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 <[EMAIL PROTECTED]>:
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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