elastic {gemR} | R Documentation |
Elastic-net modeling of GEM objects.
Description
Elastic-net modeling of GEM objects.
Usage
elastic(gem, ...)
## S3 method for class 'GEM'
elastic(
gem,
effect,
alpha = 0.5,
newdata = NULL,
validation,
segments = NULL,
measure = measure,
family = family,
...
)
Arguments
gem |
Object of class |
... |
Additional arguments for |
effect |
The effect to be used as response. |
alpha |
The elasticnet mixing parameter. |
newdata |
Optional new data matrix for prediction. |
validation |
Optional validation parameters. |
segments |
number of segments or list of segments (optional) |
measure |
Type of performance summary, default = 'class' (see |
family |
Type of model response, default = 'multinomial'. |
Value
An object of class GEMglmnet, cv.glmnet, list
containing the fitted Elastic-net model, classifications/predictions and data.
See Also
Analyses using GEM
: pca
, sca
, neuralnet
, pls
.
Confidence interval plots: confints
. Convenience knock-in and knock-out of effects: knock.in
.
Examples
## Multiple Sclerosis data
data(MS, package = "gemR")
# Subset to reduce runtime in example
MS$proteins <- MS$proteins[,20:70]
gem <- GEM(proteins ~ MS * group, data = MS)
elasticMod <- elastic(gem, 'MS', validation = "CV")
sum(elasticMod$classes == MS$MS)
plot(elasticMod) # Model fit
plot(elasticMod$glmnet.fit) # Coefficient trajectories
# Select all proteins with non-zeros coefficients
coefs <- coef(elasticMod)
(selected <- names(which(coefs[,1] != 0)))
# Time consuming due to many variables
## Diabetes data
data(Diabetes, package = "gemR")
gem.Dia <- GEM(transcriptome ~ surgery * T2D, data = Diabetes)
elasticMod <- elastic(gem.Dia, 'T2D', validation = "LOO")
[Package gemR version 1.2.1 Index]