similar to: Logistic regression and mixed effects / hierarchial structure

Dear all, I collected my data from the different agricultural fields every week over a period of a month. how can I test for spatial autocorrelation in R with data that are temporally pseudoreplicated? I used lme with correlation=corCompSymm(form=~Date) to model temporal pseudoreplication. Regards, VG

Greetings R users, My goal is to generate quartile groups of each variable in my data set. I would like each experiment to have its designated group added as a subsequent column. I can accomplish this individually with the following code: brks <- with(data_variables, cut2(var2, g=4)) #I don't want the actual numbers, I need a numbered group data$test1=factor(brks,

I have data that looks like lake,loglength,logweight 1,2.369215857,1.929418926 1,2.426511261,2.230448921 1,2.434568904,2.298853076 1,2.437750563,2.298853076 1,2.442479769,2.230448921 1,2.445604203,2.356025857 ... 102,2.722633923,3.310268367 102,2.781755375,3.502153893 102,2.836324116,3.683407299 102,2.802773725,3.583312152 102,2.790285164,3.546419267 102,2.806179974,3.599118565

Why am I getting a wrong result for quartiles? here is my code: > cbiomass = c(910, 1058, 929, 1103, 1056, 1022, 1255, 1121, 1111, 1192, > 1074, 1415) > summary(cbiomass) > IQR(cbiomass) The result R gives me is: For the summary > Min. 1st Qu. Median Mean 3rd Qu. Max. 910 1048 1088 1104 1139 1415 For IQR > 91.25 ********* The true Q1 is 1039

>[Rd] boxplot() defaults {was "boxplot in extreme cases"} >Martin Maechler maechler at stat.math.ethz.ch >Mon Nov 8 10:36:42 CET 2004 > > AndyL> Try: > > AndyL> x <- list(x1=rep(c(0,1,2),c(10,20,40)), > x2=rep(c(0,1,2),c(10,40,20))) > AndyL> boxplot(x, pars=list(medpch=20, medcex=3)) > > AndyL> (Cf ?bxp, pointed to from

Hello everybody, This is a bug involving functions in core R package: graphics::hist.default, grDevices::nclass.FD, and base::pretty.default. It is not yet on Bugzilla. I cannot submit it myself, as I do not have an account. Could somebody else add it for me, perhaps? That would be much appreciated. Kind regards, Sietse Sietse Brouwer Summary ------- Floating point errors can cause a data

I just got the same error message with > sessionInfo() R version 3.4.0 (2017-04-21) Platform: x86_64-apple-darwin15.6.0 (64-bit) Running under: macOS Sierra 10.12.4 Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

Hemant's problem is that the indicators are not distributed uniformly. With a uniform distribution, categorization gives a reasonably optimal separation of cases. One approach would be to drop categorization and calculate the overall score as the mean of the standardized indicator scores. Whether this is an option I do not know. I did offer an "eyeball" set of breaks in a previous

Hi,everyone i need to calculate quartile values of a variable grouped by the other variable . same as in aggregate function(only median,mean or functions is possible-i think so) Could you please help me to achieve the same for other quartile values(5,10,25,75,90) as for median using aggregate. Thanks in advance. data : zip price 60000 567000 60001 478654 60004 485647 60001

Hi I need some help getting results from multiple linear models into a dataframe. Let me explain the problem. I have a dataframe with ejection fraction results measured over a number of quartiles and grouped by base_study. My dataframe (800 different base_studies) looks like > afvtprelvefs basestudy quartile ef ef_std entropy CBP0908020 1 21.6 0.53 3.27

Hi all, I''ve got a simple contingency table produced with table(), and the upper and lower quartiles (quantile25 and quantile75) for the same dataset. Is there a function that tells me in which category does the value of the quartile falls into? for example: tabsp <- table(fam$I[fam$I>0]) 1 3 6 6 1 1 and for the cumulative frequency distribution tabcum <- cumsum(tabsp) 1 3

hello, I'm confused what you guys are talking about. i just want to set ideal threshold values for my RFM scores which can be done using Quantiles but i don't want to use quantiles because my data is not normally distributed so it will lead to wrong ranges of breaks. to fix this problem I'm looking for an approach which can define the ideal range to breaks to categorize RFM scores into

Hi every one, I am trying to generate a normally distributed random variable with the following descriptive statistics, min=1, max=99, variance=125, mean=38.32, 1st quartile=38, median=40, 3rd quartile=40, skewness=-0.274. I know the "rnorm" will allow me to simulate random numbers with mean 38.32 and Sd=11.18(sqrt(125)). But I need to have the above mentioned descriptive

> On Oct 13, 2017, at 2:51 AM, PIKAL Petr <petr.pikal at precheza.cz> wrote: > > Hi > > You expect us to solve your problem but you ignore advice already recieved. > > Your data are unreadable, use dput(yourdata) instead. see ?dput > >> test<-read.table("clipboard", heade=T) > Error in scan(file = file, what = what, sep = sep, quote = quote,

Hi all Well, first: happy new year... Second: I write a function in R that might interest some other people. On the other hand, I am closer to beginners than experts; I don't know how valuable my code is. I don't know how long it will take to me to create a library and I don't know if it's worth to. So before starting this long process, I would like some advices, both on the

Hi the list *** New package *** R to LaTeX : Univariate analyses r2lUniv *URL:*http://www.r-project.org, http://christophe.genolini.free.fr/r2lUniv *** Description *** r2lUniv performs some basic analyses, then generates a code to be included in a LaTeX document to print the analyses in a (so nice!) LaTeX way. r2lUniv performs the analyses automatically according to the classical statistics

Hi the list *** New package *** R to LaTeX : Univariate analyses r2lUniv *URL:*http://www.r-project.org, http://christophe.genolini.free.fr/r2lUniv *** Description *** r2lUniv performs some basic analyses, then generates a code to be included in a LaTeX document to print the analyses in a (so nice!) LaTeX way. r2lUniv performs the analyses automatically according to the classical statistics

I have the following code. > f <- cph(formula = Surv(TimeToDeath, Dead == "Yes") ~1,data=single.dat, x=T, y=T, surv=T) > for(i in c('A', 'B', 'C', 'D', 'E', 'F')){ > f <-update(f,as.formula(paste('Surv(TimeToDeath, Dead == "Yes")~',i,sep=''))) > print(summary(f, paste(i,"=1st

I've just started using R and am still a neophyte, but I found the following curious result. I'm using the current version of R (2.5.1 (2007-06-27) ). Why are the results for the third quartile different in the output from the summary and fivenum commands? For the following data set 457 514 530 530 538 560 687 745 745 778 786 790 792

Full_Name: Michael Toews Version: 2.61 OS: WinXP SP2 Submission from: (NULL) (142.58.206.114) Using boxplot on a PDF device with more than one group (or boxes) produces multiple (and overlain) 1st and 3rd quartile ticks. There are exactly the multiple of boxplot groups as there are of each 1st and 3rd quartile ticks for each boxplot (drawn as a horizontal line at the end of each boxplot), which