pcountOpen(unmarked)
pcountOpen()所属R语言包：unmarked
Fit the open N-mixture model of Dail and Madsen
适合开放式N-混合模型折射出马德森
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fit the model of Dail and Madsen (2011), which is a generalized form of the Royle (2004) N-mixture model for open
折射出马德森（2011年），这是一个广义的形式的罗伊尔（2004）N-混合模型的开放式的拟合模型
用法----------Usage----------
pcountOpen(lambdaformula, gammaformula, omegaformula, pformula,
data, mixture = c(" ", "NB", "ZIP"), K, dynamics=c("constant", "autoreg",
"notrend", "trend"), fix=c("none", "gamma", "omega"), starts,
method = "BFGS", se = TRUE, ...)
参数----------Arguments----------
参数：lambdaformula
Right-hand sided formula for initial abundance
右手侧的初始丰度的公式
参数：gammaformula
Right-hand sided formula for recruitment rate
右手侧的招聘率公式
参数：omegaformula
Right-hand sided formula for apparent survival probability
右手侧明显的生存概率公式
参数：pformula
Right-hand sided formula for detection probability
右手侧检测概率公式
参数：data
An object of class unmarkedFramePCO. See details
对象的类unmarkedFramePCO。查看详细资料
参数：mixture
character specifying mixture: "", "NB", or "ZIP" for the Poisson, negative binomial, and zero-inflated Poisson distributions.
字符，指定混合物：“P”，“NB”，或“ZIP”为泊松分布，负二项分布，以及零膨胀泊松分布。
参数：K
Integer defining upper bound of discrete integration. This should be higher than the maximum observed count and high enough that it does not affect the parameter estimates. However, the higher the value the slower the compuatation.
整数定义的离散积分的上限。这应该是高于观测到的最大计数和足够高的，它并没有影响的参数估计。然而，较高的值越慢compuatation。
参数：dynamics
Character string describing the type of population dynamics. "constant" indicates that there is no relationship between omega and gamma. "autoreg" is an auto-regressive model in which recruitment is modeled as gamma*N[i,t-1]. "notrend" model gamma as lambda*(1-omega) such that there is no temporal trend. "trend" is a model for exponential growth, N[i,t] = N[i,t-1]*gamma, where gamma in this case is finite rate of increase (normally referred to as lambda).
描述字符串类型的种群动态。 “常量”表示，欧米茄和γ之间不存在任何关系。 “autoreg”是一个自回归模型在招聘为蓝本的伽玛* N [I，T-1]。的“notrend”的模式伽玛为lambda *（1-ω），有没有时间的趋势。 “潮流”是一个指数增长模型，N [I，T] = N [I，T-1] *伽玛，在这种情况下，伽玛率的增加（通常简称为lambda）是有限的。
参数：fix
If "omega", omega is fixed at 1. If "gamma", gamma is fixed at 0.
如果“欧米茄”，欧米茄是固定在1。如果“γ”，γ是固定为0。
参数：starts
vector of starting values
向量的初始值
参数：method
Optimization method used by optim.
优化所使用的方法optim。
参数：se
logical specifying whether or not to compute standard errors.
逻辑指定是否计算标准误差。
参数：...
additional arguments to be passed to optim.
额外的参数传递给optim。
Details
----------Details----------
This model generalizes the Royle (2004) N-mixture model by relaxing the closure assumption. The model includes two additional parameters: gamma, the recruitment rate (births and immigrations), and omega, the apparent survival rate (deaths and emigrations). Estimates of population size at each time period can be derived from these parameters, and thus so can trend estimates. Or, trend can be estimated directly using dynamics="trend".
这种模式推广的罗伊尔（2004）的N-混合模型放宽封闭假设。该模型包括两个额外的参数：γ，招聘率（出生和移民）和欧米茄，明显的成活率（死亡和emigrations）。在各时间段中的种群大小预算可以来自这些参数，从而使可以趋势估计。或者，趋势，可以直接使用动态“潮流”估计。
The latent abundance distribution, f(N | theta) can be set as a Poisson, negative binomial, or zero-inflated Poisson random variable, depending on the setting of the mixture argument, mixture = "", mixture = "NB", mixture = "ZIP" respectively. For the first two distributions, the mean of N_i is lambda_i. If N_i ~ NB, then an additional parameter, alpha, describes dispersion (lower alpha implies higher variance). For the ZIP distribution, the mean is lambda_i*(1-psi), where psi is the zero-inflation parameter.
的潜在丰富分布，f(N | theta)的，可以设置为一个负二项分布，泊松分布，或零膨胀Poisson随机变量，根据mixture参数的设置，mixture = ""，<X >，mixture = "NB"“。对于前两种分布，平均mixture = "ZIP"是N_i。如果lambda_i，然后一个额外的参数，N_i ~ NB，介绍了分散（下alpha意味着更高的方差）。平均对于ZIP分配，是alpha，PSI是零通胀的参数。
The latent abundance state following the initial sampling period arises from a Markovian process in which survivors are modeled as S(i,t) ~ Binomial(N(i,t-1), omega(i,t)), and recruits follow G(i,t) ~ Poisson(gamma(i,t)). Alternative population dynamics can be specified using the dynamics argument.
