similar to: glm() with non-integer responses

Hi - I have a likelihood function that involves sums of two possions: L = a*dpois(Xi,theta1)*dpois(Yi,theta2)+b*(1-c)*a*dpois(Xi,theta1+theta3)*dpois(Yi,theta2) where a,b,c,theta1,theta2,theta3 are parameters to be estimated. (Xi,Yi) are observations. However, Xi and Yi are usually big (> 20000). This causes dpois to returns 0 depending on values of theta1, theta2 and theta3. My first

Hi everyone! I am using the mle {stats4} to estimate the parameters of distributions by MLE method. I have a problem with the examples they provided with the mle{stats4} html files. Please check the example and my question below! *Here is the mle html help file * http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html http://stat.ethz.ch/R-manual/R-devel/library/stats4/html/mle.html

Hi. Here is a piece of cpp code. It works, but I do not understand the rational for the use of "R::dpois" to call the function dpois since in the examples I have always found directly "dpois" or "Rcpp::dpois" that both do not work in my code. Could anyone be so patient to explain me why should it be like that? Thaks a lot, Enrico #include <Rcpp.h> using

Hello all, I am trying to get a better understanding of the underlying code for the stats::dpois function in R and, specifically, what happens under the hood when it is called. I finally managed to track down the C course at: https://github.com/wch/r-source/blob/trunk/src/nmath/dpois.c. It would seem that the dpois C function is taking a double for each of the x and lambda arguments so I am a bit

I'm very new to R and am trying to create my first loop. I have: x <-c(0:200) A <- dpois(x,exp(4.5355343)) B <- dpois(x,exp(4.5355343 + 0.0118638)) C <- dpois(x,exp(4.5355343 -0.0234615)) D <- dpois(x,exp(4.5355343 + 0.0316557)) E <- dpois(x,exp(4.5355343 + 0.0004716)) F <- dpois(x,exp(4.5355343 + 0.056437)) G <- dpois(x,exp(4.5355343 + 0.1225822)) and would like to

I am trying to test out several mgcv::gam models in a scalar-on-function regression analysis. The following is the 'hierarchy' of models I would like to test: (1) Y_i = a + integral[ X_i(t)*Beta(t) dt ] (2) Y_i = a + integral[ F{X_i(t)}*Beta(t) dt ] (3) Y_i = a + integral[ F{X_i(t),t} dt ] equivalents for discrete data might be: 1) Y_i = a + sum_t[ L_t * X_it * Beta_t ] (2) Y_i

Dear all, Even though one of R users answered my question, I cannot understand, so I re-ask this question. I am trying to use "do.call", but I don't think I totally understand this function. Here is an simple example. -------------------------------------------- > B <- matrix(c(.5,.1,.2,.3),2,2) > B [,1] [,2] [1,] 0.5 0.2 [2,] 0.1 0.3 > x <- c(.1,.2) >

Hi the list, According to what I know, the Canberra distance between X et Y is : sum[ (|x_i - y_i|) / (|x_i|+|y_i|) ] (with | | denoting the function 'absolute value') In the source code of the canberra distance in the file distance.c, we find : sum = fabs(x[i1] + x[i2]); diff = fabs(x[i1] - x[i2]); dev = diff/sum; which correspond to the formula : sum[ (|x_i - y_i|) /

Dear all, I am trying to use "do.call", but I don't think I totally understand this function. Here is an simple example. -------------------------------------------- > B <- matrix(c(.5,.1,.2,.3),2,2) > B [,1] [,2] [1,] 0.5 0.2 [2,] 0.1 0.3 > x <- c(.1,.2) > X <- cbind(1,x) > X x [1,] 1 0.1 [2,] 1 0.2 > > lt <-

I have a general question about coefficients estimation of the mixed model. I simulated a very basic model: Y|b=X*\beta+Z*b +\sigma^2* diag(ni); b follows N(0,\psi) #i.e. bivariate normal where b is the latent variable, Z and X are ni*2 design matrices, sigma is the error variance, Y are longitudinal data, i.e. there are ni

for learning purposes and also to help someone, i used roger peng's document to get the mle's of the gamma where the gamma is defined as f(y_i) = (1/gammafunction(shape)) * (scale^shape) * (y_i^(shape-1)) * exp(-scale*y_i) ( i'm defining the scale as lambda rather than 1/lambda. various books define it differently ). i found the likelihood to be n*shape*log(scale) +

Hi All, My dependent variable is a ratio that takes a value of 0 (zero) for 95% of the observations and positive non-integer values for the other 5%. What model would be appropriate? I'm thinking of fitting a GLM with a Poisson ~. Now, becuase it takes non-integer values, using the glm function with Poisson family issues warning messages. Warning messages: 1: In dpois(y, mu, log = TRUE) :

Hi, I'm using optim with box constraints to MLE on about 100 data points. It goes quite slow even on 4GB machine. I'm wondering if R has any faster implementation? Also, if I'd like to impose equality/nonequality constraints on parameters, which package I should use? Any help would be appreciated. Thank you. rc

Dear all, Is there any method in R to find all possible nonnegative integer solutions to the linear equation with unit coefficients as follow: X1+X2+...+Xk=N Thank you, Amin Zollanvari

Hello Why this works: ncota <- 1 nslope <- 29 resul <- matrix(rep(0,ncota*nslope*4),ncota*nslope,4) But this doesn't? ncota <- 1 sini <- 0.1; sfin <- 1.5; spaso <- 0.05; nslope <- 1+((sfin-sini)/spaso) resul <- matrix(rep(0,ncota*nslope*4),ncota*nslope,4) I guess the problem is that the division gives a noninteger number. How can I get the second one work? I

The nice new log=TRUE option in dpois appears to mess up the case where lambda=0 (I was trying to calculate the likelihood of a saturated model). Because the behavior is now always to calculate the probability in terms of exp(log(prob)), there's a test for lambda<=0 which really needs to be only lambda<0. dpois(0:5,0) ought to give 1 0 0 0 0 but gives NaNs instead. Here's

Canberra distance is defined in function `dist' (standard library `mva') as sum(|x_i - y_i| / |x_i + y_i|) Obviously this is undefined for cases where both x_i and y_i are zeros. Since double zeros are common in many data sets, this is a nuisance. In our field (from which the distance is coming), it is customary to remove double zeros: contribution to distance is zero when both x_i

Canberra distance is defined in function `dist' (standard library `mva') as sum(|x_i - y_i| / |x_i + y_i|) Obviously this is undefined for cases where both x_i and y_i are zeros. Since double zeros are common in many data sets, this is a nuisance. In our field (from which the distance is coming), it is customary to remove double zeros: contribution to distance is zero when both x_i

Is there a way to force certain formula parameters to be nonnegative? What I want to do is to estimate student capacity over time, namely by > capacity ~ Student + Student:Day I add this formula to a glm call and obtain negative learning slope estimates (Student:Day) in some cases. However, I don't want to allow for that. In such a case, glm should solve > capacity ~ Student and

Hello, I have the following function that receives a "function pointer" formal parameter name "fnc": loocv <- function(data, fnc) { n <- length(data.x) score <- 0 for (i in 1:n) { x_i <- data.x[-i] y_i <- data.y[-i] yhat <- fnc(x=x_i,y=y_i) score <- score + (y_i - yhat)^2 } score <- score/n