diagnos(RMC)
diagnos()所属R语言包：RMC
Calculation of Markov residuals for discrete-time non-stationary Markov models with simple parameterisation.
马尔可夫残差计算的离散时间非平稳马尔可夫模型用简单的参数化。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Calculates patch and movement residuals for Markov models with a simple parameterisation. The models themselves are for categorical Markov processes that are usefully described by models whose parameterisation is based on a simple reversible Markov model and that can be extended to non-stationary cases. Non-stationary models are incorporated by letting the transition matrix vary with covariates. Simulation envelopes are created using diagnos.envel.
用一个简单的参数化计算补丁和移动马尔可夫模型的残差的。模型本身是明确的马尔可夫过程是基于一个简单的可逆马尔可夫模型，并且可以扩展到非固定的情况下，其参数化模型，有效地描述。非平稳模型纳入，让过渡矩阵与协变量的变化。模拟信封的使用diagnos.envel的。
用法----------Usage----------
diagnos(obs.states, chain.id, X=NULL, fit)
diagnos.envel( obs.states, chain.id, X=NULL, fit, perc=c( 0.025, 0.975), B=100, contr=list( print.iter=50))
参数----------Arguments----------
参数： obs.states
observed ordered chained data. If there are multiple chains then chains are stacked on top of each other. Argument must be supplied
观察下令链接数据。如果有多个链，那么链堆叠在彼此的顶部。参数必须提供
参数： chain.id
vector (length matches states) of identifiers for the individual chains. If NULL then it is assumed that all observations form a single chain.
向量（长度匹配州）的个人链的标识符。如果为NULL，那么它是假定所有的意见形成一个单链。
参数： X
design matrix as passed to the model fitting routine RMC.mod. If NULL then a matrix with 1 column full of ones is assumed.
设计矩阵传递到模型拟合程序RMC.mod的。如果为NULL，那么1列的矩阵，充满的是假定的。
参数： fit
a fitted model formed by a call to the estimation function RMC.mod. Must match up with the X argument.
一个拟合模型的估计函数的调用由RMC.mod。 X参数必须匹配。
参数： perc
the percentiles of the simulations to take for the simulation envelope
的百分模拟的模拟信封
参数： B
the number of simulations for the simulation envelope
用于仿真包络线的模拟次数
参数： contr
list describing control parameters for the function. Currently consists of a single value for how often printing is to be performed
列出描述控制函数的参数。目前由一个单一的值常常以进行打印
Details
详细信息----------Details----------
Calculates the patch and movement residuals for the given Markov model. The methods are given in Foster and Bravington (2009). The patch residuals assess the model's compatibility with the data by inspecting the probabilities of observing each fully observed patch. The movement residuals assess the model's ability to describe each of the movement categories in the transition matrix.
计算的修补程序和运动马尔可夫模型的残差。给出的方法在福斯特和Bravington（2009年）。通过检查的概率观察每个完全观察到的补丁，该补丁残差的数据评估模型的兼容性。运动残差评估模型的描述能力的运动类的过渡矩阵。
Usage of diagnos.envel will produce, in addition to the functionality of diagnos, simulation envelopes
用途diagnos.envel生产，除了的诊断的功能，模拟信封
值----------Value----------
<table summary="R valueblock"> <tr valign="top"><td> *for diagnos* A list with the following elements</td> <td> </td></tr> <tr valign="top"><td> patch</td> <td> a list with number of elements equal to the number of states. Each element contains the patch residuals for each state</td></tr> <tr valign="top"><td> movement</td> <td> a square matrix containing the movement residuals from and to each state</td></tr> <tr valign="top"><td> njumps</td> <td> a square matrix containing the number of jumps from each state to each other state</td></tr> <tr valign="top"><td> *for diagnos.envel* a list with the following elements</td> <td> </td></tr> <tr valign="top"><td> patch</td> <td> a list with elements equal to the number of states. Each list element contains a matrix with observed patch residuals, expected patch quantiles and, lower and upper simulation envelope bounds. All values are given on uniform and normal deviate scales</td></tr> <tr valign="top"><td> movement</td> <td> a matrix with observed movement residuals, expected movement residuals and, lower and upper simulation envelope bounds. All values are given on uniform and normal deviate scales</td></tr> <tr valign="top"><td> njumps</td> <td> a list with length equal to the number of states. Each element contains the number of observed and simulated movements from each a particular state to each other state</td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD> *for diagnos* A list with the following elements </ TD> <TD> </ TD> </ TR> <tr valign="top"> <TD > patch </ TD> <td>一个名单的国家的数目相等的元素的数量。的每一个元素中包含的修补程序为每个状态的残差</ TD> </ TR> <tr valign="top"> <TD> movement </ TD> <td>一个方形矩阵中的运动残差和每个状态</ TD> </ TR> <tr valign="top"> <TD> njumps </ TD> <td>一个方形矩阵的数量从每个状态的跳跃彼此的状态</ TD> </ TR> <tr valign="top"> <TD> *for diagnos.envel* a list with the following elements </ TD> <TD> </ TD> </ TR> <tr valign="top"> <TD> X> </ TD> <td>一个名单的国家的数目相等的元素。每个列表元素都包含一个矩阵，观察到的补丁残留物，预期补丁位数，较低的信封上模拟界。所有的值都给出了均匀，正常的偏离尺度</ TD> </ TR> <tr valign="top"> <TD> patch </ TD> <td>一个矩阵与观测到的运动残差，预计运动残差和，下部和上部的模拟包络边界。所有的值都给出了均匀，正常的偏离尺度</ TD> </ TR> <tr valign="top"> <TD> movement </ TD> <td>一个列表长度相等的数量状态。每个元素包含的数量从每一个特定的状态观测和模拟的动作彼此的状态</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Scott D. Foster
参考文献----------References----------
<h3>See Also</h3>
实例----------Examples----------
#estimate a model for stationary example data, dataEG1[估计一个固定的示例数据模型，dataEG1]
fm.est <- RMC.mod( states=dataEG1[,2], chain.id=dataEG1[,1], X=dataEG1[,3])
#calculate residuals[计算残差]
res <- diagnos( dataEG1[,2], dataEG1[,1], X=dataEG1[,3, drop=FALSE], fit=fm.est)
res.envel <- diagnos.envel( dataEG1[,2], dataEG1[,1], X=dataEG1[,3,drop=FALSE], fit=fm.est, B=25)
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。
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发表于 2012-9-26 23:56:03
