Description Usage Arguments Details Value Author(s) References Examples
Does k-fold cross-validation for glmTLP, produces a plot, and returns a value for lambda
with pre-specified tau
.
1 2 3 4 5 6 7 | cv.glmTLP(x, y, family=c("gaussian","binomial","poisson","multinomial","cox","mgaussian"),
nfolds = 10, weights, offset=NULL, lambda, tau = 0.3,
nlambda=100, penalty.factor = rep(1, nvars),
lambda.min.ratio=ifelse(nobs<nvars,1e-3,1e-4),
standardize=TRUE,intercept=TRUE,dfmax=nvars+1,
pmax=min(dfmax*2+20,nvars), lower.limits=-Inf,upper.limits=Inf,
standardize.response=FALSE, maxIter=100, Tol=1e-4)
|
x |
|
y |
response variable. Quantitative for |
family |
Response type (see above) |
nfolds |
number of folds - default is 10. Although |
weights |
Observation weights; defaults to 1 per observation |
offset |
Offset vector (matrix) as in |
lambda |
Optional user-supplied lambda sequence; default is
|
tau |
Tuning parameter. |
nlambda |
The number of |
penalty.factor |
Separate penalty factors can be applied to each
coefficient. This is a number that multiplies |
lambda.min.ratio |
Smallest value for |
standardize |
Logical flag for x variable standardization, prior to
fitting the model sequence. The coefficients are always returned on
the original scale. Default is |
intercept |
Should intercept(s) be fitted (default=TRUE) or set to zero (FALSE) |
dfmax |
Limit the maximum number of variables in the
model. Useful for very large |
pmax |
Limit the maximum number of variables ever to be nonzero |
lower.limits |
Vector of lower limits for each coefficient;
default |
upper.limits |
Vector of upper limits for each coefficient;
default |
standardize.response |
This is for the |
maxIter |
Maximum iteration for TLP. |
Tol |
Tolerance. |
The function runs glmTLP
nfolds
+1 times; the
first to get the lambda
sequence, and then the remainder to
compute the fit with each of the folds omitted. The error is
accumulated, and the average error and standard deviation over the
folds is computed.
Note that cv.glmnet
does NOT search for
values for tau
. A specific value should be supplied, else
tau= 0.3
is assumed by default.
an object of class "cv.glmnet"
is returned, which is a
list with the ingredients of the cross-validation fit. Although the implementation is different, we try to mimic returning as "cv.glment"
in a popular package glmnet
such that users can use truncated lasso as using elastic net.
lambda |
the values of |
cvm |
The mean cross-validated error - a vector of length
|
cvsd |
estimate of standard error of |
cvup |
upper curve = |
cvlo |
lower curve = |
nzero |
number of non-zero coefficients at each |
name |
a text string indicating type of measure (for plotting purposes). |
glmnet.fit |
a fitted glmnet object for the full data. |
lambda.min |
value of |
lambda.1se |
largest value of |
fit.preval |
if |
foldid |
if |
Chong Wu
Maintainer: Chong Wu <wuxx0845@umn.edu>
Xiaotong Shen , Wei Pan and Yunzhang Zhu (2012) Likelihood-Based Selection and Sharp Parameter Estimation, Journal of the American Statistical Association, 107:497, 223-232
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