12000 1 1851 0 1851 0 19 0 17 NaN 1369
705 54 0 0.182331 0.030075 17 32500 1 0 0 NaN 9.210441 0 1 1 3088 1 2970 3000 1 35000 1 1961 1 1481 480 31 0 22 NaN 2916
706 30 0 0.182331 0.030075 16 6750 1 0 0 NaN 9.940350 0 1 0 3415 0 2970 3415 1 0 0 2363 0 2363 0 8 1 9 NaN 900
[706 rows x 30 columns]
"""
sleep_label = sleep_label[~sleep_label['var'].isin(['case', 'leis1', 'leis2', 'leis3'])]
"""
var label group
0 age in years age
1 black =1 if black other factors
3 clerical =1 if clerical worker occupation
4 construc =1 if construction worker occupation
5 educ years of schooling labor factors
6 earns74 total earnings, 1974 labor factors
7 gdhlth =1 if in good or excel. health health factors
8 inlf =1 if in labor force labor factors
12 smsa =1 if live in smsa area of residence
13 lhrwage log hourly wage labor factors
14 lothinc log othinc, unless othinc < 0 labor factors
15 male =1 if male other factors
16 marr =1 if married family factors
17 prot =1 if Protestant other factors
18 rlxall slpnaps + personal activs other sleep factors
19 selfe =1 if self employed labor factors
20 sleep mins sleep at night, per wk other factors
21 slpnaps minutes sleep, inc. naps other sleep factors
22 south =1 if live in south area of residence
23 spsepay spousal wage income other factors
24 spwrk75 =1 if spouse works other factors
25 totwrk mins worked per week labor factors
26 union =1 if belong to union labor factors
27 worknrm mins work main job labor factors
28 workscnd mins work second job labor factors
29 exper age - educ - 6 labor factors
30 yngkid =1 if children < 3 present family factors
31 yrsmarr years married family factors
32 hrwage hourly wage labor factors
33 agesq age^2 age
"""
基础绘制（单模型）¶
forestplot/examples/get-sleep.ipynb at main · LSYS/forestplot
# 计算变量间的成对相关性（Pearson相关性）
sleep_corr = pg.pairwise_corr(sleep).rename(columns={'p-unc': 'p-val'})
"""
X Y method alternative n r CI95% p-val BF10 power
0 age black pearson two-sided 706 -0.063647 [-0.14, 0.01] 9.105487e-02 0.196 0.394045
1 age clerical pearson two-sided 706 -0.011861 [-0.09, 0.06] 7.530534e-01 0.05 0.061397
2 age construc pearson two-sided 706 0.018473 [-0.06, 0.09] 6.241168e-01 0.053 0.077924
3 age educ pearson two-sided 706 -0.265831 [-0.33, -0.2] 6.951621e-13 7.005e+09 1.000000
4 age earns74 pearson two-sided 706 0.070692 [-0.0, 0.14] 6.046985e-02 0.274 0.467639
.. ... ... ... ... ... ... ... ... ... ...
430 yngkid hrwage pearson two-sided 532 -0.005156 [-0.09, 0.08] 9.055532e-01 0.055 0.051585
431 yngkid agesq pearson two-sided 706 -0.305331 [-0.37, -0.24] 1.063976e-16 3.906e+13 1.000000
432 yrsmarr hrwage pearson two-sided 532 0.110267 [0.03, 0.19] 1.092447e-02 1.373 0.721959
433 yrsmarr agesq pearson two-sided 706 0.583251 [0.53, 0.63] 1.406839e-65 1.139e+62 1.000000
434 hrwage agesq pearson two-sided 532 0.086673 [0.0, 0.17] 4.569576e-02 0.398 0.516054
[435 rows x 10 columns]
"""
# 仅保留其他变量与sleep变量的相关性
sleep_corr.query("Y == 'sleep' | X == 'sleep'", inplace=True)
"""
X Y method alternative n r CI95% p-val BF10 power
15 age sleep pearson two-sided 706 0.090373 [0.02, 0.16] 1.630887e-02 0.839 0.671578
43 black sleep pearson two-sided 706 -0.027057 [-0.1, 0.05] 4.728889e-01 0.061 0.110805
70 clerical sleep pearson two-sided 706 0.048081 [-0.03, 0.12] 2.019484e-01 0.106 0.247768
96 construc sleep pearson two-sided 706 0.041229 [-0.03, 0.11] 2.739475e-01 0.086 0.194481
121 educ sleep pearson two-sided 706 -0.095004 [-0.17, -0.02] 1.155151e-02 1.137 0.715052
145 earns74 sleep pearson two-sided 706 -0.076890 [-0.15, -0.0] 4.110934e-02 0.378 0.533501
168 gdhlth sleep pearson two-sided 706 -0.102825 [-0.18, -0.03] 6.246660e-03 1.967 0.781649
190 inlf sleep pearson two-sided 706 -0.027126 [-0.1, 0.05] 4.717698e-01 0.061 0.111121
211 smsa sleep pearson two-sided 706 -0.066997 [-0.14, 0.01] 7.524015e-02 0.229 0.428720
231 lhrwage sleep pearson two-sided 532 -0.067197 [-0.15, 0.02] 1.216222e-01 0.179 0.340792
250 lothinc sleep pearson two-sided 706 0.036661 [-0.04, 0.11] 3.306971e-01 0.076 0.163499
268 male sleep pearson two-sided 706 -0.035909 [-0.11, 0.04] 3.407214e-01 0.074 0.158760
285 marr sleep pearson two-sided 706 0.053757 [-0.02, 0.13] 1.536188e-01 0.13 0.297561
301 prot sleep pearson two-sided 706 0.027147 [-0.05, 0.1] 4.714176e-01 0.061 0.111220
316 rlxall sleep pearson two-sided 706 0.867744 [0.85, 0.88] 6.051022e-216 6.697e+211 1.000000
330 selfe sleep pearson two-sided 706 0.001782 [-0.07, 0.08] 9.623058e-01 0.047 0.050233
344 sleep slpnaps pearson two-sided 706 0.893043 [0.88, 0.91] 2.339108e-246 1.38e+242 1.000000
345 sleep south pearson two-sided 706 0.078600 [0.0, 0.15] 3.679946e-02 0.415 0.551599
346 sleep spsepay pearson two-sided 706 0.007881 [-0.07, 0.08] 8.344125e-01 0.048 0.055000
347 sleep spwrk75 pearson two-sided 706 0.007868 [-0.07, 0.08] 8.346888e-01 0.048 0.054983
348 sleep totwrk pearson two-sided 706 -0.321384 [-0.39, -0.25] 1.994095e-18 1.961e+15 1.000000
349 sleep union pearson two-sided 706 0.009965 [-0.06, 0.08] 7.915440e-01 0.049 0.058021
350 sleep worknrm pearson two-sided 706 -0.322300 [-0.39, -0.25] 1.577335e-18 2.471e+15 1.000000
351 sleep workscnd pearson two-sided 706 0.001139 [-0.07, 0.07] 9.759034e-01 0.047 0.050082
352 sleep exper pearson two-sided 706 0.104191 [0.03, 0.18] 5.587422e-03 2.175 0.792292
353 sleep yngkid pearson two-sided 706 -0.013262 [-0.09, 0.06] 7.250012e-01 0.05 0.064278
354 sleep yrsmarr pearson two-sided 706 0.063997 [-0.01, 0.14] 8.928507e-02 0.199 0.397636
355 sleep hrwage pearson two-sided 532 -0.049450 [-0.13, 0.04] 2.548774e-01 0.104 0.206828
356 sleep agesq pearson two-sided 706 0.099722 [0.03, 0.17] 8.010946e-03 1.574 0.756346
"""
# 提取变量名并删除部分无关参数
sleep_corr.insert(0, 'var', np.where(sleep_corr['X']=="sleep", sleep_corr['Y'], sleep_corr['X']))
sleep_corr.drop(columns=["X", "Y", "method", "alternative"], inplace=True)
"""
var n r CI95% p-val BF10 power
15 age 706 0.090373 [0.02, 0.16] 1.630887e-02 0.839 0.671578
43 black 706 -0.027057 [-0.1, 0.05] 4.728889e-01 0.061 0.110805
70 clerical 706 0.048081 [-0.03, 0.12] 2.019484e-01 0.106 0.247768
96 construc 706 0.041229 [-0.03, 0.11] 2.739475e-01 0.086 0.194481
121 educ 706 -0.095004 [-0.17, -0.02] 1.155151e-02 1.137 0.715052
145 earns74 706 -0.076890 [-0.15, -0.0] 4.110934e-02 0.378 0.533501
168 gdhlth 706 -0.102825 [-0.18, -0.03] 6.246660e-03 1.967 0.781649
190 inlf 706 -0.027126 [-0.1, 0.05] 4.717698e-01 0.061 0.111121
