# [R] nlme in R v.2.2.1 and S-Plus v. 7.0

From: Paolo Ghisletta <Paolo.Ghisletta_at_cig.unige.ch>
Date: Tue 24 Jan 2006 - 04:19:58 EST

Dear R-Users,

I am comparing the nlme package in S-Plus (v. 7.0) and R (v. 2.2.1, nlme package version 3.1-68.1; the lattice, Matrix, and lme4 have also just been updated today, Jan. 23, 2006) on a PC (2.40 GHz Pentium 4 processor and 1 GHz RAM) operating on Windows XP. I am using a real data set with 1,191 units with at most 4 repeated measures per unit (data are incomplete, unbalanced). I use the same code with the same starting values for both programs and obtain slightly different results. I am aware that at this stage my model is far from being well specified for the given data. Nevertheless, I wonder whether one program is more suited than the other to pursue my modeling.

Below I included the input + output code, first for S-Plus, than for R.

Many thanks and best regards,
Paolo Ghisletta

```############
#S-Plus
#min=4, max=41
```

> logistic4.a <- nlme(jugs ~ 4 + 41 / (1 + xmid*exp( -scal*I(occ-1) +
u)), fixed=scal+xmid~1, random= u~1 |id, start=c(scal=.2, xmid=155), data=jug, na.action=na.exclude, method="ML")
> summary(logistic4.a)

Nonlinear mixed-effects model fit by maximum likelihood   Model: jugs ~ 4 + 41/(1 + xmid * exp( - scal * I(occ - 1) + u))  Data: jug

AIC BIC logLik
29595.62 29621.3 -14793.81

Random effects:
Formula: u ~ 1 | id

u Residual
StdDev: 5.162391 3.718887

Fixed effects: scal + xmid ~ 1

Value Std.Error DF t-value p-value scal 4.9697 0.0823 3339 60.39508 <.0001 xmid 683.5634 125.8509 3339 5.43153 <.0001

Standardized Within-Group Residuals:

Min Q1 Med Q3 Max  -10.66576 -0.5039498 0.0002772166 0.1226745 5.453209

Number of Observations: 4531
Number of Groups: 1191

############
# R
> #min=4, max=41
> logistic4.a <- nlme(jugs ~ 4 + 41 / (1 + xmid*exp( -scal*I(occ-1)+
u)), data=jug, fixed=scal+xmid~1, random= u~1 |id, start=c(scal=.2, xmid=155), method="ML", na.action=na.exclude)
> summary(logistic4.a)

Nonlinear mixed-effects model fit by maximum likelihood   Model: jugs ~ 4 + 41/(1 + xmid * exp(-scal * I(occ - 1) + u))  Data: jug

AIC BIC logLik
29678.11 29703.78 -14835.05

Random effects:
Formula: u ~ 1 | id

u Residual
StdDev: 5.116542 3.767097

Fixed effects: scal + xmid ~ 1

```        Value Std.Error   DF  t-value p-value
scal   4.9244   0.08121 3339 60.63763       0
xmid 633.6956 115.37512 3339  5.49248       0
```
Erreur dans dim(x) : aucun slot de nom "Dim" pour cet objet de la classe "correlation"

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