similar to: Comparing factor level measurments

Hello, I need to subset my data to only look at the parts that have "holes" in it. I already have a formula to get rid of inconsistencies, but now I need to look only at the problem data to reconfigure it. In my data set where there are multiple "cycles" per "patient," and I want to highlight the patients who have a variable was not measured every cycle. Here's a

I'm attempting to change a data set by compressing rows into columns. Currently there are several rows that all have information about one "patient," but at different cycles. I'm trying to make each patient only have one row in the data set. Does anyone know a good way to combine data sets by factor level? I've separated the groups into different subsets by cycle, but not

Hi. Can anyone suggest a simple way to obtain in R a list of vector coordinates of the following form? The code below is Mathematica. In[5]:= Flatten[Table[{i,j,k},{i,3},{j,4},{k,5}], 2] Out[5]= {{1,1,1},{1,1,2},{1,1,3},{1,1,4},{1,1,5},{1,2,1},{1,2,2},{1,2,3},{1 ,2,4},{1,2, 5},{1,3,1},{1,3,2},{1,3,3},{1,3,4},{1,3,5},{1,4,1},{1,4,2},{1,4,3}, {1,4,

I have 316 files. Each file represents a patient's breathing track (respiratory signal recorded for a variable number of cycles). All files have the same are made up of a header followed by a variable number of records. Each record contains 7 comma separated fields. The patient ID is recorder in the header which is stripped off when reading the file into a R data.frame. Since I need to keep

Dear friends, I need to fill in (duplicate the whole record) the missing days with the same record values as long as AE is the same value (i.e. "1"), once AE value changes, the process of duplication should proceed with the new AE value till it changes again. e.g. I need to fill in records: day 18-day 44, all the records are carried with the new AE value of "0". At the

Dear R-helpers, This may sound simple to you, but I'm a beginner in this, so please be forgiving. I have a following problem: two analytes were measured in patient's blood on 4 occasions: ProteinA and ProteinB. How to correctly evaluate correlation between ProteinA and ProteinB? I tried: x <- data.frame(Patient.ID=rep(1:10, each=4), Visit=rep(c(1:4),10), ProteinA=rnorm(m=10,

Dear all, I have following data called "paitent" day patient1 patient4 patient5 patient6 0 -0.27842688 -0.04080808 -0.41948398 -0.04508318 56 -0.22275425 -0.01767067 -0.30977249 -0.03168185 112 -0.08217659 -0.26209243 -0.29141451 -0.09876170 252 0.08044537 -0.26701769 0.05727087 -0.09663701 where each patient have response values at four time points. I want to

Dear useRs, I have written a function that implements a Bayesian method to compare a patient's score on two tasks with that of a small control group, as described in Crawford, J. and Garthwaite, P. (2007). Comparison of a single case to a control or normative sample in neuropsychology: Development of a bayesian approach. Cognitive Neuropsychology, 24(4):343?372. The function (see

Dear R-users, I have a data frame containing 2 colums: column 1 is the patient numbers (totally 36 patients), column 2 is patient's response values (each patient has 100 response values). If I produce a boxplot for each patient on the same graph in order to compare them against each other then the boxplots are very small. How can I instead of creating one graph containing 36 boxplots,

Dear R users, A co-worker and I are writing a function to facilitate graph plotting in R. The function makes use of a lot of lists in its defaults. However, we discovered that R does not necessarily preserve the defaults if we were to input them in the form of list() when initializing the function. For example, if you feed the function codes below into R: myfunction=function( list1=list

Hello, I'm using SPSS at work but really would like to switch to R. Right now I'm trying to learn R in reproducing calculations I did with SPSS but am stuck with something that is quite simple and comprehensible in SPSS-Syntax: IF (variable1.fac = 0 AND variable2.num = 0) variable3=1. IF (variable1.fac = 0 AND variable2.num >= 1) variable3=2. IF (variable1.fac = 1 AND variable2.num =

Hello, My health care organization is looking for a way to do appointment reminders. We currently have staff members who spend part of each day manually calling patients to remind them of their upcoming appointments, and we would like to automate this process. Our electronic health record software would provide such information as the patient's name, phone number, and day and time of

I am attempting to replicate some of my experience from SAS in R and assume there are best methods for using a combination of summary(), subset, and which() to produce a subset of mean values by categorical or ordinal factors. within sas I would write proc means mean data=dataset; class factor1 factor2 var variable1 variable2; RUN; producing an output with means for each variable by factor

I am graphing longitudinal data from three time points. I'd like to draw a solid line from point 1 to point 2, and then a dashed line from point 2 to point 3. It works if I do it in two steps: > first.vector <- c(mean(year1$variable1), mean(year2$variable1)) > second.vector <- c(NA, mean(year2$variable1), mean(year3$variable1)) > plot(first.vector, type="b",

Dear list, I am trying to use lme to build the analogue of the following SAS MIXED random specification: random int+Variable1+Variable2 /subject = Subject group=Condition type=vc; which gives a Condition-blocked heterogeneity in the random effects variance-covariance matrix. Needless to say, I have a hard time in specifying Condition-specific heterogeneities in the variance-covariance

Hi, I have a dataset in which there are in all 250 variables and for each variable the data is entered over the months. I need to calculate the percentage of missing values for each variable over each month and then plot a graph for that. I am running the following code for doing the same *ds <- read.csv(file="filepath", header=TRUE) attach(ds) may <-

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Hello, I have many hundred variables in my longitudinal dataset and lots of missings. In order to plot data I need to remove missings. If I do > data <- na.omit(data) that will reduce my dataset to 2% of its original size ;) So I only need to listwise delete missings on 3 variables (the ones I am plotting). data$variable1 <-na.omit(data$variable1) does not work. Thank you

Listers, I have a question regarding correlation matrices. It is fairly straight forward to build a correlation matrix of an entire data frame. I simply use the command cor(MyDataFrame). However, what I would like to do is construct a smaller correlation matrix using just three of the variable out of my data set. When I run this: cor(MyDataFrame$variable1,

Hi there, I have a huge dataframe containing 70,000 observations. I have filtered this dataframe (let it's name be "transformed_dataframe") as I wanted to select only those observations which are greater than or equal to 60,001 regarding the very first identity column. So I have a transformed dataframe now including 10,000 obeservations (from 60,001 - to 70,000) and if you send