Blackduck(RMark)
Blackduck()所属R语言包：RMark
Black duck known fate data
黑鸭知天命数据
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A known fate data set on Black ducks that accompanies MARK as an example analysis using the Known model.
一个知天命的数据集上黑鸭子，伴随着MARK使用已知的模型为例进行分析。
格式----------Format----------
A data frame with 48 observations on the following 5 variables.
48以下5个变量的观察数据框。
ch a character vector containing the encounter history of
CH字符向量的遭遇史上的
BirdAge the age of the bird: a factor with levels 0
BirdAge的小鸟：年龄因素与水平0
Weight the weight of the bird at time
加权的鸟在时间的重量
Wing_Len the wing-length of the bird at time of marking
Wing_Len的鸟翼长的时间标记
condix the condition index of the bird at time of marking
condix条件指数在时间标记的鸟
Details
详细信息----------Details----------
This is a data set that accompanies program MARK as an example for Known fate. The data can be stratified using BirdAge as a grouping variable. The function run.Blackduck defined below in the examples creates some of the models used in the dbf file that accompanies MARK.
这是一个数据集，伴随着计划MARK知天命的一个例子。这些数据可以是分层使用BirdAge作为分组变量。的功能run.Blackduck定义在下面的例子中创建了一些，伴随着MARK dbf文件中使用的模型。
Note that in the MARK example the variable is named Age. In the R code, the fields "age" and "Age" have specific meanings in the design data related to time since release. These will override the use of a field with the same name in the individual covariate data, so the names "time", "Time", "cohort", "Cohort", "age", and "Age" should not be used in the individual covariate data with possibly the exception of "cohort" which is not defined for models with "Square" PIMS such as POPAN and other Jolly-Seber type models.
请注意，在对MARK的例子中的变量被命名为年龄。在R代码，字段“年龄”和“年龄”有特定的含义，释放的时间，因为在设计相关的数据。这将覆盖使用的领域在个别协的数据具有相同的名称，所以名称“时间”，“时间”，“队列”，“队列”，“年龄”，“年龄” ;不应使用在个人的协变量数据的可能的异常没有被定义为模型的“队列”与“广场”PIMS，如POPAN和其他的Jolly-Seber类型模型。
实例----------Examples----------
data(Blackduck)
# Change BirdAge to numeric; starting with version 1.6.3 factor variables are[更改BirdAge数字;版本1.6.3因子变量的开始]
# no longer allowed. They can work as in this example but they can be misleading[不再允许。他们可以在这个例子中，但他们可能会产生误导]
# and fail if the levels are non-numeric. The real parameters will remain[失败，如果的水平非数字的。真正的参数将保持]
# unchanged but the betas will be different.[不变，但测试版会有所不同。]
Blackduck$BirdAge=as.numeric(Blackduck$BirdAge)-1
run.Blackduck=function()
{
#[]
# Process data[过程数据]
#[]
bduck.processed=process.data(Blackduck,model="Known")
#[]
# Create default design data[创建默认的设计数据]
#[]
bduck.ddl=make.design.data(bduck.processed)
#[]
# Add occasion specific data min < 0; I have no idea what it is[添加场合特定的数据分<0，我不知道它是什么]
#[]
bduck.ddl$S$min=c(4,6,7,7,7,6,5,5)
#[]
# Define range of models for S[定义为S系列车型]
#[]
S.dot=list(formula=~1)
S.time=list(formula=~time)
S.min=list(formula=~min)
S.BirdAge=list(formula=~BirdAge)
#[]
# Note that in the following model in the MARK example, the covariates[请注意，在对MARK的例子在下面的模型，协变量]
# have been standardized. That means that the beta parameters will be different[已经标准化。这意味着，β参数是不同]
# for BirdAge, Weight and their interaction but the likelihood and real parameter[BirdAge，重量及其相互关系的可能性和实际参数]
# estimates are the same.[估计是相同的。]
#[]
S.BirdAgexWeight.min=list(formula=~min+BirdAge*Weight)
S.BirdAge.Weight=list(formula=~BirdAge+Weight)
#[]
# Create model list and run assortment of models[创建模型列表和运行各式各样的模型]
#[]
model.list=create.model.list("Known")
bduck.results=mark.wrapper(model.list,data=bduck.processed,ddl=bduck.ddl,
invisible=FALSE,threads=2)
#[]
# Return model table and list of models[回归模型的表格和列表的车型]
#[]
return(bduck.results)
}
bduck.results=run.Blackduck()
bduck.results
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 2012-9-26 23:37:58
