lms.yjn(VGAM)
lms.yjn()所属R语言包：VGAM
LMS Quantile Regression with a Yeo-Johnson Transformation to Normality
LMS位数回归正常的一个杨Johnson转换
译者：生物统计家园网 机器人LoveR
描述----------Description----------
LMS quantile regression with the Yeo-Johnson transformation to normality.
LMS位数回归与杨 - 约翰逊转变到正常的。
用法----------Usage----------
lms.yjn(percentiles = c(25, 50, 75), zero = c(1,3),
llambda = "identity", lsigma = "loge", elambda = list(),
esigma = list(), dfmu.init = 4, dfsigma.init = 2,
ilambda = 1, isigma = NULL, rule = c(10, 5),
yoffset = NULL, diagW = FALSE, iters.diagW = 6)
lms.yjn2(percentiles=c(25,50,75), zero=c(1,3),
llambda = "identity", lmu = "identity", lsigma = "loge",
elambda = list(), emu = list(), esigma = list(),
dfmu.init = 4, dfsigma.init = 2, ilambda = 1.0,
isigma = NULL, yoffset = NULL, nsimEIM = 250)
参数----------Arguments----------
参数：percentiles
A numerical vector containing values between 0 and 100, which are the quantiles. They will be returned as "fitted values".
一个数值向量的值介于0和100之间，这是分位数。他们将返回“拟合值”。
参数：zero
See lms.bcn.
见lms.bcn。
参数：llambda, lmu, lsigma
See lms.bcn.
见lms.bcn。
参数：elambda, emu, esigma
See lms.bcn.
见lms.bcn。
参数：dfmu.init, dfsigma.init
See lms.bcn.
见lms.bcn。
参数：ilambda, isigma
See lms.bcn.
见lms.bcn。
参数：rule
Number of abscissae used in the Gaussian integration scheme to work out elements of the weight matrices. The values given are the possible choices, with the first value being the default. The larger the value, the more accurate the approximation is likely to be but involving more computational expense.
数横坐标中使用的的高斯积分计划，制定出权重矩阵的元素。给出的值是可能的选择，与所述第一值，该值是默认值。该值越大，更准确的近似很可能是，但涉及更多的计算费用。
参数：yoffset
A value to be added to the response y, for the purpose of centering the response before fitting the model to the data. The default value, NULL, means -median(y) is used, so that the response actually used has median zero. The yoffset is saved on the object and used during prediction.
的值被添加到响应y，拟合模型与数据中心的响应之前的目的。默认值，NULL，指的是-median(y)被使用，因此，实际使用的响应具有中位数的零。 yoffset保存的对象和过程中使用的预测。
参数：diagW
Logical. This argument is offered because the expected information matrix may not be positive-definite. Using the diagonal elements of this matrix results in a higher chance of it being positive-definite, however convergence will be very slow. If TRUE, then the first iters.diagW iterations will use the diagonal of the expected information matrix. The default is FALSE, meaning faster convergence.
逻辑。提出这种说法是因为可能不是预期的信息矩阵正定的。使用这个矩阵的查询结果在一个较高的机会是正定的对角元素，但是收敛会很慢。如果TRUE，那么第一个iters.diagW迭代将使用预期的信息矩阵的对角线。默认为FALSE，这意味着更快的收敛速度。
参数：iters.diagW
Integer. Number of iterations in which the diagonal elements of the expected information matrix are used. Only used if diagW = TRUE.
整数。使用中，预期的信息矩阵的对角线元素的迭代数目。仅用于如果diagW = TRUE。
参数：nsimEIM
See CommonVGAMffArguments for more information.
见CommonVGAMffArguments更多信息。
Details
详细信息----------Details----------
Given a value of the covariate, this function applies a Yeo-Johnson transformation to the response to best obtain normality. The parameters chosen to do this are estimated by maximum likelihood or penalized maximum likelihood. The function lms.yjn2() estimates the expected information matrices using simulation (and is consequently slower) while lms.yjn() uses numerical integration. Try the other if one function fails.
鉴于协变量的值，这个函数施加杨荣文 - 约翰逊变换到响应，最好得到常态。选择这样做的最大似然估计的参数或惩罚最大似然法。函数lms.yjn2()估计预期的信息矩阵使用模拟（因此速度较慢），而lms.yjn()使用数值积分。如果一个函数失败，尝试其他。
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和vgam。
警告----------Warning ----------
The computations are not simple, therefore convergence may fail. In that case, try different starting values.
不是简单的计算，因此可能无法收敛。在这种情况下，尝试不同的初始值。
The generic function predict, when applied to a lms.yjn fit, does not add back the yoffset value.
通用功能predict，当应用到一个lms.yjn合适，不添加yoffset这个值。
注意----------Note----------
The response may contain both positive and negative values. In contrast, the LMS-Box-Cox-normal and LMS-Box-Cox-gamma methods only handle a positive response because the Box-Cox transformation cannot handle negative values.
响应可能包含正值和负值。相比之下，的LMS的Box-Cox正常和LMS的Box-Cox-γ的方法只处理了积极的回应，因为Box-Cox转换不能处理负值。
Some other notes can be found at lms.bcn.
其它注意事项可以在lms.bcn。
（作者）----------Author(s)----------
Thomas W. Yee
参考文献----------References----------
A new family of power transformations to improve normality or symmetry. Biometrika, 87, 954–959.
Quantile regression via vector generalized additive models. Statistics in Medicine, 23, 2295–2315.
An Implementation for Regression Quantile Estimation. Pages 3–14. In: Haerdle, W. and Ronz, B., Proceedings in Computational Statistics COMPSTAT 2002. Heidelberg: Physica-Verlag.
http://www.stat.auckland.ac.nz/~yee contains further information and examples.
参见----------See Also----------
lms.bcn, lms.bcg, qtplot.lmscreg, deplot.lmscreg, cdf.lmscreg, bmi.nz, amlnormal.
lms.bcn，lms.bcg，qtplot.lmscreg，deplot.lmscreg，cdf.lmscreg，bmi.nz，amlnormal。
实例----------Examples----------
fit = vgam(BMI ~ s(age, df = 4), lms.yjn, bmi.nz, trace = TRUE)
head(predict(fit))
head(fitted(fit))
head(bmi.nz)
# Person 1 is near the lower quartile of BMI amongst people his age[1人是附近的下四分位数之间的BMI人年龄]
head(cdf(fit))
## Not run: [＃不运行：]
# Quantile plot[分量图]
par(bty = "l", mar = c(5, 4, 4, 3) + 0.1, xpd = TRUE)
qtplot(fit, percentiles = c(5, 50, 90, 99), main = "Quantiles",
xlim = c(15, 90), las = 1, ylab = "BMI", lwd = 2, lcol = 4)
# Density plot[密度图]
ygrid = seq(15, 43, len=100) # BMI ranges[BMI范围]
par(mfrow=c(1,1), lwd=2)
(aa = deplot(fit, x0=20, y=ygrid, xlab="BMI", col="black",
main="Density functions at Age = 20 (black), 42 (red) and 55 (blue)"))
aa = deplot(fit, x0=42, y=ygrid, add=TRUE, llty=2, col="red")
aa = deplot(fit, x0=55, y=ygrid, add=TRUE, llty=4, col="blue", Attach=TRUE)
with(aa@post, deplot) # Contains density function values; == a@post$deplot[包含密度函数值; == A @文章$ deplot]
## 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。
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。
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发表于 2012-10-1 15:41:33
