summary.scaleboot(scaleboot)
summary.scaleboot()所属R语言包：scaleboot
P-value Calculation for Multiscale Bootstrap
P-值计算的多尺度引导
译者：生物统计家园网 机器人LoveR
描述----------Description----------
summary method for class "scaleboot" and "scalebootv".
summary类"scaleboot"和"scalebootv"。
用法----------Usage----------
## S3 method for class 'scaleboot':
summary(object,models=names(object$fi),k=3,s=1,sp=-1,
type=c("Frequentist","Bayesian"),...)
## S3 method for class 'scalebootv':
summary(object,models=attr(object,"models"),k=3,type="Frequentist",...)
## S3 method for class 'summary.scaleboot':
print(x,sort.by=c("aic","none"),verbose=FALSE,...)
## S3 method for class 'summary.scalebootv':
print(x,select="average",sort.by=NULL,nochisq=TRUE,...)
参数----------Arguments----------
参数：object
an object used to select a method.
使用的对象选择方法。
参数：models
character vector of model names. If numeric, names(object$fi)[models] is used for each "scaleboot" object.
模型名称的字符向量。如果是数字，names(object$fi)[models]用于每个"scaleboot"对象。
参数：k
numeric vector of k for calculating p-values.
kp值计算的数字向量。
参数：s
σ_0^2
σ_0^2
参数：sp
σ_p^2
σ_p^2
参数：type
If numeric, it is passed to sbpsi functions as lambda to specify p-value type. If "Frequentist" or "Bayesian", then equivalent to specifying lambda = 1 or 0, respectively.
如果数字，它被传递给sbpsilambda指定p值类型的功能。如果“抽样分配”或“贝叶斯”，然后相当于指定lambda= 1或0，分别。
参数：select
character of model name (such as "poly.3") or one of "average" and "best". If "average" or "best", then the averaging by Akaike weights or the best model is used, respectively.
人物的模型名称（如为“poly.3”）或“平均”和“最佳”。如果“平均”或“最好”，然后由赤池重量或最好的模型的平均使用，分别。
参数：x
object.
对象。
参数：sort.by
sort key.
排序键。
参数：verbose
logical.
逻辑。
参数：nochisq
logical.
逻辑。
参数：...
further arguments passed to and from other methods.
进一步的参数传递给其他方法。
Details
详细信息----------Details----------
For each model, a class of approximately unbiased p-values, indexed by k=1,2,..., is calculaed. The p-values are named k.1, k.2, ..., where k=1 (k.1) corresponds to the ordinary bootstrap probability, and k=2 (k.2) corresponds to the third-order accurate p-value of Shimodaira (2002). As the k value increases, the bias of testing decreases, although the p-value becomes less stable numerically and the monotonicity of rejection regions becomes worse. Typically, k=3 provides a reasonable compromise. The sbpval method is available to extract p-values from the "summary.scaleboot" object.
对于每个模型，一类约公正的p-值，索引k=1,2,...，calculaed。 p值被命名为k.1，k.2，...，k=1（k.1）对应的普通引导概率，k=2（ k.2）对应的三阶准确的P-的Shimodaira值（2002年）。作为k值增大，测试减小偏压，虽然p-值变得不太稳定的数值和拒绝区的单调性变差。通常情况下，k=3提供了一个合理的妥协。 sbpval方法是提取"summary.scaleboot"对象的p值。
The p-value is defined as
的p-值被定义为
x^j}\Bigr|_{σ_0^2} \right),</i> where ψ(σ^2|β) is the
X ^ J} \ Bigr | _ {σ_0^ 2} \正确），</ I>ψ(σ^2|β)
The p-values are justified only for good fitting models. By default, the model which minimizes the AIC value is selected. We can modify the AIC value by using the sbaic function. We also diagnose the fitting by using the plot method.
良好的拟合模型的p值是合理的。默认情况下，模型的AIC值最小的选择。我们可以通过修改AIC值使用sbaic功能。我们也诊断的拟合通过使用plot方法。
值----------Value----------
summary.scaleboot returns an object of the class "summary.scaleboot", which is inherited from the class "scaleboot". It is a list containing all the components of class "scaleboot" and the following components: <table summary="R valueblock"> <tr valign="top"><td>pv</td> <td> matrix of p-values of size length(models) * length(k) with elements p_k.</td></tr> <tr valign="top"><td>pe</td> <td> matrix of standard errors of p-values.</td></tr> <tr valign="top"><td>best</td> <td> a list consisting of components model for the best fitting model name, aic for its AIC value, pv for a vector of p-values, and pe for a vector of standard errors.</td></tr> <tr valign="top"><td>parex</td> <td> a list of components k, s, and sp.</td></tr> </table>
summary.scaleboot返回一个类的对象"summary.scaleboot"，它继承自类"scaleboot"。这是一个列表，其中包含的所有组件类"scaleboot"和以下组件：<table summary="R valueblock"> <tr valign="top"> <TD> pv</ TD> < TD>矩阵的p-值的大小length(models)*length(k)的元素p_k。</ TD> </ TR> <tr valign="top"> <TD><X > </ TD> <TD>矩阵的p-值的标准误差。</ TD> </ TR> <tr valign="top"> <TD> pe</ TD> <td>一个best最适合的型号名称，modelAIC值列表组件组成的，aicp-值的向量，pv标准的矢量错误。</ TD> </ TR> <tr valign="top"> <TD>pe </ TD> <td>一个组件列表中parex，k，和s。</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Hidetoshi Shimodaira
参见----------See Also----------
sbfit, sbpsi, sbpval,
sbfit，sbpsi，sbpval，
实例----------Examples----------
data(mam15)
## For a single hypothesis[＃如果一个假设]
a <- mam15.relltest[["t4"]] # an object of class "scaleboot"[对象类“scaleboot”]
summary(a) # calculate and print p-values (k=3)[计算和打印的p值（k = 3时）]
summary(a,k=2) # calculate and print p-values (k=2)[计算和打印的p值（k = 2时）]
summary(a,k=1:4) # up to "k.4" p-value.[到“K.4”的p-值。]
## For multiple hypotheses[＃对于多个假说]
b <- mam15.relltest[1:15] # an object of class "scalebootv"[对象类“scalebootv”]
summary(b) # calculate and print p-values (k=3)[计算和打印的p值（k = 3时）]
summary(b,k=1:4) # up to "k.4" p-value.[到“K.4”的p-值。]
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 2012-9-29 22:27:30
