similar to: Within factor & random factor

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

Hi, I'm trying to calculate 95% confidence interval of C statistic of logistic regression model using rcorr.cens in rms package. I wrote a brief function for this purpose as the followings; CstatisticCI <- function(x) # x is object of rcorr.cens. { se <- x["S.D."]/sqrt(x["n"]) Low95 <- x["C Index"] - 1.96*se Upper95 <- x["C

Hello everyone, I have a .csv file with the following format: uniqueID SubjectID Distance_miles Tag 1 1001 5.5 3 2 1001 7 1 3 1001 6.5 1 4 1001 5 1 5 1002

Hi, Is there any R package doing permutation/randomization test for PLS/PLSDA? I found some codes for MatLab, but I want to use R program. Thank you very much in advance. Kohkichi Hosoda

Last week (Wed 9/10/2003, "regression questions") I posted a question regarding the use of gnls( ) and its dissimilarity to the syntax that nls( ) will accept. No one replied, so I partly answered my own question by constructing indicator variables for use in gnls( ). The code I used to construct the indicators is at the end of this email. I do have a nagging, unanswered

Dear list, If have some repeated measurement data which looks something like: time <- rep(1:5 , each=2*4) groups <- rep(c("Case", "Control"), each=4) subjects <- factor(rep(1:(2*4), 5)) responses <- time + rnorm(5*2*4) + as.integer(factor(groups)) data <- data.frame(responses, time, groups, subjects) Now I want to plot each subject in a separate panel:

Muchas gracias, Carlos. Sobre todo por el sqldf, que seguro me ahorre mucho tiempo. La opción que se plante de primero d <- table(tips$day) y luego dim(d) me parece menos eficiente y cómo que directamente sqldf("select count(distinct day) from tips"), pero supongo que esos son gustos! También son "cómodas" las líneas: aggregate(subjectid ~ cond, data = dat, FUN = function(x)

Estoy en 3.3.0 y "sqldf" lo instala sin problemas... El 23 de junio de 2016, 1:23, Mauricio Monsalvo <m.monsalvo en gmail.com> escribió: > Malas nuevas para mi: > package ?sqldb? is not available (for R version 3.3.0) > ¿Puedo hacer algo más que esperar? No me voy a "bajar" de versión de R. > > El 22 de junio de 2016, 20:02, Mauricio Monsalvo

Hi I have a dataframe in which some subjects appear in more than one row. I want to extract the subject-rows which have the minimum date per subject. I tried the following aggregate function. attach(dataframe.xy) aggregate(Date,list(SubjectID),min) Unfortunately, the format of the Date-column changes to numeric, when I'm applying this function. How can I preserve the date format? Thanks

Hello, I have been searching for ways to obtain these for combinations of fixed factors and levels other than the 'baseline' group (contrasts coded all 0's) from a mixed-effects model in lme. I've modelled the continuous variable y as a function of a continuous covariate x, and fixed factors A, B, and C. The fixed factors have two levels each and I'd like to know whether

I am trying to plot where data points from a give subject are connected by a line. Each subject is represented by a single row of data. Each subject can have up to five observations. The first five columns of mydata give the time of observation, columns 6-10 give the values at each time point. Some subjects have all data, some are missing values. The code I wrote to draw the plot is listed below.

Hola Necesito algo simple pero no logro resolverlo en R: contar elementos distintos (unique) pero agrupados. En SQL sería: SELECT Camara, count(DISTINCT(Laboratorio)) as Lab FROM [datos] GROUP BY Camara. Puedo hacerlo para toda la matriz de datos con length(unique(datos$Laboratorio)) pero no logro hacerlo agrupado por una variable (factor) dada. Estuve intentando con el paquete data.table pero me

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

Dear Thomas, The head of my dataset > head(wsuv) parcel sp time censo treatment species 1 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1 1 2 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1 1 3 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1 1 4 S8 Poecilanthe effusa ( Hub. ) Ducke. 1 1 1

I am trying to do a simple segregation analysis using the GAP package. I have the documentation for pointer but I desperately need an example so that I can see how to format the datfile and the jobfile. For each individual, I have FamilyId, SubjectId, FatherId, MotherId, and AffectedStatus (0/1). I would like to obtain the likelihood ratio statistic for transmission. I would greatly

On Fri, 5 Aug 2005 Julia Reid wrote: > Subject: [R] GAP pointer > > I am trying to do a simple segregation analysis using the GAP package. I > have the documentation for pointer but I desperately need an example so > that I can see how to format the datfile and the jobfile. For each > individual, I have FamilyId, SubjectId, FatherId, MotherId, and > AffectedStatus (0/1). I

Hi everybody, I'm fairly new to survival analysis with R and have some questions how to apply and interpret the coxph and related functions: I have time-dependent covariates with several measurements per subject with constant delta t. The covariates change in each time step. I fitted the following model: fit <- coxph(Surv(start, stop, event) ~ ratePO + rateC + BLamp + BLP80 +

... and the mailing list is picky about attachments... whatever you attached did not conform to the stringent requirements mentioned in the Posting Guide. Pasting the code right into the email is usually safest, though you DO have to post using plain text (as the Posting Guide indicates) or your code may get mangled by the automatic html format removal. On May 15, 2018 7:04:31 AM PDT, Bert Gunter

OK, Let's try this again! Here is the reproducible script; it is long because I had to copy the panel dataset here. My question is related to systemfit; I don't know how to get the result for the entire panel. #Reproducible script Empdata<- read.csv("/Users/ngwinuiazenui/Documents/UPLOADemp.csv") View(Empdata) install.packages("systemfit")

Unless there is good reason not to, always cc the list -- there are lots of smarter folks than I on it who can help. I may or may not have time to look at this. Hopefully someone else will. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip