different models

Hi all,

This might be a very dumb question that shows I have very little idea of
what I am talking about, but I'll risk it:

What is the difference between fitting a model using these 3 different
syntaxes?

1/ fit1 <- ppm(ppp, ~covariate),
2/ fit2 <- ppm(ppp, ~x+y+Z, covariates=list(Z=covariate))
3/ fit3 <- ppm(ppp, ~x+y+covariate)

where ppp is my point pattern and "covariate" is a pixel image? I realise
the outputs of 2 and 3 are the same and different to that of 1, so I guess
the question really is

a/ Is there any difference, practical or in the actual computations of the
model, between using 2 and 3?
b/ What is the difference between (2&3) and 1?

(1) There is essentially no difference between fits 2 & 3.  The fit 2 
syntax is provided so that the user can have the relevant covariates 
bundled up in a list without any need to extract these covariates from 
that list.   With the fit 2 syntax you don't need to have all covariates 
present in your workspace.

E.g.: fit <- ppm(bei ~ elev + grad, data=bei.extra)

(2) The fit 2 syntax is essentially the same as that used by lm() and 
glm() and was designed in imitation thereof.

(3) The preferred structure of a call to ppm() is

     fit2 <- ppm(ppp ~ x + y + Z, data=list(Z=covariate))

Note:  "data" rather than "covariates"; no comma between the name of the 
response variable ("ppp") and the formula.

This makes the syntax identical to that of lm() and glm().

The syntax that you used is a remnant of earlier versions of spatstat 
and remains acceptable for reasons of backward compatibility.

(4) The difference between model 1 and models 2 and 3 is that models 2 
and 3 involve the Cartesian coordinates "x" and "y".  Model 1 is such 
that the model intensity takes the form

    exp(beta_0 + beta_1 * covariate)

In models 2 and 3 the model intensity takes the (more complex) form

    exp(beta_0 + beta_1 * x + beta_2 *y beta_3 * covariate)

Note that "x" and "y" are *reserved* names.  You cannot use these names 
for any covariates *other than* the Cartesian coordinates.

(5) The name "covariate" is probably *not* a good name for a covariate.
As fortune(77) puts it "Would you call your dog 'dog'?"

(6) Likewise (and even more so) "ppp" is *not* a good name for a point 
pattern, since it clashes the name of the creator function ppp().

cheers,

Rolf Turner

Technical Editor ANZJS
Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276

Hi Rolf,

Thank you for your very complete response. If I understand it correctly
then, I should just include the Cartesian coordinates in my covariates list
if I want to model the intensity specifically in relation to them as well
as the covariates, correct?

Oh, and just for clarification, I do not name my point patterns "ppp" and
my covariates "covariate" (although I kind of like the idea of calling my
dog "dog"). I was just trying to make a general example, but thanks for the
heads up anyway!

Best,

Virginia Morera
PhD Student
Department of Animal Biology
University of Barcelona

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2016-04-07 5:11 GMT+02:00 Rolf Turner <r.turner at auckland.ac.nz>:

On 06/04/16 22:00, Virginia Morera Pujol wrote:

Hi all,

This might be a very dumb question that shows I have very little idea of
what I am talking about, but I'll risk it:

What is the difference between fitting a model using these 3 different
syntaxes?

1/ fit1 <- ppm(ppp, ~covariate),
2/ fit2 <- ppm(ppp, ~x+y+Z, covariates=list(Z=covariate))
3/ fit3 <- ppm(ppp, ~x+y+covariate)

where ppp is my point pattern and "covariate" is a pixel image? I realise
the outputs of 2 and 3 are the same and different to that of 1, so I guess
the question really is

a/ Is there any difference, practical or in the actual computations of the
model, between using 2 and 3?
b/ What is the difference between (2&3) and 1?

(1) There is essentially no difference between fits 2 & 3.  The fit 2
syntax is provided so that the user can have the relevant covariates
bundled up in a list without any need to extract these covariates from that
list.   With the fit 2 syntax you don't need to have all covariates present
in your workspace.

E.g.: fit <- ppm(bei ~ elev + grad, data=bei.extra)

(2) The fit 2 syntax is essentially the same as that used by lm() and
glm() and was designed in imitation thereof.

(3) The preferred structure of a call to ppm() is

    fit2 <- ppm(ppp ~ x + y + Z, data=list(Z=covariate))

Note:  "data" rather than "covariates"; no comma between the name of the
response variable ("ppp") and the formula.

This makes the syntax identical to that of lm() and glm().

The syntax that you used is a remnant of earlier versions of spatstat and
remains acceptable for reasons of backward compatibility.

(4) The difference between model 1 and models 2 and 3 is that models 2 and
3 involve the Cartesian coordinates "x" and "y".  Model 1 is such that the
model intensity takes the form

   exp(beta_0 + beta_1 * covariate)

In models 2 and 3 the model intensity takes the (more complex) form

   exp(beta_0 + beta_1 * x + beta_2 *y beta_3 * covariate)

Note that "x" and "y" are *reserved* names.  You cannot use these names
for any covariates *other than* the Cartesian coordinates.

(5) The name "covariate" is probably *not* a good name for a covariate.
As fortune(77) puts it "Would you call your dog 'dog'?"

(6) Likewise (and even more so) "ppp" is *not* a good name for a point
pattern, since it clashes the name of the creator function ppp().

cheers,

Rolf Turner

--
Technical Editor ANZJS
Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276

Hi Rolf,

Thank you for your very complete response. If I understand it correctly
then, I should just include the Cartesian coordinates in my covariates
list if I want to model the intensity specifically in relation to them
as well as the covariates, correct?

Well, in a word, yes.  Dunno what more I can say without inducing 
obfuscation instead of clarification (of what is actually a simple issue.)

The best way to get an understanding of what is involved, IMHO, is to do 
some experimentation.  Fit some models to some data, plot the fitted 
surfaces (either as image plots or perspective plots) and see what the 
results look like.

<SNIP>

cheers,

Rolf

Technical Editor ANZJS
Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276