SSarrhenius(vegan)
SSarrhenius()所属R语言包：vegan
Self-Starting nls Species-Area Models
自启动免入息审查贷款计划的物种 - 面积模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
These functions provide self-starting species-area models for non-linear regression (nls). They can also be used for fitting species accumulation models in fitspecaccum. These models (and many more) are reviewed by Dengler (2009).
这些功能提供了自启动的物种 - 面积非线性回归模型（nls）。它们也可以被用于嵌合物种堆积模型fitspecaccum。这些模型（等等）进行审查Dengler（2009年）。
用法----------Usage----------
SSarrhenius(area, k, z)
SSgleason(area, k, slope)
SSgitay(area, k, slope)
SSlomolino(area, Asym, xmid, slope)
参数----------Arguments----------
参数：area
Area or size of the sample: the independent variable.
区或规模的示例：独立变量。
参数：k, z, slope, Asym, xmid
Estimated model parameters: see Details.
估计模型参数的详细信息。
Details
详细信息----------Details----------
All these functions are assumed to be used for species richness (number of species) as the independent variable, and area or sample size as the independent variable. Basically, these define least squares models of untransformed data, and will differ from models for transformed species richness or models with non-Gaussian error.
假设所有这些功能都被用于物种丰富度（种）为自变量，区域或样本量为独立变量。基本上，这些定义未转换的数据的最小二乘模型，将不同型号的转型物种丰富度和非高斯误差模型。
The Arrhenius model (SSarrhenius) is the expression k*area^z. This is the most classical model that can be found in any textbook of ecology (and also in Dengler 2009). Parameter z is the steepness of the species-area curve, and k is the expected number of species in a unit area.
阿累尼乌斯模型（SSarrhenius）的表达k*area^z。这是最经典的模型，可以发现在任何一本教科书的生态（也是2009年在Dengler）。参数z种 - 面积曲线的陡度，k是在单位面积的物种数量的预期。
The Gleason model (SSgleason) is a linear expression k + slope*log(area) (Dengler 200). This is a linear model, and starting values give the final estimates; it is provided to ease comparison with other models.
的Gleason模型（SSgleason）是一个线性表达式k + slope*log(area)（Dengler 200）。这是一个线性模型，初始值得到最终的估计;提供它是为了缓和与其他模型的比较。
The Gitay model (SSgitay) is a quadratic logarithmic expression (k + slope*log(area))^2 (Gitay et al. 1991, Dengler 2009). Parameter slope is the steepness of the species-area curve, and k is the square root of expected richness in a unit area.
Gitay模型（SSgitay）是一个二次的对数表达(k + slope*log(area))^2Dengler（Gitay等人，1991年，2009年）。参数slope种 - 面积曲线的陡度，k是在单位面积的平方根预期丰富。
The Lomolino model (SSlomolino) is Asym/(1 + slope^log(xmid/area)) (Lomolino 2000, Dengler 2009). Parameter Asym is the asymptotic maximum number of species, slope is the maximum slope of increse of richness, and xmid is the area where half of the maximum richess is achieved.
Lomolino模型（SSlomolino）Asym/(1 + slope^log(xmid/area))（Dengler Lomolino 2000年，2009年）。参数Asym的的渐近最大的物种数量，slope是量体裁衣的最大斜率的丰富，和xmid是实现一半的最大richess的区域。
In addition to these models, several other models studied by Dengler (2009) are available in standard R self-starting models: Michaelis-Menten (SSmicmen), Gompertz (SSgompertz), logistic (SSlogis), Weibull (SSweibull), and some others that may be useful.
在这些模型中，Dengler（2009）研究了其他几个车型，在标准的R自启动模式：米氏（SSmicmen），姜氏（SSgompertz），后勤（<X >），韦伯（SSlogis），和其他一些可能有用的。
值----------Value----------
Numeric vector of the same length as area. It is the value of the expression of each model. If all arguments are names of objects the gradient matrix with respect to these names is attached as an attribute named gradient.
相同的长度area数值向量。它是每个模型的表达式的值。如果所有参数是对象的名字，这些名字梯度矩阵为一个名为gradient的属性附加。
（作者）----------Author(s)----------
Jari Oksanen.
参考文献----------References----------
relationship best? A review and empirical evaluation. Journal of Biogeography 36, 728–744.
relationship in a New Zealand tussock grassland, with implications for nature reserve design and for community structure. Journal of Vegetation Science 2, 113–118.
the species-area relationship. Journal of Biogeography 27, 17–26.
参见----------See Also----------
nls, fitspecaccum.
nls，fitspecaccum。
实例----------Examples----------
## Get species area data: sipoo.area gives the areas of islands[种面积数据：sipoo.area领域的岛屿]
example(sipoo)
S <- specnumber(sipoo)
plot(S ~ sipoo.area, xlab = "Island Area (ha)", ylab = "Number of Species",
ylim = c(1, max(S)))
## The Arrhenius model[＃阿累尼乌斯模型]
marr <- nls(S ~ SSarrhenius(sipoo.area, k, z))
marr
## confidence limits from profile likelihood[从配置文件的可能性的置信区间]
confint(marr)
## draw a line[＃画一条线]
xtmp <- seq(min(sipoo.area), max(sipoo.area), len=51)
lines(xtmp, predict(marr, newdata=data.frame(sipoo.area = xtmp)), lwd=2)
## The normal way is to use linear regression on log-log data,[＃正常的方法是使用log记录数据的线性回归，]
## but this will be different from the previous:[＃这将是不同于以往：]
mloglog <- lm(log(S) ~ log(sipoo.area))
mloglog
lines(xtmp, exp(predict(mloglog, newdata=data.frame(sipoo.area=xtmp))),
lty=2)
## Gleason: log-linear[＃格里森：对数线性]
mgle <- nls(S ~ SSgleason(sipoo.area, k, slope))
lines(xtmp, predict(mgle, newdata=data.frame(sipoo.area=xtmp)),
lwd=2, col=2)
## Gitay: quadratic of log-linear[＃Gitay：二次对数线性]
mgit <- nls(S ~ SSgitay(sipoo.area, k, slope))
lines(xtmp, predict(mgit, newdata=data.frame(sipoo.area=xtmp)),
lwd=2, col = 3)
## Lomolino: using original names of the parameters (Lomolino 2000):[＃Lomolino：使用原始的参数名称（Lomolino 2000年）：]
mlom <- nls(S ~ SSlomolino(sipoo.area, Smax, A50, Hill))
mlom
lines(xtmp, predict(mlom, newdata=data.frame(sipoo.area=xtmp)),
lwd=2, col = 4)
## One canned model of standard R:[第一个罐头标准的R模型：]
mmic <- nls(S ~ SSmicmen(sipoo.area, slope, Asym))
lines(xtmp, predict(mmic, newdata = data.frame(sipoo.area=xtmp)),
lwd =2, col = 5)
legend("bottomright", c("Arrhenius", "log-log linear", "Gleason", "Gitay",
"Lomolino", "Michaelis-Menten"), col=c(1,1,2,3,4,5), lwd=c(2,1,2,2,2,2),
lty=c(1,2,1,1,1,1))
## compare models (AIC)[＃比较模型（AIC）]
allmods <- list(Arrhenius = marr, Gleason = mgle, Gitay = mgit,
Lomolino = mlom, MicMen= mmic)
sapply(allmods, AIC)
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注：
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发表于 2012-10-1 15:13:48
