To: Prof Brian Ripley <ripley at stats.ox.ac.uk>
Cc: r-help at stat.math.ethz.ch
Message-ID: <1088932892.40e7cc1c8c24d at www.mail.med.usyd.edu.au>
Content-Type: text/plain; charset=ISO-8859-1
Thank you for the very prompt response. I only included a small
part of the
output to make the message brief. I'm sorry it did not provide
enough detail to
answer my question. I have appended the summary() and anova()
outputs to the
two models I fitted in R.
Quoting Prof Brian Ripley <ripley at stats.ox.ac.uk>:
Looking at the significance of a main effect (group) in the
interaction (time:group) is hard to interpret, and in your case
not even interesting. (The `main effect' probably represents difference
in intercept for the time effect, that is the group difference
time. But see the next para.) Note that the two systems are returning
different denominator dfs.
I take your point that the main effect is probably not interesting in the
presence of an interaction. I was checking the results for
consistency to see
if I was doing the right thing. I was not 100% sure that the SAS
code was in
itself correct.
At this point you have not told us enough. My guess is that you have
complete balance with the same number of subjects in each
case the `group' effect is in the between-subjects stratum (as
the use of Error in aov, which you could also do), and thus R's 11 df
would be right (rather than 44, without W and Z). Without balance Type
III tests get much harder to interpret and the `group' effect
in two strata and there is no simple F test in the classical theory. So
further guessing, SAS may have failed to detect balance and so used the
wrong test.
I had not appreciated the need for balance: in actual fact, one
group has 5
subjects and the other 7. Will this be a problem? Would the R
analysis still be
valid in that case?
The time-dependent covariates muddy the issue more, and I
the analyses without them. Again, a crucial fact is not here: do the
covariates depend on the subjects as well?
Yes the covariates are measures of blood pressure and pulse, and
they depend on
the subjects as well.
The good news is that the results _are_ similar. You do have different
time behaviour in the two groups. So stop worrying about tests of
uninteresting hypotheses and concentrate of summarizing that difference.
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
Thank you. I was concerned that one or both methods were
incorrect given the
results were inconsistent. Perhaps reassuringly, the parameter
estimates for
the fixed effects in both SAS and R were the same.
Is the model specification OK for the model with just time, group
and their
interaction?
Is the model specification with the 2 time dependent covariates
appropriate?
Once again, I'm very grateful for the time you've taken to answer
my questions.
Keith
[Output from the 2 models fitted in R follows]
g1 = lme(Y ~ time + group + time:group, random = ~ 1 | id, data
numDF denDF F-value p-value
(Intercept) 1 44 3.387117 0.0725
time 4 44 10.620547 <.0001
group 1 11 0.508092 0.4908
time:group 4 44 3.961726 0.0079
Linear mixed-effects model fit by REML
Data: datamod
AIC BIC logLik
372.4328 396.5208 -174.2164
Random effects:
Formula: ~1 | id
(Intercept) Residual
StdDev: 11.05975 3.228684
Fixed effects: Y ~ time + group + time:group
Value Std.Error DF t-value p-value
(Intercept) 8.250 4.073428 44 2.025321 0.0489
time1 -0.250 1.614342 44 -0.154862 0.8776
time2 -8.125 1.614342 44 -5.033011 0.0000
time3 -8.875 1.614342 44 -5.497596 0.0000
time4 -4.250 1.614342 44 -2.632652 0.0116
group1 2.126 6.568205 11 0.323681 0.7523
time1:group1 -2.734 2.603048 44 -1.050307 0.2993
time2:group1 5.583 2.603048 44 2.144793 0.0375
