Hello there,
I'm trying to to a logistic regression on my missing variables to analyse for the missingness mechanism to see if a multiple imputation is needed. So I put all the variables in a logistic regression model but I the thing that I get back is:
no observations
r(2000);
I read what it means, I actually did have a string variable, which I then converted to a numerical but the problem still continues. When I do univariable analysis with the variable counting the missingness in the specific variable it works.
for example:
logistic miss_aneurysm_size sah1uia0
-> no problem
logistic miss_aneurysm_size sah1uia0 ageatdiagnosis aneurysm_loc HTN antiHTN heartdisease PVD DM preICH preSAH preStroke hyperchol statinpre antiplatelet SS HRT OCP postmeno i.premorbiddisabilitymrs i.Smoker i.Drinker fhsahuia druguse clipping coiling noth CVS Collapse consan Warfarin i.activityictus admissiondbp admissionsbp admissioninr
-> still works
logistic miss_aneurysm_size sah1uia0 ageatdiagnosis aneurysm_loc HTN antiHTN heartdisease PVD DM preICH preSAH preStroke hyperchol statinpre antiplatelet SS HRT OCP postmeno i.premorbiddisabilitymrs i.Smoker i.Drinker fhsahuia druguse clipping coiling noth CVS Collapse consan Warfarin i.activityictus admissiondbp admissionsbp admissioninr admissionsodiumana angioplast DSA coronaryrep crimatt craniectomy deathfriend durantiHTN i.ECG i.ethn i.fishergradeonadmission FNDad gcsdrop haemevac heada heartrate hemi HCP iapapa ituhduad LOC majillrel menin multipleA paped rebleed seiz stent infarct vomit i.wfnsad
-> doesnt work
With this I want to check which variables have a relation with the variable with missing values so to know which covariates to put into the imputation model. Is there a limitation to the numbers of covariates I can put into the model? Is there a minimum of observations a variable has to have to go into the model? My sample size is 1640 so this should not be a problem.
Help would me much appreciated.
Thank you
Isabel
I'm trying to to a logistic regression on my missing variables to analyse for the missingness mechanism to see if a multiple imputation is needed. So I put all the variables in a logistic regression model but I the thing that I get back is:
no observations
r(2000);
I read what it means, I actually did have a string variable, which I then converted to a numerical but the problem still continues. When I do univariable analysis with the variable counting the missingness in the specific variable it works.
for example:
logistic miss_aneurysm_size sah1uia0
-> no problem
logistic miss_aneurysm_size sah1uia0 ageatdiagnosis aneurysm_loc HTN antiHTN heartdisease PVD DM preICH preSAH preStroke hyperchol statinpre antiplatelet SS HRT OCP postmeno i.premorbiddisabilitymrs i.Smoker i.Drinker fhsahuia druguse clipping coiling noth CVS Collapse consan Warfarin i.activityictus admissiondbp admissionsbp admissioninr
-> still works
logistic miss_aneurysm_size sah1uia0 ageatdiagnosis aneurysm_loc HTN antiHTN heartdisease PVD DM preICH preSAH preStroke hyperchol statinpre antiplatelet SS HRT OCP postmeno i.premorbiddisabilitymrs i.Smoker i.Drinker fhsahuia druguse clipping coiling noth CVS Collapse consan Warfarin i.activityictus admissiondbp admissionsbp admissioninr admissionsodiumana angioplast DSA coronaryrep crimatt craniectomy deathfriend durantiHTN i.ECG i.ethn i.fishergradeonadmission FNDad gcsdrop haemevac heada heartrate hemi HCP iapapa ituhduad LOC majillrel menin multipleA paped rebleed seiz stent infarct vomit i.wfnsad
-> doesnt work
With this I want to check which variables have a relation with the variable with missing values so to know which covariates to put into the imputation model. Is there a limitation to the numbers of covariates I can put into the model? Is there a minimum of observations a variable has to have to go into the model? My sample size is 1640 so this should not be a problem.
Help would me much appreciated.
Thank you
Isabel
Comment