Re: [R] AR1 covariance structure for lme object and R/SAS differences in model output
On 11 Feb 2015, at 16:57 , anord andreas.n...@biol.lu.se wrote:
Dear R users,
We are working on a data set in which we have measured repeatedly a
physiological response variable (y)
every 20 min for 12 h (time variable; 'x') in subjects ('id') beloning to
one of five groups ('group'; 'A' to 'E'). Data are located at:
https://www.dropbox.com/s/hf455aev3teb5e0/data.csv?dl=0
We are interested to model if the response in y differences with time (i.e.
'x') for the two groups. Thus:
require(nlme)
m1-lme(y~group*x+group*I(x^2),random=~x|id,data=data.df,na.action=na.omit)
But because data are collected repeatedly over short time intervals for each
subject, it seemed prudent to consider an autoregressive covariance
structure. Thus:
m2-update(m1,~.,corr=corCAR1(form=~x|id))
AIC values indicate the latter (i.e. m2) as more appropriate:
anova(m1,m2)
# Model df AIC BIC logLikTest L.Ratio
p-value
#m1 1 19 2155.996 2260.767 -1058.9981
#m2 2 20 2021.944 2132.229 -990.9718 1 vs 2 136.0525 .0001
Fixed effects and test statistics differ between models. A look at marginal
ANOVA tables suggest inference might differ somewhat between models:
anova.lme(m1,type=m)
# numDF denDF F-value p-value
#(Intercept) 1 1789 63384.80 .0001
#group 445 1.29 0.2893
#x 1 1789 0.05 0.8226
#I(x^2)1 1789 4.02 0.0451
#group:x 4 1789 2.61 0.0341
#group:I(x^2) 4 1789 4.37 0.0016
anova.lme(m2,type=m)
# numDF denDF F-value p-value
#(Intercept) 1 1789 59395.79 .0001
#group 445 1.33 0.2725
#x 1 1789 0.04 0.8379
#I(x^2)1 1789 2.28 0.1312
#group:x 4 1789 2.09 0.0802
#group:I(x^2) 4 1789 2.81 0.0244
Now, this is all well. But: my colleagues have been running the same data
set using PROC MIXED in SAS and come up with substantially different results
when comparing SAS default covariance structure (variance components) and
AR1. Specifically, there is virtually no change in either test statistics or
fitted values when using AR1 instead of Variance Components in SAS, which
fits the observation that AIC values (in SAS) indicate both covariance
structures fit data equally well.
This is not very satisfactory to me, and I would be interesting to know what
is happening here. Realizing
this might not be the correct forum for this question, I would like to ask
you all if anyone would have any
input as to what is going on here, e.g. am I setting up my model
erroneously, etc.?
N.b. I have no desire to replicate SAS results, but I would most certainly
be interested to know what could possibly explain such a large discrepancy
between the two platforms. Any suggestions greatly welcomed.
(Data are located at:
https://www.dropbox.com/s/hf455aev3teb5e0/data.csv?dl=0)
Hmm, does SAS include a nugget effect perchance? At any rate, showing the SAS
output (or some of it) might make it easier for someone to help.
Also, R-sig-ME is a better choice for discussions of mixed effects models.
--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd@cbs.dk Priv: pda...@gmail.com
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[R] AR1 covariance structure for lme object and R/SAS differences in model output
Dear R users,
We are working on a data set in which we have measured repeatedly a
physiological response variable (y)
every 20 min for 12 h (time variable; 'x') in subjects ('id') beloning to
one of five groups ('group'; 'A' to 'E'). Data are located at:
https://www.dropbox.com/s/hf455aev3teb5e0/data.csv?dl=0
We are interested to model if the response in y differences with time (i.e.
'x') for the two groups. Thus:
require(nlme)
m1-lme(y~group*x+group*I(x^2),random=~x|id,data=data.df,na.action=na.omit)
But because data are collected repeatedly over short time intervals for each
subject, it seemed prudent to consider an autoregressive covariance
structure. Thus:
m2-update(m1,~.,corr=corCAR1(form=~x|id))
AIC values indicate the latter (i.e. m2) as more appropriate:
anova(m1,m2)
# Model df AIC BIC logLikTest L.Ratio
p-value
#m1 1 19 2155.996 2260.767 -1058.9981
#m2 2 20 2021.944 2132.229 -990.9718 1 vs 2 136.0525 .0001
Fixed effects and test statistics differ between models. A look at marginal
ANOVA tables suggest inference might differ somewhat between models:
anova.lme(m1,type=m)
# numDF denDF F-value p-value
#(Intercept) 1 1789 63384.80 .0001
#group 445 1.29 0.2893
#x 1 1789 0.05 0.8226
#I(x^2)1 1789 4.02 0.0451
#group:x 4 1789 2.61 0.0341
#group:I(x^2) 4 1789 4.37 0.0016
anova.lme(m2,type=m)
# numDF denDF F-value p-value
#(Intercept) 1 1789 59395.79 .0001
#group 445 1.33 0.2725
#x 1 1789 0.04 0.8379
#I(x^2)1 1789 2.28 0.1312
#group:x 4 1789 2.09 0.0802
#group:I(x^2) 4 1789 2.81 0.0244
Now, this is all well. But: my colleagues have been running the same data
set using PROC MIXED in SAS and come up with substantially different results
when comparing SAS default covariance structure (variance components) and
AR1. Specifically, there is virtually no change in either test statistics or
fitted values when using AR1 instead of Variance Components in SAS, which
fits the observation that AIC values (in SAS) indicate both covariance
structures fit data equally well.
