cph(rms)
cph()所属R语言包：rms
Cox Proportional Hazards Model and Extensions
Cox比例风险模型和扩展
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Modification of Therneau's coxph function to fit the Cox model and its extension, the Andersen-Gill model. The latter allows for interval time-dependent covariables, time-dependent strata, and repeated events. The Survival method for an object created by cph returns an S function for computing estimates of the survival function. The Quantile method for cph returns an S function for computing quantiles of survival time (median, by default). The Mean method returns a function for computing the mean survival time. This function issues a warning if the last follow-up time is uncensored, unless a restricted mean is explicitly requested.
修改Therneau的coxph函数来拟合Cox模型及其扩展，安德森 - 吉尔模型。后者允许间隔时间相关的协变量，随时间变化的地层，以及重复的事件。 Survivalcph创建的对象的方法返回一个S函数计算的生存函数的估计。 Quantilecph方法返回一个S函数的计算位数的生存时间（中位数，默认情况下）。 Mean方法返回一个函数，用于计算平均存活时间。此功能会发出警告，如果最后的随访时间是未经审查的，除非受限制的均值明确要求。
用法----------Usage----------
cph(formula = formula(data), data=parent.frame(),
weights, subset, na.action=na.delete,
method=c("efron","breslow","exact","model.frame","model.matrix"),
singular.ok=FALSE, robust=FALSE,
model=FALSE, x=FALSE, y=FALSE, se.fit=FALSE,
eps=1e-4, init, iter.max=10, tol=1e-9, surv=FALSE, time.inc,
type=NULL, vartype=NULL, ...)
## S3 method for class 'cph'
Survival(object, ...)
# Evaluate result as g(times, lp, stratum=1, type=c("step","polygon"))
## S3 method for class 'cph'
Quantile(object, ...)
# Evaluate like h(q, lp, stratum=1, type=c("step","polygon"))
## S3 method for class 'cph'
Mean(object, method=c("exact","approximate"), type=c("step","polygon"),
n=75, tmax, ...)
# E.g. m(lp, stratum=1, type=c("step","polygon"), tmax, \dots)
参数----------Arguments----------
参数：formula
an S formula object with a Surv object on the left-hand side. The terms can specify any S model formula with up to third-order interactions. The strat function may appear in the terms, as a main effect or an interacting factor. To stratify on both race and sex, you would include both terms strat(race) and strat(sex). Stratification factors may interact with non-stratification factors; not all stratification terms need interact with the same modeled factors.
与Surv对象的左手侧上的S公式对象。 terms可以指定任何S模型公式三阶交互。 strat函数中可能会出现的术语，作为主要的作用或相互作用因子。种族和性别进行分层，将包括这两个词strat(race)和strat(sex)。分层因素可能与非分层因素;，没有所有的分层条款都需要相同的建模因素相互作用。
参数：object
an object created by cph with surv=TRUE
一个对象创建的cph与surv=TRUE的
参数：data
name of an S data frame containing all needed variables. Omit this to use a data frame already in the S “search list”.
S的数据框包含所有需要的变量的名称。省略此使用一个数据框已经在S“搜索列表”。
参数：weights
case weights
情况下，权重
参数：subset
an expression defining a subset of the observations to use in the fit. The default is to use all observations. Specify for example age>50 & sex="male" or c(1:100,200:300) respectively to use the observations satisfying a logical expression or those having row numbers in the given vector.
表达式定义的一个子集，在拟合中使用的观测。默认值是使用所有观测。指定例如age>50 & sex="male"或c(1:100,200:300)分别使用满足一个逻辑表达式，或那些具有在给定的矢量的行数的观测。
参数：na.action
specifies an S function to handle missing data. The default is the function na.delete, which causes observations with any variable missing to be deleted. The main difference between na.delete and the S-supplied function na.omit is that na.delete makes a list of the number of observations that are missing on each variable in the model. The na.action is usally specified by e.g. options(na.action="na.delete").
