similar to: multiple comparison for interaction means

-----BEGIN PGP SIGNED MESSAGE----- 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

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

For the initial data step, assuming a data frame named stress already exists, and using base R, you can start with something like this: barcodes.to.delete <- c('16187DD4015', '16187DD6002', {complete the comma-delimited vector of barcodes you don't want} ) yield <- subset(stress, !(barcode %in% barcodes.to.delete) ) yield <- subset(yield , !(field %in%

Hi, I need to determinate HSD value from a matrix like that Thesis Days A1 Cx 0 66.07 Cx 0 60.24 Cx 0 42.86 Tw 0 66.07 Tw 0 60.24 Tw 0 42.86 Aa 0 66.07 Aa 0 60.24 Aa 0 42.86 Qe 0 66.07 Qe 0 60.24 Qe 0 42.86 Cx 56 310.86 Cx 56 223.17 Cx 56 186.77 Tw 56 149.42 Tw 56 127.75 Tw 56 138.59 Aa 56 130.24 Aa 56 214.83 Aa 56 137.95 Qe 56 186.64 Qe 56 189.09 Qe 56 187.87 this is my script

Hello all, My statistical analysis training up until this point has been entirely done in SAS. The code I frequently used was: *Yield Champagin; data yield; set stress; if field='YV' then delete; if field='HB' then delete; if barcode='16187DD4015' then delete; if barcode='16187DD6002' then delete; if barcode='16187DD2007' then delete; if

Hi, I did the analysis of variance of a split-plot and the effect of treatment was significant. I would like compare treatment means using Tukey. I can't extract the mean square to apply HSD.test to use in agricolae package. anava = aov(ganhos ~ Blocos + Trat*Supl + Error(Blocos/Trat)) names(anava) summary(anava) require(agricolae) HSD.test(ganhos, Trat, df, MSerror, alpha = 0.05)

Hello, I am really new to R and it's still a challenge to me. Currently I'm working on my Master's Thesis. My supervisor works with SAS and is not familiar with R at all. I want to run an Anova, a tukey-test and as a result I want to have the tukey-grouping ( something like A - AB - B) I came across the HSD.test in the agricolae-package, but... unfortunately I do not get an output

I wish to second this approach to learning R. I tried for several years to translate other stat programs or provide parallel analyses with R. This dabbling-in-R approach did not work . When a transferred to a research unit that could ill afford commercial software, I devoted my entire time to doing everything in R. This was a difficult learning process, but I eventually became proficient

I will offer an opinion, with which others may fairly take issue. If you are coming from SAS and wish to learn R, you should forget about SAS entirely; it is ancient and convoluted. But more to the point, as others have already suggested, you will only confuse and hamstring yourself trying to convert the programming paradigms of one language into another. Better to consider the **tasks** you wish

Regarding point 3, as a moderator I have been helping Andrew get this post out to the list over the past week. His previous attempts were encoded in some way that the listserv rejected. He sent me the post via his gmail account and viewing the source I saw it had at least both plain test and HTML an I said it was worth a try to post it. Certainly on my mail client his post displays acceptably

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;

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. >

Un texte encapsul? et encod? dans un jeu de caract?res inconnu a ?t? nettoy?... Nom : non disponible URL : <https://stat.ethz.ch/pipermail/r-help/attachments/20111102/25eb7284/attachment.pl>

Dear R experts I am running a negative-binomial GLM (glm.nb) to test the null hypotheses that species 1 and 2 are equally abundant between site 1 and site2, and between each other. So, I have a 2x2 factorial design with factors Site (1,2) and Taxon (1,2). Since the Site:Taxon interaction is significant, I need to do the equivalent to a "post-hoc test" for ANOVA, however, the same tests

Hola a todos Estoy trabajando con el package agricolae para realizar pruebas de medias y obtengo un error que no entiendo al trabajar las interacciones. Os explico: Poseo un Diseño Completamente al Azar para el arreglo de tratamientos factorial de la forma 2*2*4. El modelo consta de 6228 observaciones, el esquema para el anova es: A= 2 niveles={2,5} B= 4 niveles={F1,F2,F3,F4} C= 2

Hi, The problem is that I would like to do an interaction plot with intervals based on Tukey's honestly significant difference (HSD) procedure, but I do not know how to do it in R. I have 3 factors "A", "B" and "C" and a response variable "response". I would like to study a model where there are main effects and second order interaction effects.

Hello, My boss wants me to do a Duncan's test, which is under the agricolae package. Unfortunately I am not versed enough in R to run my data. I have 7 subspecies of deer mouse for which I have 23 measurements which are my variables of interest. I have run an ANOVA for each of the set of subspecies and variables, my data look like this: subspecies WMF 1 rowleyi 2.50 2 rowleyi 2.30

Hi there, I am doing multiple comparisons for data that is not normally distributed. For this purpose I tried both functions kruskal{agricolae} and kruskalmc{pgirmess}. It confuses me that these functions do not yield the same results although they are doing the same thing, don't they? Can anyone tell my why this happens and which function I can trust? kruskalmc() tells me that there are no

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi. Sorry for sending this question twice, but I would really like to know your opinion on this topic. I have been reading about the (in)convenience of using least-squares means (a. k. a. adjusted means) in multiple comparisons (I used to resort to them when using SAS). I even read a post in this list warning against them, but not giving much detail.

Hi, A client has a consumption measure on each of four products. The sample size is 75. The consumption distributions are highly skewed for each product. He would like a pairwise comparison test of the products, much like Tukey's HSD but using medians rather than means. Is there such a median comparison test in R? Thanks, Walt ________________________ Walter R. Paczkowski, Ph.D.