R语言 lars包 lars()函数中文帮助文档(中英文对照)
lars(lars)
lars()所属R语言包：lars
Fits Least Angle Regression, Lasso and Infinitesimal Forward Stagewise regression models
适用于最小角度回归，套索和无穷的前进Stagewise的回归模型
译者：生物统计家园网 机器人LoveR
描述———-Description———-
These are all variants of Lasso, and provide the entire sequence of coefficients and fits, starting from zero, to the least squares fit.
这些都是拉索的变种，并提供整个序列的系数和配合，从零开始的最小二乘法拟合。
用法———-Usage———-
lars(x, y, type = c(“lasso”, “lar”, “forward.stagewise”, “stepwise”),
trace = FALSE, normalize = TRUE, intercept = TRUE, Gram, eps = .Machine$double.eps, max.steps, use.Gram = TRUE)
参数———-Arguments———-
参数：x
matrix of predictors
矩阵的预测
参数：y
response
响应
参数：type
One of “lasso”, “lar”, “forward.stagewise” or “stepwise”. The names can be abbreviated to any unique substring. Default is “lasso”.
“套索”之一，“拉尔”，“forward.stagewise的”或“逐步”。名称可以缩写为任何独特的子串。默认值是“套索”。
参数：trace
If TRUE, lars prints out its progress
如果是TRUE，拉斯打印出其进展情况
参数：normalize
If TRUE, each variable is standardized to have unit L2 norm, otherwise it is left alone. Default is TRUE.
如果是TRUE，每个变量的标准化，以单位L2规范，否则将被单独留在家中。默认值是TRUE。
参数：intercept
if TRUE, an intercept is included in the model (and not penalized), otherwise no intercept is included. Default is TRUE.
如果为TRUE，截距包括在模型中（而不是处罚），否则没有截距。默认值是TRUE。
参数：Gram
The X’X matrix; useful for repeated runs (bootstrap) where a large X’X stays the same.
XX矩阵，可重复运行（引导），那里有一个很大的XX保持不变。
参数：eps
An effective zero
一个有效的零
参数：max.steps
Limit the number of steps taken; the default is 8 * min(m, n-intercept), with m the number of variables, and n the number of samples. For type=”lar” or type=”stepwise”, the maximum number of steps is min(m,n-intercept). For type=”lasso” and especially type=”forward.stagewise”, there can be many more terms, because although no more than min(m,n-intercept) variables can be active during any step, variables are frequently droppped and added as the algorithm proceeds. Although the default usually guarantees that the algorithm has proceeded to the saturated fit, users should check.
所采取的步骤的数目限制，缺省值是8 * min(m, n-intercept)，具有m的变量的数量，和n的样本数。对于type=”lar”或type=”stepwise”，最大步数为min(m,n-intercept)。对于type=”lasso”，尤其是type=”forward.stagewise”，可以有更多的条件，因为虽然没有超过min(m,n-intercept)变量可以在任何步骤中，经常droppped变量的算法进行添加。虽然默认的通常保证该算法已进入饱和的配合，用户应该检查。
参数：use.Gram
When the number m of variables is very large, i.e. larger than N, then you may not want LARS to precompute the Gram matrix. Default is use.Gram=TRUE </table>
当数米的变量是非常大的，即大于N，那么你可能不想LARS预先革兰氏矩阵。默认值是use.Gram = TRUE </ TABLE>
Details
详细信息———-Details———-
LARS is described in detail in Efron, Hastie, Johnstone and Tibshirani (2002). With the “lasso” option, it computes the complete lasso solution simultaneously for ALL values of the shrinkage parameter in the same computational cost as a least squares fit. A “stepwise” option has recently been added to LARS.
LARS埃夫隆，哈斯蒂，庄士东和Tibshirani（2002）中详细描述。随着“套索”选项，它计算的完整的解决方案，同时为所有的的收缩参数在相同的计算成本的最小二乘拟合值套索。 A“逐步”选项已被添加到LARS。
值———-Value———-
A “lars” object is returned, for which print, plot, predict, coef and summary methods exist.
A“拉斯”对象被返回，打印，绘图，预测，系数和总结的方法。
（作者）———-Author(s)———-
Brad Efron and Trevor Hastie
参考文献———-References———-
(with discussion) Annals of Statistics; see also http://www-stat.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf. Hastie, Tibshirani and Friedman (2002) Elements of Statistical Learning, Springer, NY.
参见———-See Also———-
print, plot, summary and predict methods for lars, and cv.lars
打印，绘图，总结和预测方法拉斯，和cv.lars
实例———-Examples———-
data(diabetes)
par(mfrow=c(2,2))
attach(diabetes)
object <- lars(x,y)
plot(object)
object2 <- lars(x,y,type=”lar”)
plot(object2)
object3 <- lars(x,y,type=”for”) # Can use abbreviations[可以使用缩写]
plot(object3)
detach(diabetes)
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注：
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