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December 10, 2015 00:29
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chi-square of Independence
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import pandas | |
import numpy | |
import scipy.stats | |
import seaborn | |
import matplotlib.pyplot as plt | |
# any additional libraries would be imported here | |
data = pandas.read_csv('C:\\Suresh\\Blog Posts\\datasets\\nesarc_pds1134\\SPLITDATA\\CourseData.csv', low_memory=False) | |
print (len(data)) #number of observations (rows) | |
print (len(data.columns)) # number of variables (columns) | |
print(data.shape) | |
#setting variables you will be working with to numeric | |
data['AGE'] = pandas.to_numeric(data['AGE'], errors='coerce') | |
data['SEX'] = pandas.to_numeric(data['SEX'], errors='coerce') | |
data['DYSDX12'] = pandas.to_numeric(data['DYSDX12'], errors='coerce') | |
data['S4CQ18A'] = pandas.to_numeric(data['S4CQ18A'], errors='coerce') | |
data['S4CQ18B'] = pandas.to_numeric(data['S4CQ18B'], errors='coerce') | |
data['S4CQ18C'] = pandas.to_numeric(data['S4CQ18C'], errors='coerce') | |
data['S4CQ19A'] = pandas.to_numeric(data['S4CQ19A'], errors='coerce') | |
data['S4CQ19B'] = pandas.to_numeric(data['S4CQ19B'], errors='coerce') | |
data['S4CQ19C'] = pandas.to_numeric(data['S4CQ19C'], errors='coerce') | |
data['S2AQ8A'] = pandas.to_numeric(data['S2AQ8A'], errors='coerce') | |
data['S2AQ8B'] = pandas.to_numeric(data['S2AQ8B'], errors='coerce') | |
#DYSLIFE | |
#subset of data of all young adults between 18 and 25 who drank had dysthemia | |
# in the last 12 months | |
sub1= data[(data["AGE"] >=18) & (data["AGE"] <= 25) ] | |
print(sub1.shape) | |
#SETTING MISSING DATA | |
sub1['S4CQ18A']=sub1['S4CQ18A'].replace(9, numpy.nan) | |
sub1['S4CQ18B']=sub1['S4CQ18B'].replace(9, numpy.nan) | |
sub1['S4CQ18C']=sub1['S4CQ18C'].replace(9, numpy.nan) | |
sub1['S4CQ19A']=sub1['S4CQ19A'].replace(9, numpy.nan) | |
sub1['S4CQ19B']=sub1['S4CQ19B'].replace(9, numpy.nan) | |
sub1['S4CQ19C']=sub1['S4CQ19C'].replace(9, numpy.nan) | |
sub1['S2AQ8A']=sub1['S2AQ8A'].replace(99, numpy.nan) | |
sub1['S2AQ8B']=sub1['S2AQ8B'].replace(99, numpy.nan) | |
sub1.head(10) | |
#recoding values for S3AQ3B1 into a new variable, USFREQMO | |
recode1 = {1: 365, 2: 300, 3: 162, 4: 96, 5:48 , 6: 30, 7:12, 8:9, 9:4.5,10:1.5} | |
sub1['USFREQYR']= sub1['S2AQ8A'].map(recode1) | |
# contingency table of observed counts | |
ct1=pandas.crosstab(sub1['S4CQ18A'], sub1['DYSDX12']) | |
print (ct1) | |
ct2=pandas.crosstab(sub1['S4CQ18B'], sub1['DYSDX12']) | |
print (ct2) | |
ct3=pandas.crosstab(sub1['S4CQ18C'], sub1['DYSDX12']) | |
print (ct3) | |
ct4=pandas.crosstab(sub1['S4CQ19A'], sub1['DYSDX12']) | |
print (ct4) | |
ct5=pandas.crosstab(sub1['S4CQ19B'], sub1['DYSDX12']) | |
print (ct5) | |
ct6=pandas.crosstab(sub1['S4CQ19C'], sub1['DYSDX12']) | |
print (ct6) | |
# column percentages | |
colsum=ct1.sum(axis=0) | |
colpct=ct1/colsum | |
print(colpct) | |
colsum=ct2.sum(axis=0) | |
colpct2=ct2/colsum | |
print(colpct2) | |
colsum=ct3.sum(axis=0) | |
colpct3=ct3/colsum | |
print(colpct3) | |
# chi-square | |
print ('chi-square value, p value, expected counts') | |
cs1= scipy.stats.chi2_contingency(ct1) | |
print (cs1) | |
print ('chi-square value, p value, expected counts') | |
cs2= scipy.stats.chi2_contingency(ct2) | |
print (cs2) | |
print ('chi-square value, p value, expected counts') | |
cs3= scipy.stats.chi2_contingency(ct3) | |
print (cs3) | |
print ('chi-square value, p value, expected counts') | |
cs4= scipy.stats.chi2_contingency(ct4) | |
print (cs4) | |
print ('chi-square value, p value, expected counts') | |
cs5= scipy.stats.chi2_contingency(ct5) | |
print (cs5) | |
print ('chi-square value, p value, expected counts') | |
cs6= scipy.stats.chi2_contingency(ct6) | |
print (cs6) | |
# contingency table of observed counts | |
ct=pandas.crosstab(sub1['DYSDX12'], sub1['USFREQYR']) | |
print (ct) | |
# column percentages | |
colsum=ct.sum(axis=0) | |
colpctx=ct/colsum | |
print(colpctx) | |
# chi-square | |
print ('chi-square value, p value, expected counts') | |
cs= scipy.stats.chi2_contingency(ct) | |
print (cs) | |
# set variable types | |
sub1["USFREQYR"] = sub1["USFREQYR"].astype('category') | |
# new code for setting variables to numeric: | |
sub1['USFREQYR'] = pandas.to_numeric(sub1['USFREQYR'], errors='coerce') | |
# graph percent with nicotine dependence within each smoking frequency group | |
seaborn.factorplot(x="USFREQYR", y="DYSDX12", data=sub1, kind="bar", ci=None) | |
plt.xlabel('Days drank per year') | |
plt.ylabel('Proportion Dysthemia') | |
recode2 = {1.5: 1.5, 12: 12} | |
sub1['COMP1v21']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct21=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v21']) | |
print (ct21) | |
# column percentages | |
colsum=ct21.sum(axis=0) | |
colpct21=ct21/colsum | |
print(colpct21) | |