211 smsa 706 -0.066997 [-0.14, 0.01] 7.524015e-02 0.229 0.428720
231 lhrwage 532 -0.067197 [-0.15, 0.02] 1.216222e-01 0.179 0.340792
250 lothinc 706 0.036661 [-0.04, 0.11] 3.306971e-01 0.076 0.163499
268 male 706 -0.035909 [-0.11, 0.04] 3.407214e-01 0.074 0.158760
285 marr 706 0.053757 [-0.02, 0.13] 1.536188e-01 0.13 0.297561
301 prot 706 0.027147 [-0.05, 0.1] 4.714176e-01 0.061 0.111220
316 rlxall 706 0.867744 [0.85, 0.88] 6.051022e-216 6.697e+211 1.000000
330 selfe 706 0.001782 [-0.07, 0.08] 9.623058e-01 0.047 0.050233
344 slpnaps 706 0.893043 [0.88, 0.91] 2.339108e-246 1.38e+242 1.000000
345 south 706 0.078600 [0.0, 0.15] 3.679946e-02 0.415 0.551599
346 spsepay 706 0.007881 [-0.07, 0.08] 8.344125e-01 0.048 0.055000
347 spwrk75 706 0.007868 [-0.07, 0.08] 8.346888e-01 0.048 0.054983
348 totwrk 706 -0.321384 [-0.39, -0.25] 1.994095e-18 1.961e+15 1.000000
349 union 706 0.009965 [-0.06, 0.08] 7.915440e-01 0.049 0.058021
350 worknrm 706 -0.322300 [-0.39, -0.25] 1.577335e-18 2.471e+15 1.000000
351 workscnd 706 0.001139 [-0.07, 0.07] 9.759034e-01 0.047 0.050082
352 exper 706 0.104191 [0.03, 0.18] 5.587422e-03 2.175 0.792292
353 yngkid 706 -0.013262 [-0.09, 0.06] 7.250012e-01 0.05 0.064278
354 yrsmarr 706 0.063997 [-0.01, 0.14] 8.928507e-02 0.199 0.397636
355 hrwage 532 -0.049450 [-0.13, 0.04] 2.548774e-01 0.104 0.206828
356 agesq 706 0.099722 [0.03, 0.17] 8.010946e-03 1.574 0.756346
"""
# 添加或修改某些参数
sleep_corr['hl'] = [float(ci[1]) for ci in sleep_corr['CI95%']]
sleep_corr['ll'] = [float(ci[0]) for ci in sleep_corr['CI95%']]
sleep_corr['moerror'] = sleep_corr['hl'] - sleep_corr['r']
sleep_corr['power'] = sleep_corr['power'].round(decimals=2)
sleep_corr['n'] = sleep_corr['n'].map(str)
"""
var n r CI95% p-val BF10 power hl ll moerror
15 age 706 0.090373 [0.02, 0.16] 1.630887e-02 0.839 0.67 0.16 0.02 0.069627
43 black 706 -0.027057 [-0.1, 0.05] 4.728889e-01 0.061 0.11 0.05 -0.10 0.077057
70 clerical 706 0.048081 [-0.03, 0.12] 2.019484e-01 0.106 0.25 0.12 -0.03 0.071919
96 construc 706 0.041229 [-0.03, 0.11] 2.739475e-01 0.086 0.19 0.11 -0.03 0.068771
121 educ 706 -0.095004 [-0.17, -0.02] 1.155151e-02 1.137 0.72 -0.02 -0.17 0.075004
145 earns74 706 -0.076890 [-0.15, -0.0] 4.110934e-02 0.378 0.53 -0.00 -0.15 0.076890
168 gdhlth 706 -0.102825 [-0.18, -0.03] 6.246660e-03 1.967 0.78 -0.03 -0.18 0.072825
190 inlf 706 -0.027126 [-0.1, 0.05] 4.717698e-01 0.061 0.11 0.05 -0.10 0.077126
211 smsa 706 -0.066997 [-0.14, 0.01] 7.524015e-02 0.229 0.43 0.01 -0.14 0.076997
231 lhrwage 532 -0.067197 [-0.15, 0.02] 1.216222e-01 0.179 0.34 0.02 -0.15 0.087197
250 lothinc 706 0.036661 [-0.04, 0.11] 3.306971e-01 0.076 0.16 0.11 -0.04 0.073339
268 male 706 -0.035909 [-0.11, 0.04] 3.407214e-01 0.074 0.16 0.04 -0.11 0.075909
285 marr 706 0.053757 [-0.02, 0.13] 1.536188e-01 0.13 0.30 0.13 -0.02 0.076243
301 prot 706 0.027147 [-0.05, 0.1] 4.714176e-01 0.061 0.11 0.10 -0.05 0.072853
316 rlxall 706 0.867744 [0.85, 0.88] 6.051022e-216 6.697e+211 1.00 0.88 0.85 0.012256
330 selfe 706 0.001782 [-0.07, 0.08] 9.623058e-01 0.047 0.05 0.08 -0.07 0.078218
344 slpnaps 706 0.893043 [0.88, 0.91] 2.339108e-246 1.38e+242 1.00 0.91 0.88 0.016957
345 south 706 0.078600 [0.0, 0.15] 3.679946e-02 0.415 0.55 0.15 0.00 0.071400
346 spsepay 706 0.007881 [-0.07, 0.08] 8.344125e-01 0.048 0.06 0.08 -0.07 0.072119
347 spwrk75 706 0.007868 [-0.07, 0.08] 8.346888e-01 0.048 0.05 0.08 -0.07 0.072132
348 totwrk 706 -0.321384 [-0.39, -0.25] 1.994095e-18 1.961e+15 1.00 -0.25 -0.39 0.071384
349 union 706 0.009965 [-0.06, 0.08] 7.915440e-01 0.049 0.06 0.08 -0.06 0.070035
350 worknrm 706 -0.322300 [-0.39, -0.25] 1.577335e-18 2.471e+15 1.00 -0.25 -0.39 0.072300
351 workscnd 706 0.001139 [-0.07, 0.07] 9.759034e-01 0.047 0.05 0.07 -0.07 0.068861
352 exper 706 0.104191 [0.03, 0.18] 5.587422e-03 2.175 0.79 0.18 0.03 0.075809
353 yngkid 706 -0.013262 [-0.09, 0.06] 7.250012e-01 0.05 0.06 0.06 -0.09 0.073262
354 yrsmarr 706 0.063997 [-0.01, 0.14] 8.928507e-02 0.199 0.40 0.14 -0.01 0.076003
355 hrwage 532 -0.049450 [-0.13, 0.04] 2.548774e-01 0.104 0.21 0.04 -0.13 0.089450
356 agesq 706 0.099722 [0.03, 0.17] 8.010946e-03 1.574 0.76 0.17 0.03 0.070278
"""
# 两表合并并删除无关参数
sleep_corr = pd.merge(sleep_corr, sleep_label, on='var', how='inner').drop(columns=['CI95%', 'BF10'])
"""
var n r p-val power hl ll moerror label group
0 age 706 0.090373 1.630887e-02 0.67 0.16 0.02 0.069627 in years age
1 black 706 -0.027057 4.728889e-01 0.11 0.05 -0.10 0.077057 =1 if black other factors
2 clerical 706 0.048081 2.019484e-01 0.25 0.12 -0.03 0.071919 =1 if clerical worker occupation
3 construc 706 0.041229 2.739475e-01 0.19 0.11 -0.03 0.068771 =1 if construction worker occupation
4 educ 706 -0.095004 1.155151e-02 0.72 -0.02 -0.17 0.075004 years of schooling labor factors
5 earns74 706 -0.076890 4.110934e-02 0.53 -0.00 -0.15 0.076890 total earnings, 1974 labor factors
6 gdhlth 706 -0.102825 6.246660e-03 0.78 -0.03 -0.18 0.072825 =1 if in good or excel. health health factors
7 inlf 706 -0.027126 4.717698e-01 0.11 0.05 -0.10 0.077126 =1 if in labor force labor factors
8 smsa 706 -0.066997 7.524015e-02 0.43 0.01 -0.14 0.076997 =1 if live in smsa area of residence
9 lhrwage 532 -0.067197 1.216222e-01 0.34 0.02 -0.15 0.087197 log hourly wage labor factors
10 lothinc 706 0.036661 3.306971e-01 0.16 0.11 -0.04 0.073339 log othinc, unless othinc < 0 labor factors
11 male 706 -0.035909 3.407214e-01 0.16 0.04 -0.11 0.075909 =1 if male other factors
12 marr 706 0.053757 1.536188e-01 0.30 0.13 -0.02 0.076243 =1 if married family factors
13 prot 706 0.027147 4.714176e-01 0.11 0.10 -0.05 0.072853 =1 if Protestant other factors
14 rlxall 706 0.867744 6.051022e-216 1.00 0.88 0.85 0.012256 slpnaps + personal activs other sleep factors
15 selfe 706 0.001782 9.623058e-01 0.05 0.08 -0.07 0.078218 =1 if self employed labor factors
16 slpnaps 706 0.893043 2.339108e-246 1.00 0.91 0.88 0.016957 minutes sleep, inc. naps other sleep factors
17 south 706 0.078600 3.679946e-02 0.55 0.15 0.00 0.071400 =1 if live in south area of residence
18 spsepay 706 0.007881 8.344125e-01 0.06 0.08 -0.07 0.072119 spousal wage income other factors
19 spwrk75 706 0.007868 8.346888e-01 0.05 0.08 -0.07 0.072132 =1 if spouse works other factors
20 totwrk 706 -0.321384 1.994095e-18 1.00 -0.25 -0.39 0.071384 mins worked per week labor factors
21 union 706 0.009965 7.915440e-01 0.06 0.08 -0.06 0.070035 =1 if belong to union labor factors
22 worknrm 706 -0.322300 1.577335e-18 1.00 -0.25 -0.39 0.072300 mins work main job labor factors
