Home¶

Understanding personal protective behaviours and opportunities for interventions:¶

Results from a multi-method investigation of cross-sectional data¶

Kaisa Saurio, James Twose, Gjalt-Jorn Peters, Matti Heino & Nelli Hankonen¶

Literature review of models¶

approaches used here: Linear Regression, Logistic Regression, Partial correlations, SEM¶

In [7]:
import pandas as pd
from pandas.api.types import CategoricalDtype
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import session_info

from sklearn.metrics import mean_squared_error, confusion_matrix
In [8]:
from scipy import stats
In [9]:
from sklearn.model_selection import RepeatedStratifiedKFold, cross_val_score, cross_validate, KFold, GroupKFold, GroupShuffleSplit, RepeatedKFold, train_test_split, GridSearchCV
from sklearn.linear_model import LinearRegression, LogisticRegression
In [10]:
from sklearn.model_selection import KFold
In [11]:
import statsmodels.api as sm
In [12]:
from semopy import Model, semplot, report
from semopy.examples import political_democracy
In [13]:
import itertools
In [14]:
from jmspack.frequentist_statistics import (correlation_analysis,
                                            potential_for_change_index,
                                            multiple_univariate_OLSs,
                                            correct_pvalues,
                                            partial_correlation
                                           )
from jmspack.utils import (flatten,
                           apply_scaling,
                           JmsColors
                          )
from jmspack.ml_utils import plot_confusion_matrix
In [15]:
if "jms_style_sheet" in plt.style.available:
    plt.style.use("jms_style_sheet")

# _ = sns.set_style("whitegrid")

Virtual Environments and Packages¶

In [16]:
session_info.show(req_file_name="corona_preppers-requirements.txt",
      write_req_file=False) #add write_req_file=True to function to get requirements.txt file of packages used
Out[16]:
Click to view session information 
-----
jmspack             0.1.1
matplotlib          3.5.1
numpy               1.21.5
pandas              1.4.2
scipy               1.7.3
seaborn             0.11.2
semopy              2.3.9
session_info        1.0.0
sklearn             1.0.2
statsmodels         0.13.2
-----
Click to view modules imported as dependencies 
PIL                         9.0.1
appnope                     0.1.2
asttokens                   NA
backcall                    0.2.0
beta_ufunc                  NA
binom_ufunc                 NA
bottleneck                  1.3.4
cffi                        1.15.0
colorama                    0.4.4
cycler                      0.10.0
cython_runtime              NA
dateutil                    2.8.2
debugpy                     1.5.1
decorator                   5.1.1
defusedxml                  0.7.1
entrypoints                 0.4
executing                   0.8.3
graphviz                    0.20
ipykernel                   6.9.1
ipython_genutils            0.2.0
jedi                        0.18.1
joblib                      1.1.0
jupyter_server              1.13.5
kiwisolver                  1.3.1
matplotlib_inline           NA
mkl                         2.4.0
mpl_toolkits                NA
mpmath                      1.2.1
nbinom_ufunc                NA
numexpr                     2.8.1
packaging                   21.3
parso                       0.8.3
patsy                       0.5.2
pexpect                     4.8.0
pickleshare                 0.7.5
pkg_resources               NA
prompt_toolkit              3.0.20
ptyprocess                  0.7.0
pure_eval                   0.2.2
pydev_ipython               NA
pydevconsole                NA
pydevd                      2.6.0
pydevd_concurrency_analyser NA
pydevd_file_utils           NA
pydevd_plugins              NA
pydevd_tracing              NA
pygments                    2.11.2
pyparsing                   3.0.4
pytz                        2021.3
setuptools                  61.2.0
six                         1.16.0
stack_data                  0.2.0
sympy                       1.10.1
threadpoolctl               2.2.0
tornado                     6.1
traitlets                   5.1.1
typing_extensions           NA
wcwidth                     0.2.5
zmq                         22.3.0

-----
IPython             8.2.0
jupyter_client      7.2.2
jupyter_core        4.9.2
jupyterlab          3.3.2
notebook            6.4.8
-----
Python 3.10.4 (main, Mar 31 2022, 03:38:35) [Clang 12.0.0 ]
macOS-10.16-x86_64-i386-64bit
-----
Session information updated at 2022-06-11 11:40

Read in data, show info and data head¶

In [17]:
df = pd.read_csv("data/shield_gjames_21-09-20_prepped.csv").drop("Unnamed: 0", axis=1)
In [18]:
df.head()
Out[18]:
id sampling_weight demographic_gender demographic_age demographic_4_areas demographic_8_areas demographic_higher_education behaviour_indoors_nonhouseholders behaviour_close_contact behaviour_quarantined ... intention_public_transport_recoded intention_indoor_meeting_recoded intention_restaurant_recoded intention_pa_recoded intention_composite behaviour_indoors_nonhouseholders_recoded behaviour_unmasked_recoded behavior_composite behavior_composite_recoded intention_behavior_composite
0 1 2.060959 2 60+ 2 7 0 2 5 2 ... 0 0 0 0 0 1.000000 0.000000 0.000000 0.000000 0.000000
1 2 1.784139 2 40-49 1 1 1 3 3 2 ... 0 1 1 1 3 0.785714 0.214286 0.168367 0.841837 1.920918
2 3 1.204000 1 60+ 1 2 1 4 4 2 ... 0 0 0 0 0 0.500000 0.214286 0.107143 0.535714 0.267857
3 4 2.232220 1 60+ 2 6 0 4 3 2 ... 0 2 0 2 4 0.500000 0.500000 0.250000 1.250000 2.625000
4 5 1.627940 2 18-29 1 3 0 6 3 2 ... 0 2 0 0 2 0.000000 0.214286 0.000000 0.000000 1.000000

5 rows × 106 columns

In [19]:
sdt_columns = df.filter(regex="sdt").columns.tolist()
In [20]:
drop_sdt = True
if drop_sdt:
    df=df.drop(sdt_columns, axis=1)
In [21]:
df.shape
Out[21]:
(2272, 87)

Specify the feature list, grouping variable, and specify the grouping variable as a categorical variable¶

In [22]:
target = "intention_behavior_composite"
In [23]:
df[target] = (df[target] - 10) * -1
In [24]:
features_list = df.filter(regex="^automaticity|attitude|^norms|^risk|^effective").columns.tolist()
In [28]:
meta_columns = ['Original position', 'Variable name', 'Label',
       'Item english translation ', 'Label short', 'Type', 'New variable name',
       'variable name helper',
       'Of primary interest as a predictor (i.e. feature)?', 'English lo-anchor',
       'English hi-anchor']
In [29]:
sheet_id = "1BEX4W8XRGnuDk4Asa_pdKij3EIZBvhSPqHxFrDjM07k"
sheet_name = "Variable_names"
url = f"https://docs.google.com/spreadsheets/d/{sheet_id}/gviz/tq?tqx=out:csv&sheet={sheet_name}"
meta_df = pd.read_csv(url).loc[:, meta_columns]
In [30]:
meta_list = df.filter(regex="^automaticity|attitude|^norms|^risk|^effective|^behaviour|^intention").columns.tolist()
In [31]:
pd.set_option("display.max_colwidth", 350)
pd.set_option('display.expand_frame_repr', True)
meta_df.loc[meta_df["New variable name"].isin(meta_list), ["Item english translation ", "New variable name"]]
In [32]:
pd.set_option("display.max_colwidth", 100)

