stepwise.ss: Spatial scale forward stepwise regression

In spselect: Selecting Spatial Scale of Covariates in Regression Models

Description

This function fits a spatial scale (SS) forward stepwise regression model.

Usage

stepwise.ss(y, X.3D, y.name, ss, epsilon, verbose=TRUE)

Arguments

y	A numeric response vector
X.3D	A 3-D or stacked array of numeric variables, where each stack represents a particular level of covariates (i.e., individual- and area-level variables at more than one spatial scale). In cases where values are only present for a covariate at certain levels, that covariate is assigned missing values at all other levels.
y.name	A name for y
ss	A vector of names to identify the different levels of covariates available as potential candidates for model input
epsilon	A positive value used as a stopping criterion when there is inadequate improvement in the model's performance. The algorithm stops if the difference in the Akaike information criterion (AIC) between the current model and the proposed model is less than epsilon.
verbose	If TRUE, details are printed as the algorithm progresses

Details

This function estimates coefficients using the SS forward stepwise regression approach. The function also estimates the model fit and provides summary details.

Value

A list with the following items:

beta.final	Regression coefficient estimates from final model
aic.final	AIC for final model
summary.final	Summary output of final model
stack.ss	Vector of indices to indicate the level at which each covariate enters the model

Author(s)

Lauren Grant, David Wheeler

References

Grant LP, Gennings C, Wheeler, DC. (2015). Selecting spatial scale of covariates in regression models of environmental exposures. Cancer Informatics, 14(S2), 81-96. doi: 10.4137/CIN.S17302

Examples

data(y)
data(X.3D)
y.name <- "y"
ss <- c("ind", "ss1", "ss2")
mod_forward.step.ss_1 <- stepwise.ss(y, X.3D, y.name, ss, 1)

Example output

Loading required package: tester
Loading required package: magic
Loading required package: abind
Loading required package: pracma

Attaching package: 'pracma'

The following object is masked from 'package:magic':

    magic

[1] "Results are with no intercept."
[1] "Start: AIC = -1.0259"
[1] "y ~ 0"
[1] 1
[1] "Step: AIC = -10.3344"
[1] "y ~ ss1_x2.ss1"
[1] 2
[1] "Step: AIC = -13.6769"
y ~ ss1_x2.ss1 + ind_x1.ind 
[1] "Diff in AIC = 3.3425"
[1] 3
[1] "Step: AIC = -22.6657"
y ~ ss1_x2.ss1 + ind_x1.ind + ss2_x3.ss2 
[1] "Diff in AIC = 8.9887"

Call:
lm(formula = y ~ . - 1, data = X.in, x = TRUE, y = TRUE)

Coefficients:
ss1_x2.ss1  ind_x1.ind  ss2_x3.ss2  
    0.6784      0.4703      0.4443  

[1] "Diff in AIC = 8.9887"
[1] "Epsilon = 1"
[1] "No. of vars in model = 3"
[1] "Total no. of vars possible = 3"

spselect documentation built on May 2, 2019, 3:32 a.m.