burn1000: BURN1000 data

In aplore3: Datasets from Hosmer, Lemeshow and Sturdivant, "Applied Logistic Regression" (3rd Ed., 2013)

Description

burn1000 dataset.

Usage

burn1000

Format

A data.frame with 1000 rows and 9 variables:

id

Identification code (1 - 1000)

facility

Burn facility (1 - 40)

death

Hospital discharge status (1: Alive, 2: Dead)

age

Age at admission (Years)

gender

Gender (1: Female, 2: Male)

race

Race (1: Non-White, 2: White)

tbsa

Total burn surface area (0 - 100%)

inh_inj

Burn involved inhalation injury (1: No, 2: Yes)

flame

Flame involved in burn injury (1: No, 2: Yes)

Source

Hosmer, D.W., Lemeshow, S. and Sturdivant, R.X. (2013) Applied Logistic Regression, 3rd ed., New York: Wiley

Examples

head(burn1000, n = 10)
summary(burn1000)

## Table 3.15 p. 80
summary(mod3.15 <- glm(death ~ tbsa + inh_inj + age + gender + flame + race,
                       family = binomial, data = burn1000 ))

Example output

   id facility death  age gender      race tbsa inh_inj flame
1   1       11 Alive 26.6   Male     White 25.3      No   Yes
2   2        1 Alive  2.0 Female Non-White  5.0      No    No
3   3       12 Alive 22.0 Female Non-White  2.0      No    No
4   4        1 Alive 37.3   Male     White  2.0      No    No
5   5        1 Alive 52.1   Male     White  6.0      No   Yes
6   6        6 Alive 50.2   Male     White  7.0      No    No
7   7       22 Alive  2.5 Female Non-White  7.0      No    No
8   8        1 Alive 53.8 Female     White  0.9      No   Yes
9   9        1 Alive 31.9   Male     White  2.0      No    No
10 10        1 Alive 41.1   Male     White 22.0      No   Yes
       id            facility       death          age           gender   
 Min.   :   1.0   Min.   : 1.00   Alive:850   Min.   : 0.10   Female:295  
 1st Qu.: 250.8   1st Qu.: 2.00   Dead :150   1st Qu.:10.85   Male  :705  
 Median : 500.5   Median : 8.00               Median :31.95               
 Mean   : 500.5   Mean   :11.56               Mean   :33.29               
 3rd Qu.: 750.2   3rd Qu.:18.25               3rd Qu.:51.23               
 Max.   :1000.0   Max.   :40.00               Max.   :89.70               
        race          tbsa       inh_inj   flame    
 Non-White:411   Min.   : 0.10   No :878   No :471  
 White    :589   1st Qu.: 2.50   Yes:122   Yes:529  
                 Median : 6.00                      
                 Mean   :13.54                      
                 3rd Qu.:16.00                      
                 Max.   :98.00                      

Call:
glm(formula = death ~ tbsa + inh_inj + age + gender + flame + 
    race, family = binomial, data = burn1000)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-3.01879  -0.24566  -0.08874  -0.03351   2.66144  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -7.695153   0.691169 -11.134  < 2e-16 ***
tbsa         0.089345   0.009087   9.832  < 2e-16 ***
inh_injYes   1.365277   0.361780   3.774 0.000161 ***
age          0.082890   0.008629   9.606  < 2e-16 ***
genderMale  -0.201494   0.307784  -0.655 0.512687    
flameYes     0.582578   0.354493   1.643 0.100298    
raceWhite   -0.701389   0.309781  -2.264 0.023565 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 845.42  on 999  degrees of freedom
Residual deviance: 336.46  on 993  degrees of freedom
AIC: 350.46

Number of Fisher Scoring iterations: 7

aplore3 documentation built on May 2, 2019, 8:24 a.m.