MOStest(vegan)
MOStest()所属R语言包：vegan
Mitchell-Olds \& Shaw Test for the Location of Quadratic Extreme
米切尔的孩子\＆肖测试为二次极端的位置
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Mitchell-Olds & Shaw test concerns the location of the highest (hump) or lowest (pit) value of a quadratic curve at given points. Typically, it is used to study whether the quadratic hump or pit is located within a studied interval. The current test is generalized so that it applies generalized linear models (glm) with link function instead of simple quadratic curve. The test was popularized in ecology for the analysis of humped species richness patterns (Mittelbach et al. 2001), but it is more general. With logarithmic link function, the quadratic response defines the Gaussian response model of ecological gradients (ter Braak & Looman 1986), and the test can be used for inspecting the location of Gaussian optimum within a given range of the gradient. It can also be used to replace Tokeshi's test of “bimodal” species frequency distribution.
米切尔奥尔兹＆邵氏测试涉及的最高的位置（驼背）或最低（坑）的值在给定的点的二次曲线。通常情况下，它被用来在研究的时间间隔之内，来研究是否位于二次驼峰或坑。目前的测试是广义的，因此，它适用于广义线性模型（glm）链接功能，而不是简单的二次曲线。测试是在生态分析双峰物种丰富度格局（米特尔巴赫等，2001），但它是更普遍。随着对数的链接功能，二次响应定义高斯响应模型的生态梯度（之三Braak＆Looman 1986），和测试可用于检查高斯最优的位置给定范围内的梯度。它也可以被用来取代Tokeshi的“双峰”物种频度分布的测试。
用法----------Usage----------
MOStest(x, y, interval, ...)
## S3 method for class 'MOStest'
plot(x, which = c(1,2,3,6), ...)
fieller.MOStest(object, level = 0.95)
## S3 method for class 'MOStest'
profile(fitted, alpha = 0.01, maxsteps = 10, del = zmax/5, ...)
## S3 method for class 'MOStest'
confint(object, parm = 1, level = 0.95, ...)
参数----------Arguments----------
参数：x
The independent variable or plotting object in plot.
独立变量或绘图对象的plot。
参数：y
The dependent variable.
因变量。
参数：interval
The two points at which the test statistic is evaluated. If missing, the extremes of x are used.
该检验统计量计算两点。如果缺少，这种极端的x。
参数：which
Subset of plots produced. Values which=1 and 2 define plots specific to MOStest (see Details), and larger values select graphs of plot.lm (minus 2).
生产图的子集。值which=1和2定义图的具体MOStest（见详情），和更大的价值选择plot.lm（减2图）。
参数：object, fitted
A result object from MOStest.
一个结果对象从MOStest。
参数：level
The confidence level required.
所需的信心水平。
参数：alpha
Maximum significance level allowed.
最大允许的显着性水平。
参数：maxsteps
Maximum number of steps in the profile.
在配置文件中的步骤的最大数目。
参数：del
A step length parameter for the profile (see code).
步长参数的配置文件（见代码）。
参数：parm
Ignored.
忽略。
参数：...
Other variables passed to functions. Function MOStest passes these to glm so that these can include family. The other functions pass these to underlying graphical functions.
其他变量传递给函数。功能MOStest通过这些glm，使这些可以包括family。其他功能通过这些基本的图形功能。
Details
详细信息----------Details----------
The function fits a quadratic curve μ = b_0 + b_1 x + b_2 x^2 with given family and link function. If b_2 < 0, this defines a unimodal curve with highest point at u = -b_1/(2 b_2) (ter Braak & Looman 1986). If b_2 > 0, the parabola has a minimum at u and the response is sometimes called “bimodal”. The null hypothesis is that the extreme point u is located within the interval given by points p_1 and p_2. If the extreme point u is exactly at p_1, then b_1 = 0 on shifted axis x - p_1. In the test, origin of x is shifted to the values p_1 and p_2, and the test statistic is based on the differences of deviances between the original model and model where the origin is forced to the given location using the standard anova.glm function (Oksanen et al. 2001). Mitchell-Olds & Shaw (1987) used the first degree coefficient with its significance as estimated by the summary.glm function. This give identical results with Normal error, but for other error distributions it is preferable to use the test based on differences in deviances in fitted models.
该功能符合二次曲线μ = b_0 + b_1 x + b_2 x^2给定family和链接功能。如果b_2 < 0，这定义了一个单峰曲线的最高点，u = -b_1/(2 b_2)（之三Braak和Looman的1986年）。如果b_2 > 0，抛物线有一个最低在u和反应有时也被称为“双峰”。的零假设是，极端点u点p_1和p_2给定的时间间隔内。如果极端的u点正是在p_1，然后b_1 = 0转向轴x - p_1。在测试中，起源x转移的值p_1和p_2，检验统计量的基础上的差异异常之间的原始模型和模型的产地在哪里被强制设置差异标记到给定的位置，使用的标准的anova.glm函数（奥克萨宁等人，2001）。米切尔的孩子和肖（1987）第一度系数为估计summary.glm功能的，其意义。这给了相同的结果与正常的错误，但其他的误差分布，它是优选使用在异常拟合模型中设置差异标记的差异的基础上的测试。
The test is often presented as a general test for the location of the hump, but it really is dependent on the quadratic fitted curve. If the hump is of different form than quadratic, the test may be insignificant.
