R语言拟合半监督回归模型ssr()函数-中英文对照帮助文档

MicroRbt

        R语言拟合半监督回归模型ssr()函数-中英文对照帮助文档

                                         By MicroRbt Martinez PhD

R语言函数名：ssr()
R语言函数功能：拟合半监督回归模型
来自资源库：CRAN
ssr()函数所属R语言包：所在R包具体名称、包功能的中英文双语描述见正文后面'--所在R语言包信息--'部分。

描述-----Description-----

This function implements the co-training by committee and self-learning semi-supervised regression algorithms with a set of n base regressor(s) specified by the user. When only one model is present in the list of regressors, self-learning is performed.
此函数使用用户指定的一组n基本回归变量来实现co-training by committeec和self-learning半监督回归算法。当回归模型列表中仅存在一个模型时，将执行自学习。


使用方法-----Usage-----

ssr(theFormula, L, U, regressors = list(lm = lm, knn = caret::knnreg),
regressors.params = NULL, pool.size = 20, gr = 1, maxits = 20,
testdata = NULL, shuffle = TRUE, verbose = TRUE,
plotmetrics = FALSE, U.y = NULL)

参数-----Arguments-----

参数theFormula介绍: a formula that specifies the response and the predictor variables. Two formats are supported: "Y ~ ." and "Y ~ var1 + var2 + ... + varn".
指定响应和预测变量的formula。支持两种格式："Y ~ ."和"Y ~ var1 + var2 + ... + varn"。

参数L介绍: a data frame that contains the initial labeled training set.
包含初始标记训练集的数据框。

参数U介绍: a data frame that contains the unlabeled data. If the provided data frame has the response variable as one of its columns, it will be discarded.
包含未标记数据的数据框。如果提供的数据帧将响应变量作为其列之一，则将其丢弃。

参数regressors介绍: a list of custom functions and/or strings naming the regression models to be used. The strings must contain a valid name of a regression model from the 'caret' package. The list of available regression models from the 'caret' package can be found here. Functions must be named, e.g., list(linearModel=lm). List names for models defined with strings are optional. A list can contain both, strings and functions: list("kknn", linearModel=lm). For better performance in time, it is recommended to pass functions directly rather than using 'caret' strings since 'caret' does additional preprocessing when training models. Examples can be found in the vignettes.
列出要使用的回归模型的自定义函数和/或字符串的列表。字符串必须包含来自'caret'包的回归模型的有效名称。可以从“插入符”包中找到可用的回归模型列表。函数必须被命名，例如list(linearModel=lm)。用字符串定义的模型的列表名称是可选的。列表可以包含字符串和函数：list("kknn", linearModel=lm)。为了及时获得更好的性能，建议直接传递函数，而不要使用“插入符号”字符串，因为训练模型时，“插入符号”会进行额外的预处理。可以在小插图中找到示例。

参数regressors.params介绍: a list of lists that specifies the parameters for each custom function. For 'caret' models specified as strings in regressors, parameters cannot be passed, use NULL instead. The parameters are specified with a named list. For example, if regressors = list("lm", knn=knnreg), the number of nearest neighbors for knn can be set with list(NULL, list(k = 7)).
指定每个自定义函数的参数的列表列表。对于在regressors中指定为字符串的“插入符号”模型，无法传递参数，请改用NULL。用命名列表指定参数。例如，如果使用regressors = list("lm", knn=knnreg)c，则可以使用list(NULL, list(k = 7))设置knn的最近邻居数。

参数pool.size介绍: specifies the number of candidate elements to be sampled from the unlabeled set U. The best candidate elements from the pool are labeled and added to the training set. The gr parameter controls how many of the best candidates are used to augment the training set at each iteration. This parameter has big influence in computational time since in each iteration, pool.size * length(regressors) models are trained and evaluated in order to find the best candidate data points.
指定要从无标签集合中采样的候选元素的数量U。标记池中的最佳候选元素，并将其添加到训练集中。 gr参数控制在每次迭代中使用多少最佳候选者来增强训练集。由于在每次迭代中，pool.size * length(regressors)模型都经过训练和评估才能找到最佳的候选数据点，因此该参数对计算时间有很大影响。

参数gr介绍: an integer specifying the growth rate, i.e., how many of the best elements from the pool are added to the training set for each base model at each iteration.
一个整数，指定growth rate，即在每次迭代时将池中多少个最佳元素添加到每个基本模型的训练集中。

参数maxits介绍: an integer that specifies the maximum number of iterations. The training phase will terminate either when maxits is reached

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