指定一个S函数来处理丢失的数据。默认的功能是na.delete，这会导致丢失被删除任何变量的观测。之间的主要区别na.delete和在S-提供的函数na.omit是na.delete使观测值的数量的列表，在模型中的每个变量上缺少。 na.action这就通常指定例如options(na.action="na.delete")。
参数：method
for cph, specifies a particular fitting method, "model.frame" instead to return the model frame of the predictor and response variables satisfying any subset or missing value checks, or "model.matrix" to return the expanded design matrix. The default is "efron", to use Efron's likelihood for fitting the model. For Mean.cph, method is "exact" to use numerical integration of the survival function at any linear predictor value to obtain a mean survival time. Specify method="approximate" to use an approximate method that is slower when Mean.cph is executing but then is essentially instant thereafter. For the approximate method, the area is computed for n points equally spaced between the min and max observed linear predictor values. This calculation is done separately for each stratum. Then the n pairs (X beta, area) are saved in the generated S function, and when this function is evaluated, the approx function is used to evaluate the mean for any given linear predictor values, using linear interpolation over the n X beta values.
cph，指定一个特定的拟合方法，"model.frame"，而不是返回满足任何一个子集或遗漏值检查的预测和响应变量的模型框架，或"model.matrix"返回扩展的设计矩阵。默认值是"efron"，使用埃弗龙的可能性拟合模型。对于Mean.cph，method是"exact"使用数值积分的生存函数的任何线性预测值，获得的平均存活时间。指定method="approximate"使用一种近似的方法比较慢，当Mean.cph执行，但本质上是即时其后。的近似方法，计算了该区域的n点平均间隔的最小值和最大值观察线性预测值。进行计算，分别为各阶层。然后n对（Xβ，面积）被保存在所生成的S功能，此功能时，评估，approx函数被用于评估对于任何给定的线性预测值的平均值，利用nX beta值的线性插值。
参数：singular.ok
If TRUE, the program will automatically skip over columns of the X matrix that are linear combinations of earlier columns. In this case the coefficients for such columns will be NA, and the variance matrix will contain zeros. For ancillary calculations, such as the linear predictor, the missing coefficients are treated as zeros. The singularities will prevent many of the features of the rms library from working.
如果TRUE，程序将自动跳过列的X矩阵的线性组合，以前的专栏。在这种情况下，这样的列的系数将是NA，方差矩阵将包含零。用于辅助计算，如线性预测器中，丢失的系数被视为零。奇点会阻止从工作许多rms库的功能。
参数：robust
if TRUE a robust variance estimate is returned. Default is TRUE if the model includes a cluster() operative, FALSE otherwise.
如果TRUE返回一个强大的方差估计。默认是TRUE，如果该模型包括一个cluster()手术，FALSE否则。
参数：model
default is FALSE(false). Set to TRUE to return the model frame as element model of the fit object.
默认是FALSE（假）。设置TRUE返回的模型框架元素model合适的对象。
参数：x
default is FALSE. Set to TRUE to return the expanded design matrix as element x (without intercept indicators) of the returned fit object.
默认是FALSE。设置为TRUE返回扩展的设计矩阵元素x（没有截取指标）返回的合适的对象。
参数：y
default is FALSE. Set to TRUE to return the vector of response values (Surv object) as element y of the fit.
默认是FALSE。设置为TRUE返回的响应值（Surv对象）的矢量元素y的契合。
参数：se.fit
default is FALSE. Set to TRUE to compute the estimated standard errors of the estimate of X beta and store them in element se.fit of the fit. The predictors are first centered to their means before computing the standard errors.
默认是FALSE。设置TRUE来计算估计标准误差的估计X的测试版和将它们存储在的元素se.fit契合。的预测变量为中心，以他们的方式，然后再计算标准的错误。
参数：eps
convergence criterion - change in log likelihood.
收敛准则 - 对数似然变化。
参数：init
vector of initial parameter estimates. Defaults to all zeros. Special residuals can be obtained by setting some elements of init to MLEs and others to zero and specifying iter.max=1.
矢量的初始参数估计。默认为全零。特殊残差可以通过以下方式获得设置一些MLEs及其他元素的init到零，并指定iter.max=1。
参数：iter.max
maximum number of iterations to allow. Set to 0 to obtain certain null-model residuals.
的迭代，以允许的最大数目。设置为0获得一定的空模型的残差。
参数：tol
tolerance for declaring singularity for matrix inversion (available only when survival5 or later package is in effect)
容忍声明奇异矩阵求逆（仅在survival5或更高版本的包实际上是）
参数：surv
set to TRUE to compute underlying survival estimates for each stratum, and to store these along with standard errors of log Lambda(t), maxtime (maximum observed survival or censoring time), and surv.summary in the returned object. Set surv="summary" to only compute and store surv.summary, not survival estimates at each unique uncensored failure time. If you specify x=Y and y=TRUE, you can obtain predicted survival later, with accurate confidence intervals for any set of predictor values. The standard error information stored as a result of surv=TRUE are only accurate at the mean of all predictors. If the model has no covariables, these are of course OK. The main reason for using surv is to greatly speed up the computation of predicted survival probabilities as a function of the covariables, when accurate confidence intervals are not needed.
