From: Jenny Sun <jenny.sun.sun_at_gmail.com>

Date: Wed, 02 Jul 2008 14:23:59 -0600

*>fm1 <- nlme(DIFN ~ SSlogis(SVA, Asym, R0, lrc),data = LAST,fixed = Asym + R0 + lrc ~ 1,random = Asym ~ 1,start =c(Asym = 1, R0 = 1, lrc = -5))
*

*>fm2 <- update(fm1, random = pdDiag(Asym + R0 ~ 1))
*

*>fm3 <- update(fm2, random = pdDiag(Asym + R0 + lrc ~ 1))
*

*>anova(fm1,fm2,fm3)
*

ANOVA showed:

StdDev: 2.303402e-05

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Wed 02 Jul 2008 - 21:08:28 GMT

Date: Wed, 02 Jul 2008 14:23:59 -0600

My special thanks to Chunhao Tu for the suggestions about testing significance of two locations.

Y X dir loc 0.6295 0.8667596 S A 0.7890 0.7324820 S A 0.4735 0.9688875 S A 0.7805 1.1125239 S A 0.8640 0.9506174 E A 0.9445 0.6582157 E A 0.8455 0.5558860 E A 0.9380 0.3304870 E A 0.4010 1.1763090 N A 0.2585 1.3202890 N A 0.3750 1.1763090 E A 0.3855 1.3202890 E A 0.3020 1.1763090 S A 0.2300 1.3202890 S A 0.3155 1.1763090 W A 0.8890 0.6915861 W B 0.9185 0.6149019 W B 0.9275 0.5289258 W B 0.8365 0.9507088 S B 0.7720 0.8842165 N B 0.8615 0.8245123 N B 0.9170 0.7559687 W B 0.9590 0.6772720 W B 0.9900 0.5872023 W B 0.9940 0.4849064 W B 0.7500 0.9560776 W B

The data is grouped using:

>LAST<-groupedData(Y~X|loc/dir, data=test)

I then used logistic models to define the relationship between Y and X, and got fm1, fm2, and fm3 as follows:

ANOVA showed:

*>anova(fm1,fm2,fm3)
*

Model df AIC BIC logLik Test L.Ratio p-value fm1 1 7 -1809.913 -1774.304 910.9564 fm2 2 9 -1805.774 -1758.295 910.8871 1 vs 2 0.1386696 0.9999 fm3 3 12 -1801.822 -1742.473 910.9109 2 vs 3 0.0475543 0.9666

- question: do the results show that fm1 could represent the results of fm2 and fm3?

*>coef(fm1)
*

Asym R0 lrc

AB/E 0.9148927 1.389432 -0.3009858 AB/N 0.8775250 1.389432 -0.3009858 AB/S 0.9247592 1.389432 -0.3009858 AB/W 0.8479180 1.389432 -0.3009858 BC/E 0.8791908 1.389432 -0.3009858 BC/N 0.8414229 1.389432 -0.3009858 BC/S 0.9169323 1.389432 -0.3009858 BC/W 0.8817838 1.389432 -0.3009858

- question: how could I know if any of the models is significantly different from the other ones? (eg. AB/E is different from the AB/S)?

*> summary(fm1)
*

Nonlinear mixed-effects model fit by maximum likelihood
Model: DIFN ~ SSlogis(SVA, Asym, R0, lrc)
Data: LAST

AIC BIC logLik

-1809.913 -1774.304 910.9564

Random effects:

Formula: Asym ~ 1 | loc

Asym

StdDev: 2.303402e-05

Formula: Asym ~ 1 | dir %in% loc

Asym Residual

StdDev: 0.03208693 0.1741559

Fixed effects: Asym + R0 + lrc ~ 1

Value Std.Error DF t-value p-value Asym 0.8855531 0.015375906 2783 57.59355 0 R0 1.3894322 0.009418047 2783 147.52869 0 lrc -0.3009858 0.012833066 2783 -23.45393 0Correlation:

Asym R0

R0 -0.440

lrc -0.452 0.150

Standardized Within-Group Residuals:

Min Q1 Med Q3 Max -4.1326757 -0.6117037 0.1082112 0.6575250 3.3297270

Number of Observations: 2793

Number of Groups:

loc dir %in% loc 2 8

I have marked all the codes and questions(**). Any answers and suggestions are appreciated.

Have a good day!

Jenny

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