[R] Off topic - Differences among stat packages in GLM results

[Yves Claveau]

Dear colleagues,
	I have performed the same analysis using the GLM
module of three statistical softwares: SYSTAT 10, JMP
4.0.2 and R 1.6.2 (see below for more details).
Although SYSTAT and R give roughly the same level of
significance for all variables, JMP yield a 20 percent
difference in probability for a categorical variable.
In fact, this difference is so important that I can
call this variable significant. Incidentally, Tukey's
test is in accordance with this result. Which
statistical software should I believe?

	Thank you in advance for your insight.

	Yves Claveau



DETAILS ON PERFORMED STATISTICAL ANALYSES

The categorical variable I am writing about is ESP

The model used is:

ptro=CONSTANT+classl+ht+esp+classl*ht+classl*esp+ht*esp+classl*ht*esp

Where:
- ptro is the dependent variable
- CONSTANT the constant in the model (defaut
procedure)
- classl a categorical variable with two classes
- ht a continuous variable
- esp a categorical variable with two classes


The results for each package are:

R 1.6.2

Call:
glm(formula = PTRO ~ ESP. * HT * CLASSL., family =
gaussian, 
    data = dataa)

Deviance Residuals: 
      Min         1Q     Median         3Q        Max 

-20.21973   -4.41060   -0.03971    4.77046   14.29097 


Coefficients:
                          Estimate Std. Error t value
Pr(>|t|)    
(Intercept)    35.54604    4.65265   7.640 3.41e-09
***
ESP            -13.12051   12.32455  -1.065    0.294  
 
HT             0.08005    0.04374   1.830    0.075 .  
CLASSL         1.09480    5.54809   0.197    0.845    
ESP:HT         0.01694    0.12375   0.137    0.892    
ESP:CLASSL     5.89693   15.41378   0.383    0.704    
HT:CLASSL      -0.01952    0.04682  -0.417    0.679   

ESP:HT:CLASSL  -0.05547    0.13217  -0.420    0.677   

---
Signif. codes:  0 `***' 0.001 `**' 0.01 `*' 0.05 `.'
0.1 ` ' 1 

(Dispersion parameter for gaussian family taken to be
59.17901)

    Null deviance: 4567.3  on 45  degrees of freedom
Residual deviance: 2248.8  on 38  degrees of freedom
AIC: 327.46

Number of Fisher Scoring iterations: 2


SYSTAT 10

Dep Var: PTRO   N: 49   Multiple R: 0.7241   Squared
multiple R: 0.5244
 
Analysis of Variance
Source    Sum-of-Squares  df  Mean-Square F-ratio  P
 
ESP             113.6878  1   113.6878  1.6551  
0.2055
CLASSL          20.6118   1   20.6118   0.3001  
0.5868
HT              239.7713  1   239.7713  3.4908  
0.0689
CLASSL*HT       26.3909   1   26.3909   0.3842  
0.5388
CLASSL*ESP      5.9755   1   5.9755   0.0870   0.7695
ESP*HT          2.6415   1   2.6415   0.0385   0.8455
CLASSL*ESP*HT   12.9459   1   12.9459   0.1885  
0.6665
 
Error                  2816.1893    41      68.6875


JMP 4

RSquare	0.52438
RSquare Adj	0.443177
Root Mean Square Error	8.287795
Mean of Response	42.78898
Observations (or Sum Wgts)	49

Analysis of Variance
Source	DF	Sum of Squares	Mean Square	F Ratio
Model	7	3104.9018	443.557	6.4576
Error	41	2816.1893	68.688	Prob > F
C. Total	48	5921.0910		<.0001

Effect Tests
Source   Nparm  DF  Sum of Squares  F Ratio   Prob > F
  
ESP             1   1   636.09249   9.2607   0.0041   
CLASSL          1   1   8.26185   0.1203   0.7305   
HT              1   1   239.77125   3.4908   0.0689   
HT*CLASSL       1   1   26.39087   0.3842   0.5388   
ESP*CLASSL      1   1   12.18491   0.1774   0.6758   
ESP*HT          1   1   2.64154   0.0385   0.8455   
ESP*HT*CLASSL   1   1   12.94593   0.1885   0.6665   




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