similar to: is there a similar function to perform repeated statements as in SAS PROC MIXED?

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 all, I am trying to simulate the null distribution for the likelihood ratio test statistic for testing 1 random effect versus no random effect. The asymptotic null distribution should be a mixture of a chi-squared distribution with 0 degrees of freedom and a chi-squared distribution with 1 degree of freedom. This means that I expect a point mass of 50% on 0 for the likelihood ratio

Hi, I have implemented the lme in SAS and specified the random effects covariance structure as unstructured using the statement "random / type=UN" I want to specify same in R but not able to know how to do it. Can anyone please advise me on how to proceed. Thanks -- Wills, Harry [[alternative HTML version deleted]]

find the position of the first value who equals certain number in a vector: Say a=c(0,0,0,0,0.2, 0.2, 0.4,0.4,0.5) i wish to return the index value in a for which the value in the vector is equal to 0.4 for the first time. in this case, it is 7. [[alternative HTML version deleted]]

Hello all, Simple version of my problem: I've got a list of data frames, where each data frame has the same number of columns and the same column names. I'd like to flatten the list into one large data frame. Is there an easy way to do this? Quick example code: a <- data.frame(x=c(1,2,3),y=c(5,7,9) b <- data.frame(x=c(2,4,7,9),y=c(2,3,5,4)) z <- list(a,b) # Do

Hi all, I don't know if anyone has any thoughts on this. I have been trying to move from SAS Proc Mixed to R nlme and have an unusual result. I have several subjects measured at four timepoints. I want to model the within-subject correlation using an autoregressive structure. I've attached the R and SAS code I'm using along with the results from SAS. With R lme I get an estimate of

Hi all, I have time series data in a matrix format 20 rows x 205 columns and have been trying to replace outliers with NA. My first column contains the outliers threshold (3 Standard deviations) for each row - here's a bit of the first row sd3 V1 V2 V3 V4 V5 V6 V7 V8 V9 1 13.03267 1797157 75 84 58 -1.958649 0.048775 2.056198 8.063622

http://www.nabble.com/file/p24211204/repeated.csv repeated.csv Dear all, I'm currently trying to replicate some Proc Mixed results using lme() and have a curious result I can't explain. The dataset is a repeated measures example where patients (each on one of several treatments) are measured over a number of days. Putting aside issues of the error covariance structure I'm using

I just noticed this upon startup since updating from 15.6.1 to 16.0.0 - do any of these matter? [Oct 18 12:12:18] WARNING[4489]: loader.c:2228 load_modules: Some non-required modules failed to load. [Oct 18 12:12:18] ERROR[4489]: loader.c:2243 load_modules: res_pjsip_transport_websocket declined to load. [Oct 18 12:12:18] ERROR[4489]: loader.c:2243 load_modules: cdr_sqlite3_custom declined to

Is there any easy way to pull out the row indexes for a logical matching statment? #################example code######################################### foo <- data.frame(name=c(rep("A", 25), rep("B", 25), rep("C", 25), rep("A", 25)), stuff=rnorm(100), and=rnorm(100), things=rnorm(100)) #this is what I want but I would like the row indexes

Hello everybody, I'm trying to use a if-statment on a function. For a better understanding I want to present a small example: FUN=mean # could also be median,sd or any other function if (FUN == mean) plot(...) if (FUN == median) plot(...) ... This doesn't work, because FUN is a function. I've already tried to coerce the

I have a sample of observations: > yy [1] 0.00000000 2.39722222 4.35000000 -4.19722222 0.63611111 [6] 1.08055556 5.90555556 -1.87222222 2.13333333 -1.18055556 [11] 3.61666667 0.87777778 8.33888889 3.84166667 1.11111111 [16] -3.76111111 -11.67777778 -2.03055556 6.94444444 -11.76666667 [21] 4.81111111 -7.25833333 1.42222222 5.37222222 4.68055556

To anyone that may be able to help, First I applogize if this message ends up in your inbox twice. The first one seemed to get stuck in a pending status so I deleted and started over. I am relatively new to R, however I know the basics through some classes I have taken. Unfortunately the classes left off the more complicated analysis that I need to learn and use. I am attempting to replicate

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,

Example: I have the following model > model <- lmer(response ~ time * trt * bio + (time|id), data = dat) where time = time of observation trt = treatment group (0-no treatment / 1-treated) bio = biological factor (0-absent / 1-present) and I would like to obtain an estimate (with standard error) of the change in response over time for individuals in the

Hello all, I am trying to teach myself R and replicate some previous SAS analysis. Could someone please help me translate the following SAS code into R. Proc mixed method=ml Class Group Treatment Stream Time Year; Model Logrpk=Treatment Time Treatment*Time; Random Group Stream (Group Treatment) Year(Time); Thank you to anyone that may help! -- View this message in context:

Dear R users, I need help with translating these SAS codes into R with lme()? I have a longitudinal data with repeated measures (measurements are equally spaced in time, subjects are measured several times a year). I need to allow slope and intercept vary. SAS codes are: proc mixed data = survey method=reml; class subject var1 var3 var2 time; model score = var2 score_base var4 var5 var3

Hi, I have the following find statment @client_pages, @clients = paginate :clients, :per_page => 20, :order => "organisation_id, surname" currently im ordering by organisation_id however I need to order by the field in the organisation table organisations.name how can this be done? Thanks Scott -- Posted via http://www.ruby-forum.com/.

I would like to be able to use lme to fit random effect models In which some but not all of the random effects are constrained to be independent. It seems as thought the pdMat options in lme are a promising avenue. However, none of the existing pdMat classes seem to allow what I want. As a specific example, I would like to fit a random intercept/slope mixed model to longitudinal observations in

hi, Im running the following sql statment which returns 1 result. pSql = "SELECT sum(amount) FROM payments WHERE expected_pay_date LIKE ''"+pWholeDate+"%'' AND invoice_id IS null " aResult = Payment.find_by_sql(pSql) how can I get the result of this? (when doing @aResult.inspect it