similar to: Conditional Random Fields

In a GLMM, one compares the conditional model including covariates with the unconditional model to see whether the conditional model fits the data better. (1) For my unconditional model, a different random effects term fits better (independent random effects) than for my conditional model (correlated random effects). Is this very uncommon, and how can this be explained? Can I compare these models

Using lmer in the lme4 package, you can compute the conditional variance-covariance matrix of the random effects using the bVar slot: bVar: A list of the diagonal inner blocks (upper triangles only) of the positive-definite matrices on the diagonal of the inverse of ZtZ+Omega. With the appropriate scale factor (and conversion to a symmetric matrix) these are the conditional variance-covariance

Does anyone know if there are any special considerations with Random Forest and correlated fields or rather derived fields? For example if we are trying to predict who might leave our company to go work for another company some of the variables we may look at are below (in addition to others). Do we need to be cautious with comingling these especially since, for example with Age variable, all are

dear all, i wonder whether it is possible to estimate a mixed (random parameters) logit model in R. my dataset only includes conditional explanatory (RHS) variables. i've already searched the R-help archives and found slightly comparable questions but no satisfying answers. an old fashoined conditional logit does not work due to the violation of the iia property. a short description of

I need to refresh my memory on Probability Theory, especially on conditional probability. In particular, I want to solve the following two problems. Can somebody point me some good books on Probability Theory? Thank you! 1. Z=X+Y, where X and Y are independent random variables and their distributions are known. Now, I want to compute E(X | Z = z). 2.Suppose that I have $I \times J$ random number

Hi all, This was posted originally on r-sig-mixed-models, but I thought I would post here as well as it might be of more general interest. With a colleague, I have been trying to implement the Conditional AIC described by Vaida and Blanchard 2005 Biometrika, "Conditional Akaike information for mixed-effects models". This quantity is derived in a way analogous to the AIC, but is

Hi there, Could anyone please help me to understand what should be done in order not to get this error message: Error: evaluation nested too deeply: infinite recursion / options(expressions=)? Here is my code: determinant<- function(x){det(matrix(c(1.0,0.2,0.5,0.8,0.2,1.0,0.5,0.6,0.5,0.5,0.5,1.0,x,0.8,0.6,x,1.0),ncol=4,byrow=T))} matrix<-

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi Timo, Dovecot 2.2.6 compiled on Ubuntu 10.04 fails on "make check" with the following errors. "make install" is finishing just fine and Dovecot itself works flawlessly so far. - ----- 8< ----- http header invalid [0]: parse failure ............................... : ok: Expected ':' after header field name

Hi, I have a right-censored (positive) random variable (e.g. failure times subject to right censoring) that is observed for N subjects: Y_i, I = 1, 2, ..., N. Note that Y_i = min(T_i, C_i), where T_i is the true failure time and C_i is the censored time. Let us assume that C_i is independent of T_i. Now, I would like to generate another random variable U_i, I = 1, 2, ..., N, which is

Hi all, I'm looking for some help to bias the sample function. Basically, I'd like to generate a data frame where the first column is completely random, the second, however, is conditional do the first, the third is conditional to the first and the second and so on. By conditional I mean that I shouldn't have repeated values in the line. I know it could be easily implemented using

I have tried to install the package RandomFields (using the packages menu) from CRAN and it fails to open the zip file. I then tried downloading the package from the author's site, but it still fails to install. Anyone had any success with this? Or am I doing something wrong. Thanks in advance for any help. Michael Axelrod

I am looking up a number based upon a randomly selected number and then proceed to the rest of my code if the corresponding value is greater than or equal to yet another value. so if Dev_Size = 14 and my randomly selected number is 102 and i am looking up 102 in the following table 100 21 101 4 102 9 103 52 104 29 So i select the the corresponding value of 102, which is 9 and

I have asked the same question on stackoverflow but did not get a satisfying answer. I am trying to simulate a lognormal spatial random field but I need the simulated value in a certain range. So I need some easy to use functions to generate a truncated Gaussian field to start with. To be specific, I need a function like GaussRF from the RandomFields package or grf from the geoR package to

Hi, I'm doing a generalized linear mixed model, and I currently use an R function called "glmm". However, in this function they use a standard normal distribution for the random effect, which doesn't satisfy my case, i.e. my random effect also follows a normal distribution, but observations over time are somehow correlated, so the covariance matrix would be different the

check the following code: # settings n <- 100 # number of sample units p <- 10 # number of repeated measurements N <- n * p # total number of measurements t.max <- 3 # parameter values betas <- c(0.5, 0.4, -0.5, -0.8) # fixed effects (check also 'X' below) sigma.b <- 2 # random effects variance # id, treatment & time id <- rep(1:n, each = p) treat <- rep(0:1,

Hello, I got one question on the Latin hypercube sampling. suppose there are three variables a, b, c, all of them follow the normal distribution. the mean value and standard deviation for each are  a(32, 2), b(35,5), c(37,3). I would like to use Latin hypercube sampling to random generate 1000 samples. but it needs to satisfy the condition that a<b<c. How can I implement this

if ive got an incomplete data set thats got thousands of rows and 80 columns with random missing fields...like this say... 3 b 3 4 1 1 x 2 ? how do i turn it into.... 3 b 3 4 missing 1 1 x 2 ...i.e., i want to insert the word "missing" into the fields that are empty? -- View this message in context:

Am Donnerstag, 28. Juli 2016, 18:07:32 CEST schrieb Alex Xu: Hi Alex, > Linux 4.6, also tried 4.7, qemu 2.6, using this C program: I am not sure what problem you are referring to, but that is an expected behavior. You get partial reads when reading from /dev/random with a minimum of 64 bits. On the other hand getrandom(2) is woken up after the input_pool received 128 bits of entropy. In

#Conditional inference forest ("Party" package) error message states that levels in factors of new data do not match original data, but they do... #create conditional inference forest oc_listed.fit1 <- cforest(Listed~ HabMode,controls=cforest_unbiased(ntree=500), data=oc.complete) #use predict function for subset of data #this works correctly

To any that may be interested in the implementation of the conditional expression in the expression parser (ast_expr2*) in asterisk, I've filed the patch at: http://bugs.digium.com/view.php?id=4459 Right now, a comment has been added noting that the IF func provides this capability, and asks if both would really be necessary. It's a good question. I haven't been following the