steepest(rsm)
steepest()所属R语言包：rsm
Steepest-ascent methods for response surfaces
最速上升响应面方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
These functions provide the path of steepest ascent (or descent) for a fitted response surface produced by rsm.
这些功能提供了最陡峭的上升（或下降）的拟合响应面产生的rsm路径。
用法----------Usage----------
steepest (object, dist = seq(0, 5, by = .5), descent = FALSE)
canonical.path(object, which = ifelse(descent, length(object$b), 1),
dist = seq(-5, 5, by = 0.5), descent = FALSE)
参数----------Arguments----------
参数：object
rsm object to be analyzed.
rsm对象进行分析。
参数：dist
Vector of desired distances along the path of steepest ascent or descent. In steepest, these must all be non-negative; in canonical.path, you may want both positive and negative values, which specify opposite directions from the stationary point.
矢量所需的距离，沿着陡峭的上升或下降的路径。在steepest，这些都必须是非负的;在canonical.path，你可能想正面和负面的值，用于指定固定点从相反的方向。
参数：descent
Set this to TRUE to obtain the path of steepest descent, or FALSE to obtain the path of steepest ascent. This value is ignored in canonical.path if which is specified.
设置为TRUE获得最陡下降路径，或FALSE获得最速上升的路径。该值将被忽略canonical.path如果which指定。
参数：which
Which canonical direction (eigenvector) to use.
规范方向（特征向量）使用。
Details
详细信息----------Details----------
steepest returns the linear path of steepest ascent for first-order models, or a path obtained by ridge analysis (see Draper 1963) for second-order models. In either case, the path begins at the origin.
steepest返回最陡峭的上升一阶模型的线性路径的路径，或通过岭脊分析（德雷珀1963年）为第二阶模型。在这两种情况下，该路径开始于原点。
canonical.path applies only to second-order models (at least a TWI term present). It determines a linear path along one of the canonical variables, originating at the stationary point (not the origin). We need to specify which canonical variable to use. The eigenvalues obtained in the canaonical analysis are always in decreasing order, so the first canonical direction will be the path of steepest ascent (or slowest descent, if all eigenvalues are negative) from the stationary point, and the last one will be the path of steepest descent (or slowest ascent, if all eigenvalues are positive). These are the defaults for which when descent=FALSE and descent=TRUE respectively.
canonical.path只适用于第二阶模型（至少TWI术语存在的话）。它决定沿着一个标准变量的线性路径，原产在固定点（原点）。我们需要指定使用的典型变量。的特征值中得到的canaonical分析总是以递减的顺序，所以第一个典型的方向将是最速上升（或下降最慢的，如果所有的特征值是负的），从固定点的路径，和最后一个将是路径最速下降（或上升速度最慢的，如果所有的特征值是正的）。这是默认的whichdescent=FALSE和descent=TRUE分别。
With either function, the path in uncoded units depends on how the data are coded. Accordingly, it is important to code the predictor variables appropriately before fitting the response-surface model. See coded.data and its relatives for more information.
有了这两种功能，在未编码单位取决于数据是如何编码的路径。因此，重要的是要适当地预测变量进行编码嵌合的响应面模型之前。见coded.data和其亲属的详细信息。
值----------Value----------
A data.frame of points along the path of steepest ascent (or descent). For steepest, this path originates from the center of the experiment; for canonical.path, it starts at the stationary point. If coding information is available, the data frame also includes the uncoded values of the variables.
Adata.frame点沿路径的最速上升（或下降）。对于steepest，此路径来源于实验的中心;canonical.path，它开始在固定点。如果编码信息是可用的，则该数据框还包括未编码的值的变量。
For first-order response surfaces, only steepest may be used; the path is linear in that case. For second-order surfaces, steepest uses ridge analysis, and the path may be curved.
对于一阶响应面，只有steepest可能会使用在这种情况下，路径是线性的。对于二阶的表面，steepest使用脊分析，并且可以是弯曲的路径。
注意----------Note----------
Take careful note of the fitted values along the outputted path (labeled yhat). For example, if the stationary point is a maximum (all eigenvalues negative), the fitted values from steepest will increase as far as the stationary point, then they will decrease as we proceed along what is now the path of slowest descent.
认真地注意到输出路径的拟合值（标记为yhat）。例如，如果固定点是最大的（所有的特征值负），拟合值steepest将增加为固定点，然后他们会随着我们继续沿着什么是现在最慢下降的路径。
（作者）----------Author(s)----------
Russell V. Lenth
参考文献----------References----------
Technometrics, 5, 469–479.
Journal of Statistical Software, 32(7), 1–17. http://www.jstatsoft.org/v32/i07/.
参见----------See Also----------
rsm, coded.data
rsm，coded.data
实例----------Examples----------
library(rsm)
heli.rsm = rsm (ave ~ block + SO(x1, x2, x3, x4), data = heli)
steepest(heli.rsm)
canonical.path(heli.rsm)
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 2012-9-28 22:04:18
