[R] NLME questions -- interpretation of results

[ctu at bigred.unl.edu]

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
>
>