Thanks so much for your comments. Sorry for not having sent a running
example from the start. Here it is:
library (spatstat)
#Load example data
data(demopat)
#Generate a random point pattern within the polygon
set.seed(12345)
pdat <- rpoint(200,win=demopat$window)
#Generate a distmap, which will serve as covariate information
im.cdat <- as.im(distmap(pdat))
#Now the random seed is fixed and a new set of random points is
generated for the example
set.seed(11111)
pdat <- rpoint(200,win=demopat$window)
#Fitting a model to the data
mod <- ppm (pdat ~ im.cdat)
#Now a point pattern is simulated via rmh from the fitted model an
visualized as a density surface
set.seed(22222)
plot(density(rmh(mod)))
#And here is the problem: When I repeat the exercise with different
coefs, the very same patter come out. "new.coef" has no effect.
set.seed(22222)
plot(density(rmh(mod),new.coef=c(1,200)))
What am I missing?
Thanks again,
Sebastian
On 16.09.2014 00:18, Rolf Turner wrote:
Your example is not reproducible. We don't have "cshape" or "im.pop"
(and are possibly lacking other bits and pieces; I didn't check the
details since the example fails to run from the get-go). Please
provide a *reproducible* example.
Also I am puzzled by the line
mod <- ppm (ppp, ~ pop , covariates = list (pop = im.pop))
Did you mean
mod <- ppm (dat, ~ pop , covariates = list (pop = im.pop))
???
Also please note that with versions of spatstat later than or equal to
1.37-0 you can write
ppm(dat ~ im.pop)
when the object "im.pop" is present in the global environment.
cheers,
Rolf Turner
On 16/09/14 02:30, Sebastian Schutte wrote:
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?
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