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

[Thomas Lumley]

On Mon, 6 Oct 2003, [iso-8859-1] Yves Claveau wrote:

> 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?


It looks at though you have asked for three different analyses from the
three packages. Certainly the analysis you asked R for is not the same as
the others.

If you run the anova() function on your model in R you should get one of
the other two analyses.  I think Systat gives the things SAS calls Type II
sums of squares, in which case JMP is presumably giving real sums of
squares and will agree with anova().

	=thomas


> 	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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Thomas Lumley			Assoc. Professor, Biostatistics
tlumley at u.washington.edu	University of Washington, Seattle