print ('chi-square value, p value, expected counts') | |
cs21= scipy.stats.chi2_contingency(ct21) | |
print (cs21) | |
recode2 = {1.5: 1.5, 30:30} | |
sub1['COMP1v22']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct22=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v22']) | |
print (ct22) | |
# column percentages | |
colsum=ct22.sum(axis=0) | |
colpct22=ct22/colsum | |
print(colpct22) | |
print ('chi-square value, p value, expected counts') | |
cs22= scipy.stats.chi2_contingency(ct22) | |
print (cs22) | |
recode2 = {1.5: 1.5, 48:48} | |
sub1['COMP1v23']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct23=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v23']) | |
print (ct23) | |
# column percentages | |
colsum=ct23.sum(axis=0) | |
colpct23=ct23/colsum | |
print(colpct23) | |
print ('chi-square value, p value, expected counts') | |
cs23= scipy.stats.chi2_contingency(ct23) | |
print (cs23) | |
recode2 = {1.5: 1.5, 96:96} | |
sub1['COMP1v24']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct24=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v24']) | |
print (ct24) | |
# column percentages | |
colsum=ct24.sum(axis=0) | |
colpct24=ct24/colsum | |
print(colpct24) | |
print ('chi-square value, p value, expected counts') | |
cs24= scipy.stats.chi2_contingency(ct24) | |
print (cs24) | |
recode2 = {1.5: 1.5, 162:162} | |
sub1['COMP1v25']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct25=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v25']) | |
print (ct25) | |
# column percentages | |
colsum=ct25.sum(axis=0) | |
colpct25=ct25/colsum | |
print(colpct25) | |
print ('chi-square value, p value, expected counts') | |
cs25= scipy.stats.chi2_contingency(ct25) | |
print (cs25) | |
recode2 = {1.5: 1.5, 300:300} | |
sub1['COMP1v26']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct26=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v26']) | |
print (ct26) | |
# column percentages | |
colsum=ct26.sum(axis=0) | |
colpct26=ct26/colsum | |
print(colpct26) | |
print ('chi-square value, p value, expected counts') | |
cs26= scipy.stats.chi2_contingency(ct26) | |
print (cs26) | |
recode2 = {1.5: 1.5, 365:365} | |
sub1['COMP1v27']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct27=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v27']) | |
print (ct27) | |
# column percentages | |
colsum=ct27.sum(axis=0) | |
colpct27=ct27/colsum | |
print(colpct27) | |
print ('chi-square value, p value, expected counts') | |
cs27= scipy.stats.chi2_contingency(ct27) | |
print (cs27) | |
recode2 = {4.5: 4.5, 9:9} | |
sub1['COMP1v28']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct28=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v28']) | |
print (ct28) | |
# column percentages | |
colsum=ct28.sum(axis=0) | |
colpct28=ct28/colsum | |
print(colpct28) | |
print ('chi-square value, p value, expected counts') | |
cs28= scipy.stats.chi2_contingency(ct28) | |
print (cs28) | |
recode2 = {4.5:4.5, 12:12} | |
sub1['COMP1v29']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct29=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v29']) | |
print (ct29) | |
# column percentages | |
colsum=ct29.sum(axis=0) | |
colpct29=ct29/colsum | |
print(colpct29) | |
print ('chi-square value, p value, expected counts') | |
cs29= scipy.stats.chi2_contingency(ct29) | |
print (cs29) | |
recode2 = {4.5: 4.5, 30:30} | |
sub1['COMP1v30']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct30=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v30']) | |
print (ct30) | |
# column percentages | |
colsum=ct30.sum(axis=0) | |
colpct30=ct30/colsum | |
print(colpct30) | |
print ('chi-square value, p value, expected counts') | |
cs30= scipy.stats.chi2_contingency(ct30) | |
print (cs30) | |
recode2 = {4.5: 4.5, 48:48} | |
sub1['COMP1v31']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct31=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v31']) | |
print (ct31) | |
# column percentages | |
colsum=ct31.sum(axis=0) | |
colpct31=ct31/colsum | |
print(colpct31) | |
print ('chi-square value, p value, expected counts') | |
cs31= scipy.stats.chi2_contingency(ct31) | |
print (cs31) | |
recode2 = {4.5: 4.5, 96:96} | |
sub1['COMP1v32']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct32=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v32']) | |
print (ct32) | |
# column percentages | |
colsum=ct32.sum(axis=0) | |
colpct32=ct32/colsum | |
print(colpct32) | |
print ('chi-square value, p value, expected counts') | |
cs32= scipy.stats.chi2_contingency(ct32) | |
print (cs32) | |
recode2 = {4.5: 4.5, 162:162} | |
sub1['COMP1v33']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct33=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v33']) | |
print (ct33) | |
# column percentages | |
colsum=ct33.sum(axis=0) | |
colpct33=ct33/colsum | |
print(colpct33) | |
print ('chi-square value, p value, expected counts') | |