设置为TRUE的来计算各阶层的基本生存估计，并存储这些标准错误logλ（T），maxtime（最大观察到的生存或审查时间），和surv.summary 返回的对象。设置surv="summary"只计算和存储surv.summary，而不是生存在每一个独特的未经审查的故障时间的估计。如果您指定x=Y和y=TRUE，你可以得到的预测生存后，任何一组的预测值与准确的置信区间。存储的作为surv=TRUE结果的标准错误信息只准确的所有预测变量的平均值。如果模型没有协变量，当然这些都是OK。使用surv是大大加快了计算的协变量的函数时，并不需要精确的置信区间的预测生存概率的主要原因。
参数：time.inc
time increment used in deriving surv.summary. Survival, number at risk, and standard error will be stored for t=0, time.inc, 2 time.inc, ..., maxtime, where maxtime is the maximum survival time over all strata. time.inc is also used in constructing the time axis in the survplot function (see below). The default value for time.inc is 30 if units(ftime) = "Day" or no units attribute has been attached to the survival time variable. If units(ftime) is a word other than "Day", the default for time.inc is 1 when it is omitted, unless maxtime<1, then maxtime/10 is used as time.inc. If time.inc is not given and maxtime/ default time.inc > 25, time.inc is increased.
在导出surv.summary的使用时间增量。生存，在风险和标准错误将被存储为t=0, time.inc, 2 time.inc, ..., maxtime，maxtime是在各阶层的最大生存时间。 time.inc也被用来在构建survplot函数（见下文）中的时间轴。 time.inc的默认值是30，如果units(ftime) = "Day"不units属性已连接的生存时间变量。如果units(ftime)就是一个字以外"Day"，默认为time.inc为1时，它被省略，除非maxtime<1，那么maxtime/10作为time.inc。如果time.inc没有给出maxtime/ default time.inc25time.inc增加。
参数：type
(for cph) applies if surv is TRUE or "summary". If type is omitted, the method consistent with method is used. See survfit.coxph (under survfit) or survfit.cph for details and for the definitions of values of type For Survival, Quantile, Mean set to "polygon" to use linear interpolation instead of the usual step function. For Mean, the default of step will yield the sample mean in the case of no censoring and no covariables, if type="kaplan-meier" was specified to cph. For method="exact", the value of type is passed to the generated function, and it can be overridden when that function is actually invoked. For method="approximate", Mean.cph generates the function different ways according to type, and this cannot be changed when the function is actually invoked.
如果（cph）适用于surv是TRUE或"summary"。如果type省略，使用的方法符合method。 survfit.coxph（下survfit）survfit.cph的详细信息和typeSurvival, Quantile, Mean设置为"polygon"使用线性值的定义内插，而不是通常的阶跃函数。对于Mean，默认的step将产生的样本是指在没有审查，没有协变量的情况下，如果type="kaplan-meier"被指定为cph。对于method="exact"，的价值type传递给生成的函数，和实际调用该函数时，它可以重写。对于method="approximate"，Mean.cph生成功能根据type，这不能改变实际上是调用函数时，不同的方式。
参数：vartype
see survfit.coxph
看到survfit.coxph
参数：...
other arguments passed to coxph.fit from cph. Ignored by other functions.
其他参数传递给coxph.fitcph。忽略其他功能。
参数：times
a scalar or vector of times at which to evaluate the survival estimates
一个标量或矢量的时间，以评估的生存估计
参数：lp
a scalar or vector of linear predictors (including the centering constant) at which to evaluate the survival estimates
一个标量或向量的线性预测（包括中心不变），以评估的生存估计
参数：stratum
a scalar stratum number or name (e.g., "sex=male") to use in getting survival probabilities
一个标量层数或名称（例如，"sex=male"）使用获得的生存概率
参数：q
a scalar quantile or a vector of quantiles to compute
一个标量分量或一个向量的位数计算
参数：n
the number of points at which to evaluate the mean survival time, for method="approximate" in Mean.cph.
