From: Spencer Graves <spencer.graves_at_pdf.com>

Date: Thu 01 Sep 2005 - 12:32:13 EST

[1] 5

> pchisq(sum(z0^2), 100, lower=FALSE)

[1] 0.917285*> ...
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*>
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*>
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*>
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> Linear mixed-effects model fit by REML

*> Data: d.orig
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*> AIC BIC logLik
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*> 1007.066 1153.168 -469.5329
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*>
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*> Random effects:
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*> Formula: ~1 | Method
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*> (Intercept) Residual
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*> StdDev: 0.4000478 0.4943817
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*>
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*> Fixed effects: log(value + 7.5) ~ SSPos1 + SSPos2 + SSPos6 + SSPos7 + SSPos10 + SSPos11 + SSPos13 + SSPos14 + SSPos18 + SSPos19 +
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*> Value Std.Error DF t-value p-value
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*> (Intercept) 2.8621811 0.23125065 540 12.376964 0.0000
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*> SSPos1C -0.1647937 0.06293993 540 -2.618269 0.0091
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*> SSPos1G -0.3448095 0.05922479 540 -5.822047 0.0000
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*> SSPos1T 0.1083988 0.06087095 540 1.780797 0.0755
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*> ...
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*> SSPos11C -0.1540292 0.06171635 540 -2.495761 0.0129
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*> SSPos11G -0.1428980 0.05993122 540 -2.384368 0.0175
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*> SSPos11T -0.0039434 0.06133920 540 -0.064289 0.9488
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*> ...
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*>
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*>
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*> [[alternative HTML version deleted]]
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*>
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Date: Thu 01 Sep 2005 - 12:32:13 EST

Does the following answer your question:

> set.seed(1) > z0 <- rnorm(100) > p.z <- 2*pnorm(-abs(z0)) > sum(p.z<0.05)

[1] 5

> pchisq(sum(z0^2), 100, lower=FALSE)

[1] 0.917285

Some of the 100 (in this case) normal random deviates seem statistically significant, even though the ensemble is not.

spencer graves

Arne.Muller@sanofi-aventis.com wrote:

> Hello,

*>
**> I'm posting this to receive some comments/hints about a rather statistical than R-technical question ... .
**>
**> In an anova of a lme factor SSPos11 shows up non-significant,
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but in the t-test of the summay 2 of the 4 levels (one for
constrast) are significant. See below for some truncated output.

*>
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> I realize that the two test are different (F-test/t-test),

but I'm looking for for a "meaning". Maye you have a schenario
that explains how these differences can be created and how you'd
go ahead and analyse it further.

*>
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> When I use SSPos11 as te only fixed effect, it does it is not

significant in either anova nor t-test, and a boxplot of the
factor shows that the levels are all quite similar (similar
variance and mean). Might the effect I observe be linked to
an unbalance design in the multifactorial model?

*>
*

> thanks a lot for your help,

*> +kind regards,
**>
**> Arne
**>
**>
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>>anova(fit)

>

> numDF denDF F-value p-value

> (Intercept) 1 540 323.4442 <.0001> SSPos1 3 540 15.1206 <.0001> ...> SSPos11 3 540 1.1902 0.3128

>>summary(fit)

> Linear mixed-effects model fit by REML

-- Spencer Graves, PhD Senior Development Engineer PDF Solutions, Inc. 333 West San Carlos Street Suite 700 San Jose, CA 95110, USA spencer.graves@pdf.com www.pdf.com <http://www.pdf.com> Tel: 408-938-4420 Fax: 408-280-7915 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.htmlReceived on Thu Sep 01 12:48:25 2005

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