aslm {AdaptiveSparsity} | R Documentation |
implements the adaptive sparse linear model using Figueiredo's EM algorithm for adaptive sparsity (Jeffreys prior)
## S3 method for class 'formula' aslm(formula, data=list(), na.action=na.omit, ...) ## Default S3 method: aslm(x, y, ...) getSparseModel(object)
formula |
an object of class “formula” (or one that can be coerced to that class): a symbolic description of the model to be fitted.
See |
data |
an optional data frame, list or environment containing the variables in the model. |
na.action |
action to use when data contains NAs. Options include na.omit, na.exclude, na.fail |
x |
design matrix |
y |
vector of observations |
... |
further arguments |
object |
an object of class “aslm”. |
aslm
returns an object of class c("aslm", "lm")
.
An object of class “aslm
” is a list containing at least the following components:
coefficients |
a named vector of coefficients |
residuals |
the residuals, that is response minus fitted values. |
fitted.values |
the fitted mean values |
rank |
the numeric rnak of the fitted linear model |
df |
the residual degrees of freedom |
call |
the matched call |
terms |
the terms object used |
sigma |
getSparseModel
returns an object of class "lm"
that is a model consisting of only the sparse nonzero variables from the original model.
Kristen Zygmunt, Eleanor Wong, Tom Fletcher
Figueiredo, M.A.T.; , “Adaptive sparseness for supervised learning”, Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.25, no.9, pp. 1150- 1159, Sept. 2003
summary.aslm
, logLik.aslm
, print.aslm
s = aslm(Infant.Mortality~.,data=swiss) m = getSparseModel(s) summary(s) coef(m)