similar to: lsmeans

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

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. >

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

I have used glm and stepAIC to choose a best model. I can use termplot to assess the contribution of each explanatory variable in the glm. However the final model after running stepAIC includes interaction terms, and when I do termplot I get "Error in `[.data.frame`(mf, , i) : undefined columns selected". I also see the termplot detail saying "Nothing sensible happens for

Dear R users, I am modelling the probability of error in a behavioural task using the glm() function (with the numbers of successes and failures listed for each line in the data frame). How can I plot the partial effects of the predictors? Many thanks in advance, Stav

Hi list! I did the following regression: reg1 <- glm(alti~sp + ovent + vivent + nuage, family=gaussian, data=meteo1) I was interested in knowing the effect of the species (sp) in reg1 and so I used the function «effect»: effect.sp <- effect ("sp", reg1, se=TRUE) with this output: sp AK BW NH OS RT SS 2.730101 2.885363 2.753774 2.750311

Dear all, I am quite new with R and I have a problem with plotting a binomial regression line in a plot. This is what I type in: > model<-glm(Para~Size,binomial) > par(mfrow=c(1,1)) > xv<-seq(3.2,4.5,0.01) > yv<-predict(model,list(area=xv),type="response") > plot(Size,Para) > lines(xv,yv) The error message that I get is: > Error in xy.coords(x, y) :

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

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

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

Dear all, How does the effect() function in the effects package calculate effects and standard errors for glm quasipoisson models? I was using effect() to calculate the impact of increasing x to e + epsilon, and then finding the expected percent change. I thought that this effect (as a percentage) should be exp(beta*epsilon), where beta is the appropriate coefficient from the model, but

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 +

Hello R-helpers, I would like to calculate least square means after having built a GLM with quasipoisson errors. In my model the dependent variable is continuous, I have one continuous independent variable and one categorical independent variable (that is the variable for which I would like to calculate the least square means). I've looked around for the command to calculate the least

Dear all, In connection with a stats course where we want to use R, I am trying to find out if there are analogues to the ESTIMATE and LSMEANS ("population" means) statements in SAS (in proc glm, proc genmod and proc mixed). The students are agronomists, vets and that sort of people, and most of them have some familiarity with SAS. The design package seems to go some of the way, but

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;

Hi all I'm doing some things with a colleague comparing different sorts of models. My colleague has fitted a number of glms in Genstat (which I have never used), while the glm I have been using is only available for R. He has a spreadsheet of fitted means from each of his models obtained from using the Genstat "predict" function. For example, suppose we fit the model of the type