similar to: R function for computing Simultaneous confidence intervals for multinomial proportions

Dear all, I studied in tank prey fish behavior. Using the design described below (and R code), I want to test the effects of both habitat and predator (and interaction) on prey fish's vertical distribution, which was recorded (with repeated measures) as a categorical variable. I found that package mlogit might fit to my need but I don't know how to specify my complex design in the

Hi R users! Is there a function that extracts the simultaneous confidence intervals for multinomial proportions as described by Sison and Glaz 1995? or anyone else for that matter? I have seen that SAS has macro for this ( http://www.jstatsoft.org/v05/i06) and i was wondering if R had something similar? Thank you and have a great day ahead! ---------------------------------

Hello! I want to calculate simultaneous confidence intervals for a nominal variable with three categories: "yes", "no", "partially" and I expect that far more than 5 samples fall into each category. I have read that Glaz & Sison's method is only appropriate for variables with 7 or more categories. Therefore, the Goodman method seems like a good idea. I have

Sometimes one might like to obtain pointwise bootstrap bias-corrected, accelerated (BCA) confidence intervals for a large number of statistics computed from a single dataset. For instance, one might like to get (so as to plot graphically) bootstrap confidence bands for the fitted values in a regression model. (Example: Chiu S et al., Early Acceleration of Head Circumference in Children with

Dear R user, I used the following to do a bootstrap. >bootObj<-boot(data=DAT, statistic=Lp.est, R=1000,x0=3) I have the following output from the above bootstrap. How can I extract  components of the output. For example, how can I extract the std.error? > bootObj   ORDINARY NONPARAMETRIC BOOTSTRAP   Call: boot(data = DAT, statistic = Lp.est, R = 1000, x0 = 3)   Bootstrap Statistics

Hello I am trying to add a ternary plot as a corner inset graph to a larger main ternary plot. I have successfully used add.scatter in the past for different kinds of plots but It doesn't seem to work for this particular function. It overlays the old plot rather than plotting as an inset. Here is a simple version of what I'm trying. Note that if I change the inset plot to be an ordinary

Dear all, I run a loop wrapped in try(), and for each of the rows where "try-error" is true I want to fill that row with NA (at the moment it is omitted). So I would expect to get a dataframe with 1000 rows some of which would be empty, but instead I get a dataframe with 995 rows. In this case missing 5 rows were omitted. Any suggestions? Thanks, Olga ----------------

you could try mapply mydata2<-mapply("[", mydata, lapply(mydata, function(x) !x %in% A)) mydata2[[1]]<-A #to replace the obviously deleted elements of "A" mydata2 mydata2[[1]] mydata2[[2]] mydata2[[3]] mydata2[[4]] Stefano -----Messaggio originale----- Da: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch]Per conto di jastar

Hello, does anyone know of a quick way to create a data frame "like" another, but with more rows? What I'd like to do is this: if mydata is a data.frame like a b c 1 TRUE yes 2 FALSE no 3 TRUE yes I'd like to get mydata2 with the same column names and column types, but without the values and with more rows. All I could think of was to manually do

Dear all, I have problem when reading a table into R. The total row of read in table has is much less than the original saved table. I built a 1,273,230 by 6 data set named "mydata2", it was saved in the following command, write.table(mydata2, "mydata2.txt", row.name=F,col.name=T,quote=F,sep="\t") The next day I read in above saved text file into R,

Hi all, I am trying to understand Writing R Extension... Section 1.1.5, data: I include two datasets in a package, one using 'save', the other using 'write.table': --- 8< ---- myData1 <- data.frame(x=1:10) write.table(myData1,file="myData1.txt") myData2 <- data.frame(x=2:10) save(myData2,file="myData2.Rdata") --- 8< ---- Then R CMD check aks me to

Dear all, I have problem when reading a table into R. The total row of read in table has is much less than the original saved table. I built a 1,273,230 by 6 data set named "mydata2", it was saved in the following command, write.table(mydata2, "mydata2.txt", row.name=F,col.name=T,quote=F,sep="\t") The next day I read in above saved text file into R,

R helpers I am getting to grips with R but came across a small problem today that I could not fix by myself. I have 3 text files, each with a single column of data. I read them in using: myData1<-scan("C:/Program Files/R/myData1.txt") myData2<-scan("C:/Program Files/R/myData2.txt") myData3<-scan("C:/Program Files/R/myData3.txt") I wanted to produce a

Hi After Fedora 31 upgrade to F32, samba started to break after more clients connect (up to 10). No config change, no clients change, no sharing changes.? F31 had smb 4.11.7 and was working fine after restarting service it works for a while then it breaks with various messages for each client I tried removing some old parameters (no help), I had mounted ssd drive into folder structure and I

Hi, 1/ I wonder why a anova.lme on a single lme object does not print the sum of squares (as expected from the help: "a data frame with the sums of squares, numerator degrees of freedom, denominator degrees of freedom, F-values, and P-values"). Example: > fm2 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1) > anova(fm2) numDF denDF F-value p-value

Dear useRs, How could I obtain the confidence intervals for the means of my treatments, when my data was fitted to a GLM? I need the CI's for the Poisson and Negative Binomial distributions. Here's what I have: mydata1 <- data.frame('treatments'=gl(4,20), 'value'=rpois(80, 1)) model1 <- glm(value ~ treatments, data=mydata1, family=poisson) means1 <-

Hello r-help, I am using the excellent np package to conduct a nonparametric kernel regression and am having some trouble plotting the results. I have 2 covariates, x1 and x2, and a continuous outcome variable y. I am conducting a nonparametric regression of y on x1 and x2. The one somewhat unusual feature of these data is that, to be included in the dataset, x1 must be at least as large as x2.

Dear Jim, Here is again exactly what I did and with the output of Rprof (with this reduced dataset and with a simpler function, it is here much faster than in real life). Thanks you again for your help! ## CODE ## mydata1<- structure(list(species = structure(1:8, .Label = c("alsen","gogor", "loalb", "mafas", "pacyn", "patro",

Dear R experts, I got help to build a loop but there is a bug inside it that causes one part of the mechanism to fail. It should grow once, but if keep growing on rows where $ti_all is not NA. Here is a wall of code that very crudely demonstrates the problem, there is a couple of dim() outputs at the end where you can see how it the second time around keeps adds (2) rows, but this does not

Hello I have dataframes. mydata1 <-data.frame(value=c(15,20,25,30,45,50),dates=c("2005-05-25 07:00:00 ","2005-05-25 19:00:00","2005-06-25 07:00:00","2005-06-25 19:00:00 ","2005-07-25 07:00:00","2005-8-25 19:00:00")) or mydata2 <-data.frame(value=c(15,20,25,30,45,50),dates=c("2005-05-25 00:00:00 ","2005-05-25