cs33= scipy.stats.chi2_contingency(ct33) | |
print (cs33) | |
recode2 = {4.5: 5.5, 300:300} | |
sub1['COMP1v34']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct34=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v34']) | |
print (ct34) | |
# column percentages | |
colsum=ct34.sum(axis=0) | |
colpct34=ct34/colsum | |
print(colpct34) | |
print ('chi-square value, p value, expected counts') | |
cs34= scipy.stats.chi2_contingency(ct34) | |
print (cs34) | |
recode2 = {4.5: 4.5, 365:365} | |
sub1['COMP1v35']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct35=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v35']) | |
print (ct35) | |
# column percentages | |
colsum=ct35.sum(axis=0) | |
colpct35=ct35/colsum | |
print(colpct35) | |
print ('chi-square value, p value, expected counts') | |
cs35= scipy.stats.chi2_contingency(ct35) | |
print (cs35) | |
recode2 = {9:9, 12:12} | |
sub1['COMP1v36']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct36=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v36']) | |
print (ct36) | |
# column percentages | |
colsum=ct36.sum(axis=0) | |
colpct36=ct36/colsum | |
print(colpct36) | |
print ('chi-square value, p value, expected counts') | |
cs36= scipy.stats.chi2_contingency(ct36) | |
print (cs36) | |
recode2 = {9:9,30:30} | |
sub1['COMP1v37']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct37=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v37']) | |
print (ct37) | |
# column percentages | |
colsum=ct37.sum(axis=0) | |
colpct37=ct37/colsum | |
print(colpct37) | |
print ('chi-square value, p value, expected counts') | |
cs37= scipy.stats.chi2_contingency(ct37) | |
print (cs37) | |
recode2 = {9:9, 48:48} | |
sub1['COMP1v38']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct38=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v38']) | |
print (ct38) | |
# column percentages | |
colsum=ct38.sum(axis=0) | |
colpct38=ct38/colsum | |
print(colpct38) | |
print ('chi-square value, p value, expected counts') | |
cs38= scipy.stats.chi2_contingency(ct38) | |
print (cs38) | |
recode2 = {9:9,96:96} | |
sub1['COMP1v39']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct39=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v39']) | |
print (ct39) | |
# column percentages | |
colsum=ct39.sum(axis=0) | |
colpct39=ct39/colsum | |
print(colpct39) | |
print ('chi-square value, p value, expected counts') | |
cs39= scipy.stats.chi2_contingency(ct39) | |
print (cs39) | |
recode2 = {9:9,162:162} | |
sub1['COMP1v40']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct40=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v40']) | |
print (ct40) | |
# column percentages | |
colsum=ct40.sum(axis=0) | |
colpct40=ct40/colsum | |
print(colpct40) | |
print ('chi-square value, p value, expected counts') | |
cs40= scipy.stats.chi2_contingency(ct40) | |
print (cs40) | |
recode2 = {9:9, 300:300} | |
sub1['COMP1v41']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct41=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v41']) | |
print (ct41) | |
# column percentages | |
colsum=ct41.sum(axis=0) | |
colpct41=ct41/colsum | |
print(colpct41) | |
print ('chi-square value, p value, expected counts') | |
cs41= scipy.stats.chi2_contingency(ct41) | |
print (cs41) | |
recode2 = {9:9, 365:365} | |
sub1['COMP1v42']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct42=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v42']) | |
print (ct42) | |
# column percentages | |
colsum=ct42.sum(axis=0) | |
colpct42=ct42/colsum | |
print(colpct42) | |
print ('chi-square value, p value, expected counts') | |
cs42= scipy.stats.chi2_contingency(ct42) | |
print (cs42) | |
recode2 = {12: 12, 30:30} | |
sub1['COMP1v43']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct43=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v43']) | |
print (ct43) | |
# column percentages | |
colsum=ct43.sum(axis=0) | |
colpct43=ct43/colsum | |
print(colpct43) | |
print ('chi-square value, p value, expected counts') | |
cs43= scipy.stats.chi2_contingency(ct43) | |
print (cs43) | |
recode2 = {12: 12, 48:48} | |
sub1['COMP1v44']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct44=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v44']) | |
print (ct44) | |
# column percentages | |
colsum=ct44.sum(axis=0) | |
colpct44=ct44/colsum | |
print(colpct44) | |
print ('chi-square value, p value, expected counts') | |
cs44= scipy.stats.chi2_contingency(ct44) | |
print (cs44) | |
recode2 = {12: 12, 96:96} | |
sub1['COMP1v45']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct45=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v45']) | |
print (ct45) | |
# column percentages | |
colsum=ct45.sum(axis=0) | |
colpct45=ct45/colsum | |
print(colpct45) | |
print ('chi-square value, p value, expected counts') | |
cs45= scipy.stats.chi2_contingency(ct45) | |
print (cs45) | |
