ices {ADVICE} | R Documentation |
This function provides an alternative multiple regression fitting procedure which simultaneously estimates and selects variables. The resulting coefficient estimates will tend to be slightly biased, but in a sparse setting, they can be quite accurate. A full regression model is specified by the user, and the function usually returns coefficient estimates for a reduced model, i.e., a model for which some of the coefficient estimates are exactly 0.
ices(formula, data, model = TRUE, x = FALSE, y = FALSE, qr = TRUE)
formula |
a formula object specifying the full regression model. |
data |
a data frame containing observations on the response variable and the predictor variables. |
model , x , y , qr |
logicals. If |
a QRS class object
coefficients |
a named numeric vector of coefficients |
residuals |
a numeric vector containing the response minus the fitted values. |
effects |
a numeric vector of containing the projections of the response variable under the orthogonal Q matrix coming from the QR decomposition of the model matrix. |
rank |
the numeric rank of the fitted linear model. |
fitted.values |
the estimated response values according to the fitted interrupted coefficient estimation selection regression model. |
sigma2 |
the estimated noise variance based on the n-p residual effects, where p is the size of the full model. |
std_error |
a numeric vector of standard errors. |
df.residual |
residual degrees of freedom. |
x |
a numeric matrix containing the model matrix. |
y |
a numeric vector containing the response variable values. |
qr |
the QR decomposition object coming from the model matrix (after re-ordering columns). |
coefOrder |
permutation of the sequence 1:p which gives the ascending order of the coefficients of the linear model object, as a result of the pre-screening. |
call |
the matched call. |
terms |
the terms object used. |
names |
a character vector containing the column names of the model matrix. |
model |
if requested (the default), the model frame used in the case of the full regression model. |
Ladan Tazik, W.J. Braun
lm.R
, QRS.R
myRegressionData <- rmultreg(50, k=10, p=.25, sdnoise = .5)
pairs(myRegressionData$data)
out <- ices(y ~ ., data = myRegressionData$data) # fit model to simulated data
confint(out) # calculate 95 % confidence intervals for all coefficients
myRegressionData$coefficients # compare with true coefficients