AIC.sparseLTS(robustHD)
AIC.sparseLTS()所属R语言包：robustHD
Information criteria for sparse LTS regression models
信息标准，稀疏的LTS的回归模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Compute the Akaike or Bayes information criterion for sparse least trimmed squares regression models based on the robust residual scale estimate.
计算的Akaike或稀疏至少修剪最小二乘回归模型的基础上强劲的剩余规模估计的贝叶斯信息标准的。
用法----------Usage----------
## S3 method for class 'sparseLTS'
AIC(object, ...,
fit = c("reweighted", "raw", "both"), k = 2)
## S3 method for class 'sparseLTS'
BIC(object, ...)
## S3 method for class 'sparseLTSGrid'
AIC(object, ...,
fit = c("reweighted", "raw", "both"), k = 2)
## S3 method for class 'sparseLTSGrid'
BIC(object, ...)
参数----------Arguments----------
参数：object
the model fit for which to compute the information criterion.
模型的拟合计算的信息标准。
参数：...
for the BIC method, additional arguments to be passed down to the AIC method. For the AIC method, additional arguments are currently ignored.
BIC方法，其他参数可以通过AIC方法。对于AIC方法，目前被忽略额外的参数。
参数：fit
a character string specifying for which fit to compute the information criterion. Possible values are "reweighted" (the default) for the information criterion of the reweighted fit, "raw" for the information criterion of the raw fit, or "both" for the information criteria of both fits.
一个字符串指定为适合计算标准。可能的值是"reweighted"（默认值）的信息标准的重新加权拟合，"raw"的信息标准的原料配合，或"both"的信息都适合的标准。
参数：k
a numeric value giving the penalty per parameter to be used. The default is to use 2 as in the classical definition of the AIC.
一个数字值，每个参数可以使用给予罚款。默认的是使用2中的经典定义的AIC。
Details
详细信息----------Details----------
The information criteria are computed as n (log(2 pi) + 1 + log(sigma^2)) + df k, where n denotes the number of observations, sigma is the robust residual scale estimate, df is the number of nonzero coefficient estimates, and k is penalty per parameter. The usual definition of the AIC uses k = 2, whereas the BIC uses k = log(n). Consequently, the former is used as the default penalty of the AIC method, whereas the BIC method calls the AIC method with the latter penalty.
信息标准计算n (log(2 pi) + 1 + log(sigma^2)) + df k，其中n表示的若干意见，sigma是强大的剩余规模估计，df是非零系数的估计数，并k是每个参数的处罚。通常定义的AIC使用了k = 2，，而BIC使用k = log(n)。因此，使用作为违约金AIC方法，，而BIC方法调用AIC方法，后者处罚。
值----------Value----------
A numeric vector giving the information criteria for the requested fits.
一个数字矢量提供的信息标准的要求一刀切。
注意----------Note----------
Computing information criteria for several objects supplied via the ... argument (as for the default methods of AIC and BIC) is currently not implemented.
计算对多个对象提供的信息标准，通过...参数（如为默认的方法AIC和BIC）目前尚未实现。
（作者）----------Author(s)----------
Andreas Alfons
参考文献----------References----------
Annals of the Institute of Statistical Mathematics, 22(2), 203–217.
The Annals of Statistics, 6(2), 461–464.
参见----------See Also----------
AIC, sparseLTS, sparseLTSGrid
AIC，sparseLTS，sparseLTSGrid
实例----------Examples----------
## generate data[＃生成数据]
# example is not high-dimensional to keep computation time low[例如不高维的计算时间保持低]
library("mvtnorm")
set.seed(1234) # for reproducibility[可重复性]
n <- 100 # number of observations[的观测数]
p <- 25 # number of variables[的变量数目]
beta <- rep.int(c(1, 0), c(5, p-5)) # coefficients[系数]
sigma <- 0.5 # controls signal-to-noise ratio[控制的信号 - 噪声比]
epsilon <- 0.1 # contamination level[污染水平]
Sigma <- 0.5^t(sapply(1:p, function(i, j) abs(i-j), 1:p))
x <- rmvnorm(n, sigma=Sigma) # predictor matrix[预测矩阵]
e <- rnorm(n) # error terms[误差项]
i <- 1:ceiling(epsilon*n) # observations to be contaminated[受到污染的意见]
e[i] <- e[i] + 5 # vertical outliers[垂直离群]
y <- c(x %*% beta + sigma * e) # response[响应]
x[i,] <- x[i,] + 5 # bad leverage points[坏的平衡点]
## fit sparse LTS model over a grid of values for lambda[＃适合稀疏LTS模型的lambda值的网格]
frac <- seq(0.25, 0.05, by = -0.05)
fitGrid <- sparseLTSGrid(x, y, lambda = frac, mode = "fraction")
## compute AIC and BIC[＃计算AIC和BIC]
AIC(fitGrid)
BIC(fitGrid)
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注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-27 22:21:57