23 workscnd 706 0.001139 9.759034e-01 0.05 0.07 -0.07 0.068861 mins work second job labor factors
24 exper 706 0.104191 5.587422e-03 0.79 0.18 0.03 0.075809 age - educ - 6 labor factors
25 yngkid 706 -0.013262 7.250012e-01 0.06 0.06 -0.09 0.073262 =1 if children < 3 present family factors
26 yrsmarr 706 0.063997 8.928507e-02 0.40 0.14 -0.01 0.076003 years married family factors
27 hrwage 532 -0.049450 2.548774e-01 0.21 0.04 -0.13 0.089450 hourly wage labor factors
28 agesq 706 0.099722 8.010946e-03 0.76 0.17 0.03 0.070278 age^2 age
"""
fig = plt.figure(figsize=(15, 20), dpi=100, layout="constrained")
ax = fig.add_subplot(1, 1, 1)
group_order = ["age factors", "labor factors", "occupation", "health factors", "family factors", "area of residence", "other factors", "other sleep factors"]
fp.forestplot(sleep_corr,
# 绘制估计值与置信区间的errorbar和scatter√
estimate="r", # 估计值，传递给ax.errorbar和ax.scatter的x参数
ll="ll", # 置信区间下限，计算后传递给ax.errorbar的xerr参数
hl="hl", # 置信区间上限，计算后传递给ax.errorbar的xerr参数
lw=2, # 误差线宽，传递给ax.errorbar的elinewidth参数
linecolor='r', # 误差线色，传递给ax.errorbar的ecolor参数
marker="*", # 估计值标记类型，传递给ax.scatter的marker参数
markersize=100, # 估计值标记大小，传递给ax.scatter的s参数
markercolor="k", # 估计值标记颜色，传递给ax.scatter的color参数
markeralpha=1, # 估计值标记透明度，传递给ax.scatter的alpha参数
logscale=False, # 是否x轴进行对数化（以10为底）缩放【ax.set_xscale("log", base=10)】
color_alt_rows=True, # 变量行交替显示颜色
# 设置x轴刻度与范围√
xticks=[-0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1.0], # √x轴刻度位置【ax.set_xticks(xticks)】
nticks=8, # √xticks=None时x轴最大刻度数，自动设置刻度位置【ax.xaxis.set_major_locator(plt.MaxNLocator(nticks))】
xticklabels=None, # √x轴刻度标签文本【ax.set_xticklabels(xticklabels)】
xtick_size=20, # √x轴刻度标签大小【ax.tick_params(axis="x", labelsize=xtick_size)】
xlim=[-0.4, 1], # √x轴范围【ax.set_xlim(xlim[0], xlim[1])】
# x轴标题√
xlabel="Pearson correlation coefficient", # √x轴标题文本内容，传递给ax.set_xlabel的xlabel参数
xlabel_size=20, # √x轴标题字体大小，传递给ax.set_xlabel的size参数
xlabel_fontweight="bold", # √x轴标题字体粗细，传递给ax.set_xlabel的fontweight参数
# 绘制参考线√
xline=0, # √参考线位置，传递给ax.vlines的x参数
xlinestyle='--', # √参考线线型，传递给ax.vlines的linestyle参数
xlinecolor='r', # √参考线颜色，传递给ax.vlines的color参数
xlinewidth=2, # √参考线线宽，传递给ax.vlines的linewidth参数
# 绘制左侧y轴刻度标签yticklabel
varlabel="var", # √变量标签
groupvar='group', # √分组信息列。注意分组名尽量不与变量标签名重复，否则显示出问题。
group_order=group_order, # √分组显示顺序
grouplab_size=20, # √分组文本字体大小
# grouplab_fontweight="bold", # √分组文本和右侧表头字体粗细（rightannote!=None时）
flush=True, # √如果为True，则设置ax.set_yticklabels的ha参数为"left"，否则设置为"right"
capitalize="title", # √变量标签和分组名的显示形式。可选"title", "capitalize", "lower", "upper", "swapcase"
sort=True, # √是否对变量根据sortby参数进行排序（降序），默认False。如果设置了groupvar参数，则在各自组内排序。
sortby='r', # √排序依据（若设置则默认设置sort=True），默认是estimate参数对应的列。
form_ci_report=True, # √是否自动处理并形成置信区间报告"est_ci"列（默认为True）
# ci_report=True, # √是否在图左侧显示置信区间报告列（默认为True，若ll=None则设置为False），若设置为True则默认设置form_ci_report=True
annote=['label', "est_ci"], # √图左侧显示的信息列，覆盖以上设置的置信区间报告列
annoteheaders=["Label", "Est. (95% Conf. Int.)"], # √图左侧显示的信息列的表头，覆盖以上设置的置信区间报告列
variable_header="Variable", # √变量标签列表头
# tableheader_fontweight="bold", # √图左侧显示的信息列的表头字体粗细
# 绘制右侧y轴刻度标签yticklabel2
pval='p-val', # √变量p值信息列，添加'formatted_pval'列并显示在图右侧
starpval=True, # √是否根据thresholds参数提供的阈值，在p值后添加对应符号
thresholds=[0.001, 0.01, 0.05], # √小于等于对应阈值的p值后添加symbols参数对应显著性符号
symbols=[' ***', ' **', ' *'], # √为p值添加显著性符号
# pval_title="P-value", # √p值列表头（rightannote=None时）
# pval_title_fontsize=20, # √p值表头文本大小（rightannote=None且annoteheaders！=None时）
# pval_title_fontweight="bold", # √p值表头文本粗细（rightannote=None且annoteheaders！=None时）
rightannote=["formatted_pval", "n", "power"], # √图右侧显示的信息列，覆盖以上设置的p值列
right_annoteheaders=["P-value", "N", "Power"], # √图右侧显示的信息列的表头，覆盖以上设置的p值列
# 两侧y轴刻度标签
table=True, # √是否绘制表格线
col_spacing=2, # √列间距
fontfamily="monospace", # √字体
fontsize=20, # √字体大小
# y轴标题√
ylabel="Variable Information", # √y轴标题，传递给ax.set_ylabel的ylabel参数
ylabel1_size=18, # √y轴标题字体大小，传递给ax.set_ylabel的size参数；p值表头文本大小（rightannote=None且annoteheaders=None时，默认设置字体粗细为"bold"）
ylabel1_fontweight="bold", # √y轴标题字体粗细，传递给ax.set_ylabel的fontweight参数
ylabel_loc="top", # √y轴标题位置，传递给ax.set_ylabel的loc参数
ylabel_angle="horizontal", # √y轴标题旋转角度，传递给ax.set_ylabel的rotation参数
# 其它√
decimal_precision=2, # √数据小数点精确位数
return_df=True, # √是否返回内部处理后的DataFrame结果
despine=False,
ax=ax
)
"""
( var group n r p-val power hl ll moerror label formatted_r formatted_ll formatted_hl ci_range est_ci formatted_pval formatted_label formatted_est_ci yticklabel formatted_formatted_pval formatted_n formatted_power yticklabel2
0 Rlxall Other Sleep Factors 706 0.867744 6.051022e-216 1.00 0.88 0.85 0.012256 slpnaps + personal activs 0.87 0.85 0.88 ( 0.85 to 0.88) 0.87( 0.85 to 0.88) 0.0 *** slpnaps + personal activs 0.87( 0.85 to 0.88) Rlxall slpnaps + personal activs... 0.0 *** 706 1.0 0.0 *** 706 1.0
1 Slpnaps Other Sleep Factors 706 0.893043 2.339108e-246 1.00 0.91 0.88 0.016957 minutes sleep, inc. naps 0.89 0.88 0.91 ( 0.88 to 0.91) 0.89( 0.88 to 0.91) 0.0 *** minutes sleep, inc. naps 0.89( 0.88 to 0.91) Slpnaps minutes sleep, inc. naps ... 0.0 *** 706 1.0 0.0 *** 706 1.0
2 Other Sleep Factors Other Sleep Factors NaN NaN NaN NaN NaN NaN NaN NaN nan nan nan ( nan to nan) nan( nan to nan) nan nan( nan to nan) Other Sleep Factors nan nan
3 Male Other Factors 706 -0.035909 3.407214e-01 0.16 0.04 -0.11 0.075909 =1 if male -0.04 -0.11 0.04 (-0.11 to 0.04) -0.04(-0.11 to 0.04) 0.34 =1 if male -0.04(-0.11 to 0.04) Male =1 if male ... 0.34 706 0.16 0.34 706 0.16
4 Black Other Factors 706 -0.027057 4.728889e-01 0.11 0.05 -0.10 0.077057 =1 if black -0.03 -0.10 0.05 (-0.10 to 0.05) -0.03(-0.10 to 0.05) 0.47 =1 if black -0.03(-0.10 to 0.05) Black =1 if black ... 0.47 706 0.11 0.47 706 0.11
5 Spwrk75 Other Factors 706 0.007868 8.346888e-01 0.05 0.08 -0.07 0.072132 =1 if spouse works 0.01 -0.07 0.08 (-0.07 to 0.08) 0.01(-0.07 to 0.08) 0.83 =1 if spouse works 0.01(-0.07 to 0.08) Spwrk75 =1 if spouse works ... 0.83 706 0.05 0.83 706 0.05
6 Spsepay Other Factors 706 0.007881 8.344125e-01 0.06 0.08 -0.07 0.072119 spousal wage income 0.01 -0.07 0.08 (-0.07 to 0.08) 0.01(-0.07 to 0.08) 0.83 spousal wage income 0.01(-0.07 to 0.08) Spsepay spousal wage income ... 0.83 706 0.06 0.83 706 0.06
7 Prot Other Factors 706 0.027147 4.714176e-01 0.11 0.10 -0.05 0.072853 =1 if Protestant 0.03 -0.05 0.10 (-0.05 to 0.10) 0.03(-0.05 to 0.10) 0.47 =1 if Protestant 0.03(-0.05 to 0.10) Prot =1 if Protestant ... 0.47 706 0.11 0.47 706 0.11