recode2 = {12: 12, 162:162} | |
sub1['COMP1v46']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct46=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v46']) | |
print (ct46) | |
# column percentages | |
colsum=ct46.sum(axis=0) | |
colpct46=ct46/colsum | |
print(colpct46) | |
print ('chi-square value, p value, expected counts') | |
cs46= scipy.stats.chi2_contingency(ct46) | |
print (cs46) | |
recode2 = {12: 12, 300:300} | |
sub1['COMP1v47']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct47=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v47']) | |
print (ct47) | |
# column percentages | |
colsum=ct47.sum(axis=0) | |
colpct47=ct47/colsum | |
print(colpct47) | |
print ('chi-square value, p value, expected counts') | |
cs47= scipy.stats.chi2_contingency(ct47) | |
print (cs47) | |
recode2 = {12: 12,365:365} | |
sub1['COMP1v48']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct48=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v48']) | |
print (ct48) | |
# column percentages | |
colsum=ct48.sum(axis=0) | |
colpct48=ct48/colsum | |
print(colpct48) | |
print ('chi-square value, p value, expected counts') | |
cs48= scipy.stats.chi2_contingency(ct48) | |
print (cs48) | |
recode2 = {30:30, 48:48} | |
sub1['COMP1v49']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct49=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v49']) | |
print (ct49) | |
# column percentages | |
colsum=ct49.sum(axis=0) | |
colpct49=ct49/colsum | |
print(colpct49) | |
print ('chi-square value, p value, expected counts') | |
cs49= scipy.stats.chi2_contingency(ct49) | |
print (cs49) | |
recode2 = {30:30, 96:96} | |
sub1['COMP1v50']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct50=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v50']) | |
print (ct50) | |
# column percentages | |
colsum=ct50.sum(axis=0) | |
colpct50=ct50/colsum | |
print(colpct50) | |
print ('chi-square value, p value, expected counts') | |
cs50= scipy.stats.chi2_contingency(ct50) | |
print (cs50) | |
recode2 = {30:30, 162:162} | |
sub1['COMP1v51']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct51=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v51']) | |
print (ct51) | |
# column percentages | |
colsum=ct51.sum(axis=0) | |
colpct51=ct51/colsum | |
print(colpct51) | |
print ('chi-square value, p value, expected counts') | |
cs51= scipy.stats.chi2_contingency(ct51) | |
print (cs51) | |
recode2 = {30:30, 300:300} | |
sub1['COMP1v52']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct52=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v52']) | |
print (ct52) | |
# column percentages | |
colsum=ct52.sum(axis=0) | |
colpct52=ct52/colsum | |
print(colpct52) | |
print ('chi-square value, p value, expected counts') | |
cs52= scipy.stats.chi2_contingency(ct52) | |
print (cs52) | |
recode2 = {30:30, 365:365} | |
sub1['COMP1v53']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct53=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v53']) | |
print (ct53) | |
# column percentages | |
colsum=ct53.sum(axis=0) | |
colpct53=ct53/colsum | |
print(colpct53) | |
print ('chi-square value, p value, expected counts') | |
cs53= scipy.stats.chi2_contingency(ct53) | |
print (cs53) | |
recode2 = {96:96, 162:162} | |
sub1['COMP1v54']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct54=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v54']) | |
print (ct54) | |
# column percentages | |
colsum=ct54.sum(axis=0) | |
colpct54=ct54/colsum | |
print(colpct54) | |
print ('chi-square value, p value, expected counts') | |
cs54= scipy.stats.chi2_contingency(ct54) | |
print (cs54) | |
recode2 = {96:96, 300:300} | |
sub1['COMP1v55']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct55=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v55']) | |
print (ct55) | |
# column percentages | |
colsum=ct55.sum(axis=0) | |
colpct55=ct55/colsum | |
print(colpct55) | |
print ('chi-square value, p value, expected counts') | |
cs55= scipy.stats.chi2_contingency(ct55) | |
print (cs55) | |
recode2 = {96:96, 365:365} | |
sub1['COMP1v56']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct56=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v56']) | |
print (ct56) | |
# column percentages | |
colsum=ct56.sum(axis=0) | |
colpct56=ct56/colsum | |
print(colpct56) | |
print ('chi-square value, p value, expected counts') | |
cs56= scipy.stats.chi2_contingency(ct56) | |
print (cs56) | |
recode2 = {162:162, 300:300} | |
sub1['COMP1v57']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct57=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v57']) | |
print (ct57) | |
# column percentages | |
colsum=ct57.sum(axis=0) | |
colpct57=ct57/colsum | |
print(colpct57) | |
print ('chi-square value, p value, expected counts') | |
cs57= scipy.stats.chi2_contingency(ct57) | |
print (cs57) | |
recode2 = {300:300, 365:365} | |
