toOR: Display logistic coefficient table as odds ratios and...

In LOGIT: Functions, Data and Code for Binary and Binomial Data

Description

Following the glm command, toOR() displays a table of odds ratios and related statistics including exponentiated model confidence intervals.

Usage

toOR(object)

Arguments

object

name of the fitted glm function model

Format

object

The only argument is the name of the fitted glm function model

value

or

odds ratio of predictor

delta

Model standard error using delta method

zscore

z-statistic

pvalue

probability-value based on normal distribution

exp.loci

Exponentialed lower model confidence interval

exp.upci

Expontiated upper model confidence interval

Details

toOR is a post-estimation function, following the use of glm().

Value

list

Note

toOR must be loaded into memory in order to be effectve. As a function in LOGIT, it is immediately available to a user.

Author(s)

Joseph M. Hilbe, Arizona State University, and Jet Propulsion Laboratory, California Institute of technology

References

Hilbe, Joseph M. (2015), Practical Guide to Logistic Regression, Chapman & Hall/CRC.

See Also

glm

Examples

library(MASS)
 library(LOGIT)
 data(medpar)
 mylogit <- glm(died ~ los + white + hmo, family=binomial, data=medpar)
 summary(mylogit)
 toOR(mylogit)

Example output

Call:
glm(formula = died ~ los + white + hmo, family = binomial, data = medpar)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.0258  -0.9436  -0.8655   1.3637   2.5948  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.593328   0.214017  -2.772  0.00557 ** 
los         -0.030088   0.007711  -3.902 9.54e-05 ***
white        0.255677   0.206801   1.236  0.21633    
hmo         -0.044626   0.149650  -0.298  0.76555    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1922.9  on 1494  degrees of freedom
Residual deviance: 1902.9  on 1491  degrees of freedom
AIC: 1910.9

Number of Fisher Scoring iterations: 4

                or  delta  zscore pvalue exp.loci. exp.upci.
(Intercept) 0.5525 0.1182 -2.7723 0.0056    0.3632    0.8404
los         0.9704 0.0075 -3.9020 0.0001    0.9558    0.9851
white       1.2913 0.2670  1.2363 0.2163    0.8610    1.9367
hmo         0.9564 0.1431 -0.2982 0.7656    0.7132    1.2823

LOGIT documentation built on May 29, 2017, 10:26 a.m.