similar to: R-analogue to the estimate and lsmeans statements in SAS

It is in the doBy package. Thanks From: Bert Gunter [mailto:bgunter.4567 at gmail.com] Sent: Tuesday, February 13, 2018 4:32 PM To: Pius Mwansa <pmwansa at shaw.ca> Cc: R-help <r-help at r-project.org> Subject: Re: [R] LSmeans and lsmeans Always cc the list unless there is good reason to keep your reply private. There is no LSmeans() function in the lsmeans package.

A cursory reading indicates that they are identical; but others more knowledgeable than I need to confirm or deny this. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Feb 13, 2018 at 3:38 PM, Pius Mwansa <pmwansa at

Always cc the list unless there is good reason to keep your reply private. There is no LSmeans() function in the lsmeans package. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Feb 13, 2018 at 3:20 PM, Pius Mwansa

Dear all, I search the mail list about this topic and learn that no simple way is available to get "lsmeans" in R as in SAS. Dr.John Fox and Dr.Frank E Harrell have given very useful information about "lsmeans" topic. Dr. Frank E Harrell suggests not to think about lsmeans, just to think about what predicted values wanted and to use the predict

Is there a difference between LSmeans and lsmeans functions in R? Thanks, Pius

I need help with calculating lsmeans (adjusted means) of different terms in a linear model including the main effect and the interaction effect terms. I use lm to run the linear models...I previously noted from literature that that "effects" package can be used to generate lsmeans. But I tried to use it but could not figure out which option to use to get means. If anyone can give an

Hi folks, I have a dataset that consists of counts over a ~30 year period at multiple (>200) sites. Only one count is conducted at each site in each year; however, not all sites are surveyed in all years. I need to impute the missing values because I need an estimate of the total population size (i.e., sum of counts across all sites) in each year as input to another model. >

Hello, I have different results from these two softwares for a simple binomial GLM problem. >From Genmod in SAS: LogLikelihood=-4.75, coeff(intercept)=-3.59, coeff(x)=0.95 >From glm in R: LogLikelihood=-0.94, coeff(intercept)=-3.99, coeff(x)=1.36 Is there anyone tell me what I did wrong? Here are the code and results, 1) SAS Genmod: % r: # of failure % k: size of a risk set data

Hi there, I am trying to run simulations using R with linear mixed model (lme). There are two factors in my fixed effect model, educ (treatment and control) and mth (visit 1, 2, and 3). What I want to obtain is the estimated treatment difference (treatment - control) at visit 3, plus the standard error and p-value. This can be easily obtained in SAS using lsmeans or estimate statements, but I

Dear list, Is there a function (or an equivalent way) in R resembling the lsmeans command in SAS? The objective is to get the (adjusted) means for design models. Thanks in advance. Regards, Washington Santos da Silva.

Hello, I have the next function call: lme(fixed=Error ~ Temperature * Tumour ,random = ~1|ID, data=error_DB) which returns an lme object. I am interested on carrying out some kind of lsmeans on the data returned, but I cannot find any function to do this in R. I'have seen the effect() function, but it does not work with lme objects. Any idea? Best, Dani -- Daniel Valverde Saub? Grup

Hello, I have to run a general linear mixed model which looks at 2 dependent variables at the same time (var1 divided by var2). I have tryed to search for such a kind of model structure but since I just started using R my search was not successful. Especielly since I only have an old SAS GENMOD code structure from my project supervisor as an indication. My question is no, does there exist a code

List: I'm helping a colleague with some Poisson regression modeling. He uses SAS proc GENMOD and I'm using glm() in R. Note on the SAS and R output below that our estimates, standard errors, and deviances are identical but what we get for likelihoods differs considerably. I'm assuming that these must differ just by some constant but it would be nice to have some confirmation

I'm working with a nested model (mixed). I have four factors: Patients, Tissue, sex, and tissue_stage. Totally I have 10 patients, for each patient, there are 2 tissues (Cancer vs. Normal). I think Tissue and sex are fixed. Patient is nested in sex,Tissue is nested in patient, and tissue_stage is nested in Tissue. I tried aov and lme as the following, > aov(gene ~ tissue + gender +

Hi, I wonder if anybody could help me in converting this easy SAS program into R. (I'm still trying to do that!) PROC IMPORT OUT= WORK.CHLA_italian DATAFILE= "C:\Documents and Settings\carleal\My Documents\REBECCA\stat\sas\All&nutrients.xls" DBMS=EXCEL2000 REPLACE; GETNAMES=YES; RUN; data chla_italian; set chla_italian;

I need capabilities, for my data analysis, like the Pinheiro & Bates S-Plus/R package nlme() but with binomial family and logit link. I need multiple crossed, possibly interacting fixed effects (age cohort of twin when entered study, sex of twin, sampling method used to acquire twin pair, and twin zygosity), a couple of random effects other than the cluster variable, and the ability to

I hope you'll forgive me for resurrecting this thread. My question refers to John Fox's comments in the discussion of lsmeans from https://stat.ethz.ch/pipermail/r-help/2008-June/164106.html John you said, "It wouldn't be hard, however, to do the computations yourself, using the coefficient vector for the fixed effects and a suitably constructed model-matrix to compute the

R users, What is the equivalent of SAS " '/diff' and '/slice'" in R ? I am successful in fitting the same model "PROC MIXED" fits using lme() : ###R code: model <- lme( y ~ g + h + yr + yr:h, random = ~ 1 | b, data = yield) *** SAS code: proc mixed data=yield covtest cl method=reml; class b g h yr; model y = g h yr yr*h; random b; lsmeans g / diff;

You might get better answers if you 1 - break this down into separate issues 2 - tell us what you want to achieve in words rather than SAS, we all read English but few of us speak SAS 3 - post in plain text not HTML as HTML mangles your post On 29/09/2017 13:47, Andrew Harmon wrote: > Hello all, > > My statistical analysis training up until this point has been entirely done > in

Hello, I send the message again with the data file as txt because it seems not to be accepted as csv in the R-help list. Data comes from a multiyear field experiment in which 4 levels of a treatment (2, 3, 4, 6) are compared to see the effect on yield. It is a randomized complete block design. The SAS code follows: options ls=95; data uno; infile 'data.txt'