search for: lsmeans

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...

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. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus...

...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. > > Cheers, > > Bert > > > Bert Gunter > > "The trouble with having an open mind is that people...

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 function. However, after read...

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...

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 the model objects are not "pure&quot...

...year as input to another model. > head(newdat,40) SITE YEAR COUNT 1 1 1975 12620 2 1 1976 13499 3 1 1977 45575 4 1 1978 21919 5 1 1979 33423 ... 37 2 1975 40000 38 2 1978 40322 39 2 1979 70000 40 2 1980 16244 It was suggested to me by a statistician to use LSmeans to do this; however, I do not have SAS, nor do I know anything much about SAS. I have spent DAYS reading about these "LSmeans" and while (I think) I understand what they are, I have absolutely no idea how to a) calculate them in R and b) how to use them to impute my missing values in R. A...

...ar 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 am not sure how to do this in R. The fixed effects I obtained are as follows: Fixed effects: ymth ~ educ * mth - 1 Value Std.Error DF t-value p-value educcont 0.148...

...DS listing close; %macro sort_event(cut_off,last=0); /*proc glm data=sort_dataset; class year; model logchla=year cos1 sin1 cos2 sin2 cos3 sin3 cos4 sin4 /solution; by station; where bloom=0; output out=chla_res predicted=pred student=studres cookd=cookd rstudent=rstudent u95=u95; lsmeans year / at (cos1 sin1 cos2 sin2 cos3 sin3 cos4 sin4)=(0 0 0 0 0 0 0 0); ODS output ParameterEstimates=parmest LSmeans=lsmeans; run;*/ proc glm data=sort_dataset; class year month; model chla=/solution; by station; weight w_chla; where bloom=0; output out=chla_res predicted=pred...

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 de Biologia Molecular de Llevats Facultat de Veterin?ria de la Universitat Aut?noma de Barcel...

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 effects; you could also get standard e...

...yield'; proc mixed method=reml data=yield; class rep Management Foliar_Fungicide Chemical_Treatment; model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr; random rep rep*Management rep*Management*Foliar_Fungicide; lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff; ods output diffs=ppp lsmeans=means; ods listing exclude diffs lsmeans; run; quit; %include'C:\Users\harmon12\Desktop\pdmix800.sas'; %pdmix800(ppp,means,alpha=0.10,sort=yes); ods graphics off; run; quit; proc univariate data=res...

...- Hash: SHA1 Hi. I have been using SAS for some time, and now I have discovered R. I am very happy with it, but I have not found out how to perform some of the multiple comparisons I was used to do in SAS. With the SAS/STAT, I generally used the MEANS (for comparison of arithmetic means) and the LSMEANS (for adjusted means) statements of the GLM procedure (I think it is equivalent to lm in R). They provided a lot of tests: LSD, Duncan, Tukey-Kramer, Bonferroni, Scheff??, SNK, etc. However, in R I have only discovered Tukey-HSD. I have searched for information about this, but I was not successful....

...n%tissue >lme(gene ~ tissue + gender, random = list(patients = ~1 , stage = ~ 1)) I got results from aov, but I got error message from lme which is false convergence (8). so I could not compare the results of aov with lme. Could anybody tell me whether I use the correct command? How can I get LSMeans for Cancer vs. Normal in R ( I know it is easy in SAS)? Thanks, Shirley

...anagement Foliar_Fungicide Chemical_Treatment; >> >> model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment >> Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr; >> >> random rep rep*Management rep*Management*Foliar_Fungicide; >> >> lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff; >> >> ods output diffs=ppp lsmeans=means; >> >> ods listing exclude diffs lsmeans; >> >> run; quit; >> >> %include'C:\Users\harmon12\Desktop\pdmix800.sas'; >> >> %pdmix800(ppp...

...d; > > class rep Management Foliar_Fungicide Chemical_Treatment; > > model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment > Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr; > > random rep rep*Management rep*Management*Foliar_Fungicide; > > lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff; > > ods output diffs=ppp lsmeans=means; > > ods listing exclude diffs lsmeans; > > run; quit; > > %include'C:\Users\harmon12\Desktop\pdmix800.sas'; > > %pdmix800(ppp,means,alpha=0.10,sort=yes); >...

...hemical_Treatment; >>> >>> model Grain_Yield__Mg_h_ =Management|Foliar_Fungicide|Chemical_Treatment >>> Final_Stand__Plants_A_ / outpred=resids residual ddfm=kr; >>> >>> random rep rep*Management rep*Management*Foliar_Fungicide; >>> >>> lsmeans Management|Foliar_Fungicide|Chemical_Treatment / pdiff; >>> >>> ods output diffs=ppp lsmeans=means; >>> >>> ods listing exclude diffs lsmeans; >>> >>> run; quit; >>> >>> %include'C:\Users\harmon12\Desktop\pdmix800.sas';...

...'" 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; The codes above give equivalent outputs (analysis of a 5(g) by 2(h) factorial, RCBD(b)), except for the "lsmeans g / diff" line, which I cannot reproduce in R! The below gives one of the comparisons I am looking for. anova(model, L=c("gPEN"=1, "gC5"=-1))...