8 Other Factors Other Factors NaN NaN NaN NaN NaN NaN NaN NaN nan nan nan ( nan to nan) nan( nan to nan) nan nan( nan to nan) Other Factors nan nan
9 Smsa Area Of Residence 706 -0.066997 7.524015e-02 0.43 0.01 -0.14 0.076997 =1 if live in smsa -0.07 -0.14 0.01 (-0.14 to 0.01) -0.07(-0.14 to 0.01) 0.08 =1 if live in smsa -0.07(-0.14 to 0.01) Smsa =1 if live in smsa ... 0.08 706 0.43 0.08 706 0.43
10 South Area Of Residence 706 0.078600 3.679946e-02 0.55 0.15 0.00 0.071400 =1 if live in south 0.08 0.00 0.15 ( 0.00 to 0.15) 0.08( 0.00 to 0.15) 0.04 * =1 if live in south 0.08( 0.00 to 0.15) South =1 if live in south ... 0.04 * 706 0.55 0.04 * 706 0.55
11 Area Of Residence Area Of Residence NaN NaN NaN NaN NaN NaN NaN NaN nan nan nan ( nan to nan) nan( nan to nan) nan nan( nan to nan) Area Of Residence nan nan
12 Yngkid Family Factors 706 -0.013262 7.250012e-01 0.06 0.06 -0.09 0.073262 =1 if children < 3 present -0.01 -0.09 0.06 (-0.09 to 0.06) -0.01(-0.09 to 0.06) 0.73 =1 if children < 3 present -0.01(-0.09 to 0.06) Yngkid =1 if children < 3 presen... 0.73 706 0.06 0.73 706 0.06
13 Marr Family Factors 706 0.053757 1.536188e-01 0.30 0.13 -0.02 0.076243 =1 if married 0.05 -0.02 0.13 (-0.02 to 0.13) 0.05(-0.02 to 0.13) 0.15 =1 if married 0.05(-0.02 to 0.13) Marr =1 if married ... 0.15 706 0.3 0.15 706 0.3
14 Yrsmarr Family Factors 706 0.063997 8.928507e-02 0.40 0.14 -0.01 0.076003 years married 0.06 -0.01 0.14 (-0.01 to 0.14) 0.06(-0.01 to 0.14) 0.09 years married 0.06(-0.01 to 0.14) Yrsmarr years married ... 0.09 706 0.4 0.09 706 0.4
15 Family Factors Family Factors NaN NaN NaN NaN NaN NaN NaN NaN nan nan nan ( nan to nan) nan( nan to nan) nan nan( nan to nan) Family Factors nan nan
16 Gdhlth Health Factors 706 -0.102825 6.246660e-03 0.78 -0.03 -0.18 0.072825 =1 if in good or excel. health -0.10 -0.18 -0.03 (-0.18 to -0.03) -0.10(-0.18 to -0.03) 0.01 ** =1 if in good or excel. health -0.10(-0.18 to -0.03) Gdhlth =1 if in good or excel. h... 0.01 ** 706 0.78 0.01 ** 706 0.78
17 Health Factors Health Factors NaN NaN NaN NaN NaN NaN NaN NaN nan nan nan ( nan to nan) nan( nan to nan) nan nan( nan to nan) Health Factors nan nan
18 Construc Occupation 706 0.041229 2.739475e-01 0.19 0.11 -0.03 0.068771 =1 if construction worker 0.04 -0.03 0.11 (-0.03 to 0.11) 0.04(-0.03 to 0.11) 0.27 =1 if construction worker 0.04(-0.03 to 0.11) Construc =1 if construction worker... 0.27 706 0.19 0.27 706 0.19
19 Clerical Occupation 706 0.048081 2.019484e-01 0.25 0.12 -0.03 0.071919 =1 if clerical worker 0.05 -0.03 0.12 (-0.03 to 0.12) 0.05(-0.03 to 0.12) 0.2 =1 if clerical worker 0.05(-0.03 to 0.12) Clerical =1 if clerical worker ... 0.2 706 0.25 0.2 706 0.25
20 Occupation Occupation NaN NaN NaN NaN NaN NaN NaN NaN nan nan nan ( nan to nan) nan( nan to nan) nan nan( nan to nan) Occupation nan nan
21 Worknrm Labor Factors 706 -0.322300 1.577335e-18 1.00 -0.25 -0.39 0.072300 mins work main job -0.32 -0.39 -0.25 (-0.39 to -0.25) -0.32(-0.39 to -0.25) 0.0 *** mins work main job -0.32(-0.39 to -0.25) Worknrm mins work main job ... 0.0 *** 706 1.0 0.0 *** 706 1.0
22 Totwrk Labor Factors 706 -0.321384 1.994095e-18 1.00 -0.25 -0.39 0.071384 mins worked per week -0.32 -0.39 -0.25 (-0.39 to -0.25) -0.32(-0.39 to -0.25) 0.0 *** mins worked per week -0.32(-0.39 to -0.25) Totwrk mins worked per week ... 0.0 *** 706 1.0 0.0 *** 706 1.0
23 Educ Labor Factors 706 -0.095004 1.155151e-02 0.72 -0.02 -0.17 0.075004 years of schooling -0.10 -0.17 -0.02 (-0.17 to -0.02) -0.10(-0.17 to -0.02) 0.01 * years of schooling -0.10(-0.17 to -0.02) Educ years of schooling ... 0.01 * 706 0.72 0.01 * 706 0.72
24 Earns74 Labor Factors 706 -0.076890 4.110934e-02 0.53 -0.00 -0.15 0.076890 total earnings, 1974 -0.08 -0.15 -0.00 (-0.15 to -0.00) -0.08(-0.15 to -0.00) 0.04 * total earnings, 1974 -0.08(-0.15 to -0.00) Earns74 total earnings, 1974 ... 0.04 * 706 0.53 0.04 * 706 0.53
25 Lhrwage Labor Factors 532 -0.067197 1.216222e-01 0.34 0.02 -0.15 0.087197 log hourly wage -0.07 -0.15 0.02 (-0.15 to 0.02) -0.07(-0.15 to 0.02) 0.12 log hourly wage -0.07(-0.15 to 0.02) Lhrwage log hourly wage ... 0.12 532 0.34 0.12 532 0.34
26 Hrwage Labor Factors 532 -0.049450 2.548774e-01 0.21 0.04 -0.13 0.089450 hourly wage -0.05 -0.13 0.04 (-0.13 to 0.04) -0.05(-0.13 to 0.04) 0.25 hourly wage -0.05(-0.13 to 0.04) Hrwage hourly wage ... 0.25 532 0.21 0.25 532 0.21
27 Inlf Labor Factors 706 -0.027126 4.717698e-01 0.11 0.05 -0.10 0.077126 =1 if in labor force -0.03 -0.10 0.05 (-0.10 to 0.05) -0.03(-0.10 to 0.05) 0.47 =1 if in labor force -0.03(-0.10 to 0.05) Inlf =1 if in labor force ... 0.47 706 0.11 0.47 706 0.11
28 Workscnd Labor Factors 706 0.001139 9.759034e-01 0.05 0.07 -0.07 0.068861 mins work second job 0.00 -0.07 0.07 (-0.07 to 0.07) 0.00(-0.07 to 0.07) 0.98 mins work second job 0.00(-0.07 to 0.07) Workscnd mins work second job ... 0.98 706 0.05 0.98 706 0.05
29 Selfe Labor Factors 706 0.001782 9.623058e-01 0.05 0.08 -0.07 0.078218 =1 if self employed 0.00 -0.07 0.08 (-0.07 to 0.08) 0.00(-0.07 to 0.08) 0.96 =1 if self employed 0.00(-0.07 to 0.08) Selfe =1 if self employed ... 0.96 706 0.05 0.96 706 0.05
30 Union Labor Factors 706 0.009965 7.915440e-01 0.06 0.08 -0.06 0.070035 =1 if belong to union 0.01 -0.06 0.08 (-0.06 to 0.08) 0.01(-0.06 to 0.08) 0.79 =1 if belong to union 0.01(-0.06 to 0.08) Union =1 if belong to union ... 0.79 706 0.06 0.79 706 0.06
31 Lothinc Labor Factors 706 0.036661 3.306971e-01 0.16 0.11 -0.04 0.073339 log othinc, unless othinc < 0 0.04 -0.04 0.11 (-0.04 to 0.11) 0.04(-0.04 to 0.11) 0.33 log othinc, unless othinc < 0 0.04(-0.04 to 0.11) Lothinc log othinc, unless othinc... 0.33 706 0.16 0.33 706 0.16
32 Exper Labor Factors 706 0.104191 5.587422e-03 0.79 0.18 0.03 0.075809 age - educ - 6 0.10 0.03 0.18 ( 0.03 to 0.18) 0.10( 0.03 to 0.18) 0.01 ** age - educ - 6 0.10( 0.03 to 0.18) Exper age - educ - 6 ... 0.01 ** 706 0.79 0.01 ** 706 0.79
33 Labor Factors Labor Factors NaN NaN NaN NaN NaN NaN NaN NaN nan nan nan ( nan to nan) nan( nan to nan) nan nan( nan to nan) Labor Factors nan nan
34 Age Age Factors 706 0.090373 1.630887e-02 0.67 0.16 0.02 0.069627 in years 0.09 0.02 0.16 ( 0.02 to 0.16) 0.09( 0.02 to 0.16) 0.02 * in years 0.09( 0.02 to 0.16) Age in years ... 0.02 * 706 0.67 0.02 * 706 0.67
35 Agesq Age Factors 706 0.099722 8.010946e-03 0.76 0.17 0.03 0.070278 age^2 0.10 0.03 0.17 ( 0.03 to 0.17) 0.10( 0.03 to 0.17) 0.01 ** age^2 0.10( 0.03 to 0.17) Agesq age^2 ... 0.01 ** 706 0.76 0.01 ** 706 0.76
