DeLury(VGAM)
DeLury()所属R语言包：VGAM
DeLury's Method for Population Size Estimation
DeLury的人群规模估计方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes DeLury's method or Leslie's method for estimating a biological population size.
计算DeLury的方法或者张国荣的方法来估计一个生物种群的大小。
用法----------Usage----------
DeLury(catch, effort, type=c("DeLury","Leslie"), ricker=FALSE)
参数----------Arguments----------
参数：catch, effort
Catch and effort. These should be numeric vectors of equal length.
渔获量和努力。这些应该是相等的长度的数值向量。
参数：type
Character specifying which of the DeLury or Leslie models is to be fitted. The default is the first value.
字符，指定要安装的DeLury或张国荣模型的。默认值是第一个值。
参数：ricker
Logical. If TRUE then the Ricker (1975) modification is computed.
逻辑。如果TRUE的的Ricker子（1975年）修改计算。
Details
详细信息----------Details----------
This simple function implements the methods of DeLury (1947). These are called the DeLury and Leslie models. Note that there are many assumptions. These include: (i) Catch and effort records are available for a series of consecutive time intervals. The catch for a given time interval, specified by t, is c(t), and the corresponding effort by e(t). The catch per unit effort (CPUE) for the time interval t is C(t) = c(t)/e(t). Let d(t) represent the proportion of the population captured during the time interval t. Then d(t) = k(t) e(t) so that k(t) is the proportion of the population captured during interval t by one unit of effort. Then k(t) is called the catchability, and the intensity of effort is e(t). Let E(t) and K(t) be the total effort and total catch up to interval t, and N(t) be the number of individuals in the population at time t. It is good idea to plot \log(C(t)) against E(t) for type="DeLury" and C(t) versus K(t) for type="Leslie".
这个简单的函数实现的DeLury（1947年）的方法。这些被称为DeLury和Leslie模型。需要注意的是有很多假设。这些包括：（i）捕捉和，努力记录可用的一系列连续时间间隔。对于一个给定的时间间隔，指定的t，美中不足的是c(t)，以及相应的努力e(t)。单位努力量（CPUE）的时间间隔t的是C(t) = c(t)/e(t)的渔获量。让我们d(t)代表的人口比例的时间间隔内t拍摄的。然后d(t) = k(t) e(t)k(t)一个单位的努力使t是时间间隔内拍摄的人口比例。 k(t)被称为卢振彬，和强度的努力是e(t)。让我们E(t)和K(t)是总的努力和总赶上间隔t和N(t)时间t的是在人群中的个人。这是个好主意绘制\log(C(t))对E(t)type="DeLury"和C(t)与K(t)type="Leslie"。
The other assumptions are as follows. (ii) The population is closed—the population must be closed to sources of animals such as recruitment and immigration and losses of animals due to natural mortality and emigration. (iii) Catchability is constant over the period of removals. (iv) The units of effort are independent, i.e., the individual units of the method of capture (i.e., nets, traps, etc) do not compete with each other. (v) All fish are equally vulnerable to the method of capture—source of error may include gear saturation and trap-happy or trap-shy individuals. (vi) Enough fish must be removed to substantially reduce the CPUE. (vii) The catches may remove less than 2% of the population. Also, the usual assumptions of simple regression such as (viii) random sampling, (ix) the independent variable(s) are measured without error—both catches and effort should be known, not estimated, (x) a line describes the data, (xi) the errors are independent and normally distributed.
其他假设如下。 （二）人口封闭的人口必须被关闭的动物来源，如招聘和动物自然死亡率和移民的移民和损失。 （三）捕性不变，在此期间的清除。 （iv）本单位的努力是独立的，即，捕获的方法（即，网，陷阱等）的各个单元的不与彼此竞争。 （v）所有的鱼都同样脆弱的捕获方法的误差来源可能包括齿轮饱和度和幸福陷阱或陷阱害羞的人。 （六）必须拆除足够的鱼CPUE将大幅降低。 （七）渔获量可能会删除不超过2％的人口。此外，通常假设的简单回归，如（八）随机抽样，（九）独立变量（s）是没有错误的捕捉和努力应该是已知的，而不是估计，（x）的一个行描述了数据， （十一）的错误是独立的正态分布。
值----------Value----------
A list with the following components.
