# rhohat

##### Nonparametric Estimate of Intensity as Function of a Covariate

Computes a nonparametric estimate of the intensity of a point process, as a function of a (continuous) spatial covariate.

- Keywords
- models, spatial, nonparametric

##### Usage

`rhohat(object, covariate, ...)`# S3 method for ppp
rhohat(object, covariate, ...,
baseline=NULL, weights=NULL,
method=c("ratio", "reweight", "transform"),
horvitz=FALSE,
smoother=c("kernel", "local", "decreasing", "increasing"),
subset=NULL,
dimyx=NULL, eps=NULL,
n = 512, bw = "nrd0", adjust=1, from = NULL, to = NULL,
bwref=bw,
covname, confidence=0.95, positiveCI)

# S3 method for quad
rhohat(object, covariate, ...,
baseline=NULL, weights=NULL,
method=c("ratio", "reweight", "transform"),
horvitz=FALSE,
smoother=c("kernel", "local", "decreasing", "increasing"),
subset=NULL,
dimyx=NULL, eps=NULL,
n = 512, bw = "nrd0", adjust=1, from = NULL, to = NULL,
bwref=bw,
covname, confidence=0.95, positiveCI)

# S3 method for ppm
rhohat(object, covariate, ...,
weights=NULL,
method=c("ratio", "reweight", "transform"),
horvitz=FALSE,
smoother=c("kernel", "local", "decreasing", "increasing"),
subset=NULL,
dimyx=NULL, eps=NULL,
n = 512, bw = "nrd0", adjust=1, from = NULL, to = NULL,
bwref=bw,
covname, confidence=0.95, positiveCI)

# S3 method for lpp
rhohat(object, covariate, ...,
weights=NULL,
method=c("ratio", "reweight", "transform"),
horvitz=FALSE,
smoother=c("kernel", "local", "decreasing", "increasing"),
subset=NULL,
nd=1000, eps=NULL, random=TRUE,
n = 512, bw = "nrd0", adjust=1, from = NULL, to = NULL,
bwref=bw,
covname, confidence=0.95, positiveCI)

# S3 method for lppm
rhohat(object, covariate, ...,
weights=NULL,
method=c("ratio", "reweight", "transform"),
horvitz=FALSE,
smoother=c("kernel", "local", "decreasing", "increasing"),
subset=NULL,
nd=1000, eps=NULL, random=TRUE,
n = 512, bw = "nrd0", adjust=1, from = NULL, to = NULL,
bwref=bw,
covname, confidence=0.95, positiveCI)

##### Arguments

- object
A point pattern (object of class

`"ppp"`

or`"lpp"`

), a quadrature scheme (object of class`"quad"`

) or a fitted point process model (object of class`"ppm"`

or`"lppm"`

).- covariate
Either a

`function(x,y)`

or a pixel image (object of class`"im"`

) providing the values of the covariate at any location. Alternatively one of the strings`"x"`

or`"y"`

signifying the Cartesian coordinates.- weights
Optional weights attached to the data points. Either a numeric vector of weights for each data point, or a pixel image (object of class

`"im"`

) or a`function(x,y)`

providing the weights.- baseline
Optional baseline for intensity function. A

`function(x,y)`

or a pixel image (object of class`"im"`

) providing the values of the baseline at any location.- method
Character string determining the smoothing method. See Details.

- horvitz
Logical value indicating whether to use Horvitz-Thompson weights. See Details.

- smoother
Character string determining the smoothing algorithm. See Details.

- subset
Optional. A spatial window (object of class

`"owin"`

) specifying a subset of the data, from which the estimate should be calculated.- dimyx,eps,nd,random
Arguments controlling the pixel resolution at which the covariate will be evaluated. See Details.

- bw
Smoothing bandwidth or bandwidth rule (passed to

`density.default`

).- adjust
Smoothing bandwidth adjustment factor (passed to

`density.default`

).- n, from, to
Arguments passed to

`density.default`

to control the number and range of values at which the function will be estimated.- bwref
Optional. An alternative value of

`bw`

to use when smoothing the reference density (the density of the covariate values observed at all locations in the window).- …
Additional arguments passed to

`density.default`

or`locfit`

.- covname
Optional. Character string to use as the name of the covariate.

- confidence
Confidence level for confidence intervals. A number between 0 and 1.

- positiveCI
Logical value. If

`TRUE`

, confidence limits are always positive numbers; if`FALSE`

, the lower limit of the confidence interval may sometimes be negative. Default is`FALSE`

if`smoother="kernel"`

and`TRUE`

if`smoother="local"`

. See Details.

##### Details

This command estimates the relationship between
point process intensity and a given spatial covariate.
Such a relationship is sometimes called a
*resource selection function* (if the points are organisms
and the covariate is a descriptor of habitat) or
a *prospectivity index* (if the points are mineral deposits
and the covariate is a geological variable).
This command uses nonparametric methods which do not assume a
particular form for the relationship.

