glpls1a.logit.all(gpls)
glpls1a.logit.all()所属R语言包:gpls
Fit MIRWPLS and MIRWPLSF model separately for logits
适合MIRWPLS和MIRWPLSF模型为logits分开
译者:生物统计家园网 机器人LoveR
描述----------Description----------
Apply Iteratively ReWeighted Least Squares (MIRWPLS) with an option of Firth's bias reduction procedure (MIRWPLSF) for multi-group (say C+1 classes) classification by fitting logit models for all C classes vs baseline class separately.
适用于多组选项弗斯的偏见减少程序(MIRWPLSF)(C +1类)分类为所有的C类和基线类分别拟合Logit模型的迭代加权最小二乘(MIRWPLS)。
用法----------Usage----------
glpls1a.logit.all(X, y, K.prov = NULL, eps = 0.001, lmax = 100, b.ini = NULL, denom.eps = 1e-20, family = "binomial", link = "logit", br = T)
参数----------Arguments----------
参数:X
n by p design matrix (with no intercept term)
由p的设计矩阵N(无截距项)
参数:y
response vector with class lables 1 to C+1 for C+1 group classification, baseline class should be 1
响应向量类标贴1到C +1 C +1群分类,基线类应为1
参数:K.prov
number of PLS components
PLS的组件数
参数:eps
tolerance for convergence
收敛公差
参数:lmax
maximum number of iteration allowed
迭代允许的最大数量
参数:b.ini
initial value of regression coefficients
回归系数的初始值
参数:denom.eps
small quanitity to guarantee nonzero denominator in deciding convergence
小quanitity非零分母,以保证在决定收敛
参数:family
glm family, binomial (i.e. multinomial here) is the only relevant one here
GLM家庭,binomial(即多项式)是唯一相关的一
参数:link
link function, logit is the only one practically implemented now
链接功能,logit是唯一一个现在切实执行
参数:br
TRUE if Firth's bias reduction procedure is used
TRUE,如果用于弗斯的偏见减少程序
值----------Value----------
参数:coefficients
regression coefficient matrix
回归系数矩阵
作者(S)----------Author(s)----------
Beiying Ding, Robert Gentleman
参考文献----------References----------
generalized partial least squares.
estimation for generalized linear regression. Technometrics 38(4): 374-381.
参见----------See Also----------
glpls1a.mlogit,glpls1a,glpls1a.mlogit.cv.error, glpls1a.train.test.error,
glpls1a.mlogit,glpls1a,glpls1a.mlogit.cv.error,glpls1a.train.test.error
举例----------Examples----------
x <- matrix(rnorm(20),ncol=2)
y <- sample(1:3,10,TRUE)
## no bias reduction[#没有偏见减少]
glpls1a.logit.all(x,y,br=FALSE)
## bias reduction[#偏见减少]
glpls1a.logit.all(x,y,br=TRUE)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注:
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