EDA on the target¶

Check the amount of samples in the target

In [33]:
_ = sns.violinplot(data=df[[target]].melt(), 
                    x="variable", 
                    y="value"
               )
_ = sns.stripplot(data=df[[target]].melt(), 
                    x="variable", 
                    y="value",
                  edgecolor='white',
                  linewidth=0.5
               )
In [34]:
pd.crosstab(df["demographic_gender"], df["demographic_age"])
Out[34]:
demographic_age 18-29 30-39 40-49 50-59 60+
demographic_gender
1 114 169 187 168 337
2 281 185 229 211 391
In [35]:
target_df = df[target]
target_df.describe().to_frame().T
Out[35]:
count mean std min 25% 50% 75% max
intention_behavior_composite 2272.0 8.582428 1.524704 -0.0 8.017857 8.964286 9.5 10.0
In [36]:
_ = plt.figure(figsize=(20, 5))
_ = sns.countplot(x=target_df)
_ = plt.xticks(rotation=90)
In [37]:
df = (df[["demographic_age", "demographic_higher_education"] + features_list + [target]])
In [38]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2272 entries, 0 to 2271
Data columns (total 30 columns):
 #   Column                            Non-Null Count  Dtype  
---  ------                            --------------  -----  
 0   demographic_age                   2272 non-null   object 
 1   demographic_higher_education      2272 non-null   int64  
 2   automaticity_carry_mask           2272 non-null   int64  
 3   automaticity_put_on_mask          2272 non-null   int64  
 4   inst_attitude_protects_self       2272 non-null   int64  
 5   inst_attitude_protects_others     2272 non-null   int64  
 6   inst_attitude_sense_of_community  2272 non-null   int64  
 7   inst_attitude_enough_oxygen       2272 non-null   int64  
 8   inst_attitude_no_needless_waste   2272 non-null   int64  
 9   norms_family_friends              2272 non-null   int64  
 10  norms_risk_groups                 2272 non-null   int64  
 11  norms_officials                   2272 non-null   int64  
 12  norms_people_present_indoors      2272 non-null   int64  
 13  aff_attitude_comfortable          2272 non-null   int64  
 14  aff_attitude_calm                 2272 non-null   int64  
 15  aff_attitude_safe                 2272 non-null   int64  
 16  aff_attitude_responsible          2272 non-null   int64  
 17  aff_attitude_difficult_breathing  2272 non-null   int64  
 18  effective_means_handwashing       2272 non-null   int64  
 19  effective_means_masks             2272 non-null   int64  
 20  effective_means_distance          2272 non-null   int64  
 21  effective_means_ventilation       2272 non-null   int64  
 22  risk_likely_contagion             2272 non-null   int64  
 23  risk_contagion_absent_protection  2272 non-null   int64  
 24  risk_severity                     2272 non-null   int64  
 25  risk_fear_spread                  2272 non-null   int64  
 26  risk_fear_contagion_self          2272 non-null   int64  
 27  risk_fear_contagion_others        2272 non-null   int64  
 28  risk_fear_restrictions            2272 non-null   int64  
 29  intention_behavior_composite      2272 non-null   float64
dtypes: float64(1), int64(28), object(1)
memory usage: 532.6+ KB
In [39]:
display(df[target].value_counts().head().to_frame()), df.shape[0], df[target].value_counts().head().sum()
intention_behavior_composite
10.000000 424
9.500000 228
9.000000 187
8.885204 155
9.385204 112
Out[39]:
(None, 2272, 1106)

Multivariate Linear Regression¶

In [40]:
X = df[features_list]
y = df[target]
In [41]:
mod = sm.OLS(endog=y, exog=X)
res = mod.fit()
display(res.summary())
OLS Regression Results
Dep. Variable: intention_behavior_composite R-squared (uncentered): 0.980
Model: OLS Adj. R-squared (uncentered): 0.980
Method: Least Squares F-statistic: 4050.
Date: Sat, 11 Jun 2022 Prob (F-statistic): 0.00
Time: 11:41:32 Log-Likelihood: -3705.8
No. Observations: 2272 AIC: 7466.
Df Residuals: 2245 BIC: 7620.
Df Model: 27
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
automaticity_carry_mask 0.0923 0.031 2.933 0.003 0.031 0.154
automaticity_put_on_mask 0.0787 0.034 2.331 0.020 0.012 0.145
inst_attitude_protects_self -0.0120 0.024 -0.491 0.624 -0.060 0.036
inst_attitude_protects_others 0.2237 0.032 6.901 0.000 0.160 0.287
inst_attitude_sense_of_community -0.0035 0.020 -0.173 0.863 -0.043 0.036
inst_attitude_enough_oxygen 0.1177 0.021 5.668 0.000 0.077 0.158
inst_attitude_no_needless_waste 0.0190 0.016 1.172 0.241 -0.013 0.051
norms_family_friends 0.0758 0.027 2.840 0.005 0.023 0.128
norms_risk_groups -4.061e-05 0.034 -0.001 0.999 -0.067 0.067
norms_officials 0.1520 0.032 4.817 0.000 0.090 0.214
norms_people_present_indoors 0.1691 0.024 7.160 0.000 0.123 0.215
aff_attitude_comfortable 0.0224 0.029 0.778 0.437 -0.034 0.079
aff_attitude_calm 0.0316 0.027 1.192 0.233 -0.020 0.084
aff_attitude_safe 0.0213 0.031 0.688 0.492 -0.039 0.082
aff_attitude_responsible 0.0120 0.033 0.363 0.717 -0.053 0.077
aff_attitude_difficult_breathing 0.1773 0.019 9.375 0.000 0.140 0.214
effective_means_handwashing 0.0465 0.021 2.170 0.030 0.004 0.089
effective_means_masks 0.0202 0.028 0.721 0.471 -0.035 0.075
effective_means_distance 0.1063 0.026 4.127 0.000 0.056 0.157
effective_means_ventilation 0.0497 0.020 2.507 0.012 0.011 0.089
risk_likely_contagion 0.0551 0.023 2.344 0.019 0.009 0.101
risk_contagion_absent_protection 0.0012 0.020 0.062 0.951 -0.038 0.040
risk_severity 0.1452 0.021 6.893 0.000 0.104 0.187
risk_fear_spread 0.0224 0.026 0.848 0.396 -0.029 0.074
risk_fear_contagion_self -0.0313 0.026 -1.206 0.228 -0.082 0.020
risk_fear_contagion_others -0.0046 0.025 -0.185 0.853 -0.053 0.044
risk_fear_restrictions 0.0210 0.014 1.451 0.147 -0.007 0.049
Omnibus: 288.080 Durbin-Watson: 1.928
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1109.978
Skew: -0.581 Prob(JB): 9.36e-242
Kurtosis: 6.221 Cond. No. 45.1


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.

Stepwise/ RFE multivariate linear regressions¶

In [42]:
X = df[features_list]
y = df[target]