36 Age Factors Age Factors NaN NaN NaN NaN NaN NaN NaN NaN nan nan nan ( nan to nan) nan( nan to nan) nan nan( nan to nan) Age Factors nan nan
37 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Variable Label ... NaN NaN NaN P-value N Power,
<Axes: xlabel='Pearson correlation coefficient', ylabel='Variable Information'>)
"""
plt.show()
多模型¶
forestplot/examples/get-sleep-multmodel.ipynb at main · LSYS/forestplot
# 回归参数
stdopts = {'relimp': False, 'remove_na': True}
# 保留变量
x = ["age", "black", "clerical", "construc", "educ", "gdhlth", "smsa", "lhrwage", "prot", "selfe", "south", "spsepay", "totwrk", "yrsmarr"]
# 所有样本变量与'sleep'变量的回归结果
lm_all = pg.linear_regression(sleep[x], sleep['sleep'], **stdopts).assign(model='all')
"""
names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%] model
0 Intercept 3693.605849 136.823292 26.995446 8.489175e-101 0.127289 0.103656 3424.807860 3962.403839 all
1 age 0.994889 1.969249 0.505213 6.136249e-01 0.127289 0.103656 -2.873825 4.863603 all
2 black -84.796612 82.150125 -1.032215 3.024539e-01 0.127289 0.103656 -246.185715 76.592491 all
3 clerical 22.505827 48.963263 0.459647 6.459627e-01 0.127289 0.103656 -73.685593 118.697248 all
4 construc 113.929558 105.045137 1.084577 2.786145e-01 0.127289 0.103656 -92.438240 320.297357 all
5 educ -7.210558 7.516736 -0.959267 3.378725e-01 0.127289 0.103656 -21.977660 7.556543 all
6 gdhlth -82.580434 58.204717 -1.418793 1.565620e-01 0.127289 0.103656 -196.927272 31.766405 all
7 smsa -40.653557 39.507723 -1.029003 3.039595e-01 0.127289 0.103656 -118.268971 36.961857 all
8 lhrwage 21.605697 31.989301 0.675404 4.997212e-01 0.127289 0.103656 -41.239303 84.450696 all
9 prot -9.249430 39.771462 -0.232564 8.161916e-01 0.127289 0.103656 -87.382976 68.884117 all
10 selfe -21.274179 63.303918 -0.336064 7.369588e-01 0.127289 0.103656 -145.638720 103.090363 all
11 south 82.486980 46.377571 1.778596 7.589350e-02 0.127289 0.103656 -8.624685 173.598644 all
12 spsepay -0.001005 0.002648 -0.379739 7.042954e-01 0.127289 0.103656 -0.006207 0.004196 all
13 totwrk -0.151922 0.020366 -7.459430 3.698705e-13 0.127289 0.103656 -0.191934 -0.111911 all
14 yrsmarr -0.075958 2.009356 -0.037802 9.698602e-01 0.127289 0.103656 -4.023464 3.871548 all
"""
# 男性样本变量与'sleep'变量的回归结果
lm_male = pg.linear_regression(sleep.query('male==1')[x], sleep.query('male==1')['sleep'], **stdopts).assign(model='men')
"""
names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%] model
0 Intercept 3919.280072 191.619361 20.453466 2.284744e-57 0.180382 0.139106 3542.070849 4296.489295 men
1 age 1.482893 2.885462 0.513919 6.077171e-01 0.180382 0.139106 -4.197237 7.163023 men
2 black -132.967109 132.581166 -1.002911 3.167759e-01 0.180382 0.139106 -393.957639 128.023420 men
3 clerical -229.275962 102.888221 -2.228399 2.665377e-02 0.180382 0.139106 -431.814921 -26.737003 men
4 construc 65.696059 107.889208 0.608922 5.430737e-01 0.180382 0.139106 -146.687512 278.079631 men
5 educ -7.545420 9.720341 -0.776250 4.382610e-01 0.180382 0.139106 -26.680241 11.589402 men
6 gdhlth -188.284508 80.476539 -2.339620 2.001135e-02 0.180382 0.139106 -346.705309 -29.863707 men
7 smsa -27.631427 52.418593 -0.527130 5.985235e-01 0.180382 0.139106 -130.819208 75.556354 men
8 lhrwage 3.544752 47.045923 0.075347 9.399931e-01 0.180382 0.139106 -89.066745 96.156249 men
9 prot -20.967328 52.201060 -0.401665 6.882393e-01 0.180382 0.139106 -123.726888 81.792233 men
10 selfe 3.484094 77.585707 0.044906 9.642142e-01 0.180382 0.139106 -149.246005 156.214193 men
11 south 79.401694 64.439832 1.232183 2.189217e-01 0.180382 0.139106 -47.450305 206.253693 men
12 spsepay 0.003069 0.004924 0.623344 5.335697e-01 0.180382 0.139106 -0.006624 0.012762 men
13 totwrk -0.191008 0.029765 -6.417152 5.964764e-10 0.180382 0.139106 -0.249602 -0.132414 men
14 yrsmarr 0.224116 2.934791 0.076365 9.391834e-01 0.180382 0.139106 -5.553120 6.001352 men
"""
# 女性样本变量与'sleep'变量的回归结果
lm_female = pg.linear_regression(sleep.query('male==0')[x], sleep.query('male==0')['sleep'], **stdopts).assign(model='women')
"""
names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%] model
0 Intercept 3599.064226 210.446560 17.102034 1.637481e-42 0.109105 0.053425 3184.355929 4013.772523 women
1 age 0.953200 2.943209 0.323864 7.463431e-01 0.109105 0.053425 -4.846721 6.753121 women
2 black -68.350631 108.901754 -0.627636 5.308819e-01 0.109105 0.053425 -282.953621 146.252360 women
3 clerical 106.507258 61.127321 1.742384 8.281375e-02 0.109105 0.053425 -13.950911 226.965427 women
4 construc 361.756430 428.264834 0.844703 3.991782e-01 0.109105 0.053425 -482.186946 1205.699805 women
5 educ -6.278397 11.869883 -0.528935 5.973743e-01 0.109105 0.053425 -29.669318 17.112525 women
6 gdhlth -24.491583 87.716867 -0.279212 7.803399e-01 0.109105 0.053425 -197.347402 148.364236 women
7 smsa -57.146400 61.521030 -0.928892 3.539449e-01 0.109105 0.053425 -178.380417 64.087616 women
8 lhrwage -22.580482 53.782320 -0.419850 6.749978e-01 0.109105 0.053425 -128.564512 83.403547 women
9 prot 1.066643 61.898047 0.017232 9.862667e-01 0.109105 0.053425 -120.910327 123.043614 women
10 selfe -90.685936 112.556497 -0.805693 4.212746e-01 0.109105 0.053425 -312.491002 131.119130 women
11 south 114.622934 69.300494 1.653999 9.952837e-02 0.109105 0.053425 -21.941379 251.187246 women
12 spsepay -0.000508 0.003621 -0.140324 8.885302e-01 0.109105 0.053425 -0.007644 0.006628 women
13 totwrk -0.125512 0.032713 -3.836783 1.621349e-04 0.109105 0.053425 -0.189977 -0.061048 women
14 yrsmarr -1.414137 2.931985 -0.482314 6.300539e-01 0.109105 0.053425 -7.191939 4.363665 women
"""
# 儿童样本变量与'sleep'变量的回归结果
lm_kids = pg.linear_regression(sleep.query('yngkid==1')[x], sleep.query('yngkid==1')['sleep'], **stdopts).assign(model='young kids')
"""
names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%] model
0 Intercept 3496.301163 514.490634 6.795656 5.575797e-09 0.178147 -0.013619 2467.166669 4525.435657 young kids
1 age 22.634017 15.495320 1.460700 1.493148e-01 0.178147 -0.013619 -8.361238 53.629272 young kids
2 black -119.741039 296.432953 -0.403940 6.876932e-01 0.178147 -0.013619 -712.695230 473.213151 young kids
3 clerical -239.902574 211.760200 -1.132897 2.617642e-01 0.178147 -0.013619 -663.486040 183.680893 young kids
4 construc -186.415201 358.223501 -0.520388 6.047082e-01 0.178147 -0.013619 -902.968890 530.138489 young kids
5 educ -20.972116 26.974032 -0.777493 4.399230e-01 0.178147 -0.013619 -74.928213 32.983981 young kids