测试通常的驼峰的位置，作为一般的测试，但它确实是依赖于二次拟合曲线。 ，如果驼背的不同形式大于二次，测试可能是微不足道的。
Because of strong assumptions in the test, you should use the support functions to inspect the fit. Function plot(..., which=1) displays the data points, fitted quadratic model, and its approximate 95% confidence intervals (2 times SE). Function plot with which = 2 displays the approximate confidence interval of the polynomial coefficients, together with two lines indicating the combinations of the coefficients that produce the evaluated points of x. Moreover, the cross-hair shows the approximate confidence intervals for the polynomial coefficients ignoring their correlations. Higher values of which produce corresponding graphs from plot.lm. That is, you must add 2 to the value of which in plot.lm.
由于假设在测试中，你应该使用支持功能来检查的配合。函数plot(..., which=1)显示的数据点，配有二次模型，其约95％的置信区间（2次SE）。函数plotwhich = 2显示的置信区间估计的多项式系数，再加上两行显示的组合产生的x评价分数的系数。此外，十字线显示了近似的置信区间多项式的系数忽略了他们的相关性。较高的值which产生相应的图形plot.lm。也就是说，你必须添加2到whichplot.lm。
Function fieller.MOStest approximates the confidence limits of the location of the extreme point (hump or pit) using Fieller's theorem following ter Braak & Looman (1986). The test is based on quasideviance except if the family is poisson or binomial. Function profile evaluates the profile deviance of the fitted model, and confint finds the profile based confidence limits following Oksanen et al. (2001).
功能fieller.MOStest接近的置信区间（驼背或坑）使用Fieller定理后Braak和Looman的（1986）的极值点的位置。但如果family是poisson或binomial的测试是基于quasideviance的。函数profile评估档案的拟合模型的偏差，和confint发现基于配置文件的可信限奥克萨宁等。 （2001）。
The test is typically used in assessing the significance of diversity hump against productivity gradient (Mittelbach et al. 2001). It also can be used for the location of the pit (deepest points) instead of the Tokeshi test. Further, it can be used to test the location of the the Gaussian optimum in ecological gradient analysis (ter Braak & Looman 1986, Oksanen et al. 2001).
通常，该测试是用于在评估对生产力梯度（米特尔巴赫等人，2001）的分集驼峰意义。它也可以用于代替Tokeshi试验坑（最深的点）的位置。此外，它可以被用来测试的生态梯度分析中（之三Braak＆Looman 1986，奥克萨宁等人，2001年）的高斯最优的位置。
值----------Value----------
The function is based on glm, and it returns the result of object of glm amended with the result of the test. The new items in the MOStest are:
该函数是基于glm，并返回结果的glm与测试的结果修正的对象。新项目在MOStest是：
参数：isHump
TRUE if the response is a hump.
TRUE如果响应是一个驼背。
参数：isBracketed
TRUE if the hump or the pit is bracketed by the evaluated points.
TRUE如果驼背或坑括号内的评价分数。
参数：hump
Sorted vector of location of the hump or the pit and the points where the test was evaluated.
排序条件驼背或凹坑和评价试验点的位置的矢量。
参数：coefficients
Table of test statistics and their significances.
表检验统计量和意义。
注意----------Note----------
Function fieller.MOStest is based on package optgrad in the Ecological Archives (http://www.esapubs.org/archive/ecol/E082/015/default.htm) accompanying Oksanen et al. (2001). The Ecological Archive package optgrad also contains profile deviance method for the location of the hump or pit, but the current implementation of profile and confint rather follow the example of profile.glm and confint.glm in the MASS package.
函数fieller.MOStest基于上包optgrad在生态档案（http://www.esapubs.org/archive/ecol/E082/015/default.htm）陪同奥克萨宁等。 （2001）。生态归档包optgrad还包含配置文件的驼背或坑的位置偏差的方法，但目前实施的profile和confint而仿效的profile.glm confint.glm MASS包。
（作者）----------Author(s)----------
Jari Oksanen
参考文献----------References----------
selection: statistical inference and biological interpretation. Evolution 41, 1149–1161.
H.L., Waide, R.B., Willig, R.M., Dodson, S.I. & Gough, L. 2001. What is the observed relationship between species richness and productivity? Ecology 82, 2381–2396.
intervals for the optimum in the Gaussian response function. Ecology 82, 1191–1197.
regression and the Gaussian response model. Vegetatio 65, 3–11.
参见----------See Also----------
The no-interaction model can be fitted with humpfit.
没有交互模型可以配备humpfit。
实例----------Examples----------
## The Al-Mufti data analysed in humpfit():[＃的Al-穆夫提数据分析在humpfit（）：]
mass <- c(140,230,310,310,400,510,610,670,860,900,1050,1160,1900,2480)
spno <- c(1, 4, 3, 9, 18, 30, 20, 14, 3, 2, 3, 2, 5, 2)
mod <- MOStest(mass, spno)
## Insignificant[＃无意义]
mod
## ... but inadequate shape of the curve[＃...但不足的曲线的形状]
op <- par(mfrow=c(2,2), mar=c(4,4,1,1)+.1)
plot(mod)
## Looks rather like log-link with Poisson error and logarithmic biomass[＃看起来有点像log链接的泊松错误和对数生物质]
mod <- MOStest(log(mass), spno, family=quasipoisson)
mod
plot(mod)
par(op)
## Confidence Limits[＃置信限。]
fieller.MOStest(mod)
confint(mod)
plot(profile(mod))
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 15:08:23
