micmen(VGAM)
micmen()所属R语言包：VGAM
Michaelis-Menten Model
米氏模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fits a Michaelis-Menten nonlinear regression model.
适用于一个米氏非线性的回归模型。
用法----------Usage----------
micmen(rpar = 0.001, divisor = 10, init1 = NULL, init2 = NULL,
imethod = 1, oim = TRUE,
link1 = "identity", link2 = "identity",
firstDeriv = c("nsimEIM", "rpar"),
earg1 = list(), earg2 = list(), prob.x = c(0.15, 0.85),
nsimEIM = 500,
dispersion = 0, zero = NULL)
参数----------Arguments----------
参数：rpar
Numeric. Initial positive ridge parameter. This is used to create positive-definite weight matrices.
数字。初步的积极岭参数。这是用来创建正定的权重矩阵。
参数：divisor
Numerical. The divisor used to divide the ridge parameter at each iteration until it is very small but still positive. The value of divisor should be greater than one.
数值。使用的除数在每一次迭代，直到它非常小，但仍积极岭参数来划分。 divisor的值应该是大于1。
参数：init1, init2
Numerical. Optional initial value for the first and second parameters, respectively. The default is to use a self-starting value.
数值。可选的初始值分别为第一和第二参数，。默认情况下是使用自启动值。
参数：link1, link2
Parameter link function applied to the first and second parameters, respectively. See Links for more choices.
参数链接功能分别施加到所述第一和第二参数。见Links更多的选择。
参数：earg1, earg2
List. Extra argument for each of the links. See earg in Links for general information.
列表。每个环节的额外参数。见earg中Links的一般信息。
参数：dispersion
Numerical. Dispersion parameter.
数值。分散参数。
参数：firstDeriv
Character. Algorithm for computing the first derivatives and working weights. The first is the default.
字符。计算一阶导数和工作的权重算法。第一个是默认的。
参数：imethod, prob.x
See CommonVGAMffArguments for more information.
见CommonVGAMffArguments更多信息。
参数：nsimEIM
See CommonVGAMffArguments for more information.
见CommonVGAMffArguments更多信息。
参数：oim
Use the OIM? See CommonVGAMffArguments for more information.
使用OIM？见CommonVGAMffArguments更多信息。
参数：zero
An integer-valued vector specifying which linear/additive predictors are modelled as intercepts only. The values must be from the set {1,2}. A NULL means none. See CommonVGAMffArguments for more information.
指定一个整数值向量线性/添加剂的预测模型仅作为拦截。这些值必须是从集合{1,2}。 ANULL是指没有。见CommonVGAMffArguments更多信息。
Details
详细信息----------Details----------
The Michaelis-Menten model is given by
米氏模型
where theta1 and theta2 are the two parameters.
theta1和theta2是两个参数。
The relationship between iteratively reweighted least squares and the Gauss-Newton algorithm is given in Wedderburn (1974). However, the algorithm used by this family function is different. Details are given at the Author's web site.
迭代加权最小二乘法和Gauss-Newton算法之间的关系在韦德伯恩（1974年）。然而，这个家庭功能所使用的算法是不同的。有关详情载于作者的网站。
值----------Value----------
An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, and vgam.
类的一个对象"vglmff"（见vglmff-class）。该对象被用于建模功能，如vglm，vgam。
警告----------Warning ----------
This function is not (nor could ever be) entirely reliable. Plotting the fitted function and monitoring convergence is recommended.
这个功能是不完全可靠（也可能永远不会）。绘制拟合函数和监控的融合的建议。
注意----------Note----------
The regressor values u_i are inputted as the RHS of the form2 argument. It should just be a simple term; no smart prediction is used. It should just a single vector, therefore omit the intercept term. The LHS of the formula form2 is ignored.
回归变量的值u_i输入作为form2参数的RHS。它应该只是一个简单的名词，没有使用智能预测。它应该只是一个单一的向量，因此忽略截距项。在LHS的公式form2被忽略。
To predict the response at new values of u_i one must assign the @extra$Xm2 slot in the fitted object these values, e.g., see the example below.
在新值预测响应u_i一个人必须分配@extra$Xm2插槽的拟合对象中这些值，例如，看下面的例子。
Numerical problems may occur. If so, try setting some initial values for the parameters. In the future, several self-starting initial values will be implemented.
数值可能会出现问题。如果是这样，请尝试设置一些参数的初始值。在未来，几个自启动的初始值将被实施。
（作者）----------Author(s)----------
T. W. Yee
参考文献----------References----------
Nonlinear Regression, New York: Wiley.
Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method. Biometrika, 61, 439–447.
Nonlinear Regression Analysis and Its Applications, New York: Wiley.
http://www.stat.auckland.ac.nz/~yee contains further information and examples.
参见----------See Also----------
enzyme.
enzyme。
实例----------Examples----------
fit = vglm(velocity ~ 1, micmen, enzyme, trace = TRUE, crit = "coef",
form2 = ~ conc - 1)
summary(fit)
## Not run: plot(velocity ~ conc, enzyme, xlab = "concentration", las = 1,[＃不运行图（速度浓，酶，xlab =“集中”，LAS = 1，]
col = "blue", main = "Michaelis-Menten equation for the enzyme data",
ylim = c(0, max(velocity)), xlim = c(0, max(conc)))
points(fitted(fit) ~ conc, enzyme, col = "red", pch = "+", cex = 1.5)
# This predicts the response at a finer grid:[这预示响应更精细的网格：]
newenzyme = data.frame(conc = seq(0, max(with(enzyme, conc)), len = 200))
fit@extra$Xm2 = newenzyme$conc # This assignment is needed for prediction[此分配所需的预测]
lines(predict(fit, newenzyme, "response") ~ conc, newenzyme, col = "red")
## 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。
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发表于 2012-10-1 15:44:20
