similar to: summarizing dataframe at variable/factor levels

All, When specifying colors to xyplot w/ a groups argument, using auto.key no longer maintains the colors properly. I've searched the docs and help but haven't found exactly what I need ... I saw a few examples in the archives involving par.settings but that doesn't seem to do it. I also saw some people using key instead of auto.key, but that didn't seem consistent. Is there a

Hi, I am encountering a difficulty I don't understand. Be patient, I'm very new to analysis of variance. If I load this data: example12_7=read.table("http://msemac.redwoods.edu/~darnold/math15/data/chapter12/example12_7.dat",header=TRUE) The run the oneway.test: oneway.test(time~drug,data=example12_7,var.equal=TRUE) I get these results: data: time and drug F = 4.1881, num

Hi all, thank you for your patience. I am dealing with a large dataset detailing patients and medications Medications are hard to code, as they are (usually) meaningless unless matched with doses. I have a dataframe with vectors (Drug1, Drug2..... Drug 16) and individual patients are represented by rows. The vectors are actually factors, with 100s of possible levels (all the drugs the patient

Hi As I can't find an example of my data structure I'd like some advice on which is the most appropriate test for significant effects. If I should be using either lme or anova, is the relevant example below the best/correct way to do the test? The Data... 2 groups of patients (5 in GroupA, 7 in GroupB) 3 short acting drugs, (I'm not concerned with residual effects from the previous

Please help with this error message drugbook is an 885 x 32 dataframe >names(drugbook) [1] "DRUG1" "DRUG2" "DRUG3" "DRUG4" "DRUG5" [6] "DRUG6" "DRUG7" "DRUG8" "DRUG9" "DRUG10" [11] "DRUG11" "DRUG12"

Hey all, I'm trying to replicate some plots with ggplot2. The problem is that I need to specify the color for every attribute (drug). If I use the code below the colors get automatically assigned but I need to plot drug1 in black drug2 in blue etc. How do I do that? q = qplot(days,vol,data = cellLine7064, color = drug, geom = c("line", "point")) best regards, Immanuel

Dear R-maillit, I have to perform a stastistical test to asses if two grug show the same or different effect on my prepartion. I tested on the same preparation only one drug and I have to use normalized data due to high variability in the control condictions. What type can I use? A non paramatric test right? And how can do it on R software? Thanks in advance Simone [[alternative HTML version

Hello I simply want to plot two variables against one 'year' variable in qplot. Is any way of doing this without reshaping data in long format and using facet function afterwards? Thank you Denis

Hi David: In looking at your original post it is a bit difficult to ascertain exactly what your null hypothesis was. That is, you want to assess whether there is a treatment effect at time 3, but compared to what. I think your second post clears this up. You should refer to pages 224- 225 of Pinhiero and Bates for your answer. This shows how to specify contrasts. > -----Original Message-----

Hello fellow R-ers, I have spent some time on this and it is driving me NUTS! I am sure there is a solution, so please help. I am trying to create a function that will plot different lines for subsets of a dataset. For example, I am trying to look at different drug groups (drug2), let's say 1,2,3,4, and 5. The data has 2 different rates, college students and high school students (var names cs

I'm a journalist, wondering what questions to ask about a study that contrasted the impact on serum cholesterol of two drugs. This was a 40 dog study: 5 treatment blocks of 4 dogs each, randomized to: a control block, two blocks at different doses of drug1 and two at corresponding doses of drug2. Analysis was 2-factor repeated measures ANOVA on treatment group and sampling time. Linear

All, The code below is for a pseudo dataset of repeated measures on patients where there is also a treatment factor called "drug". Time is treated as categorical. What code is necessary to test for a treatment effect at a single time point, e.g., time = 3? Does the answer matter if the design is a crossover design, i.e, each patient received drug and placebo? Finally, what would

Hi, I am running a mixed-effect model with a nested-random effect. I am interested in gut parasites in moose. I has three different type of treatment that I applied to moose which are from different "families". My response variable is gut parasites and the factors are moose families which is nested within treatment. My data is balanced. To answer this question, I used the lme function

All, I have data across 5 time points that I am graphing via xyplot, along with error bars. For one of the variables I have missing data for two of the time points. The code below is okay but I can't seem to get the lines to connect across the missing time points. Does anyone now how to rectify this? Cheers, David Afshartous library(lattice) ## the data junk = data.frame( Visit =

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 )

All, I'm re-running some analysis that has been augmented with additional data. When I use the exact same code for the augmented data, the behavior of the aggregate function is very strange, viz., one of the resulting variables is now coded as a factor while it was coded as numeric for the original data. Unfortunately, I cannot provide a reproducible code example since it only seems to occur

All, I can't seem to get auto.key to work properly in an xyplot that is employing panel.text. Specifically, I often change the default grouping colors then use auto.key accordingly, but for some reason the same functionality isn't working for this different type of plot. Any help much appreciated. Cheers, David library("lattice") dat = data.frame( Y = c(rnorm(18,1),

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 =

All, I'm using lmer for some repeated measures data and have specified the contrasts for a time factor such that say time 3 is the base. This works fine. However, when I next use the subset argument to remove the last two time values, the output indicates that the specified contrast is not maintained (see below). I can solve this by creating a new dataframe for the subset of interest

Dear List, Here is an example of survival data in counting process format (detailed record of each day) > data[data$Id == 11,] # extracted one person's record Id Event Fup Start Stop sex Drug1 601 11 0 6 0 1 0 0 602 11 0 6 1 2 0 0 603 11 0 6 2 3 0 0 604 11 0 6 3 4 0 0 605 11 0 6 4 5 0 1 606