6 gdhlth -141.814976 203.213022 -0.697864 4.879582e-01 0.178147 -0.013619 -548.301540 264.671589 young kids
7 smsa 43.429572 138.683089 0.313157 7.552474e-01 0.178147 -0.013619 -233.977910 320.837053 young kids
8 lhrwage 35.609317 122.323738 0.291107 7.719733e-01 0.178147 -0.013619 -209.074589 280.293223 young kids
9 prot -24.173124 127.566497 -0.189494 8.503450e-01 0.178147 -0.013619 -279.344111 230.997863 young kids
10 selfe -370.956512 241.153473 -1.538259 1.292427e-01 0.178147 -0.013619 -853.335279 111.422256 young kids
11 south 71.851381 136.010112 0.528280 5.992538e-01 0.178147 -0.013619 -200.209350 343.912111 young kids
12 spsepay -0.004701 0.011443 -0.410849 6.826471e-01 0.178147 -0.013619 -0.027591 0.018188 young kids
13 totwrk -0.112995 0.076363 -1.479712 1.441818e-01 0.178147 -0.013619 -0.265743 0.039753 young kids
14 yrsmarr -31.211144 16.914011 -1.845283 6.993267e-02 0.178147 -0.013619 -65.044204 2.621915 young kids
"""
sleep_lm = pd.concat([lm_all, lm_female, lm_male, lm_kids]).query('names!="Intercept"')
sleep_lm.rename(columns={'names': 'var', 'CI[2.5%]': 'll', 'CI[97.5%]': 'hl'}, inplace=True)
sleep_lm = pd.merge(sleep_lm, sleep_label, on='var', how='left', validate='m:1')
sleep_lm.sort_values(['var', 'model', 'group'], inplace=True)
sleep_lm.reset_index(drop=True, inplace=True)
"""
var coef se T pval r2 adj_r2 ll hl model label group
0 age 0.994889 1.969249 0.505213 6.136249e-01 0.127289 0.103656 -2.873825 4.863603 all in years age factors
1 age 1.482893 2.885462 0.513919 6.077171e-01 0.180382 0.139106 -4.197237 7.163023 men in years age factors
2 age 0.953200 2.943209 0.323864 7.463431e-01 0.109105 0.053425 -4.846721 6.753121 women in years age factors
3 age 22.634017 15.495320 1.460700 1.493148e-01 0.178147 -0.013619 -8.361238 53.629272 young kids in years age factors
4 black -84.796612 82.150125 -1.032215 3.024539e-01 0.127289 0.103656 -246.185715 76.592491 all =1 if black other factors
5 black -132.967109 132.581166 -1.002911 3.167759e-01 0.180382 0.139106 -393.957639 128.023420 men =1 if black other factors
6 black -68.350631 108.901754 -0.627636 5.308819e-01 0.109105 0.053425 -282.953621 146.252360 women =1 if black other factors
7 black -119.741039 296.432953 -0.403940 6.876932e-01 0.178147 -0.013619 -712.695230 473.213151 young kids =1 if black other factors
8 clerical 22.505827 48.963263 0.459647 6.459627e-01 0.127289 0.103656 -73.685593 118.697248 all =1 if clerical worker occupation
9 clerical -229.275962 102.888221 -2.228399 2.665377e-02 0.180382 0.139106 -431.814921 -26.737003 men =1 if clerical worker occupation
10 clerical 106.507258 61.127321 1.742384 8.281375e-02 0.109105 0.053425 -13.950911 226.965427 women =1 if clerical worker occupation
11 clerical -239.902574 211.760200 -1.132897 2.617642e-01 0.178147 -0.013619 -663.486040 183.680893 young kids =1 if clerical worker occupation
12 construc 113.929558 105.045137 1.084577 2.786145e-01 0.127289 0.103656 -92.438240 320.297357 all =1 if construction worker occupation
13 construc 65.696059 107.889208 0.608922 5.430737e-01 0.180382 0.139106 -146.687512 278.079631 men =1 if construction worker occupation
14 construc 361.756430 428.264834 0.844703 3.991782e-01 0.109105 0.053425 -482.186946 1205.699805 women =1 if construction worker occupation
15 construc -186.415201 358.223501 -0.520388 6.047082e-01 0.178147 -0.013619 -902.968890 530.138489 young kids =1 if construction worker occupation
16 educ -7.210558 7.516736 -0.959267 3.378725e-01 0.127289 0.103656 -21.977660 7.556543 all years of schooling labor factors
17 educ -7.545420 9.720341 -0.776250 4.382610e-01 0.180382 0.139106 -26.680241 11.589402 men years of schooling labor factors
18 educ -6.278397 11.869883 -0.528935 5.973743e-01 0.109105 0.053425 -29.669318 17.112525 women years of schooling labor factors
19 educ -20.972116 26.974032 -0.777493 4.399230e-01 0.178147 -0.013619 -74.928213 32.983981 young kids years of schooling labor factors
20 gdhlth -82.580434 58.204717 -1.418793 1.565620e-01 0.127289 0.103656 -196.927272 31.766405 all =1 if in good or excel. health health factors
21 gdhlth -188.284508 80.476539 -2.339620 2.001135e-02 0.180382 0.139106 -346.705309 -29.863707 men =1 if in good or excel. health health factors
22 gdhlth -24.491583 87.716867 -0.279212 7.803399e-01 0.109105 0.053425 -197.347402 148.364236 women =1 if in good or excel. health health factors
23 gdhlth -141.814976 203.213022 -0.697864 4.879582e-01 0.178147 -0.013619 -548.301540 264.671589 young kids =1 if in good or excel. health health factors
24 lhrwage 21.605697 31.989301 0.675404 4.997212e-01 0.127289 0.103656 -41.239303 84.450696 all log hourly wage labor factors
25 lhrwage 3.544752 47.045923 0.075347 9.399931e-01 0.180382 0.139106 -89.066745 96.156249 men log hourly wage labor factors
26 lhrwage -22.580482 53.782320 -0.419850 6.749978e-01 0.109105 0.053425 -128.564512 83.403547 women log hourly wage labor factors
27 lhrwage 35.609317 122.323738 0.291107 7.719733e-01 0.178147 -0.013619 -209.074589 280.293223 young kids log hourly wage labor factors
28 prot -9.249430 39.771462 -0.232564 8.161916e-01 0.127289 0.103656 -87.382976 68.884117 all =1 if Protestant other factors
29 prot -20.967328 52.201060 -0.401665 6.882393e-01 0.180382 0.139106 -123.726888 81.792233 men =1 if Protestant other factors
30 prot 1.066643 61.898047 0.017232 9.862667e-01 0.109105 0.053425 -120.910327 123.043614 women =1 if Protestant other factors
31 prot -24.173124 127.566497 -0.189494 8.503450e-01 0.178147 -0.013619 -279.344111 230.997863 young kids =1 if Protestant other factors
32 selfe -21.274179 63.303918 -0.336064 7.369588e-01 0.127289 0.103656 -145.638720 103.090363 all =1 if self employed labor factors
33 selfe 3.484094 77.585707 0.044906 9.642142e-01 0.180382 0.139106 -149.246005 156.214193 men =1 if self employed labor factors
34 selfe -90.685936 112.556497 -0.805693 4.212746e-01 0.109105 0.053425 -312.491002 131.119130 women =1 if self employed labor factors
35 selfe -370.956512 241.153473 -1.538259 1.292427e-01 0.178147 -0.013619 -853.335279 111.422256 young kids =1 if self employed labor factors
36 smsa -40.653557 39.507723 -1.029003 3.039595e-01 0.127289 0.103656 -118.268971 36.961857 all =1 if live in smsa area of residence
37 smsa -27.631427 52.418593 -0.527130 5.985235e-01 0.180382 0.139106 -130.819208 75.556354 men =1 if live in smsa area of residence
