tspc.predict(TSPC)
tspc.predict()所属R语言包：TSPC
Form principal components predictor from a trained tspc object
形成主要组成部分的预测从一个训练有素的TSPC对象
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes supervised principal components, using scores from "object"
计算监管的主要组成部分，使用分数“对象”
用法----------Usage----------
tspc.predict(object, data, newdata, threshold, n.components = 3, prediction.type = c("continuous", "discrete", "nonzero"), n.class = 2)
参数----------Arguments----------
参数：object
Object fit.obj returned by tspc.train
对象fit.obj返回的tspc.train
参数：data
List of projection of training data returned by tspc.train, object proj.obj$wdata.train
列表的培训返回的数据由tspc.train投影，对象proj.obj wdata.train
参数：newdata
List of projection of test data returned by tspc.train, object proj.obj$wdata.test
投影测试数据返回tspc.train的列表，对象proj.obj wdata.test
参数：threshold
Threshold for scores.
阈值的分数。
参数：n.components
Number of principal components to compute. Should be 1,2 or 3.
主成分的数量来计算。应该是1,2或3。
参数：prediction.type
"continuous" for raw principal component(s); "discrete" for principal component categorized in equal bins; "nonzero" for indices of features that pass the threshold
“连续”为原料的主要成分（S）“离散”为主要成分分类平等箱“非零”的功能，通过阈值的指数
参数：n.class
Number of classes into which predictor is binned (for prediction.type="discrete"
的预测进行分级类（prediction.type =“离散”
值----------Value----------
list(v.pred = out, u = x.sml.svd$u, d = x.sml.svd$d, which.features = which.features, v.pred.1df = v.pred.1df, n.components = n.pc, coef = result$coef, call = this.call, prediction.type = prediction.type) <table summary="R valueblock"> <tr valign="top"><td>v.pred </td> <td> Supervised principal componients predictor</td></tr> <tr valign="top"><td>u </td> <td> U matrix from svd of weighted feature matrix</td></tr> <tr valign="top"><td>d </td> <td> singual values from svd of weighted feature matrix</td></tr> <tr valign="top"><td>which.features</td> <td> Indices of features exceeding threshold</td></tr> <tr valign="top"><td>n.components</td> <td> Number of supervised principal components requested</td></tr> </table>
列表（v.pred = OUT，U = D = x.sml.svd $ D，$ U，x.sml.svd which.features = which.features，v.pred.1df = v.pred.1df，N。部件= n.pc系数=结果系数，呼叫this.call，prediction.type = prediction.type）<table summary="R valueblock"> <tr valign="top"> <TD>v.pred </ TD> <TD>的监督主要componients预测</ TD> </ TR> <tr valign="top"> <TD>u </ TD> <TD>ü矩阵奇异值分解的特征加权矩阵</ TD> </ TR> <tr valign="top"> <TD> d </ TD> <TD> singual的值奇异值分解的特征加权矩阵</ TD> </ TR> <TR VALIGN =“”> <TD> which.features </ TD> <TD>功能指标超过阈值</ TD> </ TR> <tr valign="top"> <TD>n.components <TD>号码的监督要求的主要组成部分</ TD> </ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Yuping Zhang
实例----------Examples----------
x = list()
for(i in 1:2){
set.seed(i+123)
x[[i]] = matrix(rnorm(500*100), ncol=100)
}
y = sample(c(5:100), size=100, replace=TRUE)
censoring = sample(c(0,1), size=100, replace=TRUE)
data = list(x = x, y=y, censoring.status=censoring, genenames = as.character(paste("gene", c(1:500), sep="")), geneid = as.character(paste("gene", c(1:500), sep="")))
x = list()
for(i in 1:2){
set.seed(i+133)
x[[i]] = matrix(rnorm(500*100), ncol=100)
}
y = sample(c(5:100), size=100, replace=TRUE)
censoring = sample(c(0,1), size=100, replace=TRUE)
data.test = list(x = x, y=y, censoring.status=censoring, genenames = as.character(paste("gene", c(1:500), sep="")), geneid = as.character(paste("gene", c(1:500), sep="")))
fit = tspc.train(data, data.test, type="survival")
predict.obj<- tspc.predict(fit$fit.obj, fit$proj.obj$data.train, fit$proj.obj$data.test, threshold=1.0, n.components=1)
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。
|
发表于 2012-10-1 12:46:04