潜丰富的状态的初始采样周期后出现的马氏过程中，幸存者被建模为S(i,t) ~ Binomial(N(i,t-1), omega(i,t))，和新兵的G(i,t) ~ Poisson(gamma(i,t))。 dynamics使用参数，可以指定替代的种群动态。
The detection process is modeled as binomial: y(i,j,t) ~ Binomial(N(i,t), p(i,j,t)).
在检测过程中被建模为二项：y(i,j,t) ~ Binomial(N(i,t), p(i,j,t))。
lamdba_i and gamma_it are modeled using the the log link. omega_it and p_ijt are modeled using the logit link.
lamdba_i和gamma_it的建模使用的log链接。 omega_it和p_ijt的建模使用logit的关联。
值----------Value----------
An object of class unmarkedFitPCO.
对象的类unmarkedFitPCO。
警告----------Warning----------
This function can be extremely slow, especially if there are covariates of gamma or omega. Consider testing the timing on a small subset of the data, perhaps with se=FALSE. Finding the lowest
这个功能可以非常缓慢，尤其是如果有协变量的γ射线或欧米茄。考虑测试的时间在一个小的数据子集，可能与SE = FALSE。寻找最低
注意----------Note----------
When gamma or omega are modeled using year-specific covariates, the covariate data for the final year will be ignored; however, they must be supplied.
当伽玛或omega年具体的协变量的建模使用的最后一年，协变量数据将被忽略，但是，他们必须提供。
If the time gap between primary periods is not constant, an M by T matrix of integers should be supplied to unmarkedFramePCO using the primaryPeriod argument.
如果主周期之间的时间间隔是不恒定的，应提供给unmarkedFramePCO使用primaryPeriod参数M的T矩阵的整数。
Secondary sampling periods are optional, but can greatly improve the precision of the estimates.
二次采样周期是可选的，但可大大提高估计的精度。
（作者）----------Author(s)----------
Richard Chandler <a href="mailto:rchandler@usgs.gov">rchandler@usgs.gov</a>
参考文献----------References----------
Spatially Replicated Counts. Biometrics 60, pp. 108–105.
Repeated Counts of an Open Metapopulation. Biometrics. 67, pp 577-587.
参见----------See Also----------
pcount, unmarkedFramePCO
pcount, unmarkedFramePCO
实例----------Examples----------
## Simulation[＃模拟]
## No covariates, constant time intervals between primary periods, and[＃无协变量，常量的主周期之间的时间间隔，并]
## no secondary sampling periods[＃无二次采样周期]
set.seed(3)
M <- 50
T <- 5
lambda <- 4
gamma <- 1.5
omega <- 0.8
p <- 0.7
y <- N <- matrix(NA, M, T)
S <- G <- matrix(NA, M, T-1)
N[,1] <- rpois(M, lambda)
for(t in 1 T-1)) {
S[,t] <- rbinom(M, N[,t], omega)
G[,t] <- rpois(M, gamma)
N[,t+1] <- S[,t] + G[,t]
}
y[] <- rbinom(M*T, N, p)
# Prepare data[准备数据]
umf <- unmarkedFramePCO(y = y, numPrimary=T)
summary(umf)
# Fit model and backtransform[合适的模型和backtransform]
(m1 <- pcountOpen(~1, ~1, ~1, ~1, umf, K=20)) # Typically, K should be higher[通常情况下，K应该更高]
(lam <- coef(backTransform(m1, "lambda"))) # or[或]
lam <- exp(coef(m1, type="lambda"))
gam <- exp(coef(m1, type="gamma"))
om <- plogis(coef(m1, type="omega"))
p <- plogis(coef(m1, type="det"))
# Finite sample inference. Abundance at site i, year t[有限样本推断。丰度在网站i，t年]
re <- ranef(m1)
devAskNewPage(TRUE)
plot(re, layout=c(5,5), subset = site %in% 1:25 & year %in% 1:2,
xlim=c(-1,15))
devAskNewPage(FALSE)
(N.hat1 <- colSums(bup(re)))
CI <- apply(confint(re), c(2,3), sum)
# Expected values of N[i,t][预期值N [I，T]]
N.hat2 <- matrix(NA, M, T)
N.hat2[,1] <- lam
for(t in 2:T) {
N.hat2[,t] <- om*N.hat2[,t-1] + gam
}
rbind(N=colSums(N), N.hat1=N.hat1, N.hat2=colSums(N.hat2))
plot(1:T, N.hat1, ylim=c(0,600))
points(1:T, colSums(N), col="blue", pch=16)
arrows(1:T, CI[1,], 1:T, CI[2,], code=3, length=0.05, angle=90)
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 13:25:55