sig_cutoff = 0.05
new_features_list = features_list
while len(res.pvalues[res.pvalues > sig_cutoff]):
    drop_feature = res.pvalues[res.pvalues > 0.05].sort_values().tail(1).index.values[0]
    new_features_list = df[new_features_list].drop(drop_feature, axis=1).columns.tolist()
    X = df[new_features_list]
    mod = sm.OLS(endog=y, exog=X)
    res = mod.fit()
    display(res.summary())
OLS Regression Results
Dep. Variable: intention_behavior_composite R-squared (uncentered): 0.980
Model: OLS Adj. R-squared (uncentered): 0.980
Method: Least Squares F-statistic: 4208.
Date: Sat, 11 Jun 2022 Prob (F-statistic): 0.00
Time: 11:41:32 Log-Likelihood: -3705.8
No. Observations: 2272 AIC: 7464.
Df Residuals: 2246 BIC: 7613.
Df Model: 26
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
automaticity_carry_mask 0.0923 0.031 2.934 0.003 0.031 0.154
automaticity_put_on_mask 0.0787 0.034 2.332 0.020 0.013 0.145
inst_attitude_protects_self -0.0120 0.024 -0.491 0.624 -0.060 0.036
inst_attitude_protects_others 0.2237 0.032 6.903 0.000 0.160 0.287
inst_attitude_sense_of_community -0.0035 0.020 -0.173 0.863 -0.043 0.036
inst_attitude_enough_oxygen 0.1177 0.021 5.687 0.000 0.077 0.158
inst_attitude_no_needless_waste 0.0190 0.016 1.173 0.241 -0.013 0.051
norms_family_friends 0.0757 0.025 3.085 0.002 0.028 0.124
norms_officials 0.1520 0.029 5.186 0.000 0.095 0.209
norms_people_present_indoors 0.1691 0.023 7.271 0.000 0.123 0.215
aff_attitude_comfortable 0.0224 0.029 0.778 0.436 -0.034 0.079
aff_attitude_calm 0.0316 0.027 1.193 0.233 -0.020 0.084
aff_attitude_safe 0.0213 0.031 0.688 0.491 -0.039 0.082
aff_attitude_responsible 0.0120 0.033 0.364 0.716 -0.052 0.076
aff_attitude_difficult_breathing 0.1773 0.019 9.389 0.000 0.140 0.214
effective_means_handwashing 0.0465 0.021 2.172 0.030 0.005 0.089
effective_means_masks 0.0202 0.028 0.724 0.469 -0.034 0.075
effective_means_distance 0.1063 0.026 4.130 0.000 0.056 0.157
effective_means_ventilation 0.0497 0.020 2.508 0.012 0.011 0.089
risk_likely_contagion 0.0551 0.023 2.347 0.019 0.009 0.101
risk_contagion_absent_protection 0.0012 0.020 0.062 0.951 -0.038 0.040
risk_severity 0.1452 0.021 6.897 0.000 0.104 0.187
risk_fear_spread 0.0224 0.026 0.849 0.396 -0.029 0.074
risk_fear_contagion_self -0.0313 0.026 -1.207 0.228 -0.082 0.020
risk_fear_contagion_others -0.0046 0.025 -0.185 0.853 -0.053 0.044
risk_fear_restrictions 0.0210 0.014 1.454 0.146 -0.007 0.049
Omnibus: 288.083 Durbin-Watson: 1.928
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1109.999
Skew: -0.581 Prob(JB): 9.26e-242
Kurtosis: 6.221 Cond. No. 43.8


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.
OLS Regression Results
Dep. Variable: intention_behavior_composite R-squared (uncentered): 0.980
Model: OLS Adj. R-squared (uncentered): 0.980
Method: Least Squares F-statistic: 4378.
Date: Sat, 11 Jun 2022 Prob (F-statistic): 0.00
Time: 11:41:32 Log-Likelihood: -3705.8
No. Observations: 2272 AIC: 7462.
Df Residuals: 2247 BIC: 7605.
Df Model: 25
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
automaticity_carry_mask 0.0924 0.031 2.940 0.003 0.031 0.154
automaticity_put_on_mask 0.0787 0.034 2.333 0.020 0.013 0.145
inst_attitude_protects_self -0.0121 0.024 -0.494 0.622 -0.060 0.036
inst_attitude_protects_others 0.2237 0.032 6.906 0.000 0.160 0.287
inst_attitude_sense_of_community -0.0034 0.020 -0.171 0.865 -0.043 0.036
inst_attitude_enough_oxygen 0.1176 0.021 5.701 0.000 0.077 0.158
inst_attitude_no_needless_waste 0.0190 0.016 1.173 0.241 -0.013 0.051
norms_family_friends 0.0758 0.024 3.096 0.002 0.028 0.124
norms_officials 0.1520 0.029 5.187 0.000 0.095 0.209
norms_people_present_indoors 0.1691 0.023 7.272 0.000 0.124 0.215
aff_attitude_comfortable 0.0225 0.029 0.782 0.435 -0.034 0.079
aff_attitude_calm 0.0316 0.026 1.194 0.233 -0.020 0.084
aff_attitude_safe 0.0214 0.031 0.692 0.489 -0.039 0.082
aff_attitude_responsible 0.0120 0.033 0.365 0.715 -0.052 0.076
aff_attitude_difficult_breathing 0.1773 0.019 9.395 0.000 0.140 0.214
effective_means_handwashing 0.0465 0.021 2.173 0.030 0.005 0.089
effective_means_masks 0.0203 0.028 0.731 0.465 -0.034 0.075
effective_means_distance 0.1063 0.026 4.131 0.000 0.056 0.157
effective_means_ventilation 0.0497 0.020 2.510 0.012 0.011 0.089
risk_likely_contagion 0.0556 0.022 2.585 0.010 0.013 0.098
risk_severity 0.1452 0.021 6.898 0.000 0.104 0.187
risk_fear_spread 0.0226 0.026 0.861 0.390 -0.029 0.074
risk_fear_contagion_self -0.0311 0.026 -1.206 0.228 -0.082 0.019
risk_fear_contagion_others -0.0046 0.025 -0.186 0.853 -0.053 0.044
risk_fear_restrictions 0.0209 0.014 1.453 0.146 -0.007 0.049
Omnibus: 288.273 Durbin-Watson: 1.928
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1110.666
Skew: -0.581 Prob(JB): 6.64e-242
Kurtosis: 6.222 Cond. No. 43.3


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.
OLS Regression Results
Dep. Variable: intention_behavior_composite R-squared (uncentered): 0.980
Model: OLS Adj. R-squared (uncentered): 0.980
Method: Least Squares F-statistic: 4562.
Date: Sat, 11 Jun 2022 Prob (F-statistic): 0.00
Time: 11:41:32 Log-Likelihood: -3705.8
No. Observations: 2272 AIC: 7460.
Df Residuals: 2248 BIC: 7597.
Df Model: 24
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
automaticity_carry_mask 0.0924 0.031 2.940 0.003 0.031 0.154
automaticity_put_on_mask 0.0787 0.034 2.333 0.020 0.013 0.145
inst_attitude_protects_self -0.0123 0.024 -0.505 0.614 -0.060 0.035
inst_attitude_protects_others 0.2235 0.032 6.907 0.000 0.160 0.287
inst_attitude_enough_oxygen 0.1173 0.021 5.706 0.000 0.077 0.158
inst_attitude_no_needless_waste 0.0188 0.016 1.165 0.244 -0.013 0.051
norms_family_friends 0.0758 0.024 3.096 0.002 0.028 0.124
norms_officials 0.1520 0.029 5.187 0.000 0.095 0.209
norms_people_present_indoors 0.1688 0.023 7.278 0.000 0.123 0.214
aff_attitude_comfortable 0.0220 0.029 0.770 0.442 -0.034 0.078
aff_attitude_calm 0.0316 0.026 1.194 0.232 -0.020 0.084
aff_attitude_safe 0.0209 0.031 0.679 0.497 -0.039 0.081
aff_attitude_responsible 0.0113 0.033 0.347 0.729 -0.053 0.075
aff_attitude_difficult_breathing 0.1771 0.019 9.400 0.000 0.140 0.214
effective_means_handwashing 0.0464 0.021 2.170 0.030 0.004 0.088
effective_means_masks 0.0204 0.028 0.733 0.464 -0.034 0.075
effective_means_distance 0.1063 0.026 4.131 0.000 0.056 0.157
effective_means_ventilation 0.0498 0.020 2.515 0.012 0.011 0.089
risk_likely_contagion 0.0555 0.022 2.582 0.010 0.013 0.098
risk_severity 0.1453 0.021 6.905 0.000 0.104 0.187
risk_fear_spread 0.0224 0.026 0.855 0.392 -0.029 0.074
risk_fear_contagion_self -0.0312 0.026 -1.208 0.227 -0.082 0.019
risk_fear_contagion_others -0.0046 0.025 -0.185 0.853 -0.053 0.044
risk_fear_restrictions 0.0209 0.014 1.454 0.146 -0.007 0.049
Omnibus: 287.786 Durbin-Watson: 1.928
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1107.337
Skew: -0.581 Prob(JB): 3.51e-241
Kurtosis: 6.217 Cond. No. 42.5