点的数量来评估的平均存活时间，method="approximate"中Mean.cph。
参数：tmax
For Mean.cph, the default is to compute the overall mean (and produce a warning message if there is censoring at the end of follow-up). To compute a restricted mean life length, specify the truncation point as tmax. For method="exact", tmax is passed to the generated function and it may be overridden when that function is invoked. For method="approximate", tmax must be specified at the time that Mean.cph is run. </table>
对于Mean.cph，默认值是计算的整体平均（产生一个警告信息，如果是在后续的审查）。要计算一个受限制的平均寿命长，指定截断点tmax。对于method="exact"，tmax传递给生成的函数，调用该函数时，它可能会被改写。对于method="approximate"，tmax必须指定在Mean.cph的时间运行。 </ TABLE>
Details
详细信息----------Details----------
If there is any strata by covariable interaction in the model such that the mean X beta varies greatly over strata, method="approximate" may not yield very accurate estimates of the mean in Mean.cph.
如果有任何阶层的平均值x测试了很大的变化地层模型中的协变量的相互作用，method="approximate"可能不会产生非常准确的估计平均在Mean.cph。
For method="approximate" if you ask for an estimate of the mean for a linear predictor value that was outside the range of linear predictors stored with the fit, the mean for that observation will be NA.
method="approximate"，如果你问的平均估计为线性预测值，适合与存储的线性预测的范围之外，该观察的平均值将是NA。
值----------Value----------
For Survival, Quantile, or Mean, an S function is returned. Otherwise, in addition to what is listed below, formula/design information and the components maxtime, time.inc, units, model, x, y, se.fit are stored, the last 5 depending on the settings of options by the same names. The vectors or matrix stored if y=TRUE or x=TRUE have rows deleted according to subset and to missing data, and have names or row names that come from the data frame used as input data.
对于Survival，Quantile或Mean，一个S函数的返回。否则，在除了以下面列出的是，公式/设计的信息和对元件maxtime, time.inc, units, model, x, y, se.fit被存储，最后5个根据相同名称的选项的设置。的向量或矩阵存储y=TRUE或x=TRUE已删除的行根据subset和丢失的数据，名称或行名称来作为输入数据的数据框。
参数：n
table with one row per stratum containing number of censored and uncensored observations
表中的每行阶层包含数字的审查，未经审查的意见
参数：coef
vector of regression coefficients
回归系数向量
参数：stats
vector containing the named elements Obs, Events, Model L.R., d.f., P, Score, Score P, R2, g-index, and gr, the g-index on the hazard ratio scale
向量的命名元素Obs，Events，Model L.R.，d.f.，P，Score，Score P，<X >，R2指数，和g，gr指数的危险比规模
参数：var
variance/covariance matrix of coefficients
方差/协方差矩阵的系数
参数：linear.predictors
values of predicted X beta for observations used in fit, normalized to have overall mean zero, then having any offsets added
预测值X测试版的使用在适合的观察，标准化，总体平均为零，然后添加任何偏移
参数：resid
martingale residuals
鞅残差
参数：loglik
log likelihood at initial and final parameter values
在最初和最后的参数值的对数似然
参数：score
value of score statistic at initial values of parameters
得分统计值的参数的初始值
参数：times
lists of times (if surv="T")
列表的次数（如果surv="T"）
参数：surv
lists of underlying survival probability estimates
列表的基本生存概率估计
参数：std.err
lists of standard errors of estimate log-log survival
列出的标准误差的估计数log生存
参数：surv.summary
a 3 dimensional array if surv=TRUE. The first dimension is time ranging from 0 to maxtime by time.inc. The second dimension refers to strata. The third dimension contains the time-oriented matrix with Survival, n.risk (number of subjects at risk), and std.err (standard error of log-log survival).
