search for: covaries

Hi, I've been trying to figure out a special set of covariance matrices that causes some symmetric zero elements in the inverse covariance matrix but am having trouble figuring out if that is possible. Say, for example, matrix a is a 4x4 covariance matrix with equal variance and zero covariance elements, i.e. [,1] [,2] [,3] [,4] [1,] 4 0 0 0 [2,] 0 4

Hello, R friends... I am very new to R, and I need some help. I am trying to construct a simulation for my dissertation. I need to create 1000 datasets of 1000 subjects with the following variables... Treatment variable - Drawn from a binomial distribution (1 run, prob=.13) Covariate 1 - Drawn from a normal distribution (mean=100, sd=16) Covariate 2 - Drawn from a normal distribution

Dear All,   is there a function in R that would help me convert a covariance matrix built based on arithmetic returns to a covariance matrix from log-returns?   As an example of the means and covariance from arithmetic:   mu <-c(0.094,0.006,1.337,1.046,0.263) sigma

Hi, I want to simulate a data set with similar covariance structure as my observed data, and have calculated a covariance matrix (dimensions 8368*8368). So far I've tried two approaches to simulating data: rmvnorm from the mvtnorm package, and by using the Cholesky decomposition (http://www.cerebralmastication.com/2010/09/cholesk-post-on-correlated-random-normal-generation/). The problem is

Dear All, This is not strictly R related (though I would implement the solution in R; besides, being this list so helpful for these kinds of stats questions...). I got a "strange" request from a colleage. He has a bunch (approx. 25000) subjects that belong to one of 12 possible classes. In addition, there are 8 covariates (factors) that can take as values either "absence"

I have a fairly large model: > length(Y) [1] 3051 > dim(covariates) [1] 3051 211 All of these 211 covariates need to be nested hierarchically within a grouping "class", of which there are 8. I have an accessory vector, " cov2class" that specifies the mapping between covariates and the 8 classes. Now, I understand I can break all this information up into individual

Hello everyone. I would like to find the sample covariance matrix using R. So far I read on the wikipedia what a sample_covariance is http://en.wikipedia.org/wiki/Sample_covariance according to wikipedia one vector is enough to calculate the sample covariance matrix. In R I tried cov(myvector) and I get the reply that I need to pass either two argument or one matrix with x,y values . How can I

Hello, I am trying to model an intensity function with time-varying covariates. Before, I have successfully defined a log likelihood function for a Power-Law Process (lambda(t)=alpha*beta*t^(beta-1)) with two paramters and no covariates for a repairable systems with failure times (t). This function was maximized with R optim. No problem! But now I want to include a covariate indicating a

Hello Does anybody happen to know if it is possible to use the survey package to estimate a covariance matrix from a complex survey? I have design weights and clusters (no strata), and want to get a covariance matrix with preferably the effective sample size or else an estimate of the variance-covariance matrix of the covariance matrix ("asymptotic covariance matrix"). Is this

Hi all,I have some questions about the covariants of regression. My target: To explore the trend of CD4 level through a period of time. Response variable: CD4 count Explanatory variable:time Also, the demology information is available,such as gender,occupation,income level... Q1,Are these variables of demology information called covariant? Q2,How can I correct the impact of

Hi, How can I specify a Cox proportional hazards model with a covariate which i believe its strength on survival changes/diminishes with time? The value of the covariate was only recorded once at the beginning of the study for each individual (e.g. at the diagnosis of the disease), so I do not have the time course data of the covariate for any given individual. For example, I want to state at the

Hi, Im using geoR and I'm trying to do some predictions, based on an external trend. I'm having some problems specifying my model.control, specifically how do I define my model, and also the source of the covariate data at the prediction locations? I am assuming that the covariate data at the prediction locations should be imported to a geodata object along with the prediction location

Hi all, I have data from an experiment with 3 independent variables, 2 are within and 1 is between. In addition to the dependent variable, I have a covariate that is a single measure per subject. Below I provide an example generated data set and my approach to implementing the ANCOVA. However the output confuses me; why does the covariate only appear in the first strata? Presumably it should

Dear R users, I d like to assess the effect of "treatment" covariate on a disease relapse risk with the package cmprsk. However, the effect of this covariate on survival is time-dependent (assessed with cox.zph): no significant effect during the first year of follow-up, then after 1 year a favorable effect is observed on survival (step function might be the correct way to say that ?).

I'm having trouble getting the relaimpo package to use a covariance matrix as input. I'm getting an error message that reads as follows: Error in eval(m$weights, data, parent.frame()) : numeric 'envir' arg not of length one I'm guessing there is something wrong with the structure of my covariance matrix, but it looks fine to me. Pardon my R ignorance if this is an easy

Hi all, I am trying to detect association between a covariate and a disease outcome using R. This covariate shows time-varying effect, I add a time-covariate interaction item to build Cox model as follows: COX <- coxph(as.formula("Surv(TIME,outcome)~eGFR_BASE+eGFR_BASE:TIME"),ori.data); coef exp(coef) e(coef) z p eGFR_BASE

Dear list In HLM, one can specify a covariate at one of the "levels". For example, if the data structure are repeated observations nested within students nested within schools, school size might be a covariate that is used at level 3, but not at the other levels. In HLM this is rather easy to do. However, how can one specify a covariate in R for only one of the levels? I have a

Hello, I am hoping someone can help me with the following multivariate issue: I have a model consisting of about 50 covariates. I would like to reduce this to about 5 covariate for the reduced model by combining cofactors that are strongly correlated. Is there a package or function that would help me with this in R? I appreciate any suggestions. Thanks, Ian

Dear R list, I have one very elementary question regrading correlation between two variables. x = c(44,46,46,47,45,43,45,44) y = c(44,43,41,41,46,48,44,43) > cov(x, y) [1] -2.428571 However, if I try to calculate the covariance using the formula as covariance = sum((x-mean(x))*(y-mean(y)))/8       # no of of paired obs. = 8 or     covariance = sum(x*y)/8-(mean(x)*mean(y)) gives

Dear R help forum, I am using the function 'coxph' to obtain hazard ratios for the comparison of a standard treatment to new treatments. This is easily obtained by fitting the relevant model and then calling exp(coef(fit1)) say. I now want to obtain the hazard ratio for the comparison of two non-standard treatments. >From a statistical point of view, this can be achieved by dividing