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.
OLS Regression Results
Dep. Variable: intention_behavior_composite R-squared (uncentered): 0.980
Model: OLS Adj. R-squared (uncentered): 0.980
Method: Least Squares F-statistic: 4763.
Date: Sat, 11 Jun 2022 Prob (F-statistic): 0.00
Time: 11:41:32 Log-Likelihood: -3705.9
No. Observations: 2272 AIC: 7458.
Df Residuals: 2249 BIC: 7589.
Df Model: 23
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
automaticity_carry_mask 0.0923 0.031 2.939 0.003 0.031 0.154
automaticity_put_on_mask 0.0788 0.034 2.336 0.020 0.013 0.145
inst_attitude_protects_self -0.0122 0.024 -0.499 0.618 -0.060 0.036
inst_attitude_protects_others 0.2226 0.032 6.952 0.000 0.160 0.285
inst_attitude_enough_oxygen 0.1174 0.021 5.714 0.000 0.077 0.158
inst_attitude_no_needless_waste 0.0190 0.016 1.178 0.239 -0.013 0.051
norms_family_friends 0.0755 0.024 3.091 0.002 0.028 0.123
norms_officials 0.1514 0.029 5.198 0.000 0.094 0.209
norms_people_present_indoors 0.1689 0.023 7.284 0.000 0.123 0.214
aff_attitude_comfortable 0.0220 0.029 0.767 0.443 -0.034 0.078
aff_attitude_calm 0.0316 0.026 1.193 0.233 -0.020 0.084
aff_attitude_safe 0.0210 0.031 0.684 0.494 -0.039 0.081
aff_attitude_responsible 0.0110 0.033 0.338 0.736 -0.053 0.075
aff_attitude_difficult_breathing 0.1772 0.019 9.405 0.000 0.140 0.214
effective_means_handwashing 0.0464 0.021 2.170 0.030 0.004 0.088
effective_means_masks 0.0204 0.028 0.734 0.463 -0.034 0.075
effective_means_distance 0.1059 0.026 4.131 0.000 0.056 0.156
effective_means_ventilation 0.0498 0.020 2.517 0.012 0.011 0.089
risk_likely_contagion 0.0553 0.021 2.576 0.010 0.013 0.097
risk_severity 0.1454 0.021 6.911 0.000 0.104 0.187
risk_fear_spread 0.0209 0.025 0.841 0.400 -0.028 0.069
risk_fear_contagion_self -0.0325 0.025 -1.311 0.190 -0.081 0.016
risk_fear_restrictions 0.0209 0.014 1.453 0.146 -0.007 0.049
Omnibus: 287.289 Durbin-Watson: 1.929
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1102.311
Skew: -0.580 Prob(JB): 4.33e-240
Kurtosis: 6.209 Cond. No. 41.5


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.
OLS Regression Results
Dep. Variable: intention_behavior_composite R-squared (uncentered): 0.980
Model: OLS Adj. R-squared (uncentered): 0.980
Method: Least Squares F-statistic: 4981.
Date: Sat, 11 Jun 2022 Prob (F-statistic): 0.00
Time: 11:41:33 Log-Likelihood: -3705.9
No. Observations: 2272 AIC: 7456.
Df Residuals: 2250 BIC: 7582.
Df Model: 22
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
automaticity_carry_mask 0.0924 0.031 2.942 0.003 0.031 0.154
automaticity_put_on_mask 0.0796 0.034 2.369 0.018 0.014 0.146
inst_attitude_protects_self -0.0120 0.024 -0.494 0.622 -0.060 0.036
inst_attitude_protects_others 0.2247 0.031 7.151 0.000 0.163 0.286
inst_attitude_enough_oxygen 0.1172 0.021 5.707 0.000 0.077 0.157
inst_attitude_no_needless_waste 0.0189 0.016 1.172 0.241 -0.013 0.051
norms_family_friends 0.0761 0.024 3.123 0.002 0.028 0.124
norms_officials 0.1528 0.029 5.299 0.000 0.096 0.209
norms_people_present_indoors 0.1692 0.023 7.304 0.000 0.124 0.215
aff_attitude_comfortable 0.0214 0.029 0.748 0.454 -0.035 0.077
aff_attitude_calm 0.0326 0.026 1.240 0.215 -0.019 0.084
aff_attitude_safe 0.0243 0.029 0.835 0.404 -0.033 0.081
aff_attitude_difficult_breathing 0.1770 0.019 9.402 0.000 0.140 0.214
effective_means_handwashing 0.0470 0.021 2.201 0.028 0.005 0.089
effective_means_masks 0.0215 0.028 0.781 0.435 -0.033 0.076
effective_means_distance 0.1060 0.026 4.138 0.000 0.056 0.156
effective_means_ventilation 0.0500 0.020 2.524 0.012 0.011 0.089
risk_likely_contagion 0.0551 0.021 2.571 0.010 0.013 0.097
risk_severity 0.1455 0.021 6.916 0.000 0.104 0.187
risk_fear_spread 0.0211 0.025 0.852 0.394 -0.027 0.070
risk_fear_contagion_self -0.0322 0.025 -1.298 0.194 -0.081 0.016
risk_fear_restrictions 0.0211 0.014 1.466 0.143 -0.007 0.049
Omnibus: 287.500 Durbin-Watson: 1.928
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1103.252
Skew: -0.581 Prob(JB): 2.70e-240
Kurtosis: 6.210 Cond. No. 40.3


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.
OLS Regression Results
Dep. Variable: intention_behavior_composite R-squared (uncentered): 0.980
Model: OLS Adj. R-squared (uncentered): 0.980
Method: Least Squares F-statistic: 5220.
Date: Sat, 11 Jun 2022 Prob (F-statistic): 0.00
Time: 11:41:33 Log-Likelihood: -3706.0
No. Observations: 2272 AIC: 7454.
Df Residuals: 2251 BIC: 7574.
Df Model: 21
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
automaticity_carry_mask 0.0919 0.031 2.927 0.003 0.030 0.153
automaticity_put_on_mask 0.0799 0.034 2.376 0.018 0.014 0.146
inst_attitude_protects_others 0.2201 0.030 7.341 0.000 0.161 0.279
inst_attitude_enough_oxygen 0.1168 0.021 5.695 0.000 0.077 0.157
inst_attitude_no_needless_waste 0.0184 0.016 1.144 0.253 -0.013 0.050
norms_family_friends 0.0751 0.024 3.094 0.002 0.028 0.123
norms_officials 0.1532 0.029 5.316 0.000 0.097 0.210
norms_people_present_indoors 0.1691 0.023 7.299 0.000 0.124 0.214
aff_attitude_comfortable 0.0206 0.029 0.721 0.471 -0.035 0.076
aff_attitude_calm 0.0337 0.026 1.284 0.199 -0.018 0.085
aff_attitude_safe 0.0216 0.029 0.755 0.450 -0.034 0.078
aff_attitude_difficult_breathing 0.1763 0.019 9.394 0.000 0.139 0.213
effective_means_handwashing 0.0466 0.021 2.187 0.029 0.005 0.088
effective_means_masks 0.0199 0.027 0.728 0.467 -0.034 0.074
effective_means_distance 0.1059 0.026 4.136 0.000 0.056 0.156
effective_means_ventilation 0.0501 0.020 2.533 0.011 0.011 0.089
risk_likely_contagion 0.0559 0.021 2.615 0.009 0.014 0.098
risk_severity 0.1447 0.021 6.900 0.000 0.104 0.186
risk_fear_spread 0.0213 0.025 0.860 0.390 -0.027 0.070
risk_fear_contagion_self -0.0324 0.025 -1.307 0.191 -0.081 0.016
risk_fear_restrictions 0.0210 0.014 1.459 0.145 -0.007 0.049
Omnibus: 288.080 Durbin-Watson: 1.929
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1106.001
Skew: -0.582 Prob(JB): 6.84e-241
Kurtosis: 6.214 Cond. No. 39.2


