ROC curve analysisĪnother method to evaluate the logistic regression model makes use of ROC curve analysis. In our example, the model correctly predicts 74% of the cases. In this table the observed values for the dependent outcome and the predicted values (at a user defined cut-off value, for example p=0.50) are cross-classified. The classification table is another method to evaluate the predictive accuracy of the logistic regression model. The Contingency Table for Hosmer and Lemeshow Test table shows the details of the test with observed and expected number of cases in each group. The test statistic follows a Chi-squared distribution with G−2 degrees of freedom.Ī large value of Chi-squared (with small p-value < 0.05) indicates poor fit and small Chi-squared values (with larger p-value closer to 1) indicate a good logistic regression model fit. With O g, E g and n g the observed events, expected events and number of observations for the g th risk decile group, and G the number of groups. Logistic regression generates the coefficients (and its standard errors and significance levels) of a formula to predict a logit transformation of the probability of presence of the characteristic of interest: The goal of logistic regression is to find the best fitting (yet biologically reasonable) model to describe the relationship between the dichotomous characteristic of interest (dependent variable = response or outcome variable) and a set of independent (predictor or explanatory) variables. it only contains data coded as 1 (TRUE, success, pregnant, etc.) or 0 (FALSE, failure, non-pregnant, etc.). In logistic regression, the dependent variable is binary or dichotomous, i.e. The outcome is measured with a dichotomous variable (in which there are only two possible outcomes). Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome.
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