Description Usage Arguments Details Value Author(s) References Examples
Fit a generalized linear model via penalized maximum likelihood. The regularization path is computed for the truncated lasso at a grid of values for the regularization parameter lambda. Can deal with all shapes of data, including very large sparse data matrices. Fit linear, logistic and multinomial, poisson, and Cox regression models.
1 2 3 4 5 6 | glmTLP(x, y, family=c("gaussian","binomial","poisson","multinomial","cox","mgaussian"),
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 |
input matrix, of dimension nobs x nvars; each row is an
observation vector. Can be in sparse matrix format (inherit from class |
y |
response variable. Quantitative for |
family |
Response type (see above) |
weights |
observation weights. Can be total counts if responses are proportion matrices. Default is 1 for each observation |
offset |
A vector of length |
tau |
Write something about |
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 |
lambda |
A user supplied |
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. |
Write something about the details.
An object that inherits from glmnet
.
call |
the call that produced this object |
a0 |
Intercept sequence of length |
beta |
For |
lambda |
The actual sequence of |
dev.ratio |
The fraction of (null) deviance explained (for |
nulldev |
Null deviance (per observation). This is defined to be 2*(loglike_sat -loglike(Null)); The NULL model refers to the intercept model, except for the Cox, where it is the 0 model. |
df |
The number of nonzero coefficients for each value of
|
dim |
dimension of coefficient matrix (ices) |
nobs |
number of observations |
npasses |
total passes over the data summed over all lambda values |
offset |
a logical variable indicating whether an offset was included in the model |
jerr |
error flag, for warnings and errors (largely for internal debugging). |
Chong Wu, Wei Pan
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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