hlGOF.test: Display Hosmer-Lemeshow statistic and table of probabilities...

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

Description

Provides a Hosmer-Lemeshow statistic and table following logistic regression.

Usage

hlGOF.test(observed, predicted, breaks = 15)

Arguments

observed	response variable
predicted	predicted statistic
breaks	breaks or groups

Format

x

The function has three arguments: observed term, predicted values, # groups

Details

hlGOF.test is a post-estimation function for logistic regression, following the use of glm(). Usage displays a table of observed vs predicted groups and an overall H-L goodness-of-fit statistic. The test is originally from Hilbe (2009).

Value

numeric

Note

hlGOF.test must be loaded into memory in order to be effectve. As a function in LOGIT, it is immediately available to a user. My thanks to Prof. Robert LaBudde for the initial version of this function.

Author(s)

Joseph M. Hilbe, Arizona State University, Robert LaBudde, Institute for Statisical Education (Statistics.com), provided initial code for this function for Hilbe, Logistic Regression Models, text.

References

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

Hilbe, J. M. (2009), Logistic Regression Models, 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)
medpar2 <- na.omit(medpar)
hlGOF.test(medpar2$died, predict(mylogit,medpar2, type="response"), breaks=12)

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


 Hosmer-Lemeshow GOF test 

 For # Cuts = 9   # Data = 1495 
Cut  # Total #Patterns # Resp.    # Pred.  Mean Resp. Mean Pred. 
  1      166       63       58      38.95  0.34940  0.23465 
  2      166       20       50      49.69  0.30120  0.29936 
  3      166       12       42      53.44  0.25301  0.32194 
  4      166        8       41      56.27  0.24699  0.33900 
  5      167        6       42      58.97  0.25150  0.35313 
  6      166        5       47      60.77  0.28313  0.36609 
  7      166        3       45      62.68  0.27108  0.37761 
  8      166        6       67      64.81  0.40361  0.39044 
  9      166        3      121      67.39  0.72892  0.40599 
Total # Data: 1495  Total over cuts: 1495 
Chisq: 114.4468   d.f.: 7  P-value:  0.00000 

 For # Cuts = 12   # Data = 1495 
Cut  # Total #Patterns # Resp.    # Pred.  Mean Resp. Mean Pred. 
  1      125       57       39      27.60  0.31200  0.22079 
  2      124       18       42      35.58  0.33871  0.28696 
  3      125       12       37      38.64  0.29600  0.30916 
  4      124        9       32      40.26  0.25806  0.32470 
  5      125        7       30      42.11  0.24000  0.33690 
  6      125        5       36      43.49  0.28800  0.34789 
  7      124        4       30      44.42  0.24194  0.35826 
  8      125        4       34      46.00  0.27200  0.36797 
  9      124        3       30      46.71  0.24194  0.37668 
 10      125        3       39      48.08  0.31200  0.38463 
 11      124        5       68      49.15  0.54839  0.39637 
 12      125        2       96      50.95  0.76800  0.40764 
Total # Data: 1495  Total over cuts: 1495 
Chisq: 121.3332   d.f.: 10  P-value:  0.00000 

 For # Cuts = 15   # Data = 1495 
Cut  # Total #Patterns # Resp.    # Pred.  Mean Resp. Mean Pred. 
  1      100       51       32      20.98  0.32000  0.20979 
  2       99       18       35      27.44  0.35354  0.27715 
  3      100       12       27      30.00  0.27000  0.29998 
  4      100        6       33      31.38  0.33000  0.31385 
  5       99        9       23      32.29  0.23232  0.32617 
  6      100        4       25      33.57  0.25000  0.33573 
  7      100        5       26      34.45  0.26000  0.34451 
  8       99        5       24      35.01  0.24242  0.35360 
  9      100        3       29      36.14  0.29000  0.36138 
 10      100        3       26      36.85  0.26000  0.36850 
 11       99        3       23      37.20  0.23232  0.37576 
 12      100        3       32      38.21  0.32000  0.38214 
 13      100        3       36      39.02  0.36000  0.39019 
 14       99        3       62      39.55  0.62626  0.39949 
 15      100        1       80      40.91  0.80000  0.40909 
Total # Data: 1495  Total over cuts: 1495 
Chisq: 128.9439   d.f.: 13  P-value:  0.00000 

 Minimum P-value:   0.00000 

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