Dear R and spatstat developers, Thanks so much for the time and effort that you invest into this awesome software. I have a problem simulating from a Point Process Model in spatstat. In summary, the option "new.coef" should allow me to use a fitted model and change its beta coefficients before simulating a point pattern from the model via Monte Carlo simulation. Intuitively, one would assume that the predicted point pattern changes as one fiddles with the beta coefficients. However, this does not seem to work. Please let me know what I am missing here and which screw to drive to actually change the simulation output. #owin is a polygon of country boundaries, "im.pop" is a raster with georeferenced population counts. #I am using a random point pattern for demonstration purposes #Fix random seed set.seed(12345) #Generate artificial points dat <- rpoint(500,win=cshape) #Fit a (inhomogenous spatial poisson) model to the data mod <- ppm (ppp, ~ pop , covariates = list (pop = im.pop)) #Simulate some points: plot(density(rmh(mod))) #plot(density(simulate(mod))) #Show that this is reproducible set.seed(12345) #Generate artificial points dat <- rpoint(500,win=cshape) #Fit a (inhomogenous spatial poisson) model to the data mod <- ppm (ppp, ~ pop , covariates = list (pop = im.pop)) #Simulate some points: plot(density(rmh(mod))) #As expected, the density is the same #Now change the coefs and do it again: set.seed(12345) #Generate artificial points dat <- rpoint(500,win=cshape) #Fit a (inhomogenous spatial poisson) model to the data mod <- ppm (ppp, ~ pop , covariates = list (pop = im.pop)) #Simulate some points: plot(density(rmh(mod),new.coef=c(1,200))) #Looks the same, so what am I missing? Thanks for your help, Sebastian P.S: R 3.1.1 Spatstat 1.38-1 Ubuntu 14.04 Linux 3.13.0-34-generic