If `object`

is a point pattern, and `baseline`

is missing or
null, this command assumes that `object`

is a realisation of a
point process with intensity function
\(\lambda(u)\) of the form
$$\lambda(u) = \rho(Z(u))$$
where \(Z\) is the spatial
covariate function given by `covariate`

, and
\(\rho(z)\) is the resource selection function
or prospectivity index.
A nonparametric estimator of the function \(\rho(z)\) is computed.

If `object`

is a point pattern, and `baseline`

is given,
then the intensity function is assumed to be
$$\lambda(u) = \rho(Z(u)) B(u)$$
where \(B(u)\) is the baseline intensity at location \(u\).
A nonparametric estimator of the relative intensity \(\rho(z)\)
is computed.

If `object`

is a fitted point process model, suppose `X`

is
the original data point pattern to which the model was fitted. Then
this command assumes `X`

is a realisation of a Poisson point
process with intensity function of the form
$$
\lambda(u) = \rho(Z(u)) \kappa(u)
$$
where \(\kappa(u)\) is the intensity of the fitted model
`object`

. A nonparametric estimator of
the relative intensity \(\rho(z)\) is computed.

The nonparametric estimation procedure is controlled by the
arguments `smoother`

, `method`

and `horvitz`

.

The argument `smoother`

selects the type of estimation technique.

If

`smoother="kernel"`

(the default) or`smoother="local"`

, the nonparametric estimator is a*smoothing estimator*of \(\rho(z)\), effectively a kind of density estimator (Baddeley et al, 2012). The estimated function \(\rho(z)\) will be a smooth function of \(z\). Confidence bands are also computed, assuming a Poisson point process. See the section on*Smooth estimates*.If

`smoother="increasing"`

or`smoother="decreasing"`

, we use the*nonparametric maximum likelihood estimator*of \(\rho(z)\) described by Sager (1982). This assumes that \(\rho(z)\) is either an increasing function of \(z\), or a decreasing function of \(z\). The estimated function will be a step function, increasing or decreasing as a function of \(z\). See the section on*Monotone estimates*.

See Baddeley (2018) for a comparison of these estimation techniques.

If the argument `weights`

is present, then the contribution
from each data point `X[i]`

to the estimate of \(\rho\) is
multiplied by `weights[i]`

.

If the argument `subset`

is present, then the calculations are
performed using only the data inside this spatial region.

This technique assumes that `covariate`

has continuous values.
It is not applicable to covariates with categorical (factor) values
or discrete values such as small integers.
For a categorical covariate, use
`intensity.quadratcount`

applied to the result of
`quadratcount(X, tess=covariate)`

.

The argument `covariate`

should be a pixel image, or a function,
or one of the strings `"x"`

or `"y"`

signifying the
cartesian coordinates. It will be evaluated on a fine grid of locations,
with spatial resolution controlled by the arguments
`dimyx,eps,nd,random`

.
In two dimensions (i.e.
if `object`

is of class `"ppp"`

, `"ppm"`

or
`"quad"`

) the arguments `dimyx, eps`

are
passed to `as.mask`

to control the pixel
resolution. On a linear network (i.e. if `object`

is of class
`"lpp"`

) the argument `nd`

specifies the
total number of test locations on the linear
network, `eps`

specifies the linear separation between test
locations, and `random`

specifies whether the test locations
have a randomised starting position.

##### Value

A function value table (object of class `"fv"`

)
containing the estimated values of \(\rho\)
(and confidence limits) for a sequence of values of \(Z\).
Also belongs to the class `"rhohat"`

which has special methods for `print`

, `plot`

and `predict`

.

##### Smooth estimates

Smooth estimators of \(\rho(z)\) were proposed by Baddeley and Turner (2005) and Baddeley et al (2012). Similar estimators were proposed by Guan (2008) and in the literature on relative distributions (Handcock and Morris, 1999).

The estimated function \(\rho(z)\) will be a smooth function of \(z\).

The smooth estimation procedure involves computing several density estimates
and combining them. The algorithm used to compute density estimates is
determined by `smoother`

:

If

`smoother="kernel"`

, the smoothing procedure is based on fixed-bandwidth kernel density estimation, performed by`density.default`

.If

`smoother="local"`

, the smoothing procedure is based on local likelihood density estimation, performed by`locfit`

.

The argument `method`

determines how the density estimates will be
combined to obtain an estimate of \(\rho(z)\):

If

`method="ratio"`

, then \(\rho(z)\) is estimated by the ratio of two density estimates, The numerator is a (rescaled) density estimate obtained by smoothing the values \(Z(y_i)\) of the covariate \(Z\) observed at the data points \(y_i\). The denominator is a density estimate of the reference distribution of \(Z\). See Baddeley et al (2012), equation (8). This is similar but not identical to an estimator proposed by Guan (2008).If

`method="reweight"`

, then \(\rho(z)\) is estimated by applying density estimation to the values \(Z(y_i)\) of the covariate \(Z\) observed at the data points \(y_i\), with weights inversely proportional to the reference density of \(Z\). See Baddeley et al (2012), equation (9).If

`method="transform"`

, the smoothing method is variable-bandwidth kernel smoothing, implemented by applying the Probability Integral Transform to the covariate values, yielding values in the range 0 to 1, then applying edge-corrected density estimation on the interval \([0,1]\), and back-transforming. See Baddeley et al (2012), equation (10).

