geoR (version 1.5-6)

trend.spatial: Builds the Trend Matrix

Description

Builds the trend matrix in accordance to a specification of the mean provided by the user.

Usage

trend.spatial(trend, geodata, add.to.trend)

Arguments

trend

specifies the mean part of the model. See DETAILS below.

geodata

optional. An object of the class geodata as described in as.geodata.

add.to.trend

optional. Specifies aditional terms to the mean part of the model. See details below.

Value

• An object of the class trend.spatial which is an $n \times p$ trend matrix, where $n$ is the number of spatial locations and $p$ is the number of mean parameters in the model.

eqn

$x_2$

itemize

• trend = "1st"andtrend = ~ x1 + x2
• trend = "2nd"andtrend = ~ x1 + x2 + I(x1^2) + I(x2^2) + I(x1*x2)

bold

Search path for covariates

cr

Typically, functions in the package geoR which calls trend.spatial will have the arguments geodata, coords and data. When the trend is specifed as trend = ~ model the terms included in the model will be searched for in the following path sequence (in this order):

1. in the users/session Global environment
2. in the session search path
3. as elements of the listgeodata
4. as columns in a data-framegeodata$covariates
5. as columns in a data-framegeodata$data
6. in remainder of the session search path

The argument add.to.trend adds terms to what is specified in the argument trend. This seems redundant but allow specifications of the type: trend="2nd", add.trend=~other.covariates.

Details

The implicity model assumes that there is an underlying process with mean $\mu(x)$, where $x = (x_1, x_2)$ denotes the coordinates of a spatial location. The argument trend defines the form of the mean and the following options are allowed:

• "cte"

{the mean is assumed to be constant over the region, in which case $\mu(x)= \mu$. This is the default option. } "1st"{the mean is assumed to be a first order polynomial on the coordinates: $$\mu(x)= \beta_0 + \beta_1 x_1 + \beta_2 x_2$$} "2nd"{the mean is assumed to be a second order polynomial on the coordinates: $$\mu(x)= \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \beta_3 (x_1)^2 + \beta_4 (x_2)^2 + \beta_5 x_1 * x_2$$} ~ model{a model specification. See formula for further details on how to specify a model in Rusing formulas. Notice that the model term before the ~ is not necessary. Typically used to include covariates (external trend) in the model.}

References

Further information on the package geoR can be found at: http://www.est.ufpr.br/geoR.

See Also

The section DETAILS in the documentation for likfit for more about the underlying model.

Examples

data(SIC)
# a first order polynomial trend
trend.spatial("1st", sic.100)[1:5,]
# a second order polynomial trend
trend.spatial("2nd", sic.100)[1:5,]
# a trend with a covariate
trend.spatial(~altitude, sic.100)[1:5,]
# a first degree trend plus a covariate
trend.spatial(~coords+altitude, sic.100)[1:5,]
# with produces the same as
trend.spatial("1st", sic.100, add=~altitude)[1:5,]
# and yet another exemple
trend.spatial("2nd", sic.100, add=~altitude)[1:5,]