or Smoothing of Ordinal Predictors

Description Details Author(s) References See Also Examples

Selection and/or smoothing of ordinally scaled independent variables using a group lasso or generalized ridge penalty.

Package:	ordPens
Type:	Package
Version:	0.3-1
Date:	2015-05-21
Depends:	grplasso, mgcv, RLRsim, tcltk
Suggests:	IsoGene, Biobase
License:	GPL-2
LazyLoad:	yes

Smoothing and selection of ordinal predictors is done by the function ordSelect; smoothing only, by ordSmooth. For ANOVA with ordinal factors, use ordAOV.

Jan Gertheiss

Maintainer: Jan Gertheiss, [email protected]

Gertheiss, J. (2014). ANOVA for factors with ordered levels, Journal of Agricultural, Biological and Environmental Statistics, 19, 258-277.

Gertheiss, J., S. Hogger, C. Oberhauser and G. Tutz (2011). Selection of ordinally scaled independent variables with applications to international classification of functioning core sets. Journal of the Royal Statistical Society C (Applied Statistics), 60, 377-395.

Gertheiss, J. and F. Oehrlein (2011). Testing relevance and linearity of ordinal predictors, Electronic Journal of Statistics, 5, 1935-1959.

Gertheiss, J. and G. Tutz (2009). Penalized regression with ordinal predictors. International Statistical Review, 77, 345-365.

ordSelect, ordSmooth, ordAOV

# smooth modeling of a random dataset
set.seed(123)

# generate (ordinal) predictors
x1 <- sample(1:8,100,replace=TRUE)
x2 <- sample(1:6,100,replace=TRUE)
x3 <- sample(1:7,100,replace=TRUE)

# the response
y <- -1 + log(x1) + sin(3*(x2-1)/pi) + rnorm(100)

# x matrix
x <- cbind(x1,x2,x3)

# lambda values
lambda <- c(1000,500,200,100,50,30,20,10,1)

# smooth modeling
o1 <- ordSmooth(x = x, y = y, lambda = lambda)

# results
round(o1$coef,digits=3)
plot(o1)

# If for a certain plot the x-axis should be annotated in a different way,
# this can (for example) be done as follows:
plot(o1, whichx = 1, xlim = c(0,9), xaxt = "n")
axis(side = 1, at = c(1,8), labels = c("no agreement","total agreement"))