If `horvitz=TRUE`

, then the calculations described above
are modified by using Horvitz-Thompson weighting.
The contribution to the numerator from
each data point is weighted by the reciprocal of the
baseline value or fitted intensity value at that data point;
and a corresponding adjustment is made to the denominator.

Pointwise confidence intervals for the true value of \(\rho(z)\)
are also calculated for each \(z\),
and will be plotted as grey shading.
The confidence intervals are derived using the central limit theorem,
based on variance calculations which assume a Poisson point process.
If `positiveCI=FALSE`

, the lower limit of the confidence
interval may sometimes be negative, because the confidence intervals
are based on a normal approximation to the estimate of \(\rho(z)\).
If `positiveCI=TRUE`

, the confidence limits are always
positive, because the confidence interval is based on a normal
approximation to the estimate of \(\log(\rho(z))\).
For consistency with earlier versions, the default is
`positiveCI=FALSE`

for `smoother="kernel"`

and `positiveCI=TRUE`

for `smoother="local"`

.

##### Monotone estimates

The nonparametric maximum likelihood estimator of a monotone function \(\rho(z)\) was described by Sager (1982). This method assumes that \(\rho(z)\) is either an increasing function of \(z\), or a decreasing function of \(z\). The estimated function will be a step function, increasing or decreasing as a function of \(z\).

This estimator is chosen by specifying
`smoother="increasing"`

or `smoother="decreasing"`

.
The argument `method`

is ignored this case.

To compute the estimate of \(\rho(z)\), the algorithm first computes several primitive step-function estimates, and then takes the maximum of these primitive functions.

If `smoother="decreasing"`

, each primitive step function
takes the form \(\rho(z) = \lambda\) when \(z \le t\),
and \(\rho(z) = 0\) when \(z > t\), where
and \(\lambda\) is a primitive estimate of intensity
based on the data for \(Z \le t\). The jump location \(t\)
will be the value of the covariate \(Z\) at one of the
data points. The primitive estimate \(\lambda\)
is the average intensity (number of points divided by area)
for the region of space where the covariate value is less than
or equal to \(t\).

If `horvitz=TRUE`

, then the calculations described above
are modified by using Horvitz-Thompson weighting.
The contribution to the numerator from
each data point is weighted by the reciprocal of the
baseline value or fitted intensity value at that data point;
and a corresponding adjustment is made to the denominator.

Confidence intervals are not available for the monotone estimators.

##### References

Baddeley, A., Chang, Y.-M., Song, Y. and Turner, R. (2012)
Nonparametric estimation of the dependence of a point
process on spatial covariates.
*Statistics and Its Interface* **5** (2), 221--236.

Baddeley, A. and Turner, R. (2005)
Modelling spatial point patterns in R.
In: A. Baddeley, P. Gregori, J. Mateu, R. Stoica, and D. Stoyan,
editors, *Case Studies in Spatial Point Pattern Modelling*,
Lecture Notes in Statistics number 185. Pages 23--74.
Springer-Verlag, New York, 2006.
ISBN: 0-387-28311-0.

Baddeley, A. (2018)
A statistical commentary on mineral prospectivity analysis.
Chapter 2, pages 25--65
in *Handbook of Mathematical Geosciences: Fifty Years of IAMG*,
edited by B.S. Daya Sagar, Q. Cheng and F.P. Agterberg.
Springer, Berlin.

Guan, Y. (2008) On consistent nonparametric intensity estimation
for inhomogeneous spatial point processes.
*Journal of the American Statistical Association*
**103**, 1238--1247.

Handcock, M.S. and Morris, M. (1999)
*Relative Distribution Methods in the Social Sciences*.
Springer, New York.

Sager, T.W. (1982)
Nonparametric maximum likelihood estimation of
spatial patterns. *Annals of Statistics* **10**, 1125--1136.

##### See Also

`rho2hat`

,
`methods.rhohat`

,
`parres`

.

See `ppm`

for a parametric method for the same problem.

##### Examples

```
# NOT RUN {
X <- rpoispp(function(x,y){exp(3+3*x)})
rho <- rhohat(X, "x")
rho <- rhohat(X, function(x,y){x})
plot(rho)
curve(exp(3+3*x), lty=3, col=2, add=TRUE)
rhoB <- rhohat(X, "x", method="reweight")
rhoC <- rhohat(X, "x", method="transform")
rhoM <- rhohat(X, "x", smoother="increasing")
plot(rhoM, add=TRUE, col=5)
# }
# NOT RUN {
fit <- ppm(X, ~x)
rr <- rhohat(fit, "y")
# linear network
Y <- runiflpp(30, simplenet)
rhoY <- rhohat(Y, "y")
# }
```

*Documentation reproduced from package spatstat, version 1.61-0, License: GPL (>= 2)*