pspline(survival)
pspline()所属R语言包：survival
Smoothing splines using a pspline basis
平滑样条使用pspline基础
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Specifies a penalised spline basis for the predictor. This is done by fitting a comparatively small set of splines and penalising the integrated second derivative. Traditional smoothing splines use one basis per observation, but several authors have pointed out that the final results of the fit are indistinguishable for any number of basis functions greater than about 2-3 times the degrees of freedom. Eilers and Marx point out that if the basis functions are evenly spaced, this leads to significant computational simplifications.
指定受罚样条线的基础上的预测。这是由相对较小的样条拟合和惩罚集成的第二个衍生。传统的平滑样条使用观察每一个基础，但几位作者指出，合适的最终结果是区分任意数量的基础功能大于约2-3倍的自由度。艾勒斯和马克思指出，如果均匀分布的基础功能，这将导致大量的计算简化。
用法----------Usage----------
pspline(x, df=4, theta, nterm=2.5 * df, degree=3, eps=0.1, method,
Boundary.knots=range(x), intercept=FALSE, penalty=TRUE, ...)
参数----------Arguments----------
参数：x
for psline: a covariate vector. The function does not apply to factor variables. For psplineinverse x will be the result of a pspline call.
psline：协变量的向量。该功能并不适用于因子变量。对于将psplineinverse x一个pspline调用的结果。
参数：df
the desired degrees of freedom. One of the arguments df or theta' must be given, but not both. If df=0, then the AIC = (loglik -df) is used to choose an "optimal" degrees of freedom. If AIC is chosen, then an optional argument "caic=T" can be used to specify the corrected AIC of Hurvich et. al.
自由所需的程度。一个参数df或theta必须给定的，但不能同时。如果df=0，然后在AIC =（loglik-DF）用于选择一个“最佳”的自由度。如果AIC的选择，然后一个可选的参数“CAIC = T”可以用来指定Hurvich等工商行政管理机关纠正。等。
参数：theta
roughness penalty for the fit. It is a monotone function of the degrees of freedom, with theta=1 corresponding to a linear fit and theta=0 to an unconstrained fit of nterm degrees of freedom.
适合粗糙度罚款。这是一个单调函数的自由度，θ= 1对应的线性拟合和不受约束的自由程度nterm适合θ= 0。
参数：nterm
number of splines in the basis
花键的基础上的数量
参数：degree
degree of splines
花键度
参数：eps
accuracy for df
df精度
参数：method
the method for choosing the tuning parameter theta. If theta is given, then 'fixed' is assumed. If the degrees of freedom is given, then 'df' is assumed. If method='aic' then the degrees of freedom is chosen automatically using Akaike's information criterion.
选择调整参数theta方法。如果THETA，那么“固定”假设。如果给出的自由度，那么DF假定。如果方法=“AIC”，然后自动选择自由度使用赤池信息准则。
参数：...
optional arguments to the control function
可选参数控制功能
参数：Boundary.knots
the spline is linear beyond the boundary knots. These default to the range of the data.
样条线是超越边界结的线性。这些默认的数据范围。
参数：intercept
if TRUE, the basis functions include the intercept.
如果为TRUE，基础功能，包括拦截。
参数：penalty
if FALSE a large number of attributes having to do with penalized fits are excluded. Most useful for exploring the code so as to return a matrix with few added attributes.
如果为FALSE的属性与惩罚配合做大量被排除在外。最有用的探索代码，以便与数增加属性返回一个矩阵。
值----------Value----------
Object of class pspline, coxph.penalty containing the spline basis, with the appropriate attributes to be recognized as a penalized term by the coxph or survreg functions.
Object类的pspline, coxph.penalty包含的样条线的基础上，适当的属性，确认，由coxph或survreg职能作为惩罚项。
For psplineinverse the original x vector is reconstructed.
原来的X向量为psplineinverse重建。
参考文献----------References----------
Flexible smoothing with B-splines and penalties. Statistical Science, 11, 89-121.
Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion, JRSSB, volume 60, 271–293.
参见----------See Also----------
coxph,survreg,ridge, frailty
coxph，survreg，ridge，frailty
举例----------Examples----------
lfit6 <- survreg(Surv(time, status)~pspline(age, df=2), cancer)
plot(cancer$age, predict(lfit6), xlab='Age', ylab="Spline prediction")
title("Cancer Data")
fit0 <- coxph(Surv(time, status) ~ ph.ecog + age, cancer)
fit1 <- coxph(Surv(time, status) ~ ph.ecog + pspline(age,3), cancer)
fit3 <- coxph(Surv(time, status) ~ ph.ecog + pspline(age,8), cancer)
fit0
fit1
fit3
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 2012-2-16 21:36:43