38 smsa -57.146400 61.521030 -0.928892 3.539449e-01 0.109105 0.053425 -178.380417 64.087616 women =1 if live in smsa area of residence
39 smsa 43.429572 138.683089 0.313157 7.552474e-01 0.178147 -0.013619 -233.977910 320.837053 young kids =1 if live in smsa area of residence
40 south 82.486980 46.377571 1.778596 7.589350e-02 0.127289 0.103656 -8.624685 173.598644 all =1 if live in south area of residence
41 south 79.401694 64.439832 1.232183 2.189217e-01 0.180382 0.139106 -47.450305 206.253693 men =1 if live in south area of residence
42 south 114.622934 69.300494 1.653999 9.952837e-02 0.109105 0.053425 -21.941379 251.187246 women =1 if live in south area of residence
43 south 71.851381 136.010112 0.528280 5.992538e-01 0.178147 -0.013619 -200.209350 343.912111 young kids =1 if live in south area of residence
44 spsepay -0.001005 0.002648 -0.379739 7.042954e-01 0.127289 0.103656 -0.006207 0.004196 all spousal wage income other factors
45 spsepay 0.003069 0.004924 0.623344 5.335697e-01 0.180382 0.139106 -0.006624 0.012762 men spousal wage income other factors
46 spsepay -0.000508 0.003621 -0.140324 8.885302e-01 0.109105 0.053425 -0.007644 0.006628 women spousal wage income other factors
47 spsepay -0.004701 0.011443 -0.410849 6.826471e-01 0.178147 -0.013619 -0.027591 0.018188 young kids spousal wage income other factors
48 totwrk -0.151922 0.020366 -7.459430 3.698705e-13 0.127289 0.103656 -0.191934 -0.111911 all mins worked per week labor factors
49 totwrk -0.191008 0.029765 -6.417152 5.964764e-10 0.180382 0.139106 -0.249602 -0.132414 men mins worked per week labor factors
50 totwrk -0.125512 0.032713 -3.836783 1.621349e-04 0.109105 0.053425 -0.189977 -0.061048 women mins worked per week labor factors
51 totwrk -0.112995 0.076363 -1.479712 1.441818e-01 0.178147 -0.013619 -0.265743 0.039753 young kids mins worked per week labor factors
52 yrsmarr -0.075958 2.009356 -0.037802 9.698602e-01 0.127289 0.103656 -4.023464 3.871548 all years married family factors
53 yrsmarr 0.224116 2.934791 0.076365 9.391834e-01 0.180382 0.139106 -5.553120 6.001352 men years married family factors
54 yrsmarr -1.414137 2.931985 -0.482314 6.300539e-01 0.109105 0.053425 -7.191939 4.363665 women years married family factors
55 yrsmarr -31.211144 16.914011 -1.845283 6.993267e-02 0.178147 -0.013619 -65.044204 2.621915 young kids years married family factors
"""
fig = plt.figure(dpi=100, layout="constrained")
group_order = ["age factors", "labor factors", "occupation", "health factors", "family factors", "area of residence", "other factors"]
fp.mforestplot(sleep_lm,
# 绘制估计值与置信区间的errorbar和scatter
estimate="coef", # 估计值列，传递给ax.errorbar和ax.scatter的x参数
ll="ll", # 置信区间下限，计算后传递给ax.errorbar的xerr参数
hl="hl", # 置信区间上限，计算后传递给ax.errorbar的xerr参数
model_col='model', # 模型信息列
models=['men', 'women'], # 选择绘制的模型
msymbols=['o', '*'], # 各模型估计值标记类型，分别传递给ax.scatter的marker参数
mcolor=['r', 'b'], # 各模型估计值标记和误差线颜色，分别传递给ax.scatter的c参数和ax.errorbar的ecolor参数
lw=2, # 误差线宽，传递给ax.errorbar的elinewidth参数
markersize=100, # 估计值标记大小，传递给ax.scatter的s参数
offset=0.5, # 模型绘图间距
logscale=False, # 是否x轴进行对数化（以10为底）缩放【ax.set_xscale("log", base=10)】
color_alt_rows=True, # 变量行交替显示颜色
# 绘制模型图例
legend=True, # √是否绘制模型图例
modellabels=['M', 'WM'], # √各模型图例标签。传递给ax.legend的labels参数
leg_loc='best', # √模型图例位置。传递给ax.legend的loc参数
bbox_to_anchor=None, # √模型图例bbox。传递给ax.legend的bbox_to_anchor参数
leg_markersize=8, # √模型图例标记大小。传递给Line2D的markersize参数
leg_ncol=1, # √模型图例条目列数。传递给ax.legend的ncol参数
leg_fontsize=8, # √模型图例字体大小。传递给ax.legend的fontsize参数
# 设置x轴刻度与标签
xticks=None, # √设置x轴刻度位置【ax.set_xticks(xticks)】
nticks=6, # √xticks=None时设置x轴最大刻度数自动设置刻度位置【ax.xaxis.set_major_locator(plt.MaxNLocator(nticks))】
xticklabels=None, # √设置x轴刻度标签【ax.set_xticklabels(xticklabels)】
xtick_size=12, # √设置x轴刻度标签大小【ax.tick_params(axis="x", labelsize=xtick_size)】
xlim=[-1000, 1500], # √设置x轴范围【ax.set_xlim(xlim[0], xlim[1])】
# x轴标题√
xlabel="Linear Regression coefficient", # √x轴标题，传递给ax.set_xlabel的xlabel参数
xlabel_size=20, # √x轴标题字体大小，传递给ax.set_xlabel的size参数
xlabel_fontweight="bold", # √x轴标题字体粗细，传递给ax.set_xlabel的fontweight参数
# 绘制参考线
xline=0, # √参考线位置，传递给ax.vlines的x参数
xlinestyle='--', # √参考线线型，传递给ax.vlines的linestyle参数
xlinecolor='r', # √参考线颜色，传递给ax.vlines的color参数
xlinewidth=2, # √参考线线宽，传递给ax.vlines的linewidth参数
# 绘制左侧y轴刻度标签yticklabel
varlabel="var", # √变量标签
variable_header="Variable", # √变量标签列表头
groupvar='group', # √变量分组信息列。注意分组名尽量不与变量标签名重复，否则显示出问题。
group_order=group_order, # √变量分组显示顺序
grouplab_size=20, # √分组文本字体大小
# grouplab_fontweight="bold", # √分组文本和右侧表头字体粗细（rightannote!=None时）
flush=True, # √如果为True，则设置ax.set_yticklabels的ha参数为"left"，否则设置为"right"
capitalize="title", # √变量标签和分组名的显示形式。可选"title", "capitalize", "lower", "upper", "swapcase"
sort=True, # √是否对变量根据sortby参数进行排序（降序），默认False。如果设置了groupvar参数，则在各自组内排序。
sortby='coef', # √排序依据（若设置则默认设置sort=True），默认是estimate参数对应的列。
annote=['label'], # √图左侧显示的信息列
annoteheaders=["Label"], # √图左侧显示的信息列的表头
# tableheader_fontweight="bold", # √图左侧显示的信息列的表头字体粗细
# # 绘制右侧y轴刻度标签yticklabel2
rightannote=["group"], # √图右侧显示的信息列
right_annoteheaders=["Group"], # √图右侧显示的信息列的表头
# # 两侧y轴刻度标签
table=True, # √是否绘制表格线
col_spacing=5, # √列间距
fontfamily="monospace", # √字体
fontsize=20, # √字体大小
# 其它
return_df=True, # √是否返回内部处理后的DataFrame结果
despine=False,
figsize=(5, 15)
)
"""
( var group model coef se T pval r2 adj_r2 ll hl label formatted_label yticklabel formatted_group yticklabel2
0 Spsepay other factors women -0.000508 0.003621 -0.140324 8.885302e-01 0.109105 0.053425 -0.007644 0.006628 spousal wage income spousal wage income Spsepay spousal wage income ... other factors other factors
1 Prot other factors women 1.066643 61.898047 0.017232 9.862667e-01 0.109105 0.053425 -120.910327 123.043614 =1 if Protestant =1 if Protestant Prot =1 if Protestant ... other factors other factors
2 Black other factors women -68.350631 108.901754 -0.627636 5.308819e-01 0.109105 0.053425 -282.953621 146.252360 =1 if black =1 if black Black =1 if black ... other factors other factors
3 Other Factors other factors women NaN NaN NaN NaN NaN NaN NaN NaN NaN nan Other Factors other factors
4 South area of residence women 114.622934 69.300494 1.653999 9.952837e-02 0.109105 0.053425 -21.941379 251.187246 =1 if live in south =1 if live in south South =1 if live in south ... area of residence area of residence
5 Smsa area of residence women -57.146400 61.521030 -0.928892 3.539449e-01 0.109105 0.053425 -178.380417 64.087616 =1 if live in smsa =1 if live in smsa Smsa =1 if live in smsa ... area of residence area of residence
