search for: reps

Hi, can anyone help me fit betabinomial model to the following dataset where each iD is a cluster in itself , if i use package aod 's betabinom model it gives an estimate of zero to phi(the correlation coeficient ) and if i fix it to the anova type estimate obtained from icc( in package aod) then it says system is exactly singular. And when i try to fit my loglikelihood by

Dear R people I came across a strange thing: sd(rep(0.01, 15)) #0 sd(rep(0.001, 15)) #4.489023e-19 sd(rep(0.00001, 15)) #0 sd(rep(0.00000001,15)) #1.712427e-24 sd(rep(0.01, 13)) #1.805557e-18 sd(rep(0.001, 13)) #4.513894e-19 sd(rep(0.00001, 13)) #0 sd(rep(0.00000001,13)) #0 sd(rep(5.01, 15)) #0 sd(rep(5.001, 15)) #4.489023e-19 sd(rep(5.00001, 15))

Hi, I have a dataframe containing data from individuals 1, ..., 12 (grouping variable "g" in the data frame below), which belong either to "A" or "B" (grouping variable "f"): set.seed(1) tmp <- data.frame(

Hi, I would like to construct a transition matrix from a data frame with annual transitions of marked plants. plant<-c(1:6) class<-c("seed","seed", "seed", "veg", "rep", "rep") fate<-c("dead", "veg","veg","rep", "rep", "veg") trans<-data.frame(plant, class, fate)

Dear list members, I am analyzing Affymentrix gene expression data and would like to apply the R package, VarSelRF to identifying small sets of genes that could be used for diagnostic purpose. Basically, the data matrix is composed of 22277 rows (genes) and 65 columns (samples). I did unsupervised clustering using pvclust to get 4 classes. What I would like to do is to get unique genes for each

Dear Helpers, I wonder whether you might be able to help me. I have a plot composed of ggplot (and a follow on geom_smooth call). I would like to restrict the display range of the y axis to a smaller range, a sort of zooming onto a region. I attempted to use ylim, but it will effect the range (i.e. effect the geom_smooth call). Is there any way that I can save the results up to geom_smooth call

I have a 43MB dataframe ( 5 variables) and I'm trying to summarize subsets of the data. I've RTFM ( not very clear) and looked at a variety of samples but cant seem to figure out how to make these functions work. A sample of what I want to do would be this: ids<-seq(1,50) years<-c(rep(5,10),rep(6,10),rep(7,10),rep(8,20))

Dear R-list members, I would like to pose a question about the use and results of the glm() function for logistic regression calculations. The question is based on an example provided on p. 229 in P. Dalgaard, Introductory Statistics with R, 2nd. edition, Springer, 2008. By means of this example, I was trying to practice the different ways of entering data in glm(). In his book, Dalgaard

Hi everyone! #I need a loop or a function that creates a X2 variable that is X1 without the extreme values (or X1 winsorized) by industry and year. #My reproducible example: firm<-sort(rep(1:1000,10),decreasing=F) year<-rep(1998:2007,1000) industry<-rep(c(rep(1,10),rep(2,10),rep(3,10),rep(4,10),rep(5,10),rep(6,10),rep(7,10),rep(8,10),rep(9,10), rep(10,10)),1000) X1<-rnorm(10000)

I am making the question clear. Please help. > Dear R experts > > I was using kinship package to fit mixed model with kinship matrix. > The package looks like lme4, but I could find a way to extract p-value > out of it. I need to extract is as I need to analyse large number of > variables (> 10000). > > Please help me: > > require(kinship) > > #Generating

Hi it says in R-exts that A method must have all the arguments of the generic, including ... if the generic does. A method must have arguments in exactly the same order as the generic. A method should use the same defaults as the generic. So, how come the arguments for rep() are (x, times, ...) and the arguments for rep.default() are (x, times, length.out, each, ...) ?

Would someone be able to help with this question? I'm using the Gcmrec, Survrec, and Design packages to do a power analysis on simulated data. I'm receiving an error after using the Survr function that all data must have a censoring time even after using the gcmrec function: newdata<-addCenTime(olddata). My program is below. I'd greatly appreciate any help!

hello, all. in my previous research, i have always used existing data. i am trying something new as an exploratory exercise and have never create my own variable form scratch. essentially, i am creating a variable for party affiliation. here is an example. var =party. levels= democrat, republican, other. respondents will indicate which category they fall under. for the sake of ease, i will

Hola lista, tengo una base de datos muy grande de un datalogger. Consiste en un vector con 0's y 1's, y tengo que ver cuando hay 600 o más unos seguidos. Se me ocurrió hacerlo con un loop for. Pero tarda demasiado. También intente usar which para que seleccione solo los 1 para empezar a sumar pero no hay gran diferencia. A alguien se le ocurre alguna solución para hacerlo más rápido? Acá

Dear R-Users, without going into details I tried to prepare a simple example to show you where I would need help. In particular I prepare two examples-template for a study I'm conduction on discrete-time methods for survival analysis. Each of this example has two datasets which are basically equal, with the exception that in the former one has individual data and in the latter one aggregated

suppose I have the following data x<-c(rep(.1,5),rep(.2,6),rep(.4,10),rep(.5,20)) y<-c(rep(.5,3),rep(.6,8),rep(1.2,8),rep(2.5,18),rep(3,4)) If I plot(x,y) in R, I will only get seven distinct points. What I want to do is to use different symbols to show the frequency at each point. e.g. if the frequncey is between 1 and 5, then I plot the point as a circle; if the frequency is between 6

Hello R-world, I am facing a peculiar problem and hope someone out there can comment on it. In goodness-of-fit tests for evaluation of distributions, there are three well-known methods: 1. Chi-square 2. Anderson-Darling 3. Kolmogorov-Sminrov I am trying to use the second test. Many researchers have reported results using this test. I wrote programs in C and now in R to do this. I run into

Dear List I am trying to draw a heatmap using ggplot2. In this heatmap I have faceted my data by 'infection' of which I have four. These four infections break down into two types and I would like to reorder the 'infection' column of my data to reflect this. Toy example below: library(ggplot2) # test data for ggplot reordering genes <- (rep (c(rep('a',4),

Hola a todos, me ha gustado mucho la solución de Carlos, muy eficiente y muy ingeniosa al utilizar la funcion col() que o no la conocia o no me acordaba de ella. La parte mas "lenta" sigue siendo el apply que en el fondo no es mas que un ciclo for a traves de las filas, asi que inspirado por el metodo de Carlos pense que podria ser mas rapido si iteramos a traves de las columnas por lo

Hello R user and helper! I would like to get a 3d plot with coloured points. I did that: colors<-c(rep("2",7), rep("3",12), rep("4", 24), rep("5", 13), rep ("6", 8), rep("7", 51), rep("8", 1), rep("9", 15), rep("10", 53), rep ("11",3), rep("12",3), rep("13", 8),