Description Usage Arguments Details Value Author(s) References See Also Examples
Fits a linear ridge regression model. Optionally, the ridge regression parameter is chosen automatically using the method proposed by Cule et al (2012).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  linearRidge(formula, data, lambda = "automatic", nPCs = NULL,
scaling = c("corrForm", "scale", "none"), ...)
## S3 method for class 'ridgeLinear'
coef(object, all.coef = FALSE, ...)
## S3 method for class 'ridgeLinear'
plot(x, y = NULL, ...)
## S3 method for class 'ridgeLinear'
predict(object, newdata, na.action = na.pass, all.coef = FALSE, ...)
## S3 method for class 'ridgeLinear'
print(x, all.coef = FALSE, ...)
## S3 method for class 'ridgeLinear'
summary(object, all.coef = FALSE, ...)
## S3 method for class 'summary.ridgeLinear'
print(x, digits = max(3,
getOption("digits")  3),
signif.stars = getOption("show.signif.stars"), ...)

formula 
a formula expression as for regression models, of the form 
data 
an optional data frame in which to interpret the variables occuring in 
lambda 
A ridge regression parameter. May be a vector. If 
nPCs 
The number of principal components to use to choose the ridge regression parameter, following the method of
Cule et al (2012). It is not possible to specify both 
scaling 
The method to be used to scale the predictors. One of

object 
A ridgeLinear object, typically generated by a call to 
newdata 
An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used. 
na.action 
function determining what should be done with missing values
in 
all.coef 
Logical. Should results be returned for all ridge regression penalty
parameters ( 
x 
An object of class 
y 
Dummy argument for compatibility with the default 
digits 
minimum number of significant digits to be used for most numbers 
signif.stars 
logical; if 
... 
Additional arguments to be passed to or from other methods. 
If an intercept is present in the model, its coefficient is not penalised. If you want to penalise an intercept, put in your own constant term and remove the intercept.
An object of class "ridgeLinear"
, with components:
automatic 
Logical. Was 
call 
The matched call. 
coef 
A named vector of fitted coefficients. 
df 
A vector of degrees of freedom of the model fit, degrees of freedom for variance, and residual degrees of freedom of the fitted model. 
Inter 
Was an intercept included? 
isScaled 
Were the predictors scaled before the model was fitted? 
lambda 
The ridge regression parameter(s). 
scales 
The scales used to standardize the predictors. 
terms 
The 
x 
The scaled predictor matrix. 
xm 
A vector of means of the predictors. 
y 
The response. 
ym 
The mean of the response. 
And optionally the components
max.nPCs 
The maximum number of principal components for which a ridge regression parameter was computed. 
chosen.nPCs 
The number of principal components used to compute the ridge parameter. 
Erika Cule
A semiautomatic method to guide the choice of ridge parameter in ridge regression. Cule, E. and De Iorio, M. (2012) arXiv:1205.0686v1 [stat.AP]
1 2 3  data(GenCont)
mod < linearRidge(Phenotypes ~ ., data = as.data.frame(GenCont))
summary(mod)

Call:
linearRidge(formula = Phenotypes ~ ., data = as.data.frame(GenCont))
Coefficients:
Estimate Scaled estimate Std. Error (scaled) t value (scaled)
(Intercept) 1.533386 NA NA NA
SNP1 0.277296 4.045409 0.266120 15.201
SNP2 0.110458 1.256154 0.216332 5.807
SNP3 0.110458 1.256154 0.216332 5.807
SNP4 0.005230 0.011635 0.371693 0.031
SNP5 0.531173 6.323006 0.315368 20.050
SNP6 0.119164 1.373227 0.223047 6.157
SNP7 0.113844 0.113730 0.372181 0.306
SNP8 0.099149 1.028581 0.355807 2.891
SNP9 0.008321 0.008312 0.372386 0.022
SNP10 0.058562 0.101128 0.371567 0.272
SNP11 0.096526 1.495699 0.329250 4.543
SNP12 0.334279 0.333945 0.372248 0.897
Pr(>t)
(Intercept) NA
SNP1 < 2e16 ***
SNP2 6.38e09 ***
SNP3 6.38e09 ***
SNP4 0.97503
SNP5 < 2e16 ***
SNP6 7.43e10 ***
SNP7 0.75993
SNP8 0.00384 **
SNP9 0.98219
SNP10 0.78549
SNP11 5.55e06 ***
SNP12 0.36966

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Ridge parameter: 2.206848, chosen automatically, computed using 1 PCs
Degrees of freedom: model 3.121 , variance 1.205 , residual 5.036
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