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.
OLS Regression Results
Dep. Variable: intention_behavior_composite R-squared (uncentered): 0.980
Model: OLS Adj. R-squared (uncentered): 0.980
Method: Least Squares F-statistic: 5482.
Date: Sat, 11 Jun 2022 Prob (F-statistic): 0.00
Time: 11:41:33 Log-Likelihood: -3706.3
No. Observations: 2272 AIC: 7453.
Df Residuals: 2252 BIC: 7567.
Df Model: 20
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
automaticity_carry_mask 0.0925 0.031 2.950 0.003 0.031 0.154
automaticity_put_on_mask 0.0804 0.034 2.392 0.017 0.014 0.146
inst_attitude_protects_others 0.2198 0.030 7.333 0.000 0.161 0.279
inst_attitude_enough_oxygen 0.1204 0.020 6.043 0.000 0.081 0.159
inst_attitude_no_needless_waste 0.0194 0.016 1.212 0.226 -0.012 0.051
norms_family_friends 0.0760 0.024 3.132 0.002 0.028 0.124
norms_officials 0.1525 0.029 5.297 0.000 0.096 0.209
norms_people_present_indoors 0.1695 0.023 7.318 0.000 0.124 0.215
aff_attitude_calm 0.0409 0.024 1.686 0.092 -0.007 0.088
aff_attitude_safe 0.0241 0.028 0.849 0.396 -0.032 0.080
aff_attitude_difficult_breathing 0.1745 0.019 9.383 0.000 0.138 0.211
effective_means_handwashing 0.0474 0.021 2.228 0.026 0.006 0.089
effective_means_masks 0.0205 0.027 0.749 0.454 -0.033 0.074
effective_means_distance 0.1055 0.026 4.122 0.000 0.055 0.156
effective_means_ventilation 0.0505 0.020 2.555 0.011 0.012 0.089
risk_likely_contagion 0.0573 0.021 2.691 0.007 0.016 0.099
risk_severity 0.1441 0.021 6.877 0.000 0.103 0.185
risk_fear_spread 0.0214 0.025 0.866 0.387 -0.027 0.070
risk_fear_contagion_self -0.0326 0.025 -1.319 0.187 -0.081 0.016
risk_fear_restrictions 0.0209 0.014 1.453 0.146 -0.007 0.049
Omnibus: 286.987 Durbin-Watson: 1.929
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1103.279
Skew: -0.579 Prob(JB): 2.67e-240
Kurtosis: 6.211 Cond. No. 38.7


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.
OLS Regression Results
Dep. Variable: intention_behavior_composite R-squared (uncentered): 0.980
Model: OLS Adj. R-squared (uncentered): 0.980
Method: Least Squares F-statistic: 5772.
Date: Sat, 11 Jun 2022 Prob (F-statistic): 0.00
Time: 11:41:33 Log-Likelihood: -3706.6
No. Observations: 2272 AIC: 7451.
Df Residuals: 2253 BIC: 7560.
Df Model: 19
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
automaticity_carry_mask 0.0933 0.031 2.975 0.003 0.032 0.155
automaticity_put_on_mask 0.0826 0.033 2.469 0.014 0.017 0.148
inst_attitude_protects_others 0.2252 0.029 7.745 0.000 0.168 0.282
inst_attitude_enough_oxygen 0.1199 0.020 6.023 0.000 0.081 0.159
inst_attitude_no_needless_waste 0.0205 0.016 1.282 0.200 -0.011 0.052
norms_family_friends 0.0773 0.024 3.196 0.001 0.030 0.125
norms_officials 0.1519 0.029 5.277 0.000 0.095 0.208
norms_people_present_indoors 0.1708 0.023 7.399 0.000 0.126 0.216
aff_attitude_calm 0.0408 0.024 1.685 0.092 -0.007 0.088
aff_attitude_safe 0.0269 0.028 0.959 0.338 -0.028 0.082
aff_attitude_difficult_breathing 0.1731 0.019 9.356 0.000 0.137 0.209
effective_means_handwashing 0.0475 0.021 2.233 0.026 0.006 0.089
effective_means_distance 0.1117 0.024 4.603 0.000 0.064 0.159
effective_means_ventilation 0.0506 0.020 2.558 0.011 0.012 0.089
risk_likely_contagion 0.0573 0.021 2.693 0.007 0.016 0.099
risk_severity 0.1450 0.021 6.932 0.000 0.104 0.186
risk_fear_spread 0.0234 0.025 0.953 0.341 -0.025 0.072
risk_fear_contagion_self -0.0329 0.025 -1.331 0.183 -0.081 0.016
risk_fear_restrictions 0.0194 0.014 1.361 0.174 -0.009 0.047
Omnibus: 286.047 Durbin-Watson: 1.929
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1090.649
Skew: -0.580 Prob(JB): 1.47e-237
Kurtosis: 6.190 Cond. No. 37.4


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.
OLS Regression Results
Dep. Variable: intention_behavior_composite R-squared (uncentered): 0.980
Model: OLS Adj. R-squared (uncentered): 0.980
Method: Least Squares F-statistic: 6093.
Date: Sat, 11 Jun 2022 Prob (F-statistic): 0.00
Time: 11:41:33 Log-Likelihood: -3707.0
No. Observations: 2272 AIC: 7450.
Df Residuals: 2254 BIC: 7553.
Df Model: 18
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
automaticity_carry_mask 0.0940 0.031 2.999 0.003 0.033 0.155
automaticity_put_on_mask 0.0825 0.033 2.466 0.014 0.017 0.148
inst_attitude_protects_others 0.2281 0.029 7.887 0.000 0.171 0.285
inst_attitude_enough_oxygen 0.1196 0.020 6.010 0.000 0.081 0.159
inst_attitude_no_needless_waste 0.0201 0.016 1.258 0.209 -0.011 0.051
norms_family_friends 0.0787 0.024 3.257 0.001 0.031 0.126
norms_officials 0.1530 0.029 5.323 0.000 0.097 0.209
norms_people_present_indoors 0.1709 0.023 7.404 0.000 0.126 0.216
aff_attitude_calm 0.0398 0.024 1.644 0.100 -0.008 0.087
aff_attitude_safe 0.0292 0.028 1.045 0.296 -0.026 0.084
aff_attitude_difficult_breathing 0.1721 0.018 9.316 0.000 0.136 0.208
effective_means_handwashing 0.0472 0.021 2.216 0.027 0.005 0.089
effective_means_distance 0.1124 0.024 4.636 0.000 0.065 0.160
effective_means_ventilation 0.0509 0.020 2.574 0.010 0.012 0.090
risk_likely_contagion 0.0594 0.021 2.803 0.005 0.018 0.101
risk_severity 0.1449 0.021 6.924 0.000 0.104 0.186
risk_fear_contagion_self -0.0184 0.019 -0.944 0.345 -0.057 0.020
risk_fear_restrictions 0.0192 0.014 1.346 0.178 -0.009 0.047
Omnibus: 287.168 Durbin-Watson: 1.929
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1088.350
Skew: -0.584 Prob(JB): 4.65e-237
Kurtosis: 6.183 Cond. No. 36.6


