At 07:36 AM 11/19/2003 -0500, Nirmal Govind wrote:
I ran a small data set from a factorial experiment through R, Minitab and JMP... the result from R is significantly different from what Minitab or JMP give... The data set is at the following link:
http://www.personal.psu.edu/nug107/Uploads/2x3_16repsANOVA.txt
The first 5 columns are the factors and the next three are responses.
In particular, for the response beta11MSE, two of the significant effects in the Minitab (and JMP) ANOVA table are not significant in R... What may be the cause for such a difference? I used the lm function in R to do the model fitting and fit a model with main effects and 2-way interactions in the 5 factors. The design that generated this data was a 32-run 2^5 full factorial.
From your description, it's not possible to tell what you did or exactly what happened -- either in R or in Minitab (and JMP).
Moreover, bcause the design is orthogonal and the factors are dichotomous, it's very difficult to get the analysis wrong, at least in R. I believe that you should get correct sums of squares even if you don't treat the factors as factors, regardless of contrast coding, and even if you use "sequential" SSs, as calculated by the anova function (all of which could get you in trouble more generally).
Here are the results that I get from R and SAS (I don't have Minitab or JMP):
> anova(lm(beta11MSE ~ (Cycles + Replns + VarZ + NormB + NormG)^2)) Analysis of Variance Table
Response: beta11MSE
Df Sum Sq Mean Sq F value Pr(>F)
Cycles 1 0.00102 0.00102 0.1602 0.694250
Replns 1 0.18457 0.18457 28.9170 6.164e-05 ***
VarZ 1 0.26800 0.26800 41.9879 7.597e-06 ***
NormB 1 0.00491 0.00491 0.7689 0.393527
NormG 1 0.33606 0.33606 52.6497 1.921e-06 ***
Cycles:Replns 1 0.00004 0.00004 0.0059 0.939751
Cycles:VarZ 1 0.00131 0.00131 0.2060 0.656054
Cycles:NormB 1 0.00008 0.00008 0.0131 0.910348
Cycles:NormG 1 0.00088 0.00088 0.1377 0.715457
Replns:VarZ 1 0.07781 0.07781 12.1897 0.003018 **
Replns:NormB 1 0.00360 0.00360 0.5636 0.463697
Replns:NormG 1 0.08693 0.08693 13.6199 0.001982 **
VarZ:NormB 1 0.00351 0.00351 0.5498 0.469155
VarZ:NormG 1 0.08990 0.08990 14.0843 0.001737 **
NormB:NormG 1 0.00268 0.00268 0.4206 0.525839
Residuals 16 0.10213 0.00638
proc glm; class Cycles Replns VarZ NormB NormG; model beta11MSE = Cycles|Replns Cycles|VarZ Cycles|NormB Cycles|NormG Replns|VarZ Replns|NormB Replns|NormG VarZ|NormB VarZ|NormG NormB|NormG; run;
Source DF Type I SS Mean Square F Value Pr > F
Cycles 1 0.00102265 0.00102265 0.16 0.6942
Replns 1 0.18457369 0.18457369 28.92 <.0001
Cycles*Replns 1 0.00003763 0.00003763 0.01 0.9398
VarZ 1 0.26800351 0.26800351 41.99 <.0001
Cycles*VarZ 1 0.00131456 0.00131456 0.21 0.6561
NormB 1 0.00490793 0.00490793 0.77 0.3935
Cycles*NormB 1 0.00008353 0.00008353 0.01 0.9103
NormG 1 0.33605652 0.33605652 52.65 <.0001
Cycles*NormG 1 0.00087885 0.00087885 0.14 0.7155
Replns*VarZ 1 0.07780526 0.07780526 12.19 0.0030
Replns*NormB 1 0.00359764 0.00359764 0.56 0.4637
Replns*NormG 1 0.08693408 0.08693408 13.62 0.0020
VarZ*NormB 1 0.00350913 0.00350913 0.55 0.4692
VarZ*NormG 1 0.08989860 0.08989860 14.08 0.0017
NormB*NormG 1 0.00268461 0.00268461 0.42 0.5258
Source DF Type III SS Mean Square F Value Pr > F
Cycles 1 0.00102265 0.00102265 0.16 0.6942
Replns 1 0.18457369 0.18457369 28.92 <.0001
Cycles*Replns 1 0.00003763 0.00003763 0.01 0.9398
VarZ 1 0.26800351 0.26800351 41.99 <.0001
. . .
So, what's the problem?
I hope that this helps, John ----------------------------------------------------- John Fox Department of Sociology McMaster University Hamilton, Ontario, Canada L8S 4M4 email: [EMAIL PROTECTED] phone: 905-525-9140x23604 web: www.socsci.mcmaster.ca/jfox
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