Comparison of strategies for validating binary logistic regression models Granny datingfor sex

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Comparison of strategies for validating binary logistic regression models

Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.Visit Stack Exchange Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Sign up to join this community I have a question regarding calibration plot for a binary logistic regression model (calibrate) in the rms(regression modelling strategies) package.After you’ve mastered linear regression, this comes as the natural following step in your journey.It’s also easy to learn and implement, but you must know the science behind this algorithm.The Bias-corrected curve (see below) shows if the apparent fit of the model is overfited. the explanation I found on page 270-271: "The nonparametric estimate is evaluated at a sequence of predicted probability levels.Then the distances from the 45◦ line are compared with the differences when the current model is evaluated back on the whole sample (or omitted sample for cross-validation).

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The role of link function is to ‘link’ the expectation of y to linear predictor.

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