Hi Daniel
and thank you very much for your reply!
I went over the documents you sent me, though couldn't find the sdtalt
package :(
I think using lme is more suitable for my purposes since it is important
for me include random effects in my model.
I am still stuck with understanding the right code in R to specify a
homogeneous variance structure with correlations structured as AR1 (model
13 in your paper
https://www.researchgate.net/publication/23134911_Multilevel_modelling_Beyond_the_basic_applications
).
As I wrote in my previous message specifying this model in R (if I got the
code right - I assume not...) and in SPSS produces completely different
results.
I will be grateful if you or anybody else can point me towards a solution.
I am repeating here my previous example to make my difficulty easier to
address:
for example using the Glucose data from the nlme package:
data(Glucose)
fGlucose<-filter(Glucose,Meal=="10am")
summary(
lme(
fixed=conc~Time,
random=~1+Time|Subject,
method="REML",
data=fGlucose,
na.action="na.omit",
correlation=corAR1(form=~1+Time|Subject))
)
I get an output of:
Correlation Structure: ARMA(1,0)
Formula: ~1 + Time | Subject
Parameter estimate(s):
Phi1
0.4334469
I am assuming Phi1 is equivalent to Rho (am I right?). What makes me
suspect I am wrong, and confuses me, is that when estimating AR1
diagonal (homogeneous
level one error variance) and AR1 rho (the auto regression parameter)
using SPSS I received 1.349 and -0.942 respectively.
here is the SPSS syntax I am using on the same data set (Glucose):
COMPUTE filter_$=(Meal="10am").
VARIABLE LABELS filter_$ 'Meal="10am" (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.
MIXED conc WITH Time
/CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1)
SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
PCONVERGE(0.000001, ABSOLUTE)
/FIXED=INTERCEPT Time | SSTYPE(3)
/METHOD=REML
/PRINT=SOLUTION TESTCOV
/RANDOM=INTERCEPT Time | SUBJECT(Subject)
/REPEATED=Time | SUBJECT(Subject)COVTYPE(AR1).
It would be really great if you or anybody else can assist me here!
Thank you!
Best
Asher Strauss
On Mon, May 18, 2015 at 9:01 PM, Daniel Wright <Daniel.Wright at act.org>
wrote:
It may also be convenient to use the gls function in nlme.
This is used in
r-sig-mixed-models-bounces at r-project.org]
On Behalf Of Asher Strauss
Sent: Saturday, May 16, 2015 1:19 AM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] estimating AR1 parameters of level one error using
Hi all,
I am new to using R for mixed effects (have been using SPSS until now),
please for give me if this is a trivial question.
I am trying to understand how to estimate AR1 parameters of level one
error using lme (I have understood that specifying level one
variance-covariance matrix is not easily possible in lmer, is this
When using SPSS one estimates two parameters: AR1 diagonal and AR1 rho. I
am searching for an equivalent command in R.
for example using the Glucose data from the nlme package:
data(Glucose)
fGlucose<-filter(Glucose,Meal=="10am")
summary(
lme(
fixed=conc~Time,
random=~1+Time|Subject,
method="REML",
data=fGlucose,
na.action="na.omit",
correlation=corAR1(form=~1+Time|Subject))
)
I get an out put of:
Correlation Structure: ARMA(1,0)
Formula: ~1 + Time | Subject
Parameter estimate(s):
Phi1
0.4334469
is Phi1 equivalent to Rho? I do not believe so, since when estimating
diagonal and AR1 rho using SPSS I received 1.349 and -0.942 respectively.
here is the SPSS syntax I am using:
COMPUTE filter_$=(Meal="10am").
VARIABLE LABELS filter_$ 'Meal="10am" (FILTER)'.
VALUE LABELS filter_$ 0 'Not Selected' 1 'Selected'.
FORMATS filter_$ (f1.0).
FILTER BY filter_$.
EXECUTE.
MIXED conc WITH Time
/CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1)
SINGULAR(0.000000000001) HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
PCONVERGE(0.000001, ABSOLUTE)
/FIXED=INTERCEPT Time | SSTYPE(3)
/METHOD=REML
/PRINT=SOLUTION TESTCOV
/RANDOM=INTERCEPT Time | SUBJECT(Subject)
/REPEATED=Time | SUBJECT(Subject)COVTYPE(AR1).
Thank you!
Asher
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