This is not very satisfactory to me, and I would be interesting to know what
is happening here. Realizing
this might not be the correct forum for this question, I would like to ask
you all if anyone would have any
input as to what is going on here, e.g. am I setting up my model
erroneously, etc.?
N.b. I have no desire to replicate SAS results, but I would most certainly
be interested to know what could possibly explain such a large discrepancy
between the two platforms. Any suggestions greatly welcomed.
(Data are located at:
https://www.dropbox.com/s/hf455aev3teb5e0/data.csv?dl=0)
With all best wishes,
Andreas
--
View this message in context:
http://r.789695.n4.nabble.com/AR1-covariance-structure-for-lme-object-and-R-SAS-differences-in-model-output-tp4703103.html
Sent from the R help mailing list archive at Nabble.com.
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Re: [R] AR1 covariance structure for lme object and R/SAS differences in model output
anord andreas.nord at biol.lu.se writes:
[snip snip]
We are working on a data set in which we have measured repeatedly a
physiological response variable (y)
every 20 min for 12 h (time variable; 'x') in subjects ('id') beloning to
one of five groups ('group'; 'A' to 'E'). Data are located at:
https://www.dropbox.com/s/hf455aev3teb5e0/data.csv?dl=0
We are interested to model if the response in y differences
with time (i.e.
'x') for the two groups. Thus:
require(nlme)
m1-lme(y~group*x+group*I(x^2),random=~x|id,
data=data.df,na.action=na.omit)
But because data are collected repeatedly over
short time intervals for each
subject, it seemed prudent to consider an autoregressive covariance
structure. Thus:
m2-update(m1,~.,corr=corCAR1(form=~x|id))
AIC values indicate the latter (i.e. m2) as more appropriate:
anova(m1,m2)
# Model df AIC BIC logLikTest L.Ratio
p-value
#m1 1 19 2155.996 2260.767 -1058.9981
#m2 2 20 2021.944 2132.229 -990.9718 1 vs 2 136.0525 .0001
Fixed effects and test statistics differ between models.
A look at marginal
ANOVA tables suggest inference might differ somewhat between models:
anova.lme(m1,type=m)
# numDF denDF F-value p-value
#(Intercept) 1 1789 63384.80 .0001
#group 445 1.29 0.2893
#x 1 1789 0.05 0.8226
#I(x^2)1 1789 4.02 0.0451
#group:x 4 1789 2.61 0.0341
#group:I(x^2) 4 1789 4.37 0.0016
anova.lme(m2,type=m)
# numDF denDF F-value p-value
#(Intercept) 1 1789 59395.79 .0001
#group 445 1.33 0.2725
#x 1 1789 0.04 0.8379
#I(x^2)1 1789 2.28 0.1312
#group:x 4 1789 2.09 0.0802
#group:I(x^2) 4 1789 2.81 0.0244
Now, this is all well. But: my colleagues have been running
the same data
set using PROC MIXED in SAS and come up with
substantially different results
when comparing SAS default covariance structure (variance components) and
AR1. Specifically, there is virtually no change
in either test statistics or
fitted values when using AR1 instead of Variance Components in SAS, which
fits the observation that AIC values (in SAS) indicate both covariance
structures fit data equally well.
This is not very satisfactory to me, and I would be
interesting to know what
is happening here. Realizing
this might not be the correct forum for this question, I would like to ask
you all if anyone would have any
input as to what is going on here, e.g. am I setting up my model
erroneously, etc.?
N.b. I have no desire to replicate SAS results, but I would most certainly
be interested to know what could possibly explain
such a large discrepancy
between the two platforms. Any suggestions greatly welcomed.
(Data are located at:
https://www.dropbox.com/s/hf455aev3teb5e0/data.csv?dl=0)
Could you repost this on r-sig-mixed-mod...@r-project.org ?
It would be useful to see some of the comparisons (fixed effects,
RE variance-covariance, correlation parameter) between SAS and
R; are they actually fitting the same model? (e.g., does SAS
allow for covariance between the slope and intercept random effects?)
Maybe they're getting the same estimates but computing df/p-values
in different ways?
I thought I would try this with orthogonal polynomials in case
some of the fits were unstable ...
data.df - read.csv2(ar1_data.csv)
library(nlme)
m1 - lme(y~group*x+group*I(x^2),random=~x|id,
data=data.df,na.action=na.omit,method=ML)
## use method=ML so we can compare orthog. and non-orthog. polynomials
m1B - update(m1,.~group*poly(x,2))
m2 - update(m1,corr=corCAR1(form=~x|id))
m2B - update(m1B,corr=corCAR1(form=~x|id))
AIC(m1,m1B,m2,m2B)
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