predict.sparseLTS(robustHD)
predict.sparseLTS()所属R语言包：robustHD
Predict from sparse LTS regression models
稀疏LTS回归模型的预测从
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Make predictions from sparse least trimmed squares regression models.
至少修剪稀疏最小二乘回归模型进行预测。
用法----------Usage----------
## S3 method for class 'sparseLTS'
predict(object, newdata,
fit = c("reweighted", "raw", "both"), ...)
## S3 method for class 'sparseLTSGrid'
predict(object, newdata, s,
fit = c("reweighted", "raw", "both"), ...)
参数----------Arguments----------
参数：object
the model fit from which to make predictions.
模型拟合，以便作出预测。
参数：newdata
new data for the predictors. If the model fit was computed with the formula method, this should be a data frame from which to extract the predictor variables. Otherwise this should be a matrix containing the same variables as the predictor matrix used to fit the model (possibly including a column of ones in case of a model with intercept).
新的预测数据。如果模型拟合，计算公式法，这应该是一个数据框，从中提取的预测变量。否则，这应该是一个矩阵包含相同的变量的预测矩阵，用来拟合模型（可能包括那些在一列的模型与拦截的情况下）。
参数：s
an integer vector giving the indices of the models for which to make predictions. If fit is "both", this can be a list with two components, with the first component giving the indices of the reweighted fits and the second the indices of the raw fits. The default is to use the optimal model for each of the requested estimators. Note that the optimal models may not correspond to the same value of the penalty parameter for the reweighted and the raw estimator.
整数向量，指数的模型来进行预测。 fit如果是"both"，这可以是一个表两部分组成，第一部分给指数的重新加权拟合和第二个指标的原始适合。在默认情况下是使用最优化模型，为每个请求的估计。请注意，优化模型可能不符合相同的刑罚为重加权参数和原始估计值。
参数：fit
a character string specifying for which fit to make predictions. Possible values are "reweighted" (the default) for predicting values from the reweighted fit, "raw" for predicting values from the raw fit, or "both" for predicting values from both fits.
一个字符串指定为适应做出预测。可能的值是"reweighted"（默认值）的重加权拟合预测值，"raw"预测值从原始的配合，或"both"预测值都适合的。
参数：...
currently ignored.
目前被忽略。
Details
详细信息----------Details----------
The newdata argument defaults to the matrix of predictors used to fit the model such that the fitted values are computed.
newdata参数默认为矩阵用来拟合模型的拟合值计算的预测。
值----------Value----------
If predictions for only one model are requested, they are returned in the form of a numeric vector.
如果只有一个模型预测的要求，它们在一个数值向量的形式返回。
Otherwise a numeric matrix is returned in which each column contains the predicted values from the corresponding model.
否则返回数值矩阵，其中每个列中包含的预测值从相应的模型。
（作者）----------Author(s)----------
Andreas Alfons
参见----------See Also----------
predict, sparseLTS, sparseLTSGrid
predict，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]
## sparse LTS[＃稀疏LTS]
# fit model[拟合模型]
fit <- sparseLTS(x, y, lambda = 0.05, mode = "fraction")
# compute fitted values via predict method[通过预测方法计算拟合值]
predict(fit)
predict(fit, fit = "both")
## sparse LTS over a grid of values for lambda[＃稀疏LTS为lambda值一格的]
# fit model[拟合模型]
frac <- seq(0.25, 0.05, by = -0.05)
fitGrid <- sparseLTSGrid(x, y, lambda = frac, mode = "fraction")
# compute fitted values via predict method[通过预测方法计算拟合值]
predict(fitGrid)
predict(fitGrid, fit = "both")
predict(fitGrid, s = NULL)
predict(fitGrid, fit = "both", s = NULL)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
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 22:24:14