sub1['COMP1v58']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct58=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v58']) | |
print (ct58) | |
# column percentages | |
colsum=ct58.sum(axis=0) | |
colpct58=ct58/colsum | |
print(colpct58) | |
print ('chi-square value, p value, expected counts') | |
cs58= scipy.stats.chi2_contingency(ct58) | |
print (cs58) | |
recode2 = {162:162, 365:365} | |
sub1['COMP1v59']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct59=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v59']) | |
print (ct59) | |
# column percentages | |
colsum=ct59.sum(axis=0) | |
colpct59=ct59/colsum | |
print(colpct59) | |
print ('chi-square value, p value, expected counts') | |
cs59= scipy.stats.chi2_contingency(ct59) | |
print (cs59) | |
recode2 = {1.5: 1.5, 4.5: 4.5} | |
sub1['COMP1v60']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct60=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v60']) | |
print (ct60) | |
# column percentages | |
colsum=ct60.sum(axis=0) | |
colpct60=ct60/colsum | |
print(colpct60) | |
print ('chi-square value, p value, expected counts') | |
cs60= scipy.stats.chi2_contingency(ct60) | |
print (cs60) | |
recode2 = {1.5: 1.5, 9: 9} | |
sub1['COMP1v61']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct61=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v61']) | |
print (ct61) | |
# column percentages | |
colsum=ct61.sum(axis=0) | |
colpct61=ct61/colsum | |
print(colpct61) | |
print ('chi-square value, p value, expected counts') | |
cs61= scipy.stats.chi2_contingency(ct61) | |
print (cs61) | |
recode2 = {48: 48, 365: 365} | |
sub1['COMP1v62']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct62=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v62']) | |
print (ct62) | |
# column percentages | |
colsum=ct62.sum(axis=0) | |
colpct62=ct62/colsum | |
print(colpct62) | |
print ('chi-square value, p value, expected counts') | |
cs62= scipy.stats.chi2_contingency(ct62) | |
print (cs62) | |
recode2 = {48: 48, 300: 300} | |
sub1['COMP1v63']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct63=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v63']) | |
print (ct63) | |
# column percentages | |
colsum=ct63.sum(axis=0) | |
colpct63=ct63/colsum | |
print(colpct63) | |
print ('chi-square value, p value, expected counts') | |
cs63= scipy.stats.chi2_contingency(ct63) | |
print (cs63) | |
recode2 = {48: 48, 162: 162} | |
sub1['COMP1v64']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct64=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v64']) | |
print (ct64) | |
# column percentages | |
colsum=ct64.sum(axis=0) | |
colpct64=ct64/colsum | |
print(colpct64) | |
print ('chi-square value, p value, expected counts') | |
cs64= scipy.stats.chi2_contingency(ct64) | |
print (cs64) | |
recode2 = {48: 48, 96: 96} | |
sub1['COMP1v65']= sub1['USFREQYR'].map(recode2) | |
# contingency table of observed counts | |
ct65=pandas.crosstab(sub1['DYSDX12'], sub1['COMP1v65']) | |
print (ct65) | |
# column percentages | |
colsum=ct65.sum(axis=0) | |
colpct65=ct65/colsum | |
print(colpct65) | |
print ('chi-square value, p value, expected counts') | |
cs65= scipy.stats.chi2_contingency(ct65) | |
print (cs65) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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''' | |
43093 | |
25 | |
(43093, 25) | |
(5838, 25) | |
DYSDX12 0 1 | |
S4CQ18A | |
1 19 26 | |
2 83 94 | |
DYSDX12 0 1 | |
S4CQ18B | |
1 3 16 | |
2 99 104 | |
DYSDX12 0 1 | |
S4CQ18C | |
1 19 24 | |
2 83 96 | |
DYSDX12 0 1 | |
S4CQ19A | |
1 18 21 | |
2 84 99 | |
DYSDX12 0 1 | |
S4CQ19B | |
1 2 9 | |
2 95 106 | |
DYSDX12 0 1 | |
S4CQ19C | |
1 13 16 | |
2 84 99 | |
DYSDX12 0 1 | |
S4CQ18A | |
1 0.186275 0.216667 | |
2 0.813725 0.783333 | |
DYSDX12 0 1 | |
S4CQ18B | |
1 0.029412 0.133333 | |
2 0.970588 0.866667 | |
DYSDX12 0 1 | |
S4CQ18C | |
1 0.186275 0.2 | |
2 0.813725 0.8 | |
chi-square value, p value, expected counts | |
(0.15511964107677009, 0.6936900824150003, 1, array([[ 20.67567568, 24.32432432], | |
[ 81.32432432, 95.67567568]])) | |
chi-square value, p value, expected counts | |
(6.3384979944790985, 0.011814488426313271, 1, array([[ 8.72972973, 10.27027027], | |
[ 93.27027027, 109.72972973]])) | |
chi-square value, p value, expected counts | |
(0.0076559622159894907, 0.93027539253689573, 1, array([[ 19.75675676, 23.24324324], | |
[ 82.24324324, 96.75675676]])) | |
chi-square value, p value, expected counts | |
(0.021979699824444317, 0.8821411156218304, 1, array([[ 17.91891892, 21.08108108], | |
[ 84.08108108, 98.91891892]])) | |
chi-square value, p value, expected counts | |
(2.4787122616005983, 0.11539669868109208, 1, array([[ 5.03301887, 5.96698113], | |
[ 91.96698113, 109.03301887]])) | |
chi-square value, p value, expected counts | |
(0.0085982359504715913, 0.92612070603106611, 1, array([[ 13.26886792, 15.73113208], | |
[ 83.73113208, 99.26886792]])) | |
