similar to: Repeated measures

Hi, I'm analyzing a longitudinal data set with 387 cows were observed in 63 days divided into 6 groups, and every 30 days was found to produce milk. Does not aim to model the time using regression. Only compare the groups differ in terms of milk production. There are many missing observations. Because the data are correlated I used the SAS program: proc mixed data=univar

Does lme prediction work correctly with poly() terms? In the following simulated example, the predictions are wildly off. Or am I doing something daft? Milk yield for five cows is measured weekly for 45 weeks. Yield is simulated as cubic function of weekno + random cow effect (on intercept) + residual error. I want to recover an estimate of the fixed curve. ############### library(nlme)

Dear list, I am trying to do a repeated analysis using lme in R and a little bit unsure if I have set up the right statement. The problem is the IL6 (interleukin 6) was measured 5 times on each individual in each of 6 companies. The hypotheses are to see whether there is a relationship between IL6 and the total dust in each of the companies and if there is any change in IL6 across time

Hi, I am new to this list as a poster, but a reader for some time. I've using R for several weeks now, and I have a lot of questions about certain procedures. Here I go: I want to test if there are differences in the time spent by pollinators visiting flowers of a given plant species, according to a number of experimental manipulations made on those flowers. All experimental

Hello- I'm trying to do some repeated measures ANOVAs. In the past, using SAS, I have used the framework outlined in Littell et al.'s "SAS System for Mixed Models", using the REPEATED statement in PROC MIXED to model variation across time within an experimental unit. SAS allows you to specify different within-unit covariance structures (e.g., compound symmetric, AR(1), etc.) to

Hi All, I'm working with some SAS code to analyze an experiment set up as follows: 66 subjects (colonies) treated with a random treatment (1-8) and measured at three time points. The data structure looks like: input colony tmt y1 y2 y3; y=y1; date=*1*; output; y=y2; date=*2*; output; y=y3; date=*3*; output; datalines; 1

Carsten, This is a common issue in technology when you have complex products that the majority of consumers of those products do not understand how they operate. If most UPS customers understood the importance of standardization they would have refused to purchase non-standard UPSes and all UPSes would have long ago standardized on a single management protocol. The normal thing is that product

Hi: I have a dataframe containing comma seperated group of words such as milk,bread bread,butter beer,diaper beer,diaper milk,bread beer,diaper I want to output the frequency of occurrence of comma separated words for each row and collapse duplicate rows, to make the output as shown in the following dataframe: milk,bread 2 bread,butter 1 beer,diaper 3 milk,bread 2 Thanks for help! deb

Hello dear R-help members. While trying to avoid posting on this list about news from r-bloggers.com, I hope it is o.k. by you if I share the list I made of 100 "most read" R articles from the blogosphere, based on the analytics data gathered on r-bloggers.com You can see the full list at this link:

I have a data set in which I have 5000 repeated measures on 6 subjects over time (varying intervals, but measurements for all individuals are at the same times). There are two states, a "resting" state (the majority of the time), and a perturbed state. I have a continuous measurement at each time point for each of the individuals. I would like to determine the "state"

Hello, I'm looking for a solution for the following problem: I have two vectors V1 <- c("apple","honey","milk","bread","butter") V2 <- c("bread","milk") now, I would like to know for each element in V1 if it's equal to one of the elements in V2 I could do: which(V1 == V2[1] | V1 == V2[2]) but what if I

Hi again, thank you for your previous answers. Just another question, though ... I get the following variance components after fitting a mixed model. Groups Name Variance Std.Dev. Corr PlantID TreatmCtrl 0.51784 0.71961 TreatmNoAccess 4.77469 2.18511 -0.063 TreatmNoKeel 4.22726 2.05603 0.513 0.751 TreatmNoSpur 0.45918

Hi, I am wanting to look at frequent item sets using the arules package. I need to transform my data into a "transactions" object. The data I read in from a file has 2 columns, an ID and an item. How do I convert data like this into a transactions object? I've tried class? transactions but it only confuses me. My data is like this.... basketID item 1 bread 1 cheese 1 milk 2

Many Thanks for your explanation, Ted! All the necessaries to produce (and live from) new UPS-Equippment I understand. Because of this I decided years ago to sell MGE-UPS. MGE did have the greatness to pay Arnaud Quette to improve the Metasoftware NUT. So we have one Software for brand new models and over 20years old IT dinosaur. We earn money with replacement of accumulators and save in

Dear All, I hope this is not too off-topic. Essentially, I need to know if there is any R package which can help me with a deformulation project. Suppose e.g. that you know from a chemical analysis the fat, mineral, vitamin, energy [and so on] content of a certain food product. You also know the ingredients of this product (e.g. milk, lactose, vegetable oil) and you know the chemical

HI, You may try this: dat1<- read.table(text=" CustID TripDate Store Bread Butter Milk Eggs 1 2-Jan-12 a 2 0 2 1 1 6-Jan-12 c 0 3 3 0 1 9-Jan-12 a 3 3 0 0 1 31-Mar-13 a 3 0 0 0 2 31-Aug-12 a 0 3 3 0 2 24-Sep-12 a 3 3 0 0 2 25-Sep-12 b 3 0 0 0 ",sep="",header=TRUE,stringsAsFactors=FALSE) dat2<- dat1[,-c(1:3)] res<- lapply(seq_len(ncol(dat2)),function(i)

Hi, I have been able to generate association rules for Market Basket Analysis using the following codes: **************************************************************************** ******************************************* library("arules") rules <- read.csv("write1.csv",na.strings=c(".", "NA", "", "?"),header=TRUE)

Hi all, I don't know if anyone has any thoughts on this. I have been trying to move from SAS Proc Mixed to R nlme and have an unusual result. I have several subjects measured at four timepoints. I want to model the within-subject correlation using an autoregressive structure. I've attached the R and SAS code I'm using along with the results from SAS. With R lme I get an estimate of

I feel a bit embarassed by this, but I have not managed to find out how: I have a data frame containing measurements over time for a number of different locations: > str(milk) `data.frame': 845 obs. of 3 variables: $ date : num 1987 1987 1987 1987 1987 ... $ value : num 5 1 2 5 1 1 2 3 2 4 ... $ location: Factor w/ 36 levels "Alta","And?y",..: 1 1 1 1 1 1

In xyplot, I would like to get the "data into the panel function" in the following sense: Consider xyplot(scc~time|cowidp, data=cow.s,type=c("l"), panel=function(x,y,subscripts,...){ panel.xyplot(x,y,...) vvv<-cow.s[which(!is.na(cow.s[subscripts,"mastreat"])),"time"] panel.abline(v=vvv,col="red",lwd=2) } ) If I want to use a