time3:group1 5.549 2.603048 44 2.131732 0.0387
time4:group1 3.634 2.603048 44 1.396056 0.1697
Correlation:
(Intr) time1 time2 time3 time4 group1 tm1:g1
tm2:g1 tm3:g1
time1 -0.198
time2 -0.198 0.500
time3 -0.198 0.500 0.500
time4 -0.198 0.500 0.500 0.500
group1 -0.620 0.123 0.123 0.123 0.123
time1:group1 0.123 -0.620 -0.310 -0.310 -0.310 -0.198
time2:group1 0.123 -0.310 -0.620 -0.310 -0.310 -0.198 0.500
time3:group1 0.123 -0.310 -0.310 -0.620 -0.310 -0.198 0.500
0.500
time4:group1 0.123 -0.310 -0.310 -0.310 -0.620 -0.198 0.500
0.500 0.500
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.63416413 -0.42033405 0.03577472 0.46164486 1.74068368
Number of Observations: 65
Number of Groups: 13
g2 = lme(Y ~ time + group + time:group + W + Z, random = ~ 1 |
numDF denDF F-value p-value
(Intercept) 1 42 5.54545 0.0233
time 4 42 16.41069 <.0001
group 1 11 0.83186 0.3813
W 1 42 0.07555 0.7848
Z 1 42 45.23577 <.0001
time:group 4 42 3.04313 0.0273
Linear mixed-effects model fit by REML
Data: datamod
AIC BIC logLik
355.2404 382.8245 -163.6202
Random effects:
Formula: ~1 | id
(Intercept) Residual
StdDev: 8.639157 2.597380
Fixed effects: Y ~ time + group + time:group + W + Z
Value Std.Error DF t-value p-value
(Intercept) 10.056433 9.583658 42 1.049331 0.3000
time1 0.209668 1.301306 42 0.161121 0.8728
time2 4.111435 2.556420 42 1.608278 0.1153
time3 0.423056 2.077066 42 0.203679 0.8396
time4 -3.976417 1.300572 42 -3.057437 0.0039
group1 4.677706 5.162006 11 0.906180 0.3843
W 0.377142 0.127146 42 2.966212 0.0050
Z -0.531895 0.093276 42 -5.702395 0.0000
time1:group1 -0.845857 2.126289 42 -0.397809 0.6928
time2:group1 -5.145361 2.962470 42 -1.736848 0.0897
time3:group1 -3.261241 2.597008 42 -1.255769 0.2161
time4:group1 4.153245 2.096587 42 1.980956 0.0542
Correlation:
(Intr) time1 time2 time3 time4 group1 W Z
tm1:g1
tm2:g1
time1 -0.051
time2 0.199 0.308
time3 0.023 0.361 0.817
time4 -0.029 0.501 0.293 0.342
group1 -0.202 0.131 0.136 0.146 0.129
W -0.790 0.019 0.243 0.366 -0.015 0.044
Z -0.146 -0.063 -0.853 -0.779 -0.041 -0.086 -0.409
time1:group1 -0.028 -0.601 -0.043 -0.074 -0.302 -0.187 0.147
-0.144
time2:group1 -0.293 -0.262 -0.818 -0.642 -0.255 -0.198 -0.051
0.665 0.276
time3:group1 -0.016 -0.286 -0.626 -0.774 -0.273 -0.214 -0.277
0.590 0.308
0.668
time4:group1 0.065 -0.306 -0.116 -0.159 -0.616 -0.199 0.002
-0.046 0.497
0.318
tm3:g1
time1
time2
time3
time4
group1
W
Z
time1:group1
time2:group1
time3:group1
time4:group1 0.376
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.11181231 -0.43210237 0.04949838 0.32444580 2.77710590
Number of Observations: 65
Number of Groups: 13
------------------------------
Message: 25
Date: Sun, 4 Jul 2004 10:24:47 +0100 (BST)
From: Prof Brian Ripley <ripley at stats.ox.ac.uk>
Subject: Re: [R] Re: Seasonal ARMA model
To: Ajay Shah <ajayshah at mayin.org>
Cc: r-help at stat.math.ethz.ch
Message-ID: <Pine.LNX.4.44.0407041021440.9904-100000 at gannet.stats>
Content-Type: TEXT/PLAIN; charset=US-ASCII
On Sun, 4 Jul 2004, Ajay Shah wrote:
It might clarify your thinking to note that a seasonal ARIMA model
is just an ``ordinary'' ARIMA model with some coefficients
constrained to be 0 in an efficient way. E.g. a seasonal AR(1) s =
4 model is the same as an ordinary (nonseasonal) AR(4) model with
coefficients theta_1, theta_2, and theta_3 constrained to be 0. You
can get the same answer as from a seasonal model by using the
``fixed'' argument to arima. E.g.:
set.seed(42)
x <- arima.sim(list(ar=c(0,0,0,0.5)),300)
f1 = arima(x,seasonal=list(order=c(1,0,0),period=4))
f2 =
arima(x,order=c(4,0,0),fixed=c(0,0,0,NA,NA),transform.pars=FALSE)
Is there a convenient URL which shows the mathematics of the seasonal
ARMA model, as implemented by R?
