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

Date: Wed, 02 Jul 2008 20:03:16 -0700

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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 Thu 03 Jul 2008 - 02:07:59 GMT

Date: Wed, 02 Jul 2008 20:03:16 -0700

Thank you for your reply Chunhao!

I have a couple more questions:

>fm1 <- nlme(DIFN ~ SSlogis(SVA, Asym, R0, lrc),data = LAST,fixed = Asym + R0 + lrc ~ dir %in% loc,random = Asym ~ 1,start =list(Asym = c(1,1,1,1), R0 = c(1,1,1,1), lrc = c(-5,-2,-2,-2)))

Error in nlme.formula(DIFN ~ SSlogis(SVA, Asym, R0, lrc), data = LAST, :
start must have a component called "fixed"

I've got two loc levels (A,B) with four group levels(N,E,S,W); How I am gonna define the list and the component called"fixed"?

My another question is about the fitted value of the model. If I want to calculate adjusted R square, I have to get fitted(fm1). WHich has values like this;

>fitted(fm1)[1:40]

AB/N AB/N AB/E AB/S AB/W AB/W AB/W AB/W AB/W AB/W 0.6541876 0.7421748 0.8408251 0.5879220 0.4889387 0.6129576 0.5097593 0.6195679 0.5152567 0.5680860

AB/W AB/W AB/W AB/W AB/W AB/W AB/N AB/N AB/N AB/E 0.4724423 0.8128148 0.7674529 0.7106698 0.6553155 0.6074771 0.5036201 0.5464105 0.6062978 0.6878438

AB/N AB/N AB/N AB/S AB/S AB/S AB/S AB/S AB/S AB/S 0.7792725 0.8411961 0.7942503 0.7354845 0.5895700 0.6781973 0.6286886 0.5212052 0.8864748 0.8370021

AB/S AB/S AB/N AB/N AB/N AB/N AB/E AB/E AB/E AB/E 0.7750731 0.7147024 0.6625288 0.5492599 0.5959280 0.6612426 0.7501786 0.8498928 0.6274681 0.7118615

My question is how to get the fitted values for specified group levels (eg. values for AB/E)?

Thank all very much!

Jenny

>Hi Jenny,

*>I try your code but I did not get in converge in fm3 (see the below).
**>For the first question, you could use fm1 to interpret the result
**>without bothering fm2 and fm3. It means that R0 and lrc can be treated
**>as pure fixed effects (Pinherir and Bates, 2000 Book).
**>
**>For the second question, your want to know "is AB/E different from the AB/S"
**>
**>The simplest way is to change your fixed statement:
**>fixed = Asym+R0+lrc ~ dir %in% loc
**>and specify the correct length of starting values.
**>
**>If I am wrong please correct me~
**>
**>Hope this helpful.
**>
**>Chunhao Tu
**>
**>> test<-read.table(file="C:\\Documents and
**>> Settings\\ado_cabgfaculty\\Desktop\\sun.txt", header=T)
**>> LAST<-groupedData(Y~X|loc/dir, data=test)
**>>
**>> fm1 <- nlme(Y ~ SSlogis(X, Asym, R0, lrc),data = LAST,
**>+ random = Asym ~1,
**>+ fixed = Asym+R0+lrc ~ 1,
**>+ start=c(Asym = 0.97, R0 = 1.14, lrc = -0.18))
**>> fm2 <- update(fm1, random = pdDiag(Asym + R0 ~ 1))
**>> fm3 <- update(fm2, random = pdDiag(Asym+R0+lrc~ 1))
**>Error in nlme.formula(model = Y ~ SSlogis(X, Asym, R0, lrc), data = LAST, :
**> Step halving factor reduced below minimum in PNLS step
**>
**>
**>
**>
**>Quoting Jenny Sun <jenny.sun.sun_at_gmail.com>:
**>
**>> My special thanks to Chunhao Tu for the suggestions about testing
**>> significance of two locations.
**>>
**>> I used logistic models to describe relationships between Y and X at
**>> two locations (A & B). And within each location, I have four groups
**>> (N,E,S,W)representing directions. So the test data can be arranged as:
**>>
**>> 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:
**>>
**>> --------------------------
**>>> 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:
**>>
**>>> 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 0
**>> Correlation:
**>> 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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