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.
OLS Regression Results
Dep. Variable: intention_behavior_composite R-squared (uncentered): 0.980
Model: OLS Adj. R-squared (uncentered): 0.980
Method: Least Squares F-statistic: 6452.
Date: Sat, 11 Jun 2022 Prob (F-statistic): 0.00
Time: 11:41:33 Log-Likelihood: -3707.5
No. Observations: 2272 AIC: 7449.
Df Residuals: 2255 BIC: 7546.
Df Model: 17
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
automaticity_carry_mask 0.0926 0.031 2.957 0.003 0.031 0.154
automaticity_put_on_mask 0.0827 0.033 2.472 0.014 0.017 0.148
inst_attitude_protects_others 0.2266 0.029 7.847 0.000 0.170 0.283
inst_attitude_enough_oxygen 0.1201 0.020 6.038 0.000 0.081 0.159
inst_attitude_no_needless_waste 0.0199 0.016 1.247 0.213 -0.011 0.051
norms_family_friends 0.0772 0.024 3.203 0.001 0.030 0.124
norms_officials 0.1533 0.029 5.331 0.000 0.097 0.210
norms_people_present_indoors 0.1697 0.023 7.364 0.000 0.125 0.215
aff_attitude_calm 0.0414 0.024 1.716 0.086 -0.006 0.089
aff_attitude_safe 0.0277 0.028 0.991 0.322 -0.027 0.082
aff_attitude_difficult_breathing 0.1733 0.018 9.404 0.000 0.137 0.209
effective_means_handwashing 0.0476 0.021 2.235 0.025 0.006 0.089
effective_means_distance 0.1130 0.024 4.661 0.000 0.065 0.161
effective_means_ventilation 0.0501 0.020 2.538 0.011 0.011 0.089
risk_likely_contagion 0.0562 0.021 2.688 0.007 0.015 0.097
risk_severity 0.1339 0.017 7.682 0.000 0.100 0.168
risk_fear_restrictions 0.0191 0.014 1.340 0.180 -0.009 0.047
Omnibus: 286.019 Durbin-Watson: 1.929
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1081.242
Skew: -0.582 Prob(JB): 1.63e-235
Kurtosis: 6.173 Cond. No. 35.9


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.
OLS Regression Results
Dep. Variable: intention_behavior_composite R-squared (uncentered): 0.980
Model: OLS Adj. R-squared (uncentered): 0.980
Method: Least Squares F-statistic: 6855.
Date: Sat, 11 Jun 2022 Prob (F-statistic): 0.00
Time: 11:41:33 Log-Likelihood: -3708.0
No. Observations: 2272 AIC: 7448.
Df Residuals: 2256 BIC: 7540.
Df Model: 16
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
automaticity_carry_mask 0.0942 0.031 3.012 0.003 0.033 0.155
automaticity_put_on_mask 0.0829 0.033 2.479 0.013 0.017 0.149
inst_attitude_protects_others 0.2327 0.028 8.247 0.000 0.177 0.288
inst_attitude_enough_oxygen 0.1211 0.020 6.091 0.000 0.082 0.160
inst_attitude_no_needless_waste 0.0214 0.016 1.344 0.179 -0.010 0.053
norms_family_friends 0.0781 0.024 3.245 0.001 0.031 0.125
norms_officials 0.1555 0.029 5.424 0.000 0.099 0.212
norms_people_present_indoors 0.1705 0.023 7.402 0.000 0.125 0.216
aff_attitude_calm 0.0481 0.023 2.072 0.038 0.003 0.094
aff_attitude_difficult_breathing 0.1737 0.018 9.432 0.000 0.138 0.210
effective_means_handwashing 0.0489 0.021 2.300 0.022 0.007 0.091
effective_means_distance 0.1153 0.024 4.782 0.000 0.068 0.163
effective_means_ventilation 0.0497 0.020 2.515 0.012 0.011 0.088
risk_likely_contagion 0.0555 0.021 2.655 0.008 0.015 0.097
risk_severity 0.1357 0.017 7.823 0.000 0.102 0.170
risk_fear_restrictions 0.0182 0.014 1.280 0.201 -0.010 0.046
Omnibus: 283.656 Durbin-Watson: 1.931
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1064.353
Skew: -0.579 Prob(JB): 7.56e-232
Kurtosis: 6.147 Cond. No. 34.9


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.
OLS Regression Results
Dep. Variable: intention_behavior_composite R-squared (uncentered): 0.980
Model: OLS Adj. R-squared (uncentered): 0.980
Method: Least Squares F-statistic: 7310.
Date: Sat, 11 Jun 2022 Prob (F-statistic): 0.00
Time: 11:41:33 Log-Likelihood: -3708.8
No. Observations: 2272 AIC: 7448.
Df Residuals: 2257 BIC: 7534.
Df Model: 15
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
automaticity_carry_mask 0.0921 0.031 2.949 0.003 0.031 0.153
automaticity_put_on_mask 0.0839 0.033 2.508 0.012 0.018 0.150
inst_attitude_protects_others 0.2334 0.028 8.272 0.000 0.178 0.289
inst_attitude_enough_oxygen 0.1227 0.020 6.188 0.000 0.084 0.162
inst_attitude_no_needless_waste 0.0187 0.016 1.188 0.235 -0.012 0.050
norms_family_friends 0.0763 0.024 3.172 0.002 0.029 0.123
norms_officials 0.1627 0.028 5.789 0.000 0.108 0.218
norms_people_present_indoors 0.1692 0.023 7.352 0.000 0.124 0.214
aff_attitude_calm 0.0470 0.023 2.027 0.043 0.002 0.092
aff_attitude_difficult_breathing 0.1803 0.018 10.184 0.000 0.146 0.215
effective_means_handwashing 0.0499 0.021 2.350 0.019 0.008 0.092
effective_means_distance 0.1153 0.024 4.781 0.000 0.068 0.163
effective_means_ventilation 0.0508 0.020 2.575 0.010 0.012 0.089
risk_likely_contagion 0.0582 0.021 2.797 0.005 0.017 0.099
risk_severity 0.1347 0.017 7.772 0.000 0.101 0.169
Omnibus: 278.533 Durbin-Watson: 1.930
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1032.141
Skew: -0.571 Prob(JB): 7.47e-225
Kurtosis: 6.098 Cond. No. 34.4


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.
OLS Regression Results
Dep. Variable: intention_behavior_composite R-squared (uncentered): 0.980
Model: OLS Adj. R-squared (uncentered): 0.980
Method: Least Squares F-statistic: 7830.
Date: Sat, 11 Jun 2022 Prob (F-statistic): 0.00
Time: 11:41:33 Log-Likelihood: -3709.5
No. Observations: 2272 AIC: 7447.
Df Residuals: 2258 BIC: 7527.
Df Model: 14
Covariance Type: nonrobust
coef std err t P>|t| [0.025 0.975]
automaticity_carry_mask 0.0943 0.031 3.026 0.003 0.033 0.155
automaticity_put_on_mask 0.0850 0.033 2.542 0.011 0.019 0.151
inst_attitude_protects_others 0.2346 0.028 8.321 0.000 0.179 0.290
inst_attitude_enough_oxygen 0.1254 0.020 6.359 0.000 0.087 0.164
norms_family_friends 0.0780 0.024 3.252 0.001 0.031 0.125
norms_officials 0.1608 0.028 5.730 0.000 0.106 0.216
norms_people_present_indoors 0.1693 0.023 7.354 0.000 0.124 0.214
aff_attitude_calm 0.0504 0.023 2.190 0.029 0.005 0.096
aff_attitude_difficult_breathing 0.1797 0.018 10.156 0.000 0.145 0.214
effective_means_handwashing 0.0494 0.021 2.326 0.020 0.008 0.091
effective_means_distance 0.1170 0.024 4.860 0.000 0.070 0.164
effective_means_ventilation 0.0513 0.020 2.601 0.009 0.013 0.090
risk_likely_contagion 0.0570 0.021 2.742 0.006 0.016 0.098
risk_severity 0.1391 0.017 8.216 0.000 0.106 0.172
Omnibus: 276.781 Durbin-Watson: 1.931
Prob(Omnibus): 0.000 Jarque-Bera (JB): 1013.318
Skew: -0.571 Prob(JB): 9.14e-221
Kurtosis: 6.066 Cond. No. 33.7


Notes:
[1] R² is computed without centering (uncentered) since the model does not contain a constant.
[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.

