Binary logistic regression.

blr_regress(object, ...)

# S3 method for glm
blr_regress(object, odd_conf_limit = FALSE, ...)

Arguments

object

An object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted or class glm.

...

Other inputs.

odd_conf_limit

If TRUE, odds ratio confidence limts will be displayed.

Examples

# using formula blr_regress(object = honcomp ~ female + read + science, data = hsb2)
#> <U+2714> Creating model overview. #> <U+2714> Creating response profile. #> <U+2714> Extracting maximum likelihood estimates. #> <U+2714> Estimating concordant and discordant pairs. #>
#> Model Overview #> ------------------------------------------------------------------------ #> Data Set Resp Var Obs. Df. Model Df. Residual Convergence #> ------------------------------------------------------------------------ #> data honcomp 200 199 196 TRUE #> ------------------------------------------------------------------------ #> #> Response Summary #> -------------------------------------------------------- #> Outcome Frequency Outcome Frequency #> -------------------------------------------------------- #> 0 147 1 53 #> -------------------------------------------------------- #> #> Maximum Likelihood Estimates #> ----------------------------------------------------------------- #> Parameter DF Estimate Std. Error z value Pr(>|z|) #> ----------------------------------------------------------------- #> (Intercept) 1 -12.7772 1.9755 -6.4677 0.0000 #> female1 1 1.4825 0.4474 3.3139 9e-04 #> read 1 0.1035 0.0258 4.0186 1e-04 #> science 1 0.0948 0.0305 3.1129 0.0019 #> ----------------------------------------------------------------- #> #> Association of Predicted Probabilities and Observed Responses #> --------------------------------------------------------------- #> % Concordant 0.8561 Somers' D 0.7147 #> % Discordant 0.1425 Gamma 0.7136 #> % Tied 0.0014 Tau-a 0.2794 #> Pairs 7791 c 0.8568 #> --------------------------------------------------------------- #>
# using a model built with glm model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) blr_regress(model)
#> <U+2714> Creating model overview. #> <U+2714> Creating response profile. #> <U+2714> Extracting maximum likelihood estimates. #> <U+2714> Estimating concordant and discordant pairs. #>
#> Model Overview #> ------------------------------------------------------------------------ #> Data Set Resp Var Obs. Df. Model Df. Residual Convergence #> ------------------------------------------------------------------------ #> data honcomp 200 199 196 TRUE #> ------------------------------------------------------------------------ #> #> Response Summary #> -------------------------------------------------------- #> Outcome Frequency Outcome Frequency #> -------------------------------------------------------- #> 0 147 1 53 #> -------------------------------------------------------- #> #> Maximum Likelihood Estimates #> ----------------------------------------------------------------- #> Parameter DF Estimate Std. Error z value Pr(>|z|) #> ----------------------------------------------------------------- #> (Intercept) 1 -12.7772 1.9755 -6.4677 0.0000 #> female1 1 1.4825 0.4474 3.3139 9e-04 #> read 1 0.1035 0.0258 4.0186 1e-04 #> science 1 0.0948 0.0305 3.1129 0.0019 #> ----------------------------------------------------------------- #> #> Association of Predicted Probabilities and Observed Responses #> --------------------------------------------------------------- #> % Concordant 0.8561 Somers' D 0.7147 #> % Discordant 0.1425 Gamma 0.7136 #> % Tied 0.0014 Tau-a 0.2794 #> Pairs 7791 c 0.8568 #> --------------------------------------------------------------- #>
# odds ratio estimates blr_regress(model, odd_conf_limit = TRUE)
#> <U+2714> Creating model overview. #> <U+2714> Creating response profile. #> <U+2714> Extracting maximum likelihood estimates. #> <U+2714> Computing odds ratio estimates. #> <U+2714> Estimating concordant and discordant pairs. #>
#> Model Overview #> ------------------------------------------------------------------------ #> Data Set Resp Var Obs. Df. Model Df. Residual Convergence #> ------------------------------------------------------------------------ #> data honcomp 200 199 196 TRUE #> ------------------------------------------------------------------------ #> #> Response Summary #> -------------------------------------------------------- #> Outcome Frequency Outcome Frequency #> -------------------------------------------------------- #> 0 147 1 53 #> -------------------------------------------------------- #> #> Maximum Likelihood Estimates #> ----------------------------------------------------------------- #> Parameter DF Estimate Std. Error z value Pr(>|z|) #> ----------------------------------------------------------------- #> (Intercept) 1 -12.7772 1.9755 -6.4677 0.0000 #> female1 1 1.4825 0.4474 3.3139 9e-04 #> read 1 0.1035 0.0258 4.0186 1e-04 #> science 1 0.0948 0.0305 3.1129 0.0019 #> ----------------------------------------------------------------- #> #> Odds Ratio Estimates #> --------------------------------------------------------- #> Effects Estimate 95% Wald Conf. Limit #> --------------------------------------------------------- #> female1 4.4039 1.8955 11.0521 #> read 1.1091 1.0569 1.1699 #> science 1.0994 1.0377 1.1702 #> --------------------------------------------------------- #> #> Association of Predicted Probabilities and Observed Responses #> --------------------------------------------------------------- #> % Concordant 0.8561 Somers' D 0.7147 #> % Discordant 0.1425 Gamma 0.7136 #> % Tied 0.0014 Tau-a 0.2794 #> Pairs 7791 c 0.8568 #> --------------------------------------------------------------- #>