similar to: Multiple comparisons; rank-based anova

Dear R-help, I posted this on 4 Oct but got no response (I wasn't even told to go away and do some more background reading ;) ). I am reposting it in the, perhaps, vain hope that someone with knowledge of the subject will reply, if only to point me in a different direction to which I am now facing. Earlier Posting:--- I am sorry if this is more of a stats question than an R-question, but I

Dear R-help, I am sorry if this is more of a stats question than an R-question, but I have found it difficult to get a clear answer by other means. Q. Would it be "wrong" to specify a nested model and retain a common intercept, e.g. lm(NH4 ~ Site/TideCode + 1) I am aware (?) that my Site-coefficients are now calculated relative to my reference Site (treatment.contrasts), *but* that

Dear R-help, Has anyone implemented sparklines in the strips of a lattice plot? What I have in mind is, say, highlighting that part of a time series that one is examining in more detail in a set of lattice plots. Regads,. Mark Difford. PS: (Andreas Loffler has implemented a simple but functional version for TeX/LaTeX: http://www.tug.org/tex-archive/help/Catalogue/entries/sparklines.html)

Dear List, I would appreciate help on the following matter: I am aware that higher dimensional contingency tables can be analysed using either log-linear models or as a poisson regression using a generalized linear model: log-linear: loglm(~Age+Site, data=xtabs(~Age+Site, data=SSites.Rev, drop.unused.levels=T)) GLM: glm.table <- as.data.frame(xtabs(~Age+Site, data=SSites.Rev,

Dear List, I would greatly appreciate help on the following matter: The RuleFit program of Professor Friedman uses partial dependence plots to explore the effect of an explanatory variable on the response variable, after accounting for the average effects of the other variables. The plot method [plot(summary(rq(y ~ x1 + x2, t=seq(.1,.9,.05))))] of Professor Koenker's quantreg program

Hello, all R-users! I am working on fitting a survival analysis model using the coxph function for Cox proportional hazards regression model. Data look like usual: ========================== group block death censor Group1 1 4 1 Group1 1 12 1 ... Group2 30 4 1 Group2 30 4 1 ... Group3 57 16

Hi everyone, I am new to R and have a question that relates to unplanned post-hoc comparisons using the multcomp package after a mixed effects model. I couldn't find the answer to it in the archive or in any manual. I have a dataset in which several plants have been treated in a particular way and a continuous response variable has been measured depending on several leaves per plant. I am

I'm trying to do an ANCOVA with two factors (clipping treatment with two levels, and plot with 4 levels) and a covariate (stem diameter). The response variable is fruit number. The minimal adequate model looks like this: model3<-lm(fruit~clip + plot + st.dia + clip:plot) I'd like to get some multiple comparisons like the ones from TukeyHSD, but TukeyHSD doesn't work with the

Hi all: There's a question about glht function. My data:data_ori,which inclue CD4, GROUP,time. f_GROUP<-factor(data_ori$GROUP) f_GROUP is a factor of 3 levels(0,1,2,3) result <- lme(sqrt(CD4) ~ f_GROUP*time ,random = ~time|ID,data=data_ori) glht(result, linfct = mcp(f_GROUP="Tukey") ) Error in `[.data.frame`(mf, nhypo[checknm]) : undefined columns selected I can't

Hi I have some experimental data where I have counts of the number of insects collected to different trap types rotated through 5 different location (variable -location), 4 different chemical attractants [A, B, C, D] were applied to the traps (variable - semio) and all were trialled at two different CO2 release rates [1, 2] (variable CO2) I also have a selection of continuous variables

Dear Colleagues, I have scoured the help files and been unable to find an answer to my question. Please forgive me if I have missed something obvious. I have run the following two models, where "category" has 3 levels and "comp" has 8 levels: mod1 <- lmer(x~category+comp+(1|id),data=impchiefsrm) mod2 <- lmer(x~category+comp+category*comp+(1|id),data=impchiefsrm)

Dear fellow R.users/.lovers, I am very new to both R and this list, so I hope you will be patient with me in the beginning if my enquiries are inappropriate/unclear. I am trying to perform some rather complex statistical modelling using mixed-effects models. I have, after a rather difficult beginning, finally boiled down my model (using the lme function in nlme) to a couple of fixed effects

Good day, I read some data from a PostgreSQL database by a following script: library(Rdbi) library(RdbiPgSQL) # conn becomes an object which contains the DB connection: conn <- dbConnect(PgSQL(), host="localhost", dbname="BVS", user="postgres", password = "*******") query_duj_kal <- dbSendQuery(conn, "select zdroj as well, cas as date, fe2,

Hi R Forum I am a newbie to R and I have been amazed by what I can get my team to accomplish just by implementing Scripting routines of R in all my team's areas of interest.. Recently i have been trying to adopt R scripting routine for some analysis with longitudanal data.. I am presenting my R script below that I have tried to make to automate data analysis for longitudanal data by employing

Hello everybody, problem solved, there was a typo. I wrote Type instead of Material Best ----Messaggio originale---- Da: angelo.arcadi@virgilio.it Data: 22-giu-2012 11.05 A: <r-help@r-project.org> Ogg: Error with glht function: Error in mcp2matrix(model, linfct = linfct) : Variable(s) 'Type' have been specified in 'linfct' but cannot be found in 'model'!

An amusing afterthought : What is a rival software (ahem!) was planting this, hoping for a divide between S and R communities.or at the very minimum hoping for some amusement. an assumption or even a pretense of stealing credit is one of the easiest ways of sparking intellectual discord Most users of softwares don't really care about who gets credit ( Who wrote Windows Vista ,or Mac OS or

Dear list members, I have a question concerning multiple comparisons after using glm. My response variable is days until emergence of an insect species. The explanatory variables are sex (two levels), parasitoids added (two levels) and populations (34 levels). I would like to know now which populations are different in days until insect emergence. For this I used multiple comparisons as

DeaR experts, I was asked for a log-rank pairwise survival comparison. I've a straightforward way to do this using the SAS system: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#/documentation/cdl/en/statug/63033/HTML/default/statug_lifetest_sect019.htm What I've found in R is shown below, but it's not a logrank test, I suppose. (The documentation

Good day, I'm trying to get more time series in one plot. As there are bigger differences in values of variables I need logaritmic y axis. The code I use is the following: nvz_3_data <- read.csv('/home/tomas/R_outputs/nvz_3.csv') date <- (nvz_3_data$date) NO3 <- (nvz_3_data$NO3) NH4 <- (nvz_3_data$NH4) date_p <- as.POSIXct(date, "CET") par(mfrow=c(2,1), ylog

I am not sure if this is an R-users question, but since most of you here are statisticians, I decided to give it a shot. I am using the lm() function in R to fit a dependent variable to a set of 3 to 5 independent variables. For this, I used the following commands: >model1<-lm(function=PBW~SO4+NO3+NH4) Coefficients: (Intercept) SO4 NO3 NH4 0.01323 0.01968