similar to: logistic regression in an incomplete dataset

Dear list, I'd like to use multiple imputations to try and save a somewhat badly mangled dataset (lousy data collection, worse than lousy monitoring, you know that drill... especially when I am consulted for the first time about one year *after* data collection). My dataset has 231 observations of 53 variables, of which only a very few has no missing data. Most variables have 5-10% of

Hello: I am working with a stratified survey dataset with sampling weights and I want to use multiple imputation to help with missingness. 1. Is there a way to run an ordered logistic regression using both a multiply imputed dataset (i.e. from mice) and adjust for the survey characteristics using the weight variable? The Zelig package is able to do binary logistic regressions for survey

Dear all, I have been attempting to use multiple imputation (MI) to handle missing data in my study. I use the mice package in R for this. The deeper I get into this process, the more I realize I first need to understand some basic concepts which I hope you can help me with. For example, let us consider two arbitrary variables in my study that have the following missingness pattern: Variable 1

Dear list, I'm trying to learn how to use nlme to be able to fit ad analyse mixed-model logistic regressions. In order to keep things simple, I started by the simplest possible model : a one (fixed-effect ...) continuous variable. This problem is, of course, solved by glm, but I wanted to look at a "hand-made" nls fit, in order to be able to "generalize" to nlme

Dear list, I have the following problem : I want to model a series of observations of a given hospital activity on various days under various conditions. among my "outcomes" (dependent variables) is the number of patients for which a certain procedure is done. The problem is that, when no relevant patient is hospitalized on said day, there is no observation (for which the "number

Hi there, I have subscribed to R-help but am not sure how to view or post questions? I think this is the right way. I am planning on doing a multivariate regression investigating the relationship between depression (a continuous variable) and social support variables (mostly continuous, some categorical) among older people. I have a number of demographic and health-related variables that I am

Hi, I want to impute the missing values in my data set multiple times, and then combine the results (like multiple imputation, but manually) to get a mean of the parameter(s) from the multiple imputations. Does anyone know how to do this? I have the following script: y1 <- rnorm(20,0,3) y2 <- rnorm(20,3,3) y3 <- rnorm(20,3,3) y4 <- rnorm(20,6,3) y <- c(y1,y2,y3,y4) x1 <-

I have a data matrix X (n x k, say) each row of which constitutes an observation of a k-dimensional random variable which I am willing, if not happy, to assume to be Gaussian, with mean ``mu'' and covariance matrix ``Sigma''. Distinct rows of X may be assumed to correspond to independent realizations of this random variable. Most rows of X (all but 240 out of 6000+ rows)

Dear all I want to create dataset with MCAR type from my dataset. I have my dataset with 100 records, and I want to create dataset from this dataset to missing 5 records. How I can do it. THX Jumlong -- Jumlong Vongprasert Institute of Research and Development Ubon Ratchathani Rajabhat University Ubon Ratchathani THAILAND 34000 [[alternative HTML version deleted]]

Dear list members, I try to simulate an incomplete block design in which every participants receives 3 out of 4 possible treatment. The outcome in binary. Assigning a binary outcome to the BIB or PBIB dataset of the package SASmixed gives the appropriate output. With the code below, fixed treatment estimates are not given for each of the 4 possible treatments, instead a kind of summary

Dear all, I have a multivariate dataset containing 100,000 or more points. I want find the p-value for the dataset of points coming from a particular multivariate normal distribution With mean vector u Covariance matrix s2 So H0: points ~ MVN( u, s2) H1: points not ~ MVN( u, s2) How do I find the p-value in R? To me this is a likelihood ratio test problem. In H0 the parameters are

Dear list, Inspired by the original Knuth tools, and for paedaogical reasons, I wish to produce a document presenting some source code with interspersed comments in the source (see Knuth's books rendering TeX and metafont sources to see what I mean). I seemed to remember that a code chunk could be defined piecewise, like in Comments... <<Chunk1, eval=FALSE, echo=TRUE>>=

A new release, called wine 0.9.3, is available on Debian/Ubuntu directory of the sf site. However, such a release has not beeen announced : winehq.org still advertises 0.9.2 as the last release. Is this a legitimate release with a late announcement, or an intrusion ? Scott ? Emmanuel Charpentier

Hi all, I am attempting to apply a nonlinear model developed using nls to a new dataset and assess the fit of that model. At the moment, I am using the fitted model from my fit dataset as the starting point for an nls fit for my test dataset (see below). I would like to be able to view the t-statistic and p-values for each of the iterations using the trace function, but have not yet worked out

Hello All! I currently use wine version 20040716-1.2, a rather old release, but up to now I had no problems with that. Today I tried to install Office 2000, i need primarily Excel. Installation seems to work, after this I added the directories for vbe6.dll and msoeuro.dll to the path (...program files/common files/microsoft shared/...), because on a previous excel execution wine said it

A consensus seems to emerge : R would excel to exploratory work on small/middle-sized datasets, while SAS would be able to munch much larger datasets. However, I see the "size" problem as a red herring. The objects that have to stay "in core" are usually much smaller than the dataset. For example, for problems involving fixed-effects linear models, you need only some

Dear list, perusing the GMane archive shows that the issue with XML 3.4.x still bugs odfWeave users. I just checked that the newer XML 3.4.2 version still give the same problem. Using it to weave a bit of documentation written with LibreOffice 3.3.3 (current in Debian testing) leads me to a 192 Kb, 590 pages document, whereas reinstalling XML 3.2.0 gives me a 4 pages, 24Kb file (not

Dear List, I have had problems inserting some (not all !) figures via odfWeave (using print(someLatticeFunction)...). The figure was correctly displayed in a R device window but the resulting ODF document displayed the correct space for the figure and an empty frame with a "broken image" icon and a "read error" mention. Exploration of the odf (.odt, in my case) file showed

Dear list, I wish to define a set of functions *auxilliary* to another set of "main" ones, and I wonder if there is some "clever" way do do this using lexical scoping. Looking for that in the list's archives did not get me easily understood answers. Perusing MASS (1st, 2nd, 3rd and 4th editions!) and "Programming S" wasn't of much help either... R easily

Dear lists, It seems that RODBC has a problem : when reading through ODBC from an Access table with missing values, *character* missing values are (correctly) reported as NA factor values. *Numeric* values, however, retain the value of the same variable in the previous row. My setup : R 1.1.1 under Win95, RODBC as compiled with the R Win CRAN distribution, MS Access 97. I just discovered this,