smoothLUR: smoothLUR: Functions and data for smooth land use regression...

In markusfritsch/smoothLUR: Functions and data for smooth Land Use Regression Modeling

smoothLUR: Functions and data for smooth land use regression modeling

Description

The smoothLUR package provides functions and datasets for smooth land use regression modeling and parametric benchmarks to reproduce the results reported in Fritsch and Behm (2021a). The main functions of the package are parLUR, smoothLUR, and the evaluation functions kFoldCV and looCV. Details on the included datasets are provided in Fritsch and Behm (2021b)

smoothLUR fits a smooth land use regression (LUR) model using the gam() function from the mgcv package. The procedure is outlined in \insertCiteFritsch2021smooth;textualsmoothLUR

Usage

smoothLUR(data, x, x.discr = NA, spVar1, spVar2, y, thresh = 0.95)

Arguments

data	A data set which contains the dependent variable and the potential predictors.
x	A character vector stating the variable names of the potential continuous predictors (names have to match the column names of 'data').
x.discr	A character vector stating the variable names of the potential discrete predictors (names have to match the column names of 'data').
spVar1	A character vector stating the variable name referring to longitude (name has to match the column name of 'data').
spVar2	A character vector stating the variable name referring to latitude (name has to match the column name of 'data').
y	A character string indicating the name of the dependent variable (name has to match the column name of 'data').
thresh	A numeric value that indicates the maximum share of zero values; if the share is exceeded, the corresponding potential predictor is excluded.

Value

An object of class 'smoothLUR' with the following elements:

coefficients

a vector containing the coefficient estimates

It has '...', '...', and '...' methods.

Author(s)

Svenia Behm and Markus Fritsch

References

\insertAllCited

See Also

parLUR for parametric land use regression (LUR) models. kFoldCV for k-fold cross-validation for parLUR and smoothLUR objects.

Examples

## Not run: 
## Load data set
data(monSitesDE, package="smoothLUR")
set.seed(42)

## Code example
dat <- monSitesDE[sample(1:nrow(monSitesDE), 40),]
m1 <- smoothLUR(data = dat
                 ,x = c("Lon", "Lat", "Alt", "HighDens"
                         ,"LowDens", "Ind", "Transp", "Seap", "Airp", "Constr"
                         ,"UrbGreen", "Agri", "Forest", "PopDens"
                         ,"PriRoad", "SecRoad", "FedAuto", "LocRoute")
                 ,spVar1 = "Lon"
                 ,spVar2 = "Lat"
                 ,y = "Y"
                 ,thresh = 0.95)

summary(m1)
summary(m1)$adj.r.squared
BIC(m1)
AIC(m1)

\donttest{
## Load data set
data(monSitesDE, package="smoothLUR")
dat <- monSitesDE
m1 <- smoothLUR(data = dat,
                 ,x = c("Lon", "Lat", "Alt", "HighDens"
                         ,"LowDens", "Ind", "Transp", "Seap", "Airp", "Constr"
                         ,"UrbGreen", "Agri", "Forest", "PopDens"
                         ,"PriRoad", "SecRoad", "FedAuto", "LocRoute")
                 ,spVar1 = "Lon"
                 ,spVar2 = "Lat"
                 ,y = "Y"
                 ,thresh = 0.95)

summary(m1)
summary(m1)$adj.r.squared
BIC(m1)
AIC(m1)

}

## End(Not run)

markusfritsch/smoothLUR documentation built on Nov. 5, 2022, 3:42 p.m.