similar to: lme help

To anyone who can help, I have two stupid questions, and one fairly intelligent question Stupid question (1): is there an R function to calculate a factorial of a number? That is...is there a function g(.) such that g(3) = 6, g(4) = 24, g(6) = 720, etc? Stupid question (2): how do you extract the estimated covariance matrix of the random effects in an lme object? Intelligent question

Thanks for the help Dimitris, However I still have a question, this time I'll be more specific, the following is my SAS code proc mixed data=Reg; class ID; model y=Time Time*x1 Time*x2 Time*x3 /S; random intercept Time /S type=UN subject=ID G GCORR V; repeated /subject = ID R RCORR; run; ** (Type =UN for random effects) The eqivalent lme statement I

Hi All I am trying to translate a proc mixed into a lme() syntax. It seems that I was able to do it for part of the model, but a few things are still different. It is a 2-level bivariate model (some call it a pseudo-3-level model). PROC MIXED DATA=psdata.bivar COVTEST METHOD = ml; CLASS cluster_ID individual_id variable_id ; MODEL y = Dp Dq / SOLUTION NOINT; RANDOM Dp Dq / SUBJECT = cluster_ID

Dear R users, I would be interested in using the lme() function to fit a linear mixed model to a longitudinal dataset. I know this function allows for the specification of a within-group covariance structure. However, does it allow for the explicit specification of a between-group covariance structure? Being able to specify both separately would be very important in the context of my project

Hi all, I have the output of summary() of an lme object called "lme.exp1", for example ############################################# > summary(lme.exp1) Linear mixed-effects model fit by REML Data: DATA Log-restricted-likelihood: -430.8981 Fixed: fixed.exp1 .... Random effects: Formula: ~-1 + mu1 + log.sig1 | animID Structure: Diagonal mu1 log.sig1

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,

Hello, We are attempting to use nlme to fit a linear mixed model to explain bird abundance as a function of habitat: lme(abundance~habitat-1,data=data,method="ML",random=~1|sampleunit) The data consist of repeated counts of birds in sample units across multiple years, and we have two questions: 1) Is it necessary (and, if so, how) to specify the repeated measure (years)? As written,

Dear R users, I have a question about the patterned variance-covariance structure for the random effects in linear mixed effect model. I am reading section 4.2.2 of "Mixed-Effects Models in S and S-Plus" by Jose Pinheiro and Douglas Bates. There is an example of defining a compound symmetry variance-covariance structure for the random effects in a split-plot experiment on varieties of

This question comes up periodically, probably enough to give it a proper thread and maybe point to this thread for reference (similar to the 'conservative anova' thread not too long ago). Moving from lme syntax, which is the function found in the nlme package, to lmer syntax (found in lme4) is not too difficult. It is probably useful to first explain what the differences are between the

Dear R list members, I've turned up a strange discrepancy between results obtained from the lme function in the nlme package in R and results obtained with lme in S-PLUS. I'm using version 3.1-24 of nlme in R 1.4.1 under Windows 2000, and both S-PLUS 2000 and 6.0, again under Windows 2000. I've noticed discrepancies in a couple of instances. Here's one, using data from Bryk

Dear all, I've got a problem in including square variables in lme function. I've tried to work on Dialyzer data of Pinheiro and Bates'book. We fit the heteroscedastic model with: > data(Dialyzer) > fm2Dial.lme<-lme(rate~(pressure+pressure^2+pressure^3+pressure^4)*QB, + Dialyzer,~pressure+pressure^2,weights=varPower(form=~pressure)) We Obtain > fm2Dial.lme Linear

Hi, All,   My data collection is from 4 regions (a, b, c, d). Within each region, it has 2 or 3 units. Within each unit, it has measurement from about 25 sample site. I was trying to use lme function to discribe relationship between y and a few covariates. Both y and covariates were measured at the sample site level. My question is when I use exactlly the same model but choose different estimation

Dear R gurus, I am trying to work out the problem given in Nested design - Montgomery - Design of Experiments p.561 I have attached a pdf of the data as well the anova table. It is a mixed model with Supplier as fixed effect and batches within the supplier as random effects. I am able to work out the error stratums as below using aov. Which agrees perfectly with the book example

Hi, I was wondering if there is a way to figure out why in SAS random beta coefficients are 0 vs. in R the beta-s are non zero. The variables of the data are nidl, time, and sub (for subject). Time and nidl are continuous variables. I am applying random coefficients model. Any input is greatly appreciated, Thanks, Aldi 1. mixed model in SAS: ====================== ods output SolutionR =

Hi, this is my first time using the nlme package, and I ran into the following puzzling problem. I estimated a mixed effects model using lme, once using groupedData, once explicitly stating the equations. I had the following outputs. All the coefficients were similar, but they're always slightly different, making me think that it's not due to numerical error. Also, what is the

Dear R-Users I used the nlme library to fit a linear mixed model (lme). The random effect standard errors and correlation reported are based on a Log-Cholesky parametrization. Can anyone tell me how to get the Covariance matrix of the random effects, given the above mentioned parameters based on the Log-Cholesky parametrization?? Thanks in advance Pryseley

Hi R users! I will try to state my question again. I have longitudinal data and fitted the following model with lme: Y = X*beta + U + W(t) + Z where U ~ N(0, nu*I) I is the identity matrix, so this is the random intercept W(t)~ N(0, sigma*H) and H is a matrix which incorporates a Gaussian serial correlation (covariance) in the offdiagonal

I am analyzing data on a study of the effects of Coronary Artery Bypass Graft (CABG) on cognitive function, as measured by a score from an objective test. I have 140 people who receive the CABG surgery and 92 controls, with four measurements of cognitive function over time (at 0, 3, 12 and 36 months). I have fitted a linear mixed model using lme with a random intercept for subject and a random

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,

I want to compare the following two models in AIC (Treat, Spotter are categorial, p is pressure, Pain is continuous) PainW.lme<-lme(Pain~p+Treat*Spotter,data=saw,random=~p|Pat, weights=varPower(form=~Pain)) # AIC= -448 Pain.lme<-lme(Pain~p+Treat*Spotter,data=saw,random=~p|Pat) #AIC = -19.7 Note the huge differences in AIC, and the estimated power of 6. A plot of the residual