I am struggling with the package spatstat and would really appreciate some help.

I do not know how to interpret the "Fitted trend" and "Estimated SE" plots that one can get with the following code:

library(spatstat)
ppp <- rpoispp(100)
model <- ppm(ppp, ~x+y)
plot(model, pause = F)

I have looked everywhere but everyone seems to assume the reader already knows what they are and what they are for. I am aware the spatstat book Spatial Point Patterns: Methodology and Applications with R might have all the answers I need, but I do not have access to it at the moment.

Any insight will be much appreciated.

I am struggling with the package spatstat and would really appreciate some help.

I do not know how to interpret the "Fitted trend" and "Estimated SE" plots that one can get with the following code:

library(spatstat)
ppp <- rpoispp(100)
model <- ppm(ppp, ~x+y)
plot(model, pause = F)

I have looked everywhere but everyone seems to assume the reader already knows what they are and what they are for. I am aware the spatstat book Spatial Point Patterns: Methodology and Applications with R might have all the answers I need, but I do not have access to it at the moment.

Any insight will be much appreciated.

edit: I have also looked at the function effectfun which you can plot with:

plot(effectfun(model, "y", se.fit=TRUE))

and which, according to the help page,

Computes the trend or intensity of a fitted point process model as a function of one of its covariates.

but this still doesn't mean much to me, although I understand they must be related in some way.