gaussianff(VGAM)
gaussianff()所属R语言包：VGAM
Gaussian (normal) Family Function
高斯（正常）家庭功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fits a generalized linear model to a response with Gaussian (normal) errors. The dispersion parameter may be known or unknown.
适用于广义线性模型与高斯（正态）错误响应。分散参数可以是已知或未知的。
用法----------Usage----------
gaussianff(dispersion = 0, parallel = FALSE, zero = NULL)
参数----------Arguments----------
参数：parallel
A logical or formula. If a formula, the response of the formula should be a logical and the terms of the formula indicates whether or not those terms are parallel.
一个逻辑或公式。如果一个公式，公式的响应应该是一个逻辑公式表示，不论这些条款是平行的。
参数：dispersion
Dispersion parameter. If 0 then it is estimated and the moment estimate is put in object@misc$dispersion; it is assigned the value <p align="center"> sum_{i=1}^n (y_i - eta_i)^T W_i (y_i - η_i) / (nM-p) where p is the total number of parameters estimated (for RR-VGLMs the value used is the number of columns in the large X model matrix; this may not be correct). If the argument is assigned a positive quantity then it is assumed to be known with that value.
分散参数。如果为0，然后是估计的矩估计在object@misc$dispersion，它是分配的价值<p ALIGN="CENTER"> sum_{i=1}^n (y_i - eta_i)^T W_i (y_i - η_i) / (nM-p) 其中p是参数估计总数（为RR-VGLMs，使用的值是在大X模型矩阵的列数，这可能是正确的）。如果该参数被分配一个正的数量，然后它被假定为与该值被称为。
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,...,M} where M is the number of columns of the matrix response.
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，...，M}，其中M是数列的矩阵响应。
Details
详细信息----------Details----------
This function is usually used in conjunction with vglm, else vlm is recommended instead. The notation M is used to denote the number of linear/additive predictors. This function can handle any finite M, and the default is to use ordinary least squares. A vector linear/additive model can be fitted by minimizing
该功能通常用于结合使用vglm，否则vlm建议，而不是。符号M是用来表示线性/添加剂预测因子的数目。此功能可以处理任何有限M，默认是使用普通最小二乘法。一个向量线性/添加剂模型中，可以最大限度地减少安装
where y_i is a M-vector, eta_i is the vector of linear/additive predictors. The W_i is any positive-definite matrix, and the default is the order-M identity matrix. The W_i can be inputted using the weights argument of vlm/vglm/vgam etc., and the format is the matrix-band format whereby it is a n * A matrix with the diagonals are passed first, followed by next the upper band, all the way to the (1,M) element. Here, A has maximum value of M(M+1)/2 and a minimum value of M. Usually the weights argument of vlm/vglm/vgam/rrvglm is just a vector, in which case each element is multiplied by a order-M identity matrix. If in doubt, type something like weights(object, type="working") after the model has been fitted.
y_i是M向量，eta_i是向量的线性/添加剂的预测。 W_i是正定矩阵，默认情况下是为了M的的身份矩阵。 W_i使用weights参数vlm/vglm/vgam等，和格式是矩阵带格式，从而它是可以被输入n * A矩阵对角线通过第一，其次是下一个上带，所有的方式来(1,M)元素。在这里，AM(M+1)/2具有最大值和最小值的M。通常情况下，weights参数vlm/vglm/vgam/rrvglm只是一个向量，在这种情况下，每一个元素都乘以一个命令 X>矩阵。如果有疑问，M后，该模型已安装的类型的东西。
值----------Value----------
An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, rrvglm and vgam.
类的一个对象"vglmff"（见vglmff-class）。该对象被用于建模功能，如vglm，rrvglm和vgam。
注意----------Note----------
This VGAM family function is supposed to be similar to gaussian but is is not compatible with glm. The "ff" in the name is added to avoid any masking problems.
这VGAM家庭功能应该是类似gaussian，但不兼容glm。 "ff"的名义加入，以避免任何掩蔽的问题。
（作者）----------Author(s)----------
Thomas W. Yee
参考文献----------References----------
Generalized Linear Models, 2nd ed. London: Chapman & Hall.
Vector generalized additive models. Journal of the Royal Statistical Society, Series B, Methodological, 58, 481–493.
参见----------See Also----------
normal1, huber, lqnorm, binormal, vlm, vglm, vgam, rrvglm.
normal1，huber，lqnorm，binormal，vlm，vglm，vgam，rrvglm。
实例----------Examples----------
mydat = data.frame(x = sort(runif(n <- 40)))
mydat = transform(mydat, y1 = 1 + 2*x + rnorm(n, sd=0.1),
y2 = 3 + 4*x + rnorm(n, sd=0.1),
y3 = 7 + 4*x + rnorm(n, sd=0.1))
fit = vglm(cbind(y1,y2) ~ x, gaussianff, data=mydat)
coef(fit, matrix=TRUE)
# For comparison:[对于比较：]
coef( lmfit <- lm(y1 ~ x, data=mydat))
coef(glmfit <- glm(y2 ~ x, data=mydat, gaussian))
vcov(fit)
vcov(lmfit)
t(weights(fit, type="prior")) # Unweighted observations[未加权的观察]
head(weights(fit, type="working")) # Identity matrices[单位矩阵]
# Reduced-rank VLM (rank-1)[降秩VLM（排名1）]
fit2 = rrvglm(cbind(y1,y2,y3) ~ x, gaussianff, data=mydat)
Coef(fit2)
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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 15:35:51
