mylgg

Description

The data has 11 grouped observations and 6 variables. Grouped subset of medpar data.

Usage

data(mylgg)

Format

A data frame with 11 observations with the following 6 variables.

white

0=identified as non-white; identified as white

hmo

0=patient not an HMO member; 1=patient member of HMO

type

Type of admission: 1=elective;2=urgent; 3=emergency

alive

# patients alive per patient profile

dead

# patients died within 48 hrs admit per patient profile

m

# patients in each patient profile (same predictor values)

Details

mylgg is saved as a data frame. Used to assess odds ratios and predict survival folllowing surgery

Source

Subset of medpar data, grouped format.

References

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

Hilbe, Joseph M (2009), Logistic Regression Models, Chapman & Hall/CRC.

Examples

library(MASS)   # if not automatically loaded
# LOGISTIC REGRESSION
library(LOGIT)
data(mylgg)
mylgg
summary(lg <- glm(cbind(alive, dead) ~ white + hmo + factor(type),
                family=binomial, data=mylgg))
toOR(lg)
P__disp(lg)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(LOGIT)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/LOGIT/mylgg.rd_%03d_medium.png", width=480, height=480)
> ### Name: mylgg
> ### Title: mylgg
> ### Aliases: mylgg
> ### Keywords: datasets
> 
> ### ** Examples
> 
> library(MASS)   # if not automatically loaded
> # LOGISTIC REGRESSION
> library(LOGIT)
> data(mylgg)
> mylgg
   white hmo type alive dead   m
1      0   0    1    55   17  72
2      0   0    2    22   11  33
3      0   0    3     6    4  10
4      0   1    1     7    1   8
5      0   1    2     1    3   4
6      1   0    1   580  277 857
7      1   0    2   119   82 201
8      1   0    3    43   40  83
9      1   1    1   128   69 197
10     1   1    2    19    8  27
11     1   1    3     2    1   3
> summary(lg <- glm(cbind(alive, dead) ~ white + hmo + factor(type),
+                 family=binomial, data=mylgg))

Call:
glm(formula = cbind(alive, dead) ~ white + hmo + factor(type), 
    family = binomial, data = mylgg)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-1.71409  -0.15592  -0.06327   0.42482   1.26685  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
(Intercept)    1.09058    0.20535   5.311 1.09e-07 ***
white         -0.35617    0.20712  -1.720  0.08551 .  
hmo           -0.04758    0.15032  -0.317  0.75161    
factor(type)2 -0.33963    0.14217  -2.389  0.01690 *  
factor(type)3 -0.64421    0.21582  -2.985  0.00284 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 21.0503  on 10  degrees of freedom
Residual deviance:  6.0958  on  6  degrees of freedom
AIC: 59.243

Number of Fisher Scoring iterations: 3

> toOR(lg)
                  or  delta  zscore pvalue exp.loci. exp.upci.
(Intercept)   2.9760 0.6111  5.3108 0.0000    1.9899    4.4507
white         0.7004 0.1451 -1.7196 0.0855    0.4667    1.0510
hmo           0.9535 0.1433 -0.3165 0.7516    0.7102    1.2802
factor(type)2 0.7120 0.1012 -2.3888 0.0169    0.5389    0.9409
factor(type)3 0.5251 0.1133 -2.9849 0.0028    0.3440    0.8015
> P__disp(lg)

 Pearson Chi2 =  6.135795 
 Dispersion   =  1.022633 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>