与以下组件的列表。
参数：catch, effort
Catch and effort. Same as the original vectors. These correspond to c(t) and e(t) respectively.
渔获量和努力。相同的原始矢量。这些c(t)和e(t)分别对应。
参数：type, ricker
Same as input.
作为输入相同。
参数：N0
an estimate of the population size at time 0. Only valid if the assumptions are satisfied.
人口的大小在时间0时的估计。只有有效的，如果假设是满意的。
参数：CPUE
Catch Per Unit Effort =C(t).
赶上单位工作=C(t)。
参数：K, E
K(t), E(t). Only one is computed depending on type.
K(t)，E(t)。只有一个是根据type计算。
参数：lmfit
the lm object from the fit of log(CPUE) on K (when type="Leslie"). Note that the x component of the object is the model matrix.
的lm对象的拟合log(CPUE)K（当type="Leslie"）。请注意的对象是x组件的模型矩阵。
注意----------Note----------
The data in the example below comes from DeLury (1947), and some plots of his are reproduced. Note that he used log to base 10 whereas natural logs are used here. His plots had some observations obscured by the y-axis!
下面的例子中的数据来自DeLury（1947），和他的一些图被再现。请注意，他用log以10为基数，而使用天然原木这里。他的图有一些由y轴遮蔽观测！
The DeLury method is not applicable to the data frame wffc.nc since the 2008 World Fly Fishing Competition was strictly catch-and-release.
DeLury方法是不适用的数据框wffc.nc自2008年世界钓鱼比赛，严格捕获和释放。
（作者）----------Author(s)----------
T. W. Yee.
参考文献----------References----------
On the estimation of biological populations. Biometrics, 3, 145–167.
Computation and interpretation of biological statistics of fish populations. Bull. Fish. Res. Bd. Can., 191, 382–
VGLMs and VGAMs: an overview for applications in fisheries research. In press.
参见----------See Also----------
wffc.nc.
wffc.nc。
实例----------Examples----------
pounds = c( 147, 2796, 6888, 7723, 5330, 8839, 6324, 3569, 8120, 8084,
8252, 8411, 6757, 1152, 1500, 11945, 6995, 5851, 3221, 6345,
3035, 6271, 5567, 3017, 4559, 4721, 3613, 473, 928, 2784,
2375, 2640, 3569)
traps = c( 200, 3780, 7174, 8850, 5793, 9504, 6655, 3685, 8202, 8585,
9105, 9069, 7920, 1215, 1471, 11597, 8470, 7770, 3430, 7970,
4740, 8144, 7965, 5198, 7115, 8585, 6935, 1060, 2070, 5725,
5235, 5480, 8300)
table1 = DeLury(pounds/1000, traps/1000)
## Not run: [＃不运行：]
with(table1, plot(1+log(CPUE) ~ E, las=1, pch=19, main="DeLury method",
xlab="E(t)", ylab="1 + log(C(t))", col="blue"))
## End(Not run)[＃（不执行）]
omitIndices = -(1:16)
table1b = DeLury(pounds[omitIndices]/1000, traps[omitIndices]/1000)
## Not run: [＃不运行：]
with(table1b, plot(1+log(CPUE) ~ E, las=1, pch=19, main="DeLury method",
xlab="E(t)", ylab="1 + log(C(t))", col="blue"))
mylmfit = with(table1b, lmfit)
lines(mylmfit$x[,2], 1 + predict.lm(mylmfit), col="red", lty="dashed")
## End(Not run)[＃（不执行）]
omitIndices = -(1:16)
table2 = DeLury(pounds[omitIndices]/1000, traps[omitIndices]/1000, type="L")
## Not run: [＃不运行：]
with(table2, plot(CPUE ~ K, las=1, pch=19,
main="Leslie method; Fig. III",
xlab="K(t)", ylab="C(t)", col="blue"))
mylmfit = with(table2, lmfit)
abline(a=coef(mylmfit)[1], b=coef(mylmfit)[2], col="red", lty="dashed")
## End(Not run)[＃（不执行）]
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 2012-10-1 15:31:38