USFREQYR 1.5 4.5 9.0 12.0 30.0 48.0 96.0 162.0 300.0 365.0 | |
DYSDX12 | |
0 536 468 258 427 649 520 463 350 121 128 | |
1 14 10 13 9 11 8 12 10 7 6 | |
USFREQYR 1.5 4.5 9.0 12.0 30.0 48.0 96.0 \ | |
DYSDX12 | |
0 0.974545 0.979079 0.95203 0.979358 0.983333 0.984848 0.974737 | |
1 0.025455 0.020921 0.04797 0.020642 0.016667 0.015152 0.025263 | |
USFREQYR 162.0 300.0 365.0 | |
DYSDX12 | |
0 0.972222 0.945312 0.955224 | |
1 0.027778 0.054688 0.044776 | |
chi-square value, p value, expected counts | |
(17.494163340774072, 0.041516886183372334, 9, array([[ 536.31840796, 466.10945274, 264.25870647, 425.15422886, | |
643.58208955, 514.86567164, 463.1840796 , 351.04477612, | |
124.8159204 , 130.66666667], | |
[ 13.68159204, 11.89054726, 6.74129353, 10.84577114, | |
16.41791045, 13.13432836, 11.8159204 , 8.95522388, | |
3.1840796 , 3.33333333]])) | |
COMP1v21 1.5 12.0 | |
DYSDX12 | |
0 536 427 | |
1 14 9 | |
COMP1v21 1.5 12.0 | |
DYSDX12 | |
0 0.974545 0.979358 | |
1 0.025455 0.020642 | |
chi-square value, p value, expected counts | |
(0.081110397918773974, 0.77579823589352459, 1, array([[ 537.1703854, 425.8296146], | |
[ 12.8296146, 10.1703854]])) | |
COMP1v22 1.5 30.0 | |
DYSDX12 | |
0 536 649 | |
1 14 11 | |
COMP1v22 1.5 30.0 | |
DYSDX12 | |
0 0.974545 0.983333 | |
1 0.025455 0.016667 | |
chi-square value, p value, expected counts | |
(0.75186779184247543, 0.38588552038873458, 1, array([[ 538.63636364, 646.36363636], | |
[ 11.36363636, 13.63636364]])) | |
COMP1v23 1.5 48.0 | |
DYSDX12 | |
0 536 520 | |
1 14 8 | |
COMP1v23 1.5 48.0 | |
DYSDX12 | |
0 0.974545 0.984848 | |
1 0.025455 0.015152 | |
chi-square value, p value, expected counts | |
(0.96145872790404086, 0.32681962580316493, 1, array([[ 538.7755102, 517.2244898], | |
[ 11.2244898, 10.7755102]])) | |
COMP1v24 1.5 96.0 | |
DYSDX12 | |
0 536 463 | |
1 14 12 | |
COMP1v24 1.5 96.0 | |
DYSDX12 | |
0 0.974545 0.974737 | |
1 0.025455 0.025263 | |
chi-square value, p value, expected counts | |
(0.032311134284818535, 0.85734649774401372, 1, array([[ 536.04878049, 462.95121951], | |
[ 13.95121951, 12.04878049]])) | |
COMP1v25 1.5 162.0 | |
DYSDX12 | |
0 536 350 | |
1 14 10 | |
COMP1v25 1.5 162.0 | |
DYSDX12 | |
0 0.974545 0.972222 | |
1 0.025455 0.027778 | |
chi-square value, p value, expected counts | |
(5.4034513684625857e-06, 0.99814529307319433, 1, array([[ 535.49450549, 350.50549451], | |
[ 14.50549451, 9.49450549]])) | |
COMP1v26 1.5 300.0 | |
DYSDX12 | |
0 536 121 | |
1 14 7 | |
COMP1v26 1.5 300.0 | |
DYSDX12 | |
0 0.974545 0.945312 | |
1 0.025455 0.054688 | |
chi-square value, p value, expected counts | |
(2.0626432685465224, 0.15094813001296287, 1, array([[ 532.96460177, 124.03539823], | |
[ 17.03539823, 3.96460177]])) | |
COMP1v27 1.5 365.0 | |
DYSDX12 | |
0 536 128 | |
1 14 6 | |
COMP1v27 1.5 365.0 | |
DYSDX12 | |
0 0.974545 0.955224 | |
1 0.025455 0.044776 | |
chi-square value, p value, expected counts | |
(0.81817284334079832, 0.36571492563005281, 1, array([[ 533.91812865, 130.08187135], | |
[ 16.08187135, 3.91812865]])) | |
COMP1v28 4.5 9.0 | |
DYSDX12 | |
0 468 258 | |
1 10 13 | |
COMP1v28 4.5 9.0 | |
DYSDX12 | |
0 0.979079 0.95203 | |
1 0.020921 0.04797 | |
chi-square value, p value, expected counts | |
(3.3913302621065133, 0.065540052123220491, 1, array([[ 463.32176235, 262.67823765], | |
[ 14.67823765, 8.32176235]])) | |
COMP1v29 4.5 12.0 | |
DYSDX12 | |
0 468 427 | |
1 10 9 | |
COMP1v29 4.5 12.0 | |
DYSDX12 | |
0 0.979079 0.979358 | |
1 0.020921 0.020642 | |
chi-square value, p value, expected counts | |
(0.041058300124229889, 0.83942542943834042, 1, array([[ 468.06345733, 426.93654267], | |
[ 9.93654267, 9.06345733]])) | |
COMP1v30 4.5 30.0 | |
DYSDX12 | |
0 468 649 | |
1 10 11 | |
COMP1v30 4.5 30.0 | |
DYSDX12 | |
0 0.979079 0.983333 | |
1 0.020921 0.016667 | |
chi-square value, p value, expected counts | |
(0.091887608780024402, 0.76179102883057059, 1, array([[ 469.17926186, 647.82073814], | |
[ 8.82073814, 12.17926186]])) | |
COMP1v31 4.5 48.0 | |
DYSDX12 | |
0 468 520 | |
1 10 8 | |
COMP1v31 4.5 48.0 | |
DYSDX12 | |
0 0.979079 0.984848 | |
1 0.020921 0.015152 | |
chi-square value, p value, expected counts | |
(0.20356003325923222, 0.65186250579499883, 1, array([[ 469.4473161, 518.5526839], | |
[ 8.5526839, 9.4473161]])) | |
COMP1v32 4.5 96.0 | |
DYSDX12 | |
0 468 463 | |
1 10 12 | |
COMP1v32 4.5 96.0 | |
DYSDX12 | |
0 0.979079 0.974737 | |
1 0.020921 0.025263 | |
chi-square value, p value, expected counts | |
(0.053197032823638704, 0.81759074518497599, 1, array([[ 466.96537251, 464.03462749], | |
[ 11.03462749, 10.96537251]])) | |
COMP1v33 4.5 162.0 | |
DYSDX12 | |
0 468 350 | |
1 10 10 | |
COMP1v33 4.5 162.0 | |
DYSDX12 | |
0 0.979079 0.972222 | |
1 0.020921 0.027778 | |
chi-square value, p value, expected counts | |
(0.17238506440400136, 0.67800076631344419, 1, array([[ 466.59188544, 351.40811456], | |
[ 11.40811456, 8.59188544]])) | |
COMP1v34 5.5 300.0 | |
