similar to: Simulating correlations with varying sample sizes

Hi all, I''m using ferret 11.4 together with acts_as_ferret and I''ve indexed the geonames.org country files. These files contain worldwide locations in UTF-8 with all their different spellings each. Model definition is like this: class location acts_as_ferret :fields => {:location_names => {}}, :single_index => true ... end The instance method location_names

Hi Does anyone know of any R code for estimating a *multivariate* state space model using a Kalman filter where the output matrix H(t) is time-varying but predictable (i.e. measurable w.r.t information at time t-1) in the observation equation y(t) = H(t) z(t) + R w(t)? [Here y(t) are the observations, z(t) is the state variable, w(t) the observation error and R R' the observation error

Dear R users, I have repeated measurements on individuals. I want to estimate the time-varying effect of a factor variable X (taking three levels), e.g. a model in the spirit of Hastie and Tibshirani (1993). I am considering using the package "mgvc" which implements generalized additive models, especially the function gamm, which estimates generalized additive mixed models, and thus,

Hi, I've seen in the NestCohort package that one can do a hazard model with a binary outcome using covariates. I am interested in multistate hazard models with time-varying covariates, but can't seem to find this already implemented in an R package. Is this included somewhere but called something else? I feel like I've looked all over. Thanks! Michaela Gulemetova-Swan

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

Dear R-users, I am trying to estimate a state space model of the form (1) b_t = G * b_t-1 + w_t w_t ~ N(0,W) (2) y_t= A' * x_t + H' * b_t + v_t v_t ~ N(0,V) (Hamilton 1984: 372) In particular my estimation in state space form looks like (3) a3_t = 1 * a3_t-1 + w_t w_t ~ N(0,W) (4) g_t = (a1, a2) * (1, P_t)' + u_t * a3_t + v_t v_t ~ N(0,V) where g_t is the

Hi, I've been searching CRAN and the web for a recursive multivariate filter with time-varying coefficients. What I mean is the following: I have a series of square matrices A_t an initial value vector y_0 and I need to compute y_t =A_t%*%y_t-1 As these y_t may diverge quickly and/or lead to underflow problems, the y_t need to be scaled by eg y_t =y_t/sum(y_t-1) Is anyone aware

Hi, I''ve been reading lots of documents and examples of setting up traffic shaping, and found that they _all_ refer to specific bandwidths for queues. I am using a dialup connection, and therefore can connect at different rates, and also have a dynamically changing ''bandwidth'' dependent on compression. What I want to do is to specify that interactive traffic (telnet,

Hi! I want to estimate a shared gamma frailty model with left truncated data. I use a parametric baseline hazard so that I can use simple ML estimation. As I have a big data set it is ok to assume piecewise constant baseline hazards. As my data are left truncated I have modified the definition of the risk set. Do I also have to modifiy the frailty distribution if I have left truncated data?

Question: When survfit() function is used upon a coxph object, the 'n' returned is vastly smaller (n=6) than the number of distinct loans in the dataset used. I am trying to estimate a Cox proportional hazards model for a set of loans (over 6000) using using time varying covariates. For this 6000+ loans, I have some 62,000 different vectors representing the loans at different periods of

nvfx_fragprog_assign_generic only allows for up to 10/8 texcoords for nv40/nv30. This fixes compilation of the varying-packing tests (although they still fail). Signed-off-by: Ilia Mirkin <imirkin at alum.mit.edu> Cc: 9.1 9.2 10.0 <mesa-stable at lists.freedesktop.org> --- src/gallium/drivers/nouveau/nv30/nv30_screen.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git

I just realised my previous email didn't go to the list for some reason. Here it is again: Currently, I have three liquadsoap ".liq" files, and they all look very similar to this: -------- 8< -------- jake at beastie:/etc/liquidsoap$ cat jake.liq #!/usr/bin/liquidsoap #set("log.file.path","/tmp/basic-radio.log") def my_request_function() = # Get the

Hi, I am using R a lot to make plots relating to radioactivity, I am often using expression() to label the plots with nuclide names written with superscripts, e.g. expression(paste("Releases of ", { }^{99},Tc," (TBq/year)"))->ywtext But, is there any simple way to change the number and name of the nuclide through a variable? I tried nuccode=expression({ }^{99},Tc)

I'd like to estimate time varying coefficients in a linear regression using a Kalman filter. Even if the Kalman Filter seems to be available in some packages I can't figure out how to use it to estimate the coefficients. Is there anyway to do that in R? Any help appreciated Thanks

I am using a syntax like coxph(Surv(start, stop, event) ~ X, data) to estimate the effect of X, which may change at each measurement (every 6 months). Is there anyone who knows a way to test the proportional hazard assumption in that case? Thank you in advance Jean-François Boudreau Sherbrooke University [[alternative HTML version deleted]]

Dear R useres, I'm interested in getting a series of time-varying correlation, simply between two random variables. Could you please introduce a package to do this task? Thank you so much for any help. Amir --------------------------------- Don't pick lemons. [[alternative HTML version deleted]]

Folks, I have a matrix of historicalReturns, where entry (i, j) is the daily return corresponding to date i and equity j. I also have a matrix startOffset, where entry (1, k) is the row offset in historicalReturns where I entered into equity k. So we have that NCOL(startOffset) = NCOL(historicalReturns). Now I would like compute for each column in historicalReturns, the cumulative return

A question about vectorized operations (avoiding loops, for speed)... I need to run a simple recursive (autoregressive) filter with a time-varying coefficient. It is just a one-step recursive filter, so it would be an exponential decay if the filter was constant. I just want to do this, where 'x' is the data and 'w' is the weight to apply to the previous time step: x <- c(1,

Hi I'm trying to use the fold function as described here: http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-cox-regression.pdf Page9 It does say that you can use this when you have more than one time varying covariate: in the description of the argument cov it says: "cov: A vector giving the column numbers of the time-dependent covariate in data, or a list of vectors if there

Dear R users, I know, this is the second time i return on this topic. Sorry, but this analysis is of great value for me, and i hope someone can help me. I need to model a time-varying effect in a Cox model. Briefly explained here: http://books.google.com/books?id=9kY4XRuUMUsC&lpg=PP1&hl=it&pg=PA147#v=onepage&q&f=false