similar to: mixed-effects survival

Hi, Is there any R library/package that calculates tetrachoric correlations from given marginals and Pearson correlations among ordinal variables? Inputs to polychor function in polycor package are either contingency tables or ordinal data themselves. I am looking for something that takes marginal distributions and Pearson correlation as inputs. For example, Y1=(1,2,3) with P(Y1=1)=0.3,

Hi, If a matrix is not positive definite, make.positive.definite() function in corpcor library finds the nearest positive definite matrix by the method proposed by Higham (1988). However, when I deal with correlation matrices whose diagonals have to be 1 by definition, how do I do it? The above-mentioned function seem to mess up the diagonal entries. [I haven't seen this complication, but

Hi, Is there an R library that has the same functionalities of Splus7.0+ library correlatedData? I'd appreciate any input. Hakan Demirtas [[alternative HTML version deleted]]

Hi, Is there any Fortran compiler that generates R-compatible object files ? (For Windows based systems) Thanks, Hakan Demirtas Pennsylvania State University Department of Statistics [[alternate HTML version deleted]]

Hi, Is there any R library that is capable of handling polygamma function (Hurwitz zeta function also works)? I am aware of digamma(0 and trigamma(), but could not find more advanced versions. I'd appreciate any help. Hakan Demirtas

Didn't get any useful response to the following question. Trying again. -------------------------------------------------------------------------------- I can't seem to get computationally stable estimates for the following system: Y=a+bX+cX^2+dX^3, where X~N(0,1). (Y is expressed as a linear combination of the first three powers of a standard normal variable.) Assuming that E(Y)=0 and

Hi, Suppose x and y are vectors whose elements are known. I know that there is a sinusoidal relation between them. In other words, y=a*sin(bx) where b is probably a function of pi. How do I find a and b in R? Hakan [[alternative HTML version deleted]]

Does lmer do three-level mixed-effects models? What forms of outcome variables can it handle (continuous, survival, binary)? I'd appreciate any help.

I am trying to incorporate random effects (random intercept is good enough) to a proportional odds logistic regression model for ordinal outcomes. Could lme4 do this? I'd appreciate any input. Hakan Demirtas

Hello,I would like to know if there is residue analysis after the Cox model and make the adjustment for my random cluster? [[alternative HTML version deleted]]

Hi, I am running a Cox Mixed Effects Hazard model using the library coxme. I am trying to model time to onset (age_sym1) of thought problems (e.g. hearing voices) (sym1). As I have siblings in my dataset, I have decided to account for this by including a random effect for family (famid). My covariate of interest is Mother's diagnosis where a 0 is bipolar, 1 is control, and 2 is major

Assume that we collect below data : - subjects = 20 males + 20 females, every single individual is independence, and difference events = 1, 2, 3... n covariates = 4 blood types A, B, AB, O http://r.789695.n4.nabble.com/file/n4245397/CodeCogsEqn.jpeg ?m = hazards rates for male ?n = hazards rates for female Wm = Wn x ?, frailty for males, where ? is the edge ratio of male compare to female Wn =

Using a p-value to make any kind of decision is questionable to begin with, and especially unreliable in choosing covariates in regression. Old studies, e.g. by Walls and Weeks and by Bendel and Afifi, have shown that if predictive ability is the criterion of interest and one wishes to use p-values for deciding whether to include a covariate, one should set the p-value bar very large, at 0.25 and

I have an experiment I'm trying to analyze that's turning out to be more complicated than I anticipated, so I was hoping for some suggestions about how to handle it. The lab experiment is a comparison between two search interfaces. After a little training, each subject performs 12 information search tasks, 6 with one interface and 6 with the other, and we measure time to

Hi, This is my first post here, so if I make any mistakes, warnings are always welcome. I'm working in an organization and we are migrating are domain controllers from windows to linux. I'm trying to deploy a working configuration of samba. Using openSuSE 10.0. I was following the Keith Robertson's how-to from IBM-Developerworks but in the end; the finished installation fails to

Dear all, Please let me start by indicating that I am not from a technical background, so please be gentle and patient with me. I am trying to get a snapshot from my virtual machines (vm) and the following command works for all of them bar one: # virsh snapshot-create-as --quiesce --no-metadata --domain myvm myvm-state --diskspec vda,file=overlay.qcow2 --disk-only --atomic The only exception is

Hello all, thanks for your time and patience. I'm looking for a method in R to analyse the following data: Time to waking after anaesthetic for medical procedures repeated on the same individual. > str(mysurv) labelled [1:740, 1:2] 20 20 15 20 30+ 40+ 50 30 15 10 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:2] "time" "status" -

Hello all, I am trying to understand how to specify nested factors when using coxph(), and if it is appropriate to nest these factors in my situation. In the simplest form, I am testing two different temperatures, with each temperature being performed twice in different experimental periods (e.g. Temp5 performed in Period A and C, Temp4 performed in Period B and D) I am trying to see if survival

Dear all, I have encountered the following problem where coxme seems to allow model with only random effect in R 2.11.1 but not in R 2.13.0. Following is the error message using rat example data. Any comment on this is appreciated. In R2.13 > library(coxme) > rat1 <- coxme(Surv(time, status) ~ rx + (1|litter), rats) > rat0 <- coxme(Surv(time, status) ~ (1|litter), rats)

Dear R users: I'm new to R and am trying to fit a mixed model Cox regression model with coxme function. I have one two-level factor (treat) and one covariate (covar) and 32 different groups (centers). I'd like to fit a random coefficients model, with treat and covar as fixed factors and a random intercept, random treat effect and random covar slope per center. I haver a couple of