[R] glm under R versions 1.0.1 and 1.1.0

mjantti@abo.fi mjantti at abo.fi
Fri Jun 16 14:50:52 CEST 2000


I have fitted a number of models with receipt of social assictance
(toim1) during a year (values 0 or 1) with a number of covariates. 
The data include sampling weights which I use in the models. Using the
exact same data, glm() under 1.0.1 and 1.1.0 give different results in
many (but not all) of the models. I have re-installed 1.0.1 to check
this and I found now mention in the NEWS file that indicated a change
of that would account for this in 1.1.0.] 

I show the function calls and summary() results below for each version,
using the models that only allow for the probability of receipt to vary
by year ( the factor Vuosi1 below) . The information given by version
(for 1.0.1 here) is
 

> version
         _                 
platform i686-unknown-linux
arch     i686              
os       linux             
system   i686, linux       
status                     
major    1                 
minor    0.1               
year     2000              
month    April             
day      14                
language R       

This is what R 1.0.1 gives:

> glm.toim.0 <- glm(toim1 ~ Vuosi1,
+                 data = Data, #subset =  Vuosi1 == "1993",
+                 family=binomial("probit"),
+                 na.action = na.omit , weights = pko1, model = FALSE)
> summary(glm.toim.0)

Call:
glm(formula = toim1 ~ Vuosi1, family = binomial("probit"), data = Data, 
    weights = pko1, na.action = na.omit, model = FALSE)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-27.05   -9.11   -7.31   -5.26  118.85  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.50660    0.00104 -1447.4   <2e-16 ***
Vuosi11994   0.09489    0.00143    66.2   <2e-16 ***
Vuosi11995   0.10624    0.00143    74.3   <2e-16 ***
Vuosi11996   0.13442    0.00142    94.8   <2e-16 ***
Vuosi11997   0.12126    0.00142    85.3   <2e-16 ***
---
Signif. codes:  0  `***'  0.001  `**'  0.01  `*'  0.05  `.'  0.1  ` '  1 

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 9540324  on 40869  degrees of freedom
Residual deviance: 9529262  on 40865  degrees of freedom
AIC: 9529272

Number of Fisher Scoring iterations: 4

In R 1.1.0, I get 

> glm.toim.0 <- glm(toim1 ~ Vuosi1,
+                 data = Data, #subset =  Vuosi1 == "1993",
+                 family=binomial("probit"),
+                 na.action = na.omit , weights = pko1, model = FALSE)
Warning message: 
fitted probabilities numerically 0 or 1 occurred in: (if (is.empty.model(mt)) glm.fit.null else glm.fit)(x = X, y = Y,  
> summary(glm.toim.0)

Call:
glm(formula = toim1 ~ Vuosi1, family = binomial("probit"), data = Data, 
    weights = pko1, na.action = na.omit, model = FALSE)

Deviance Residuals: 
      Min         1Q     Median         3Q        Max  
-1.35e-06  -4.76e-07  -3.82e-07  -2.75e-07   4.54e+02  

Coefficients:
             Estimate Std. Error   z value Pr(>|z|)    
(Intercept) -3.91e+15   3.61e+04 -1.08e+11   <2e-16 ***
Vuosi11994   1.18e+14   5.11e+04  2.30e+09   <2e-16 ***
Vuosi11995   1.33e+14   5.11e+04  2.60e+09   <2e-16 ***
Vuosi11996   1.72e+14   5.10e+04  3.36e+09   <2e-16 ***
Vuosi11997   1.53e+14   5.10e+04  3.01e+09   <2e-16 ***
---
Signif. codes:  0  `***'  0.001  `**'  0.01  `*'  0.05  `.'  0.1  ` '  1 

(Dispersion parameter for binomial family taken to be 1)

    Null deviance:  9540324  on 40869  degrees of freedom
Residual deviance: 98216118  on 40865  degrees of freedom
AIC: 98216128

Number of Fisher Scoring iterations: 4

The (unweighted) empirical pattern is

> table(Vuosi1, toim1)
      toim1
Vuosi1    0   1
  1993 8206 429
  1994 6524 383
  1995 8564 494
  1996 6716 369
  1997 8756 429
> 

It would be helpful to understand what is going on.  

While the R FAQ kind of warned against this (i.e., trying to explain
the problem rather than describe it accurately), I can add the when I do
the same function call but do not use the weights, results are
identical. 

Regards,

Markus
-- 
Markus Jantti				|	Department of Statistics
markus.jantti at abo.fi			|	Abo Akademi University
http://www.abo.fi/~mjantti		|	FIN 20500 Turku, Finland
358-9-643 747  (Home/Voice)		|	358-2-2154 161	(Office/Voice)
					|	358-2-2154 677	(Office/Fax)
PGP public key: http://www.abo.fi/~mjantti/pubring.asc

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