From: <ctu_at_bigred.unl.edu>

Date: Wed, 02 Jul 2008 23:44:25 -0500

*> summary(fmAB)
*

Nonlinear mixed-effects model fit by maximum likelihood

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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 - 04:47:28 GMT

Date: Wed, 02 Jul 2008 23:44:25 -0500

Hi Jenny, (I use the data you provide in the previous e-mail) For the 1st question, let me assume you only want to compare loc: A vs. B So you could specified your code like this: fmAB <- nlme(Y ~ SSlogis(X, Asym, R0, lrc),data = LAST,

random = Asym ~1, fixed = Asym+R0+lrc ~ loc, start=c(0.97,0, 1.14,0, -0.18,0))

Nonlinear mixed-effects model fit by maximum likelihood

Model: Y ~ SSlogis(X, Asym, R0, lrc)

Data: LAST

AIC BIC logLik

-31.02303 -19.70017 24.51152

Random effects:

Formula: Asym ~ 1 | loc

Asym.(Intercept) StdDev: 1.549779e-06 Formula: Asym ~ 1 | dir %in% loc Asym.(Intercept) Residual StdDev: 6.124237e-08 0.09426086 Fixed effects: Asym + R0 + lrc ~ loc Value Std.Error DF t-value p-value Asym.(Intercept) 0.9477404 0.1037337 14 9.136280 0.0000 Asym.locB 0.0982456 0.4175971 14 0.235264 0.8174 R0.(Intercept) 1.1289387 0.0652189 14 17.310003 0.0000 R0.locB 0.1390656 0.3946717 14 0.352358 0.7298 lrc.(Intercept) -0.2110057 0.0656513 14 -3.214036 0.0062 lrc.locB -0.0820484 0.6826382 14 -0.120193 0.9060

Then you know Asym, R0, and lrc of loc B are not significant. Moreover, you can test the joint fixed effect by anova(fmAB)(Pinherio and Bate, 2000 Book, p 374)

for the 2nd question, How to get the fitted value for particular level? Based on this example, let me assume you want to get the fitted value of A/N.

then you could write a small code like this:

*> FV<-data.frame(F.V=fitted(fmAB), group=summary(fmAB)$groups$dir)
**> A.N<-FV[is.element(FV$group, c("A/N")),]
*

F.V group

9 0.4209011 A/N

10 0.2726129 A/N

hope this is helpful~

Chunhao

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

> Thank you for your reply Chunhao!

*>
**> I attached only part of the test data and that is why you might not
**> be able to get convergence. Sorry.
**>
**> I have a couple more questions:
**>
**> For the second question you answered, how to specify the correct
**> length of starting values. I tried using the length of levels in
**> each of the parameters in the start list but found:
**>
**>> 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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