search for: trialing

I'm trying to learn how to do a repeated measures ANOVA in R using lme(). A data set that comes from the book Design and Analysis has the following structure: Measurements (DV) were taken on 8 subjects (SUB) with two experimental levels (GROUP) at four times (TRIAL). In SAS, I use the code: PROC MIXED DATA=[data set below]; CLASS sub group trial; MODEL dv = group trial group*trial;

Dear R-help, I am writing some simulation code to create multiple sets of time-to-event clinical trial data (for use in meta-analysis). Within each trial, I want to apply censoring via simulation of uniform variables (with minimum zero and maximum the median outcome time for that particular trial). I have started by pre-allocating a matrix which has 3 columns; one for trial number, one for

Hello, I am confused about the order of nesting in mixed models using functions like aov(), lme(), lmer(). I have the following data: n subjects in either condition A or B each subject tested at each of 3 numerical values ("distance" = 40,50,60), repeated 4 times for each of the 3 numerical values ("trial" = 1,2,3,4) Variable summary: Condition: 2 level factor Distance:

I am extremely puzzled by this behavior in R. I have a data frame called Trials in which I have results from an experiment. I am trying to do a subjects analysis, but getting weird results. Each row has 1 trial in it, which includes a column for the subject number I get the list of subject numbers like so: > Subj=unique(sort(Trials$Subj)) Then I loop over them. But I get strange results. As

Hello, I have been struggling for quite some time to find a solution for the following problem. I have a data frame which is organized by block and trial. Each trial is represented across several rows in this data frame. I'd like to extract the first x rows per trial and block. For example block trial x y 1 1 1 605 150 2 1 1 603 148 3 1 1 604 140 4 1 1 600 140 5 1 1 590 135 6 1 1 580 135 7

Hi, I am create a new DF that summarizes the mean angle per treatment/trial, of the original DF (see below). I have had some success using: (tapply(df$Angle, INDEX=interaction(df$State, df$Trial), FUN=mean)); however, this gives the answer as a list, which means I would have to split the name to get the categories back. Does anyone know a simple way to transform the Original DF into Summary

My name is Giovanna and I am a PhD student in Norway. I am a beginner with statistics and R, hence my ignorance. Apologies from now..... I have been collecting data on time performances of 5 subjects using a 1:3 scale tower yarder. The task was consisting in yarding 5 small logs placed on permanently marked course. Four subjects had different previous experiences (None, Some) and the fifth was a

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Dear R users, I recently stumbled upon problems of reproducibility while running GAM analyses in different R and gam package versions. In the example below, a small dataset is created in which the y and x1 variables are 100% correlated. The intents of this example were primarily for regression testing and, secondarily, to evaluate how the gam algorithm behaves under extreme/limit conditions. I

This code worked fine for me, then did some cleaning up of formatting using ESS (Emacs) and now I get this error, no idea what is causing it, all the brackets/parentheses seem to be balanced. What have I done wrong? Thanks Jim p0.trial01 <- 0.25 TruOR01 <- 0.80 num.patients.01 <- 50 num.trials.01 <- 5 LOR01.het.in <- 0.00 num.sims <- 1 simLOR01 <-

I'm trying to get the following section of code to work, I think the problem is being caused by the assignment of data to the lm function not evaluating to "train" in the parent environment but I can't seem to figure out how to do this. fitmodel <- function(trial,data) { wrap.lm <- function(formula,data,...) { cat("in wrap lm",NROW(data),"\n");

Hi, I'm working with step.gam in gam package. I'm interested both in spline and lowess functions and when I define all the models that I'm interested in I get something like that: > gam.object.ALC<-gam(X143S~ALC,data=dane,family=binomial) >

Hi list, I have a function that detects saccadic eye movements in a time series of eye positions sampled at a rate of 250Hz. This function needs three vectors: x-coordinate, y-coordinate, trial-id. This information is usually contained in a data frame that also has some other fields. The names of the fields are not standardized. > head(eyemovements) time x y trial 51

Hello R users, I am relatively new to the R program, and I hope some of you can offer me some suggestions on how to organize my data in R using some of the more advanced data structuring technique. Here's my scenario: I have date set of 50 participants (each with conditions and demographic data), each participant performed 2x16 trials, for each trial, there was specific information about the

Dear all, I'm a newbie in R. I have a 45x2x2x8 design. A dataframe stores the metadata of trials. And each trial has its own data file: I used "read.table" to import every trial into R as a dataframe (variable). Now I dynamically ask R to retrieve trials that fit certain selection criteria, so I use "subset", e.g. tmptrialinfo <- subset(trialinfo, (Subject==24 &

Hello everybody! I use R 1.6.2 in Windows, and have a problem controlling the na.action. In a dataset with twelve trials, one of the trials lack any readings of the variable "STS.SH" (standing power at harvest) Fitting an aov() object with the call: led1t7sts.aov <- aov(STS.SH ~ Trial/Block + Treatment + Treatment:Trial, data = led1t7, na.action=na.exclude) seems to work as it

Hello, As I am new with R I am completely stuck in resolving a, no doubt, easy problem. I have a dataset with an enormous amount of rows and 17 columns. I need to know per trial and subject number if another variable (tt) exceeds a maximum. If this is true than the last 5000 rows of that specific trial for that subject number needs to be deleted. But I am completely stuck on how to do so.

Hi there, I need some help to figure out what is the proper model in survival analysis for my data. Subjects were randomized to 3 treatments in trial 1, some of them experience the event during the trial; After period of time those subjects were randomized to 3 treatments again in trial 2, but different from what they got in 1st trial, some of them experience the event during the 2nd trial (I

Here is a runable program. When I plot Day and Wgt, it graphs all the data points. All I need is daily.sub1 plotted. I also need each "Tanks" to have its own col or pch. When I run it with the line with pch, it gives me nothing. rm(list=ls()) Trial<-rep(c(1,2),each=12) Tanks=rep(c("a3","a4","c4","h4"),each=3,2) Day=rep(c(1:12),2)

Hi folks, I am trying to figure out how run a repeated measures ANOVA on the following data set. subject trial frequency dplvl 1 FSI052A A 1 NA 2 FSI052B B 1 NA 3 FSI053A A 1 NA 4 FSI055A A 1 NA 5 FSI055B B 1 NA 6 FSI057A A 1 NA 7 FSI057B B 1 NA 8 FSI058A A 1 NA 9