6 Area Of Residence area of residence women NaN NaN NaN NaN NaN NaN NaN NaN NaN nan Area Of Residence area of residence
7 Yrsmarr family factors women -1.414137 2.931985 -0.482314 6.300539e-01 0.109105 0.053425 -7.191939 4.363665 years married years married Yrsmarr years married ... family factors family factors
8 Family Factors family factors women NaN NaN NaN NaN NaN NaN NaN NaN NaN nan Family Factors family factors
9 Gdhlth health factors women -24.491583 87.716867 -0.279212 7.803399e-01 0.109105 0.053425 -197.347402 148.364236 =1 if in good or excel. health =1 if in good or excel. health Gdhlth =1 if in good or excel. ... health factors health factors
10 Health Factors health factors women NaN NaN NaN NaN NaN NaN NaN NaN NaN nan Health Factors health factors
11 Clerical occupation women 106.507258 61.127321 1.742384 8.281375e-02 0.109105 0.053425 -13.950911 226.965427 =1 if clerical worker =1 if clerical worker Clerical =1 if clerical worker ... occupation occupation
12 Construc occupation women 361.756430 428.264834 0.844703 3.991782e-01 0.109105 0.053425 -482.186946 1205.699805 =1 if construction worker =1 if construction worker Construc =1 if construction worke... occupation occupation
13 Occupation occupation women NaN NaN NaN NaN NaN NaN NaN NaN NaN nan Occupation occupation
14 Educ labor factors women -6.278397 11.869883 -0.528935 5.973743e-01 0.109105 0.053425 -29.669318 17.112525 years of schooling years of schooling Educ years of schooling ... labor factors labor factors
15 Selfe labor factors women -90.685936 112.556497 -0.805693 4.212746e-01 0.109105 0.053425 -312.491002 131.119130 =1 if self employed =1 if self employed Selfe =1 if self employed ... labor factors labor factors
16 Totwrk labor factors women -0.125512 0.032713 -3.836783 1.621349e-04 0.109105 0.053425 -0.189977 -0.061048 mins worked per week mins worked per week Totwrk mins worked per week ... labor factors labor factors
17 Lhrwage labor factors women -22.580482 53.782320 -0.419850 6.749978e-01 0.109105 0.053425 -128.564512 83.403547 log hourly wage log hourly wage Lhrwage log hourly wage ... labor factors labor factors
18 Labor Factors labor factors women NaN NaN NaN NaN NaN NaN NaN NaN NaN nan Labor Factors labor factors
19 Age age factors women 0.953200 2.943209 0.323864 7.463431e-01 0.109105 0.053425 -4.846721 6.753121 in years in years Age in years ... age factors age factors
20 Age Factors age factors women NaN NaN NaN NaN NaN NaN NaN NaN NaN nan Age Factors age factors
21 NaN NaN women NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Variable Label ... NaN Group
22 Spsepay other factors men 0.003069 0.004924 0.623344 5.335697e-01 0.180382 0.139106 -0.006624 0.012762 spousal wage income spousal wage income Spsepay spousal wage income ... other factors other factors
23 Prot other factors men -20.967328 52.201060 -0.401665 6.882393e-01 0.180382 0.139106 -123.726888 81.792233 =1 if Protestant =1 if Protestant Prot =1 if Protestant ... other factors other factors
24 Black other factors men -132.967109 132.581166 -1.002911 3.167759e-01 0.180382 0.139106 -393.957639 128.023420 =1 if black =1 if black Black =1 if black ... other factors other factors
25 Other Factors other factors men NaN NaN NaN NaN NaN NaN NaN NaN NaN nan Other Factors other factors
26 South area of residence men 79.401694 64.439832 1.232183 2.189217e-01 0.180382 0.139106 -47.450305 206.253693 =1 if live in south =1 if live in south South =1 if live in south ... area of residence area of residence
27 Smsa area of residence men -27.631427 52.418593 -0.527130 5.985235e-01 0.180382 0.139106 -130.819208 75.556354 =1 if live in smsa =1 if live in smsa Smsa =1 if live in smsa ... area of residence area of residence
28 Area Of Residence area of residence men NaN NaN NaN NaN NaN NaN NaN NaN NaN nan Area Of Residence area of residence
29 Yrsmarr family factors men 0.224116 2.934791 0.076365 9.391834e-01 0.180382 0.139106 -5.553120 6.001352 years married years married Yrsmarr years married ... family factors family factors
30 Family Factors family factors men NaN NaN NaN NaN NaN NaN NaN NaN NaN nan Family Factors family factors
31 Gdhlth health factors men -188.284508 80.476539 -2.339620 2.001135e-02 0.180382 0.139106 -346.705309 -29.863707 =1 if in good or excel. health =1 if in good or excel. health Gdhlth =1 if in good or excel. ... health factors health factors
32 Health Factors health factors men NaN NaN NaN NaN NaN NaN NaN NaN NaN nan Health Factors health factors
33 Clerical occupation men -229.275962 102.888221 -2.228399 2.665377e-02 0.180382 0.139106 -431.814921 -26.737003 =1 if clerical worker =1 if clerical worker Clerical =1 if clerical worker ... occupation occupation
34 Construc occupation men 65.696059 107.889208 0.608922 5.430737e-01 0.180382 0.139106 -146.687512 278.079631 =1 if construction worker =1 if construction worker Construc =1 if construction worke... occupation occupation
35 Occupation occupation men NaN NaN NaN NaN NaN NaN NaN NaN NaN nan Occupation occupation
36 Educ labor factors men -7.545420 9.720341 -0.776250 4.382610e-01 0.180382 0.139106 -26.680241 11.589402 years of schooling years of schooling Educ years of schooling ... labor factors labor factors
37 Totwrk labor factors men -0.191008 0.029765 -6.417152 5.964764e-10 0.180382 0.139106 -0.249602 -0.132414 mins worked per week mins worked per week Totwrk mins worked per week ... labor factors labor factors
38 Selfe labor factors men 3.484094 77.585707 0.044906 9.642142e-01 0.180382 0.139106 -149.246005 156.214193 =1 if self employed =1 if self employed Selfe =1 if self employed ... labor factors labor factors
39 Lhrwage labor factors men 3.544752 47.045923 0.075347 9.399931e-01 0.180382 0.139106 -89.066745 96.156249 log hourly wage log hourly wage Lhrwage log hourly wage ... labor factors labor factors
40 Labor Factors labor factors men NaN NaN NaN NaN NaN NaN NaN NaN NaN nan Labor Factors labor factors
41 Age age factors men 1.482893 2.885462 0.513919 6.077171e-01 0.180382 0.139106 -4.197237 7.163023 in years in years Age in years ... age factors age factors
42 Age Factors age factors men NaN NaN NaN NaN NaN NaN NaN NaN NaN nan Age Factors age factors
43 NaN NaN men NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Variable Label ... NaN Group ,
<Axes: xlabel='Linear Regression coefficient'>)
<Figure size 640x480 with 0 Axes>
"""
plt.show()
