compute.real(RMark)
compute.real()所属R语言包：RMark
Compute estimates of real parameters
计算实际参数估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes real estimates and var-cov from design matrix (design) and coefficients (beta) using specified link functions
使用指定的链接函数，计算实际的估计和var覆盖从设计矩阵（设计）系数（β）
用法----------Usage----------
compute.real(model, beta = NULL, design = NULL,
data = NULL, se = TRUE, vcv = FALSE)
参数----------Arguments----------
参数：model
MARK model object
MARK模型对象
参数：beta
estimates of beta parameters for real parameter computation
实际参数计算的β参数的估计
参数：design
design matrix for MARK model
MARK模型的设计矩阵
参数：data
dataframe with covariate values that are averaged for estimates
数据框的协变量值的平均值估算
参数：se
if TRUE returns std errors and confidence interval of real estimates
如果真正的回报STD真正的估计误差和置信区间
参数：vcv
logical; if TRUE, sets se=TRUE and returns v-c matrix of real estimates
逻辑，如果为TRUE，设置SE = TRUE，并返回VC矩阵实时估计
Details
详细信息----------Details----------
The estimated real parameters can be derived from the estimated beta parameters, a completed design matrix, and the link function specifications. MARK produces estimates of the real parameters, se and confidence intervals but there are at least 2 situations in which it is useful to be able to compute them after running the analysis in MARK: 1) adjusting confidence intervals for estimated over-dispersion, and 2) making estimates for specific values of covariates. The first case is done in get.real with a call to this function. It is done by adjusting the estimated standard error of the beta parameters by multiplying it by the square root of chat to adjust for over-dispersion. A normal 95 confidence interval is computed for the link estimate (estimate +/- 1.96*se) and this is then back-transformed to the real parameters using inverse.link with the appropriate inverse link function for the parameter to construct a 95 real parameter. There is one exception. For parameters using the mlogit transformation, a logit transformation of each individual real Psi and its se are used to derive the confidence interval. The estimated standard error for the real parameter is also scaled by the square root of the over-dispersion constant chat stored in model$chat. But, the code actually computes the variance-covariance matrix rather than relying on the values from the MARK output because real estimates will depend on any individual covariate values used in the model which is the second reason for this function.
估计真正的参数，可以是来自于估计的beta参数，完成的设计矩阵，和链接功能规格。 MARK产生的实际参数，Se和置信区间的估计，但有至少2个情况下，它是有用的，可以计算它们运行分析MARK后：1）调整估计过度分散的置信区间，和2 ）的协变量的特定值作出估计。第一种情况是在get.real调用此功能。它是通过调整通过它乘以调整过度分散的平方根chat的测试参数的估计的标准误差。一个正常的95置信区间计算的链路估计（估计+ / - 1.96 *本身），这是然后再转化为真正的参数使用inverse.link与适当的逆联接函数为参数构建一个95真正的参数。但有一个例外。 mlogit转型，logit改造每个人的幽和其本身的参数得出的置信区间。真正的参数估计的标准误差也按比例过度分散常数的平方根chat存储在model$chat。但是，该代码实际上计算方差 - 协方差矩阵，而不是依靠MARK输出的值，因为真正的估计依赖于任何单个协变量在模型中使用的值是这个函数的第二个原因。
New values of the real parameter estimates can easily be computed by simply changing the values of the covariate values in the design matrix and computing the inverse-link function using the beta parameter estimates. The covariate values to be used can be specified in one of 2 ways. 1) Prior to making a call to this function, use the functions find.covariates to extract the rows of the design matrix with covariate values and either fill in those values aautomatically with the options provided by find.covariates or edit those values to be the ones you want and then use fill.covariates to replace the values into the design matrix and use it as the value for the argument design, or 2) automate this step by specifying a value for the argument data which is used to take averages of the covariate values to fill in the covariate entries of the design matrix. In computing real parameter estimates from individual covariate values it is important to consider the scale of the individual covariates. By default, an analysis with MARK will standardize covariates by subtracting the mean and dividing by the standard deviation of the covariate value. However, in the RMark library all calls to MARK.EXE do not standardize the covariates and request real parameter estimates based on the mean covariate values. This was done because there are many instances in which it is not wise to use the standardization implemented in MARK and it is easy to perform any standardization of the covariates with R commands prior to fitting the models. Also, with pre-standardized covariates there is no confusion in specifying covariate values for computation of real estimates. If the model contains covariates and the argument design is not specified, the design matrix is extracted from model and all individual covariate values are assigned their mean value to be consistent with the default in the MARK analysis.
新的实际参数估计值可以很容易地通过简单地改变设计矩阵的协变量值的值，并计算反向链接功能使用的测试参数估计值计算。在一个有2种方法可以指定要使用的协变量值。 1）在调用此功能，使用该功能find.covariates提取的设计与协变量值的矩阵的行和填充这些值aautomatically的选项提供find.covariates或编辑这些值是你想要的，然后使用fill.covariates的值替换到设计矩阵，并用它的值作为参数design，或2）通过指定的值自动完成这步参数data采取的协变量值的平均值，以填补在协项目的设计矩阵。从单个协变量值计算的实际参数估计，重要的是要考虑规模的个人变项。默认情况下，MARK规范分析的协变量，减去均值和协变量的值除以标准差。但是，在RMark库，调用MARK.EXE不规范的协变量，并要求实时参数估计值的平均协变量值的基础上。这样做是因为有很多情况下，它不是明智的做法到使用标准化实施MARK，它很容易进行任何标准化的R命令的协变量拟合的模型前。此外，标准化的协变量有没有在指定的协变量值计算的实际估计的混乱。如果模型包含协变量和参数design不指定，设计矩阵提取model和所有个人的协变量值赋予其平均值是一致的与MARK分析中的默认。
If a value for beta is given, those values are used in place of the estimates model$results$beta$estimate.
如果给定的值beta，这些值的估计model$results$beta$estimate。
值----------Value----------
A data frame (real) is returned if vcv=FALSE; otherwise, a list is returned also
一个数据框（real），则返回vcv=FALSE，否则，列表返回也
参数：real
data frame containing estimates, and if se=TRUE or vcv=TRUE it also contains standard errors and confidence intervals and notation of whether parameters are fixed or at a boundary
数据框包含的估计，如果SE = TRUE或VCV = TRUE，也包含标准误差和置信区间和符号是否是固定的参数，或在边界
参数：vcv.real
variance-covariance matrix of real estimates
方差 - 协方差矩阵实时估计
（作者）----------Author(s)----------
Jeff Laake
参见----------See Also----------
get.real,fill.covariates,find.covariates,inverse.link,deriv_inverse.link
get.real，fill.covariates，find.covariates，inverse.link，deriv_inverse.link
实例----------Examples----------
# see examples in fill.covariates[看到的例子fill.covariates]
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 23:38:56