DYSDX12 | |
0 468 121 | |
1 10 7 | |
COMP1v34 5.5 300.0 | |
DYSDX12 | |
0 0.979079 0.945312 | |
1 0.020921 0.054688 | |
chi-square value, p value, expected counts | |
(3.074504735762964, 0.079528813603024121, 1, array([[ 464.59075908, 124.40924092], | |
[ 13.40924092, 3.59075908]])) | |
COMP1v35 4.5 365.0 | |
DYSDX12 | |
0 468 128 | |
1 10 6 | |
COMP1v35 4.5 365.0 | |
DYSDX12 | |
0 0.979079 0.955224 | |
1 0.020921 0.044776 | |
chi-square value, p value, expected counts | |
(1.4962180738636439, 0.22125419123797496, 1, array([[ 465.50326797, 130.49673203], | |
[ 12.49673203, 3.50326797]])) | |
COMP1v36 9 12 | |
DYSDX12 | |
0 258 427 | |
1 13 9 | |
COMP1v36 9 12 | |
DYSDX12 | |
0 0.95203 0.979358 | |
1 0.04797 0.020642 | |
chi-square value, p value, expected counts | |
(3.2830471896409597, 0.069998852719869734, 1, array([[ 262.56718529, 422.43281471], | |
[ 8.43281471, 13.56718529]])) | |
COMP1v37 9 30 | |
DYSDX12 | |
0 258 649 | |
1 13 11 | |
COMP1v37 9 30 | |
DYSDX12 | |
0 0.95203 0.983333 | |
1 0.04797 0.016667 | |
chi-square value, p value, expected counts | |
(6.3015162206896074, 0.012063474767743424, 1, array([[ 264.01396348, 642.98603652], | |
[ 6.98603652, 17.01396348]])) | |
COMP1v38 9 48 | |
DYSDX12 | |
0 258 520 | |
1 13 8 | |
COMP1v38 9 48 | |
DYSDX12 | |
0 0.95203 0.984848 | |
1 0.04797 0.015152 | |
chi-square value, p value, expected counts | |
(6.3092001713192047, 0.012011301386107615, 1, array([[ 263.87734668, 514.12265332], | |
[ 7.12265332, 13.87734668]])) | |
COMP1v39 9 96 | |
DYSDX12 | |
0 258 463 | |
1 13 12 | |
COMP1v39 9 96 | |
DYSDX12 | |
0 0.95203 0.974737 | |
1 0.04797 0.025263 | |
chi-square value, p value, expected counts | |
(2.0906484676205248, 0.14820315104250653, 1, array([[ 261.91823056, 459.08176944], | |
[ 9.08176944, 15.91823056]])) | |
COMP1v40 9 162 | |
DYSDX12 | |
0 258 350 | |
1 13 10 | |
COMP1v40 9 162 | |
DYSDX12 | |
0 0.95203 0.972222 | |
1 0.04797 0.027778 | |
chi-square value, p value, expected counts | |
(1.2660757639067748, 0.26050415686771855, 1, array([[ 261.12202853, 346.87797147], | |
[ 9.87797147, 13.12202853]])) | |
COMP1v41 9 300 | |
DYSDX12 | |
0 258 121 | |
1 13 7 | |
COMP1v41 9 300 | |
DYSDX12 | |
0 0.95203 0.945312 | |
1 0.04797 0.054688 | |
chi-square value, p value, expected counts | |
(0.0017029976301370822, 0.96708272844891252, 1, array([[ 257.4160401, 121.5839599], | |
[ 13.5839599, 6.4160401]])) | |
COMP1v42 9 365 | |
DYSDX12 | |
0 258 128 | |
1 13 6 | |
COMP1v42 9 365 | |
DYSDX12 | |
0 0.95203 0.955224 | |
1 0.04797 0.044776 | |
chi-square value, p value, expected counts | |
(0.011378165416546245, 0.91505198627505746, 1, array([[ 258.28641975, 127.71358025], | |
[ 12.71358025, 6.28641975]])) | |
COMP1v43 12 30 | |
DYSDX12 | |
0 427 649 | |
1 9 11 | |
COMP1v43 12 30 | |
DYSDX12 | |
0 0.979358 0.983333 | |
1 0.020642 0.016667 | |
chi-square value, p value, expected counts | |
(0.062868106734959933, 0.8020188962236966, 1, array([[ 428.04379562, 647.95620438], | |
[ 7.95620438, 12.04379562]])) | |
COMP1v44 12 48 | |
DYSDX12 | |
0 427 520 | |
1 9 8 | |
COMP1v44 12 48 | |
DYSDX12 | |
0 0.979358 0.984848 | |
1 0.020642 0.015152 | |
chi-square value, p value, expected counts | |
(0.1590637539965693, 0.69001996225727391, 1, array([[ 428.31120332, 518.68879668], | |
[ 7.68879668, 9.31120332]])) | |
COMP1v45 12 96 | |
DYSDX12 | |
0 427 463 | |
1 9 12 | |
COMP1v45 12 96 | |
DYSDX12 | |
0 0.979358 0.974737 | |
1 0.020642 0.025263 | |
chi-square value, p value, expected counts | |
(0.059193068164174065, 0.80777590458431159, 1, array([[ 425.94950604, 464.05049396], | |
[ 10.05049396, 10.94950604]])) | |
COMP1v46 12 162 | |
DYSDX12 | |
0 427 350 | |
1 9 10 | |
COMP1v46 12 162 | |
DYSDX12 | |
0 0.979358 0.972222 | |
1 0.020642 0.027778 | |
chi-square value, p value, expected counts | |
(0.17907042035482401, 0.67217332657531625, 1, array([[ 425.59296482, 351.40703518], | |
[ 10.40703518, 8.59296482]])) | |
COMP1v47 12 300 | |
DYSDX12 | |
0 427 121 | |
1 9 7 | |
COMP1v47 12 300 | |
DYSDX12 | |
0 0.979358 0.945312 | |
1 0.020642 0.054688 | |
chi-square value, p value, expected counts | |
(3.0174434413819222, 0.08237322806184387, 1, array([[ 423.63120567, 124.36879433], | |
[ 12.36879433, 3.63120567]])) | |
COMP1v48 12 365 | |
DYSDX12 | |
0 427 128 | |
1 9 6 | |
COMP1v48 12 365 | |
DYSDX12 | |
0 0.979358 0.955224 | |
1 0.020642 0.044776 | |
chi-square value, p value, expected counts | |
(1.4832158942456082, 0.22327204289175773, 1, array([[ 424.52631579, 130.47368421], | |
[ 11.47368421, 3.52631579]])) | |
COMP1v49 30 48 | |
DYSDX12 | |
0 649 520 | |
1 11 8 | |
COMP1v49 30 48 | |
DYSDX12 | |
0 0.983333 0.984848 | |
1 0.016667 0.015152 | |
chi-square value, p value, expected counts | |
(0.00066858763675655254, 0.97937134843065576, 1, array([[ 649.44444444, 519.55555556], | |
[ 10.55555556, 8.44444444]])) | |
COMP1v50 30 96 | |
DYSDX12 | |
0 649 463 | |
1 11 12 | |
