similar to: plot function creating bars instead of lines

I could do this in various hacky ways, but what's the right way? I have a nice application of the by function, which does what I want. The output looks like this: > auc_stress lab.samples.stress$subid: 2 cortisol amylase 1 919.05 6834.8 ---------------------------------------------------------------------------------------------------------------------------

Hello, I apologize in advance for not providing sample data, I'm a very new to R and can't easily generate appropriate sample data quickly. I'm hoping someone can offer advice without it. This code below works and does what I want it to do, which is for a given row in my dataframe, where the variable "peak.cort" = max, it makes the value of another variable

Hi guys, I am using the cor() function to see if there are correlations between March cortisol levels and December cortisol levels and I'm trying to figure out if the function is doing what I want it to do. Each sample has it's own separate row in the CSV file that I'm working out of. March Cort and December Cort are different columns and they come from separate samples, therefore

Greetings, Is it possible to match several columns in a merge statement? Here is my problem: data1 looks like this... SUBID TARGID ITEM RATING 1 1 1 4 1 1 2 5 1 1 3 3 1 1 4 2 1 1 5 5 ...... SUBID is the ID for the raters, TARGID is the ID for the targets being rated, ITEM ranges from 1 to 64 crossed

I have another question about drawing samples from a data frame. This might sound really tricky. Let me use a data frame I have posted earlier as an example: SubID CSE1 CSE2 CSE3 CSE4 WSE1 WSE2 WSE3 WSE4 1 6 5 6 2 6 2 2 4 2 6 4 7 2 6 6 2 3 3 5 5 5 5

I was running a sampling syntax based on a data frame (ago) of 160 rows and 25 columns. Below are the column names: > names(ago) [1] "SubID" "AGR1" "AGR2" "AGR3" "AGR4" "AGR5" "AGR6" "AGR7" "AGR8" [10] "AGR9" "AGR10" "WAGR1" "WAGR2"

Hi all, I have a large dataset that has >10k entries. The dataset is stored in a dataframe with the headers: SubID Condition1 Condition2 Result1 Result2 There are multiple entries for a given SubID(Subject ID). Condition 1 has 3 levels and condition2 has 2 levels (therefore there are 6 possible combinations all together e.g. Cond1 Level1 x Cond2 Level 1 etc.) and i need to compute for 1. The

Dear R-users, I have written the following code to generate some trellis plots. It works perfectly fine except that it is quite slow when it is apply to my typical datasets (over several thousands of lines). I believe the problem comes from the loops I am using to subset my data.frame. I read in the archives that the tapply function is often more efficient than a loop in R. Unfortunately ,

Hi , I am new to R. I am trying to run a simple R script as shown below: aov.R ------ data1<-c(49,47,46,47,48,47,41,46,43,47,46,45,48,46,47,45,49,44,44,45,42,45,45,40 ,49,46,47,45,49,45,41,43,44,46,45,40,45,43,44,45,48,46,40,45,40,45,47,40) matrix(data1, ncol= 4, dimnames = list(paste("subj", 1:12), c("Shape1.Color1", "Shape2.Color1", "Shape1.Color2",

Hi, I have written the following function to check whether a vector has elements satisfying monotonicity. is.monotone <- function(vec, increase=T){ # check for monotonicity in time-stamp data for cortisol collection ans <- TRUE vec.nomis <- vec[!is.na(vec)] if (increase & any(diff(vec.nomis,1) < 0, na.rm=T)) ans <- FALSE if (!increase & any(diff(vec.nomis,1) > 0,

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

I have a really simple question I have a data frame of 8 variables (the first column is the subjects' id): SubID G1 G2 G3 G4 W1 W2 W3 W4 1 6 5 6 2 6 2 2 4 2 6 4 7 2 6 6 2 3 3 5 5 5 5 5 5 4 5

Dear all, In lme, models in which a factor is fully "contained" in another lead to an error. This is not the case when using lm/aov. I understand that these factors are aliased, but believe that such models make sense when the factors are fitted sequentially. For example, I sometimes fit a factor first as linear term (continuous variable with discrete levels, e.g. 1,2,4,6), and

All, How does one extract the level-1 variance from a model fit via lmer()? In the code below the level-2 variance component may be obtained via subscripting, but what about the level-1 variance, viz., the 3.215072 term? (actually this term squared) Didn't see anything in the archives on this. Cheers, David > fm <- lmer( dv ~ time.num*drug + (1 | Patient.new), data=dat.new )

Hello: I am new R user stumped why the R code after this paragraph generates "Error: syntax error" messages after each of the last 2 lines. I have tried searching the manuals, Hmisc documentation, contributed manuals, help archives, and Internet. I am running R 1.7.1 under Windows 2000 (I will upgrade when my imminent OS upgrade happens). My data was successfully entered and

Dear R-help, I'm facing a problem with defining a repeated measures anova with weighted data. Here's the code to reproduce the problem: # generate some data seed=11 rtrep <- data.frame(rt=rnorm(100),ti=rep(1:5,20),subj=gl (20,5,100),we=runif(100)) # model with within factor for subjects/repeated measurements, no problem aov(rt~ti + Error(subj/ti),data=rtrep) #model with weights

All, I'm trying to plot an all.effects() object, as shown in the help for all.effects and also Crawley's R book (p.178, 2007). The data has a repeated measures structure, but I'm using all.effects for the simple lm() fit here. Below is a reproducible example that yields the error message. fm.ex = lm(dv ~ time.num*drug*X, data = dat.new) fm.effects = all.effects(fm.ex, xlevels =

Dear all: I m a newer on R.? I have some problem when I use?anova function.? I use anova function to get Type I SS results, but I also need to get Type III SS results.? However, in my code, there is some different between the result of Type I SS and Type III SS.? I don?t know why the ?seqe? factor disappeared in the result of Type III SS.? How can I do?? Here is my example and result.

Hi all, I have a strange problem and rigth now I can't figure out a solution. Trying to calculate an ANOVA with one between subject factor (group) and one within (hemisphere). My dependent variable is source localization (data). My N = 25. My data.frame looks like this: > ML.dist.stack subj group hemisphere data 1 1 tin left 0.7460840 2 2 tin left

Hi All. I'm trying to run a simple model from Baayan, Davidson, & Bates and getting a confusing error message. Any ideas what I'm doing wrong here? # Here's the data..... Subj <- factor(rep(1:3,each=6)) Item <- factor(rep(1:3,6)) SOA <- factor(rep(0:1,3,each=3)) RT <- c(466,520,502,475,494,490,516,566,577,491,544,526,484,529,539,470,511,528) priming