parLUR: Function for deriving parametric land use regression (LUR)...

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

Function for deriving parametric land use regression (LUR) model.

Description

parLUR fits a parametric land use regression (LUR) model employing parametric polynomials in a forward stepwise approach. The function allows to carry out the ESCAPE procedure outlined in, for example, \insertCiteBeelen.2013;textualsmoothLUR, \insertCiteEeftens.2016;textualsmoothLUR, \insertCiteWolf.2017;textualsmoothLUR, and \insertCiteFritsch2021smooth;textualsmoothLUR.

Usage

parLUR(data, x, y, dirEff, thresh = 0.95, thresh_pval = 0.1)

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 predictors (names have to match the column names of 'data').
y	A character string indicating the name of the dependent variable (name needs to match the column names of 'data').
dirEff	A vector that contains one entry for each potential predictor and indicates the expected direction of the effect of the potential predictor (1 for positive, -1 for negative and 0 if the expected effect sign is unclear).
thresh	A numeric value that indicates the maximum share of zero values; if the share is exceeded, the corresponding potential predictor is excluded (defaults to 0.95).
thresh_pval	A numeric value that indicates a threshold for removing predictors after the forward stepwise modeling procedure; the threshold is the p-value of a standard t-Test (defaults to 0.1).

Value

An object of class 'parLUR' 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

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

Examples

## Load data from package
data(monSitesDE, package="smoothLUR")
set.seed(42)

## Code example
dat <- monSitesDE[sample(1:nrow(monSitesDE), 40),]
m1 <- parLUR(data = dat
                 ,x = c("Lon", "Lat", "Alt", "HighDens"
                         ,"LowDens", "Ind", "Transp", "Seap", "Airp", "Constr"
                         ,"UrbGreen", "Agri", "Forest", "PopDens"
                         ,"PriRoad", "SecRoad", "FedAuto", "LocRoute")
                 ,y = "Y"
                 ,dirEff = c(0,0,-1,1,1,1,1,1,1,1,-1,0,-1,1,1,1,1,1)
                 ,thresh = 0.95
                 ,thresh_pval = 0.1)

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


## Load data from package
data(monSitesDE, package="smoothLUR")
dat <- monSitesDE
m1 <- parLUR(data = dat
                 ,x = c("Lon", "Lat", "Alt", "HighDens"
                         ,"LowDens", "Ind", "Transp", "Seap", "Airp", "Constr"
                         ,"UrbGreen", "Agri", "Forest", "PopDens"
                         ,"PriRoad", "SecRoad", "FedAuto", "LocRoute")
                 ,y = "Y"
                 ,dirEff = c(0,0,-1,1,1,1,1,1,1,1,-1,0,-1,1,1,1,1,1)
                 ,thresh = 0.95
                 ,thresh_pval = 0.1)

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

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