COMP1v50 30 96 | |
DYSDX12 | |
0 0.983333 0.974737 | |
1 0.016667 0.025263 | |
chi-square value, p value, expected counts | |
(0.64071491012931314, 0.42345203638584983, 1, array([[ 646.62555066, 465.37444934], | |
[ 13.37444934, 9.62555066]])) | |
COMP1v51 30 162 | |
DYSDX12 | |
0 649 350 | |
1 11 10 | |
COMP1v51 30 162 | |
DYSDX12 | |
0 0.983333 0.972222 | |
1 0.016667 0.027778 | |
chi-square value, p value, expected counts | |
(0.92838509505176137, 0.3352829089888848, 1, array([[ 646.41176471, 352.58823529], | |
[ 13.58823529, 7.41176471]])) | |
COMP1v52 30 300 | |
DYSDX12 | |
0 649 121 | |
1 11 7 | |
COMP1v52 30 300 | |
DYSDX12 | |
0 0.983333 0.945312 | |
1 0.016667 0.054688 | |
chi-square value, p value, expected counts | |
(5.3443028362630649, 0.020790095513174879, 1, array([[ 644.92385787, 125.07614213], | |
[ 15.07614213, 2.92385787]])) | |
COMP1v53 30 365 | |
DYSDX12 | |
0 649 128 | |
1 11 6 | |
COMP1v53 30 365 | |
DYSDX12 | |
0 0.983333 0.955224 | |
1 0.016667 0.044776 | |
chi-square value, p value, expected counts | |
(2.9660516422149432, 0.085029151995431748, 1, array([[ 645.86901763, 131.13098237], | |
[ 14.13098237, 2.86901763]])) | |
COMP1v54 96 162 | |
DYSDX12 | |
0 463 350 | |
1 12 10 | |
COMP1v54 96 162 | |
DYSDX12 | |
0 0.974737 0.972222 | |
1 0.025263 0.027778 | |
chi-square value, p value, expected counts | |
(4.2657673386938429e-05, 0.99478882621881659, 1, array([[ 462.48502994, 350.51497006], | |
[ 12.51497006, 9.48502994]])) | |
COMP1v55 96 300 | |
DYSDX12 | |
0 463 121 | |
1 12 7 | |
COMP1v55 96 300 | |
DYSDX12 | |
0 0.974737 0.945312 | |
1 0.025263 0.054688 | |
chi-square value, p value, expected counts | |
(1.9777037344390345, 0.1596325227118105, 1, array([[ 460.0331675, 123.9668325], | |
[ 14.9668325, 4.0331675]])) | |
COMP1v56 96 365 | |
DYSDX12 | |
0 463 128 | |
1 12 6 | |
COMP1v56 96 365 | |
DYSDX12 | |
0 0.974737 0.955224 | |
1 0.025263 0.044776 | |
chi-square value, p value, expected counts | |
(0.79049923040421732, 0.37394921188019159, 1, array([[ 460.96059113, 130.03940887], | |
[ 14.03940887, 3.96059113]])) | |
COMP1v57 162 300 | |
DYSDX12 | |
0 350 121 | |
1 10 7 | |
COMP1v57 162 300 | |
DYSDX12 | |
0 0.972222 0.945312 | |
1 0.027778 0.054688 | |
chi-square value, p value, expected counts | |
(1.3120675658798562, 0.25202065226590509, 1, array([[ 347.45901639, 123.54098361], | |
[ 12.54098361, 4.45901639]])) | |
COMP1v58 300 365 | |
DYSDX12 | |
0 121 128 | |
1 7 6 | |
COMP1v58 300 365 | |
DYSDX12 | |
0 0.945312 0.955224 | |
1 0.054688 0.044776 | |
chi-square value, p value, expected counts | |
(0.0071774973026434005, 0.93248390673692383, 1, array([[ 121.64885496, 127.35114504], | |
[ 6.35114504, 6.64885496]])) | |
COMP1v59 162 365 | |
DYSDX12 | |
0 350 128 | |
1 10 6 | |
COMP1v59 162 365 | |
DYSDX12 | |
0 0.972222 0.955224 | |
1 0.027778 0.044776 | |
chi-square value, p value, expected counts | |
(0.4396236163585836, 0.5073041756642318, 1, array([[ 348.34008097, 129.65991903], | |
[ 11.65991903, 4.34008097]])) | |
COMP1v60 1.5 4.5 | |
DYSDX12 | |
0 536 468 | |
1 14 10 | |
COMP1v60 1.5 4.5 | |
DYSDX12 | |
0 0.974545 0.979079 | |
1 0.025455 0.020921 | |
chi-square value, p value, expected counts | |
(0.074596244159914046, 0.78475856723511073, 1, array([[ 537.15953307, 466.84046693], | |
[ 12.84046693, 11.15953307]])) | |
COMP1v61 1.5 9.0 | |
DYSDX12 | |
0 536 258 | |
1 14 13 | |
COMP1v61 1.5 9.0 | |
DYSDX12 | |
0 0.974545 0.95203 | |
1 0.025455 0.04797 | |
chi-square value, p value, expected counts | |
(2.2291791087153672, 0.13542578166343269, 1, array([[ 531.91230207, 262.08769793], | |
[ 18.08769793, 8.91230207]])) | |
COMP1v62 48 365 | |
DYSDX12 | |
0 520 128 | |
1 8 6 | |
COMP1v62 48 365 | |
DYSDX12 | |
0 0.984848 0.955224 | |
1 0.015152 0.044776 | |
chi-square value, p value, expected counts | |
(3.2129624239064007, 0.073057097904547216, 1, array([[ 516.83383686, 131.16616314], | |
[ 11.16616314, 2.83383686]])) | |
COMP1v63 48 300 | |
DYSDX12 | |
0 520 121 | |
1 8 7 | |
COMP1v63 48 300 | |
DYSDX12 | |
0 0.984848 0.945312 | |
1 0.015152 0.054688 | |
chi-square value, p value, expected counts | |
(5.5465875604721164, 0.01851675302058986, 1, array([[ 515.92682927, 125.07317073], | |
[ 12.07317073, 2.92682927]])) | |
COMP1v64 48 162 | |
DYSDX12 | |
0 520 350 | |
1 8 10 | |
COMP1v64 48 162 | |
DYSDX12 | |
0 0.984848 0.972222 | |
1 0.015152 0.027778 | |
chi-square value, p value, expected counts | |
(1.1413595727388834, 0.28536522618096383, 1, array([[ 517.2972973, 352.7027027], | |
[ 10.7027027, 7.2972973]])) | |
COMP1v65 48 96 | |
DYSDX12 | |
0 520 463 | |
1 8 12 | |
COMP1v65 48 96 | |
DYSDX12 | |
0 0.984848 0.974737 | |
1 0.015152 0.025263 | |
chi-square value, p value, expected counts | |
(0.84198678587624431, 0.35882916664842746, 1, array([[ 517.47158524, 465.52841476], | |
[ 10.52841476, 9.47158524]])) | |
''' |
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