search for: subjectid

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...

...bject factor and location is a within subject factor. So, controls: 28 patients: 25 (unbalanced data set) respone measure: X values (ranging 0 to 1) fixed factor: control vs. patient (between subject factor) random factor: location (level: A,B,C,D ;no order) (within subject factor) random factor: subjectID 1-53 My data looks like this; CorP X location subjectID control 0.708 A 1 control 0.648 A 2 patient 0.638 C 3 control 0.547 D 4 patient 0.632 B 5 control 0.723 C 6 ........... I want to know (a) if there is a significant difference between controls and patients in X values. (b) wher...

...I am running gnls( ) 24 separate times on separate data sets). Much thanks in advance, david paul #Constructing Indicator Variables indicator <- paste( "foo$X <- sapply(foo$subject.id, FUN = function(x) if(x == X) 1 else 0)" ) indicator <- parse( text = indicator )[[1]] subjectID.foo <- as.factor(as.character(unique(foo$animal.id))) for(i in subjectID.foo) { INDICATOR <- do.call("substitute", list(indicator, list(i = i, X = as.character(subjectID.foo[i])))) eval(INDICATOR) } foo$Overall.Effect <- rep(1,length(foo$dose.group))

...norm(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: library(lattice) xyplot(responses~time|subjects*groups, data = data, aspect="xy") but this introduces empty panels. I tried this: data$subjectID <- factor(rep(1:4, 2*5)) xyplot(responses~time|subjectID*groups, data = data, aspect="xy") This is what I want but now I don't have the correct subject id's. I couldn't find a solution in the help of strip.default. Does anyone has a solution to this? Thanks in advance,...

...seguro me ahorre mucho tiempo. La opción que se plante de primero d <- table(tips$day) y luego dim(d) me parece menos eficiente y cómo que directamente sqldf("select count(distinct day) from tips"), pero supongo que esos son gustos! También son "cómodas" las líneas: aggregate(subjectid ~ cond, data = dat, FUN = function(x) length(unique(x))) o bien tapply(a$subjectid, a$cond, function(x) length(unique(x))), pero tienen algo de R que intuitivamente nunca me sale... supongo que por no comprender cuándo corresponde el uso de function(x). ?Adjunto una selección de mis datos en .csv p...

...ción que >> se plante de primero d <- table(tips$day) y luego dim(d) me parece menos >> eficiente y cómo que directamente sqldf("select count(distinct day) from >> tips"), pero supongo que esos son gustos! >> También son "cómodas" las líneas: aggregate(subjectid ~ cond, data = dat, >> FUN = function(x) length(unique(x))) o bien tapply(a$subjectid, a$cond, >> function(x) length(unique(x))), pero tienen algo de R que intuitivamente >> nunca me sale... supongo que por no comprender cuándo corresponde el uso de >> function(x). >>...

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 Erich

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.

...ixed factors have two levels each and I'd like to know whether the relationship between y and x varies between levels of the factors, and whether there are any interactions between these factors. I've therefore setup the model as this: lme.fit <- lme(y ~ x*A*B*C, data=df, random=~x | subjectID) The contrasts are default ("contr.treatment" and "contr.poly"). As usual, the summary provides the coefficients for the 'baseline' group. The rest of coefficients correspond to *differences* and their standard error with respect to this group. One can calculate the...

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

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 appreciate any help on this subject. Best to all, Julia Reid

...e: > 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 would like to obtain the likelihood ratio > statistic for transmission. > I would greatly appreciate any help on this subject. > Best to all, > Julia Reid > I wouldn't use Pointer myself (there are lots of more recent packages...

...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 + cluster(subjectID), data=dat) and get n= 1081, number of events= 10 coef exp(coef) se(coef) robust se z Pr(>|z|) ratePO -0.50189 0.60539 0.25195 0.17696 -2.836 0.004565 ** BLamp -0.05340 0.94800 0.02877 0.01470 -3.632 0.000281 *** rateC 0.82888 2.29076 0.38111...

Hi, Sorry to bother you again. I would like to estimate the effect of several categorical factors (two between subjects and one within subjects) on two continuous dependent variables that probably covary, with subjects as a random effect. *I want to control for the covariance between those two DVs when estimating the effects of the categorical predictors** on those two DVs*. The thing is, i