gmultmix(unmarked)
gmultmix()所属R语言包：unmarked
Generalized multinomial-mixture model
广义多项式混合物模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A three level hierarchical model for designs involving repeated counts that yield multinomial outcomes. Possible data collection methods include repeated removal sampling and double observer sampling. The three model parameters are abundance, availability, and detection probability.
一个三级分层模型的设计，包括重复计数，产生多项成果。可能的数据收集方法，包括重复拆卸采样和双观察员的采样。三个模型参数丰富，可用性和检测概率。
用法----------Usage----------
gmultmix(lambdaformula, phiformula, pformula, data,
mixture = c("P", "NB"), K, starts, method = "BFGS", se = TRUE, ...)
参数----------Arguments----------
参数：lambdaformula
Righthand side (RHS) formula describing abundance covariates
右手边（RHS）的公式描述了丰富的协变量
参数：phiformula
RHS formula describing availability covariates
RHS描述可用性的协变量的公式
参数：pformula
RHS formula describing detection covariates
的RHS公式描述检测的协变量
参数：data
An object of class unmarkedFrameGMM
一个对象的类unmarkedFrameGMM
参数：mixture
Either "P" or "NB" for Poisson and Negative Binomial mixing distributions.
无论是“P”或“NB”泊松分布和负二项分布的混合分布。
参数：K
The upper bound of integration
上界的整合
参数：starts
Starting values
起始值
参数：method
Optimization method used by optim
最优化方法使用的optim
参数：se
Logical. Should standard errors be calculated?
逻辑。标准误差计算出来的？
参数：...
Additional arguments to optim, such as lower and upper bounds
其他参数OPTIM，如上限和下限
Details
详细信息----------Details----------
The latent transect-level super-population abundance distribution f(M | theta) can be set as either a Poisson or a negative binomial random variable, depending on the setting of the mixture argument. mixture = "P" or mixture = "NB" select the Poisson or negative binomial distribution respectively. The mean of M_i is lambda_i. If M_i ~ NB, then an additional parameter, alpha, describes dispersion (lower alpha implies higher variance).
潜样级别的超级人口数量分布f(M | theta)可以设置为的泊松或负二项随机变量，根据mixture参数的设置。 mixture = "P"或mixture = "NB"选择泊松或负二项分布。 M_i的平均lambda_i。如果M_i ~ NB，然后一个额外的参数，alpha，介绍了分散（下alpha意味着更高的方差）。
The number of individuals available for detection at time t is a modeled as binomial: N(i,t) ~ Binomial(M(i), phi(i,j)).
可用于检测在时间t的个人的数量是建模二项式：N(i,t) ~ Binomial(M(i), phi(i,j))的。
The detection process is modeled as multinomial: y(i,1:J,t) ~ Multinomial(N(i,t), pi(i,1,t), pi(i,2,t), ..., pi(i,J,t)), where pi(ijt) is the multinomial cell probability for plot i at time t on occasion j.
在检测过程中被建模为多项式：y(i,1:J,t) ~ Multinomial(N(i,t), pi(i,1,t), pi(i,2,t), ..., pi(i,J,t))，其中pi(ijt)是地，我有时Ĵ在时间t的多项单元的概率。
Cell probabilities are computed via a user-defined function related to the sampling design. Alternatively, the default functions removalPiFun or doublePiFun can be used for equal-interval removal sampling or double observer sampling. Note that the function for computing cell probabilites is specified when setting up the data using unmarkedFrameGMM.
单元的概率计算通过一个用户定义的函数的抽样设计。可替代地，默认功能removalPiFun或doublePiFun可用于相等的间隔去除采样或双观察员取样。注意，指定计算单元probabilites的功能设置数据时，使用unmarkedFrameGMM。
Parameters lambda, phi and p can be modeled as linear functions of covariates using the log, logit and logit links respectively.
参数lambda，phi和p可以建模为线性函数的协变量分别使用log，Logit和罗吉特链接的。
值----------Value----------
An object of class unmarkedFitGMM.
对象的类unmarkedFitGMM。
注意----------Note----------
Three types of covariates can be supplied, site-level, site-by-year-level, and observation-level. These must be formatted correctly when organizing the data with unmarkedFrameGMM
可以提供三种类型的协变量，网站，网站年水平，并观察水平。这些都必须正确的格式组织的数据时，用unmarkedFrameGMM
（作者）----------Author(s)----------
Richard Chandler <a href="mailto:rchandler@usgs.gov">rchandler@usgs.gov</a> and Andy Royle
参考文献----------References----------
survey data. Animal Biodiversity and Conservation 27, pp. 375–386.
density and temporary emigration in unmarked populations. Ecology 92:1429-1435.
参见----------See Also----------
unmarkedFrameGMM for setting up the data and metadata. multinomPois for surveys where no secondary sampling periods were used. Example functions to calculate multinomial cell probabilities are described piFuns
unmarkedFrameGMM建立数据和元数据。 multinomPois无二次采样周期的调查。示例函数来计算多项式单元的概率描述的piFuns
实例----------Examples----------
# Simulate data using the multinomial-Poisson model with a[使用多项泊松模型与模拟数据]
# repeated constant-interval removal design.[去除固定间隔重复设计。]
n <- 100 # number of sites[一些网站]
T <- 4 # number of primary periods[主周期数]
J <- 3 # number of secondary periods[第二时期的数量]
lam <- 3
phi <- 0.5
p <- 0.3
#set.seed(26)[set.seed（26）]
y <- array(NA, c(n, T, J))
M <- rpois(n, lam) # Local population size[本地人口规模]
N <- matrix(NA, n, T) # Individuals available for detection[个人可用于检测]
for(i in 1:n) {
N[i,] <- rbinom(T, M[i], phi)
y[i,,1] <- rbinom(T, N[i,], p) # Observe some[观察到了一些]
Nleft1 <- N[i,] - y[i,,1] # Remove them[删除他们]
y[i,,2] <- rbinom(T, Nleft1, p) # ...[...]
Nleft2 <- Nleft1 - y[i,,2]
y[i,,3] <- rbinom(T, Nleft2, p)
}
y.ijt <- cbind(y[,1,], y[,2,], y[,3,], y[,4,])
umf1 <- unmarkedFrameGMM(y=y.ijt, numPrimary=T, type="removal")
(m1 <- gmultmix(~1, ~1, ~1, data=umf1, K=30))
backTransform(m1, type="lambda") # Individuals per plot[每块地的个人]
backTransform(m1, type="phi") # Probability of being avilable[概率是AVILABLE的]
(p <- backTransform(m1, type="det")) # Probability of detection[检测概率]
p <- coef(p)
# Multinomial cell probabilities under removal design[下切除设计的多项单元的可能性]
c(p, (1-p) * p, (1-p)^2 * p)
# Or more generally:[或者更普遍：]
head(getP(m1))
# Empirical Bayes estimates of super-population size[经验贝叶斯估计的超级人口规模]
re <- ranef(m1)
plot(re, layout=c(5,5), xlim=c(-1,20), subset=site%in%1:25)
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。
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。
|
发表于 2012-10-1 13:24:00
