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10.21037/atm.2016.03.36 http://scihub22266oqcxt.onion/10.21037/atm.2016.03.36 C4885900!4885900 !27294091     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27294091 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Ann+Transl+Med 2016 ; 4 (10 ): 195 Nephropedia Template TP {{tp|p=27294091 |t=2016. Residuals and regression diagnostics: focusing on logistic regression |pdf=|usr=}}{{27294091 }} gab.com Text Residuals and regression diagnostics: focusing on logistic regression JO: Ann Transl Med http://www.kidney.de/pget.php?&tw=1&pmid=27294091 #FOAvip Up to now I have introduced most steps in regression model building and validation. The last step is to check whether there are observations that have significant impact on model coefficient and specification. The article firstly describes plotting Pearson residual against predictors. Such plots are helpful in identifying non-linearity and provide hints on how to transform predictors. Next, I focus on observations of outlier, leverage and influence that may have significant impact on model building. Outlier is such an observation that its response value is unusual conditional on covariate pattern. Leverage is an observation with covariate pattern that is far away from the regressor space. Influence is the product of outlier and leverage. That is, when influential observation is dropped from the model, there will be a significant shift of the coefficient. Summary statistics for outlier, leverage and influence are studentized residuals, hat values and Cook's distance. They can be easily visualized with graphs and formally tested using the car package. Twit Text FOAVip Residuals and regression diagnostics: focusing on logistic regression ????Ann Transl Med http://www.kidney.de/pget.php?&tw=1&pmid=27294091 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27294091 #FOAvip English Wikipedia <ref>{{Cite pmid|27294091 }}</ref> Residuals and regression diagnostics: focusing on logistic regression #MMPMID27294091 Zhang Z Ann Transl Med 2016[May]; 4 (10 ): 195 PMID27294091 show ga Up to now I have introduced most steps in regression model building and validation. The last step is to check whether there are observations that have significant impact on model coefficient and specification. The article firstly describes plotting Pearson residual against predictors. Such plots are helpful in identifying non-linearity and provide hints on how to transform predictors. Next, I focus on observations of outlier, leverage and influence that may have significant impact on model building. Outlier is such an observation that its response value is unusual conditional on covariate pattern. Leverage is an observation with covariate pattern that is far away from the regressor space. Influence is the product of outlier and leverage. That is, when influential observation is dropped from the model, there will be a significant shift of the coefficient. Summary statistics for outlier, leverage and influence are studentized residuals, hat values and Cook's distance. They can be easily visualized with graphs and formally tested using the car package. äResiduals and regression diagnostics: focusing on logistic regression (epmc).pdf DeepDyve Pubget Overpricing