SEM¶

In [43]:
# automaticity|attitude|^norms|^risk|^effective
In [44]:
aut = df.filter(regex="automaticity").columns.tolist()
att = df.filter(regex="attitude").columns.tolist()
nor = df.filter(regex="^norms").columns.tolist()
ris = df.filter(regex="^risk").columns.tolist()
eff = df.filter(regex="^effective").columns.tolist()
In [45]:
aut_corrs = '\n'.join([' ~~ '.join(x) for x in itertools.combinations(aut, 2)])
att_corrs = '\n'.join([' ~~ '.join(x) for x in itertools.combinations(att, 2)])
nor_corrs = '\n'.join([' ~~ '.join(x) for x in itertools.combinations(nor, 2)])
ris_corrs = '\n'.join([' ~~ '.join(x) for x in itertools.combinations(ris, 2)])
eff_corrs = '\n'.join([' ~~ '.join(x) for x in itertools.combinations(eff, 2)])
In [46]:
desc = f"""
# measurement model
automaticity =~ {' + '.join(aut)}
attitude =~ {' + '.join(att)}
norms =~ {' + '.join(nor)}
risk =~ {' + '.join(ris)}
effective =~ {' + '.join(eff)}

# regression
{target} =~ automaticity + attitude + norms + risk + effective

# residual correlations
{aut_corrs}
{att_corrs}
{nor_corrs}
{ris_corrs}
{eff_corrs}

"""
In [47]:
print(desc[:888])

# measurement model
automaticity =~ automaticity_carry_mask + automaticity_put_on_mask
attitude =~ inst_attitude_protects_self + inst_attitude_protects_others + inst_attitude_sense_of_community + inst_attitude_enough_oxygen + inst_attitude_no_needless_waste + aff_attitude_comfortable + aff_attitude_calm + aff_attitude_safe + aff_attitude_responsible + aff_attitude_difficult_breathing
norms =~ norms_family_friends + norms_risk_groups + norms_officials + norms_people_present_indoors
risk =~ risk_likely_contagion + risk_contagion_absent_protection + risk_severity + risk_fear_spread + risk_fear_contagion_self + risk_fear_contagion_others + risk_fear_restrictions
effective =~ effective_means_handwashing + effective_means_masks + effective_means_distance + effective_means_ventilation

# regression
intention_behavior_composite =~ automaticity + attitude + norms + risk + effective
In [48]:
mod = Model(desc)
res_opt = mod.fit(df, obj="FIML")
estimates = mod.inspect()
report(mod, "mask-behaviour-SEM")

estimates.head()

WARNING:root:Fisher Information Matrix is not PD.Moore-Penrose inverse will be used instead of Cholesky decomposition. See 10.1109/TSP.2012.2208105.
WARNING:root:Fisher Information Matrix is not PD.Moore-Penrose inverse will be used instead of Cholesky decomposition. See 10.1109/TSP.2012.2208105.
WARNING:root:Fisher Information Matrix is not PD.Moore-Penrose inverse will be used instead of Cholesky decomposition. See 10.1109/TSP.2012.2208105.
WARNING:root:Could not plot model. Possible Graphviz installation issues. failed to execute PosixPath('dot'), make sure the Graphviz executables are on your systems' PATH
Out[48]:
lval op rval Estimate Std. Err z-value p-value
0 automaticity ~ intention_behavior_composite 1.000000 - - -
1 attitude ~ intention_behavior_composite 0.964387 0.006314 152.727256 0.0
2 norms ~ intention_behavior_composite 1.056695 0.006666 158.512645 0.0
3 risk ~ intention_behavior_composite 0.404973 0.005085 79.64134 0.0
4 effective ~ intention_behavior_composite 1.074518 0.007191 149.418578 0.0
In [49]:
estimates[estimates["rval"].str.contains(target)]
Out[49]:
lval op rval Estimate Std. Err z-value p-value
0 automaticity ~ intention_behavior_composite 1.000000 - - -
1 attitude ~ intention_behavior_composite 0.964387 0.006314 152.727256 0.0
2 norms ~ intention_behavior_composite 1.056695 0.006666 158.512645 0.0
3 risk ~ intention_behavior_composite 0.404973 0.005085 79.64134 0.0
4 effective ~ intention_behavior_composite 1.074518 0.007191 149.418578 0.0
37 intention_behavior_composite ~~ intention_behavior_composite 32.357818 1.011179 32.000098 0.0
In [50]:
estimates["p-value"] = estimates["p-value"].replace("-", np.nan)
In [51]:
estimates[estimates["p-value"] < 0.05]
Out[51]:
lval op rval Estimate Std. Err z-value p-value
1 attitude ~ intention_behavior_composite 0.964387 0.006314 152.727256 0.0
2 norms ~ intention_behavior_composite 1.056695 0.006666 158.512645 0.0
3 risk ~ intention_behavior_composite 0.404973 0.005085 79.64134 0.0
4 effective ~ intention_behavior_composite 1.074518 0.007191 149.418578 0.0
6 automaticity_put_on_mask ~ automaticity 1.021863 0.003245 314.915754 0.0
... ... ... ... ... ... ... ...
138 effective_means_distance ~~ effective_means_distance 1.273639 0.035755 35.621441 0.0
139 norms_people_present_indoors ~~ norms_people_present_indoors 1.426859 0.039876 35.782723 0.0
140 effective_means_ventilation ~~ effective_means_ventilation 1.991198 0.057158 34.83665 0.0
142 aff_attitude_difficult_breathing ~~ aff_attitude_difficult_breathing 3.642437 0.110116 33.078206 0.0
143 risk_fear_restrictions ~~ risk_fear_restrictions 3.824372 0.134973 28.334362 0.0

122 rows × 7 columns

In [52]:
# g = semplot(mod, filename='semplot.png')
# g

Example political democracy SEM¶

In [53]:
# from semopy.examples import political_democracy
# from semopy import ModelMeans, report

# desc = political_democracy.get_model()
# data = political_democracy.get_data()
# model = ModelMeans(desc)
# model.fit(data)
# report(model, "Political Democracy")

Spearman Correlations with FDR-BH correction¶

In [54]:
dict_results = correlation_analysis(data=df, 
                     col_list=features_list, 
                     row_list=[target], 
                     check_norm=False, 
                     method = 'spearman', 
                     dropna = 'pairwise'
                     )
dict_results['summary']

/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/jmspack/frequentist_statistics.py:213: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  info = info.append(
Out[54]:
analysis feature1 feature2 r-value p-value stat-sign N
0 Spearman Rank automaticity_carry_mask intention_behavior_composite 0.373645 3.383126e-76 True 2272
1 Spearman Rank automaticity_put_on_mask intention_behavior_composite 0.364111 3.578368e-72 True 2272
2 Spearman Rank inst_attitude_protects_self intention_behavior_composite 0.311923 1.871267e-52 True 2272
3 Spearman Rank inst_attitude_protects_others intention_behavior_composite 0.309530 1.221566e-51 True 2272
4 Spearman Rank inst_attitude_sense_of_community intention_behavior_composite 0.242064 1.183843e-31 True 2272
5 Spearman Rank inst_attitude_enough_oxygen intention_behavior_composite 0.272911 4.298332e-40 True 2272
6 Spearman Rank inst_attitude_no_needless_waste intention_behavior_composite 0.332918 6.244255e-60 True 2272
7 Spearman Rank norms_family_friends intention_behavior_composite 0.365861 6.684440e-73 True 2272
8 Spearman Rank norms_risk_groups intention_behavior_composite 0.263058 2.841374e-37 True 2272
9 Spearman Rank norms_officials intention_behavior_composite 0.183851 1.010440e-18 True 2272
10 Spearman Rank norms_people_present_indoors intention_behavior_composite 0.417490 1.573553e-96 True 2272
11 Spearman Rank aff_attitude_comfortable intention_behavior_composite 0.303818 1.002616e-49 True 2272
12 Spearman Rank aff_attitude_calm intention_behavior_composite 0.293379 2.454444e-46 True 2272
13 Spearman Rank aff_attitude_safe intention_behavior_composite 0.305514 2.736484e-50 True 2272
14 Spearman Rank aff_attitude_responsible intention_behavior_composite 0.315279 1.308758e-53 True 2272
15 Spearman Rank aff_attitude_difficult_breathing intention_behavior_composite -0.179358 7.028834e-18 True 2272
16 Spearman Rank effective_means_handwashing intention_behavior_composite 0.135161 9.885530e-11 True 2272
17 Spearman Rank effective_means_masks intention_behavior_composite 0.398325 2.909620e-87 True 2272
18 Spearman Rank effective_means_distance intention_behavior_composite 0.270365 2.362092e-39 True 2272
19 Spearman Rank effective_means_ventilation intention_behavior_composite 0.203761 1.018722e-22 True 2272
20 Spearman Rank risk_likely_contagion intention_behavior_composite 0.023366 2.655871e-01 False 2272
21 Spearman Rank risk_contagion_absent_protection intention_behavior_composite 0.254880 5.071212e-35 True 2272
22 Spearman Rank risk_severity intention_behavior_composite 0.367338 1.609304e-73 True 2272
23 Spearman Rank risk_fear_spread intention_behavior_composite 0.317754 1.798916e-54 True 2272
24 Spearman Rank risk_fear_contagion_self intention_behavior_composite 0.330269 5.911028e-59 True 2272
25 Spearman Rank risk_fear_contagion_others intention_behavior_composite 0.246729 7.409840e-33 True 2272
26 Spearman Rank risk_fear_restrictions intention_behavior_composite -0.235336 5.812125e-30 True 2272
In [55]:
p_values = dict_results['summary'].loc[:, "p-value"].values
In [56]:
_ = correct_pvalues(pvals=p_values, 
                alpha = 0.05, 
                method = 'fdr_bh', 
                plot = True, 
                title = 'FDR Benjamini-Hochberg p-value correction for the spearman correlations', 
                figsize = (10, 5))