No, but there is a book, MASS4 (see the FAQ). Although the
software is in
base R it was in fact written by me to support MASS4.
R follows S-PLUS in some of its choices of signs, which do differ between
accounts.
I understand f2 fine. I understand that you are saying that f1 is just
an AR(4) with the lags 1,2,3 constrained to 0. But I'm unable to
generalise this. What would be the meaning of mixing up both order and
seasonal? E.g. what would it mean to do something like:
arima(x,order=c(2,0,0),seasonal=list(order=c(2,0,0),period=12))
That is in MASS4 and most of the books referenced on the help page.
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
------------------------------
Message: 26
Date: Sun, 04 Jul 2004 11:38:30 +0200
From: Peter Mathe <mathe at wias-berlin.de>
Subject: [R] Is there rpm for suse 9.1 under x86_64?
To: R-help at stat.math.ethz.ch
Message-ID: <40E7D016.4060705 at wias-berlin.de>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed
I recently upgraded to Suse 9.1 for Amd64.
So far I could not find precompiled binaries of R-1.9.1 for this case.
So I tried installation from source, but could not succeed. Although the
configuration/installation procedure ran without problems, the make
check always ended with errors. When trying to run R , to see what's
going on, the eigen() reported error code -18.
So, is a rpm for R-base-1.9.1 under x86_64 for Suse available, or how
can I succesfully install from sources?
Thank's for reading this message, Peter
------------------------------
Message: 27
Date: 04 Jul 2004 11:53:54 +0200
From: Peter Dalgaard <p.dalgaard at biostat.ku.dk>
Subject: Re: [R] Is there rpm for suse 9.1 under x86_64?
To: Peter Mathe <mathe at wias-berlin.de>
Cc: R-help at stat.math.ethz.ch
Message-ID: <x2ekns2j4d.fsf at biostat.ku.dk>
Content-Type: text/plain; charset=us-ascii
Peter Mathe <mathe at wias-berlin.de> writes:
I recently upgraded to Suse 9.1 for Amd64.
So far I could not find precompiled binaries of R-1.9.1 for this case.
So I tried installation from source, but could not succeed. Although
the configuration/installation procedure ran without problems, the
make check always ended with errors. When trying to run R , to see
what's going on, the eigen() reported error code -18.
So, is a rpm for R-base-1.9.1 under x86_64 for Suse available, or how
can I succesfully install from sources?
I can't get the upgrade working for me (SATA trouble -- again!) but I
have 9.0 on a system. This has run cleanly for a while with a
home-built RPM based on Detlef's SPEC file, as well as several local
builds.
A good guess is that they upgraded GCC and something got broken --
again. You could try reducing the optimization levels on the relevant
files, or as the first thing on everything (-O0 in CFLAGS and FFLAGS).
Thank's for reading this message, Peter
How did you know I would, Peter? ;-)
--
O__ ---- Peter Dalgaard Blegdamsvej 3
c/ /'_ --- Dept. of Biostatistics 2200 Cph. N
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907
------------------------------