similar to: subsets

Hello, I have a maybe trivial question, but I simply don't understand well enought how to work with text/strings: I have a rather compelx data structure, a big list with several sub-lists/dataframes and for certain calculations (which I do in loops), I only need a certain group of sub-lists/dataframes, which I want to specify with a name vector and paste together the object name and pass it

Fit NLE - was: [R] computer algebra in R Original post: https://stat.ethz.ch/pipermail/r-help/2023-November/478619.html Dear Kornad, I think I have started to understand what you try to achieve. The problem is to fit a NLE and compute the parameters of the NL-Eq. I have included the R Help-list back in the loop, as I am not an expert in optimization. Goal: y ~ I0 + IHD * hd + ID * d; where: y

Hi R-users Is there any functions in R that can implement "expert systems"? The aim of an expert system is to produce a probable diagnosis for a patient with certain symptoms. In the classical expert system a mumber of "experts" are asked to make "statements" on the probabilities for different diseases when a combination of systems would appear. One typical

Hi all, I have this microarray large microarray data set (ALL) from which I would like to subset or extract a set of data based on a factor ($mol.biol). I looked up some example of subsetting in, picked up two commands and tried both but I got error messages as follows > testset <- subset(ALL, ALL$mol.biol %in% c("BCR/ABL","ALL1/AF4")) >> Error in

I'm looking for a function in R that extends kappa to multiple raters when there is more than one response per subject. For example, say a group of doctors have to assign diseases to patients. Each patient will be assigned one to many diseases, and the number of doctors assigning diseases to any one patient will be two to many. Here's an extremely simple example of the type of data I

Hi All: Could anyone point me to a package that can calculate stratified kappa? My design is like this, 4 raters, 30 types of diagnosis scores, 20 patients. Each rater will rate each patient for each type of diagnosis score. The rater's value is nominal. I know I can measure the agreement between raters for each type of diagnosis score, e.g., calculate out 30 kappa values. My problem is I

I have a large dataset (~1 million rows) of three variables: ID (patient's name), DATE (of appointment) and DIAGNOSIS (given on that date). Patients may have been assigned more than one diagnosis at any one appointment - leading to two rows, same ID and DATE but different DIAGNOSIS. The diagnoses may change between appointments. I want to subset the data in two ways: - define groups

Dear all, I have been stuck on this problem, am rather struggling and would appreciate some advice if anyone can help. I apologise if this is a bit long-winded, but I've tried to limit it to the bare essentials, but don't know how to make it more generic! I have some slightly odd real world data that I'm looking at representing number of positive diagnoses for different diseases,

Hi all, I'm trying to do model reduction for logistic regression. I have 13 predictor (4 continuous variables and 9 binary variables). Using subject matter knowledge, I selected 4 important variables. Regarding the rest 9 variables, I tried to perform data reduction by principal component analysis (PCA). However, 8 of 9 variables were binary and only one continuous. I transformed the data by

Dear all, i searched for some classification methods and I have no glue if i took the right once. My problem: I have a matrix with 17000 rows and 33 colums (genes and patients). The patients are grouped into 3 diseases. No I want to classify the patients and for sure i want to know which rows are more helpful for the classification than others. I tried SVM and random forest. Do you think this

Hello, I have a data which looks like below: Some of the patients have multiple diagnosis. ID(200 patients)   Diag (100 unique Diag-200 in general)   Proc (50 uniqe Proc)  DOB (200)   Gender (200)    a                           daig1 b                           diag2 c                            diag1 I want to reformat this data to : ID   diag1 diag 2 diag 3..  diagx   proc1   proc2  

I am sure this is a trivial problem for a rails guru! all I want to do is display an appropriately cheched check_box in a table, so: instead of <% for t in @patient.histories%> <tr> <td><%= t.ihd %></td> </tr> <%end%> I want something like: <% for t in @patient.histories%> <tr>

Hello all! I am once again analyzing patient survival data with chronic liver disease. The severity of the liver disease is given by a number which is continuously variable. I have referred to this number as "meld"--model for end stage liver disease--which is the result of a mathematical calculation on underlying laboratory values. So, for example, I can generate a Kaplan-Meier plot

mejino at u.washington.edu wrote: > > >>> A canonical human body will have canonical parts and those canonical >>> parts will >>> have canonical subparts and so on. >> >> ... and? >> >>> Can't think of anyone who would fit that >>> description. >> >> is this considered an argument for that there cannot possibly an

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

I have a longitudinal competing risk data of the form: ID COMPL SEX HEREDITY 1 0 1 2 1 0 1 2 1 3 1 2 2 0 0 1 2 1 0 1 2 2 0 1 2 2 0 1 3 0 0 1 3 0 0 1 3 0 0 1 3 0 0 1 3 2 0 1 4 0 1 2 4 0 1

Dear R Users, I have a question based on my research. I am analyzing reader-based diagnostic data set. My study involves diabetic patients who were evaluated for treatable diabetic retinopathy based on the presence or absence of two pathologies in their eyes. Pathologies were identified using the clinical examination (Gold standard method). In addition it can be identified by taking digital

Hi list, I'm new to R and I'm currently using ROCR package. Data in input look like this: DIAGNOSIS SCORE 1 0.387945 1 0.50405 1 0.435667 1 0.358057 1 0.583512 1 0.387945 1 0.531795 1 0.527148 0 0.526397 0 0.372935 1 0.861097 And I run the following simple code: d <- read.table("inputFile", header=TRUE); pred <- prediction(d$SCORE, d$DIAGNOSIS); perf <- performance(

Hello, I am just a beginner of R 1.9.0. I try to construct a predictive score for the development of liver cancer in cirrhotic patients. So dependant variable is binanry (cancer yes or no). Independant variables are biological data. The aim is to find out a cut-off value which differentiate (theoratically) from normal to pathological state for each biological data. How can I step in procedue to

I am trying to determine how to display a list returned from a Rails find(:include => [:children]). I am using the following (which works in the console): @patients = Account.find(session[:account_id]).patients.find(:all, :include => [:patient_details]) How can I address the patient_details to display them in the view? The console even shows a @patient_details instance variable