Partial Correlations¶

In [57]:
partial_corr_matrix_rvals_df, partial_corr_matrix_pvals_df = partial_correlation(df=df[features_list+[target]])
In [58]:
par_cor_df = (pd.merge(partial_corr_matrix_rvals_df.loc[:, [target]], 
                       partial_corr_matrix_pvals_df.loc[:, [target]], 
                       suffixes=["_r-values", "_p-values"],
                       left_index=True, 
                       right_index=True)
              .round(4)
              .sort_values(by=f"{target}_p-values", ascending=True)
              .drop(target)
              )
In [59]:
par_cor_df.style.bar(subset=[f"{target}_r-values"], 
            align='mid', 
            color=['#d65f5f', '#5fba7d'])
Out[59]:
  intention_behavior_composite_r-values intention_behavior_composite_p-values
inst_attitude_protects_others 0.088200 0.000000
risk_fear_restrictions -0.088500 0.000000
norms_people_present_indoors 0.155800 0.000000
norms_family_friends 0.090100 0.000000
risk_severity 0.121300 0.000000
automaticity_put_on_mask 0.070600 0.000800
automaticity_carry_mask 0.068500 0.001100
effective_means_masks 0.059600 0.004500
effective_means_distance 0.052600 0.012100
norms_risk_groups -0.043800 0.036900
risk_fear_spread 0.038000 0.070300
effective_means_handwashing -0.027800 0.185000
inst_attitude_no_needless_waste 0.025300 0.228200
aff_attitude_difficult_breathing -0.023700 0.258900
inst_attitude_enough_oxygen 0.021600 0.303800
risk_fear_contagion_others -0.018400 0.379800
risk_contagion_absent_protection 0.017000 0.418300
aff_attitude_safe -0.016500 0.430500
inst_attitude_protects_self -0.016200 0.441200
aff_attitude_comfortable -0.014500 0.490900
effective_means_ventilation 0.008500 0.685500
aff_attitude_calm 0.008300 0.692300
norms_officials -0.008100 0.701000
inst_attitude_sense_of_community 0.005500 0.794500
risk_fear_contagion_self -0.003900 0.853100
risk_likely_contagion -0.002500 0.904200
aff_attitude_responsible -0.001100 0.957600
In [60]:
cmap = cmap=sns.diverging_palette(5, 250, as_cmap=True)
par_cor_df.style.background_gradient(cmap, 
                            subset=[f"{target}_r-values"], 
                            axis=1, 
                            vmin=-0.1, 
                            vmax=0.16)
Out[60]:
  intention_behavior_composite_r-values intention_behavior_composite_p-values
inst_attitude_protects_others 0.088200 0.000000
risk_fear_restrictions -0.088500 0.000000
norms_people_present_indoors 0.155800 0.000000
norms_family_friends 0.090100 0.000000
risk_severity 0.121300 0.000000
automaticity_put_on_mask 0.070600 0.000800
automaticity_carry_mask 0.068500 0.001100
effective_means_masks 0.059600 0.004500
effective_means_distance 0.052600 0.012100
norms_risk_groups -0.043800 0.036900
risk_fear_spread 0.038000 0.070300
effective_means_handwashing -0.027800 0.185000
inst_attitude_no_needless_waste 0.025300 0.228200
aff_attitude_difficult_breathing -0.023700 0.258900
inst_attitude_enough_oxygen 0.021600 0.303800
risk_fear_contagion_others -0.018400 0.379800
risk_contagion_absent_protection 0.017000 0.418300
aff_attitude_safe -0.016500 0.430500
inst_attitude_protects_self -0.016200 0.441200
aff_attitude_comfortable -0.014500 0.490900
effective_means_ventilation 0.008500 0.685500
aff_attitude_calm 0.008300 0.692300
norms_officials -0.008100 0.701000
inst_attitude_sense_of_community 0.005500 0.794500
risk_fear_contagion_self -0.003900 0.853100
risk_likely_contagion -0.002500 0.904200
aff_attitude_responsible -0.001100 0.957600

Logistic Regression¶

In [61]:
ordinal_cat = CategoricalDtype(categories=df[target].sort_values().unique().tolist(), ordered=True)
X = df[features_list]
# y = df[target]#.astype(ordinal_cat)
y = pd.cut(df[target], 4).cat.codes.astype(int)
In [62]:
mod = LogisticRegression(fit_intercept=True, solver='sag', multi_class='multinomial')
In [63]:
_ = mod.fit(X, y)
y_pred = mod.predict(X)

/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
In [64]:
cf = confusion_matrix(y_true=y, y_pred=y_pred)
In [65]:
_ = plot_confusion_matrix(cf)
In [66]:
cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=10, random_state=1)
# import sklearn
# sorted(sklearn.metrics.SCORERS.keys())
scoring_list = ('accuracy',
 'balanced_accuracy',
#  'f1',
#  'f1_weighted',
#  'precision',
#  'precision_weighted',
#  'recall',
#  'recall_weighted',
#  'roc_auc',
               )
tmp_out = cross_validate(mod, 
                         X, 
                         y, 
                         scoring=scoring_list, 
                         return_train_score=False, 
                         cv=cv, 
                         n_jobs=-1,
#                         fit_params={"sample_weight": sampling_weights} # fit_params is returning nans for some reason :/
                        )

/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:352: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
  warnings.warn(
In [67]:
cv_metrics_df = pd.DataFrame(tmp_out).drop(["fit_time", "score_time"], axis=1).melt(var_name="Metric")
In [68]:
_ = plt.figure(figsize=(10,5))
_ = sns.boxplot(data = cv_metrics_df,
                x = "Metric",
                y = "value")
_ = sns.swarmplot(data = cv_metrics_df,
                x = "Metric",
                y = "value", edgecolor="white", linewidth=1)
_ = plt.title(f"All performance metrics {mod.__class__.__name__} with cross validation")

/opt/miniconda3/envs/ds_env/lib/python3.10/site-packages/seaborn/categorical.py:1296: UserWarning: 8.0% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
In [69]:
!jupyter nbconvert --to html regular_methods_clean.ipynb

[NbConvertApp] Converting notebook regular_methods_clean.ipynb to html
[NbConvertApp] WARNING | Non-unique cell id '6cbb5314' detected. Corrected to 'b87bd1a9'.
[NbConvertApp] Writing 1304278 bytes to regular_methods_clean.html