similar to: Expressions in lattice conditional variables

Hi all-- We are planning an intervention study for adolescent alcohol use, and I am planning to use simulations based on a hurdle model (using the hurdle() function in package pscl) for sample size estimation. The simulation code and power code are below -- note that at the moment the "power" code is just returning the coefficients, as something isn't working quite right. The

Hello R-users, I would like to use subscript in chemical formulas for the different treatments in a boxplot. Fot title, xlab and ylab sub- and superscript is no problem, but for the different treatments of the following example I cannot get subscript. Example: weight <- c(6,5,7,2,7,3,9,4,2,7,8,9,2,3,4,5) treatments <- as.factor(rep(c('Control',

Hello, R users, I have two factors (treat, section) anova design experiment where there are 3 replicates. The objective of the experiment is to test if there is significant difference of yield between top (section 9 to 11) and bottom (section 9 to 11) of the fruit tree under treatment. I found that there are interaction between two factors. I wonder if I can contrast means from levels of

Dear Thomas Lumley, and R-help list members, I have read your article "Network meta-analysis for indirect treatment comparisons" (Statist Med, 2002) with great interest. I found it very helpful that you included the R code to replicate your analysis; however, I have had a problem replicating your example and wondered if you are able to give me a hint. When I use the code from the

Hello, R experts, I have a list called dataHP which has 30 elements (m1, m2, ..., m30). Each element is a 7x6 matrix holding yield data from two factors experimental design, with treatment in column, position in row. For instance, the element 20 is: dataHP[[20]] col1 col2 col3 trt1 trt2 trt3 [1,] 22.0 20.3 29.7 63.3 78.5 76.4 [2,]

I am confused whether Student's sleep data "show the effect of two soporific drugs" or Control against Treatment (one drug). The reason is the next: > require(stats) > data(sleep) > attach(sleep) > extra[group==1] numeric(0) > group [1] Ctl Ctl Ctl Ctl Ctl Ctl Ctl Ctl Ctl Ctl Trt Trt Trt Trt Trt Trt Trt Trt Trt [20] Trt Levels: Ctl Trt > sleep$group [1] 1 1 1 1 1

I am trying to define groupings from levels of factor variables and this the warning message that R give "& not meaningful for factors". The nature of my task is this. I have a variable stage which has the levels (1B, 2A, 2B) - these are the AJCC TNM stages of cancer, and another variable diameter with factor levels ("=< 4", "4 - 6.5, > 6.5; limit values are

Hi all, Take this simple script, which I'm trying to use to understand when modification in-place occurs, and when a new object is created: message("Global") x <- 1:3 .Internal(inspect(x)) x[2] <- 1L .Internal(inspect(x)) message("In function") (function() { x <- 1:3 .Internal(inspect(x)) x[2] <- 1L .Internal(inspect(x)) })() If I run it from the

Dear, I have a dataset with 4 subjects (see ID in example), and 4 treatment (see TRT in example) which are tested on 2 locations and in 3 blocs. By using Lattice dotplot, I made a graph that shows the raw data per location and per bloc. In that graph, I would like to have a reference line per bloc that refers to the first treatment (T1). However, I can not find how to do that. I can make

Hello, I probably have a syntax error in trying to generate an expected survival curve from a weighted cox model, but I can't see it. I used the help sample code to generate a weighted model, with the addition of a "weights=albumin" argument (I only chose albumin because it had no missing values, not because of any real relevance). Below are my code with the resulting error

Hi, I would like to know if it's ok to use .Internal(inspect(x)) in order to detect vector copying. Take for example the following silly code: f <- function() { x = seq(10) print(.Internal(inspect(x))) for(i in seq(10)) { x[i] <- x[i] + 1 print(.Internal(inspect(x))) } } The output of f() was: @bd7acf0 13 INTSXP g0c4 [NAM(1)] (len=10, tl=0) 1,2,3,4,5,... [1] 1 2

I have a vaiable named NAM having value : 1,2,3,4,5,6,7,8,9. I want to make an indicator variable that will take value 1 if NAM=7 or NAM=8 or NAM=9. How can I do that? I usually do: Var001<- ifelse(NAM==7,1,0) for the simplest case. [[alternative HTML version deleted]]

Hi All? I was looking at a posting from June-17. I managed to solve it. However, when I changed the example in the posting, my solution will work only once at a time which was mentioned by Jim Lemon on his response to the original posting. This means that my solution will have to be repeated as many times as the maximum number of spaces on each gap; something that may not work well for large

Hello, I'm having trouble in using "assign(paste ..." command . I could create several dataframes following trinomial distribution using it but it could not be used to check their row means of the created dataframe. For example, the following works: probTrt=matrix(0,4,3); probTrt; #malf, death, normal probTrt[1,]=c(0.064,0.119,0.817);#for Trt 1 probTrt[2,]=c(0.053,0.125,0.823);#for

You need to give the model formula that gave your output. There are two sources of variation (at least), within and between locations; though it looks as though your analysis may have tried to account for this (but if so, the terms are not laid out in a way that makes for ready interpretation. The design is such (two locations) that you do not have much of a check that effects are consistent over

Dear R users, I am trying to use xyplot to draw group mean and CI. The following is the sample code. But I want: 1. Use different colors and symbols to draw individual points, CI and the lines connect group means from different time points; 2. Add jitters to x axis to allow CIs not be overlapped Could anyone modify the attached code to achieve this? Thanks library(lattice)

Hi there, The following data is obtained from a long-term experiments. > mydata <- read.table(textConnection(" + y year Trt + 9.37 1993 A + 8.21 1995 A + 8.11 1999 A + 7.22 2007 A + 7.81 2010 A + 10.85 1993 B + 12.83 1995 B + 13.21 1999 B + 13.70 2007 B + 15.15 2010 B + 5.69 1993 C + 5.76 1995 C + 6.39 1999

I have been using lme from nlme to do a 3-way anova with all the effects treated as random. I was wondering if someone could direct me to an example of how to do this using lmer from lme4. I have 3 main effects, tim, trt, ctr, and all the interaction effects tim*trt*ctr. The response variable is ge. Here is my lme code: dat <-

I'm running an ANOVA on some data for respiration in a forest. I am having a problem with my degrees of freedom. For one of my variables I get one fewer degrees of freedom than I should. I have 12 plots and I therefore expected 11 degrees of freedom, but instead I got 10. Any ideas? I have some code and output below: > class(Combined.Plot) [1] "character" >

Hi all, I have problems in a dataframe variables types. Look: from a loop function: for(...){ ... dados.fin <- rbind(dados.fin, c(L=j, A=j^2, Nsp=nsps, N=length(amosfin$SP), AmT="am",NAm=nam, AMST=amst)) dados.fin <- rbind(dados.fin, c(L=j, A=j^2,