How to interpret the coefficient (beta value) and pseudo R2 (R squire) value in binary logistic regression?
Hi dear
In binary regression the interpretation of psudoR2 and also B coefficients are different because you are regressing 0 1 values and not real value of dependent variable. then you can not set a range for PsudoR2 like OLS. then the higher sums are welcomed and favorable. The B coefficient shows the likelihood or probability or odds of change in dependent variable versus independent variables. only you can interpret this. If you want to calculate the exact change, then you have to calculate marginal effects for each coefficient.
http://www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm
Hi dear
In binary regression the interpretation of psudoR2 and also B coefficients are different because you are regressing 0 1 values and not real value of dependent variable. then you can not set a range for PsudoR2 like OLS. then the higher sums are welcomed and favorable. The B coefficient shows the likelihood or probability or odds of change in dependent variable versus independent variables. only you can interpret this. If you want to calculate the exact change, then you have to calculate marginal effects for each coefficient.
http://www.ats.ucla.edu/stat/mult_pkg/faq/general/Psuedo_RSquareds.htm
After fitting the model, if B is the coefficient of individual predictor:
Predictor is continuous, exp(B) is the odds ratio (OR) for increasing one unit of the predictor.\
Predictor is category, B1 for category 1 B2 for category 2, then EXP(B2-B1) is the odds ratio between the category 2 and category 1.
OR=1, no effct,
OR>1: risk factor
OR
Say that there are two possible events, 1 and 0. You have estimated a logit, i.e.
Pr(1) = exp(Xb)/(1 + exp(Xb)). For simplicity say that we have just one explanatory variable X, so that b is a scalar. b gives you the marginal effect of X on the log-odd ratio, i.e.
ln(Pr(1)/Pr(0) = Xb.
The pseudo R2 can be interpreted as a measure of how well your model fits the data.
Should write it as
Pr(1|X=x) = exp(Xb)/(1 + exp(Xb)). then conditional probability when X=x
more clear.
Beta expresses the relative importance of each independent variable in standardized terms. R squared value indicates the proportion of the variance in the dependent variable that is predictable from the independent variables. It is a statistical measure of how close the data is to the fitted regression line. When R squared value increases, standard error of the estimate decreases.
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