一个3维数组，如果surv=TRUE。第一个维度是时间，范围从0到maxtime的time.inc。第二个维度是指地层。第三个维度包含的时间为导向的矩阵Survival, n.risk（风险科目），并std.err（标准差的loglog中生存）。
参数：center
centering constant, equal to overall mean of X beta. </table>
围绕常数，等于总体平均的X测试版。 </ TABLE>
（作者）----------Author(s)----------
Frank Harrell<br>
Department of Biostatistics, Vanderbilt University<br>
<a href="mailto:f.harrell@vanderbilt.edu">f.harrell@vanderbilt.edu</a>
参见----------See Also----------
coxph, survival-internal, Surv, residuals.cph, cox.zph, survfit.cph, survest.cph, survfit.coxph, survplot, datadist, rms, rms.trans, anova.rms, summary.rms, Predict, fastbw, validate, calibrate, plot.Predict, specs.rms, lrm, which.influence, na.delete, na.detail.response, print.cph, latex.cph, vif, ie.setup, GiniMd
coxph，survival-internal，Surv，residuals.cph，cox.zph，survfit.cph，survest.cph，survfit.coxph，survplot，datadist，rms，rms.trans，anova.rms，summary.rms，Predict，fastbw，validate ，calibrate，plot.Predict，specs.rms，lrm，which.influence，na.delete，na.detail.response，print.cph， latex.cph，vif，ie.setup，GiniMd
实例----------Examples----------
# Simulate data from a population model in which the log hazard[从人口模型的模拟数据，在该log的危险]
# function is linear in age and there is no age x sex interaction[函数是线性的，在年龄，有没有年龄的X性互动]
n <- 1000
set.seed(731)
age <- 50 + 12*rnorm(n)
label(age) <- "Age"
sex <- factor(sample(c('Male','Female'), n,
rep=TRUE, prob=c(.6, .4)))
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
dt <- -log(runif(n))/h
label(dt) <- 'Follow-up Time'
e <- ifelse(dt <= cens,1,0)
dt <- pmin(dt, cens)
units(dt) <- "Year"
dd <- datadist(age, sex)
options(datadist='dd')
Srv <- Surv(dt,e)
f <- cph(Srv ~ rcs(age,4) + sex, x=TRUE, y=TRUE)
cox.zph(f, "rank") # tests of PH[测试PH]
anova(f)
plot(Predict(f, age, sex)) # plot age effect, 2 curves for 2 sexes[图年龄效应，曲线2男女]
survplot(f, sex) # time on x-axis, curves for x2[在x-轴，曲线为x2时]
res <- resid(f, "scaledsch")
time <- as.numeric(dimnames(res)[[1]])
z <- loess(res[,4] ~ time, span=0.50) # residuals for sex[残差性]
plot(time, fitted(z))
lines(supsmu(time, res[,4]),lty=2)
plot(cox.zph(f,"identity")) #Easier approach for last few lines[最后几行简单的方法]
# latex(f)[胶乳（六）]
f <- cph(Srv ~ age + strat(sex), surv=TRUE)
g <- Survival(f) # g is a function[g是一个函数]
g(seq(.1,1,by=.1), stratum="sex=Male", type="poly") #could use stratum=2[可以使用层= 2]
med <- Quantile(f)
plot(Predict(f, age, fun=function(x) med(lp=x))) #plot median survival[图中位生存期]
# Fit a model that is quadratic in age, interacting with sex as strata[拟合模型，是二次在年龄，性别阶层与]
# Compare standard errors of linear predictor values with those from[与那些从比较线性预测值的标准误差]
# coxph[coxph]
# Use more stringent convergence criteria to match with coxph[使用更严格的收敛准则，以配合coxph]
f <- cph(Srv ~ pol(age,2)*strat(sex), x=TRUE, eps=1e-9, iter.max=20)
coef(f)
se <- predict(f, se.fit=TRUE)$se.fit
require(lattice)
xyplot(se ~ age | sex, main='From cph')
a <- c(30,50,70)
comb <- data.frame(age=rep(a, each=2),
sex=rep(levels(sex), 3))
p <- predict(f, comb, se.fit=TRUE)
comb$yhat <- p$linear.predictors
comb$se <- p$se.fit
z <- qnorm(.975)
comb$lower <- p$linear.predictors - z*p$se.fit
comb$upper <- p$linear.predictors + z*p$se.fit
comb
age2 <- age^2
f2 <- coxph(Srv ~ (age + age2)*strata(sex))
coef(f2)
se <- predict(f2, se.fit=TRUE)$se.fit
xyplot(se ~ age | sex, main='From coxph')
comb <- data.frame(age=rep(a, each=2), age2=rep(a, each=2)^2,
sex=rep(levels(sex), 3))
p <- predict(f2, newdata=comb, se.fit=TRUE)
comb$yhat <- p$fit
comb$se <- p$se.fit
comb$lower <- p$fit - z*p$se.fit
comb$upper <- p$fit + z*p$se.fit
comb
# g <- cph(Surv(hospital.charges) ~ age, surv=TRUE)[G < - CPH（的监测（hospital.charges）年龄，存活率= TRUE）]
# Cox model very useful for analyzing highly skewed data, censored or not[Cox比例风险模型非常有用的高度倾斜的数据分析，审查或不]
# m <- Mean(g)[M < - 平均（G）]
# m(0) # Predicted mean charge for reference age[M（0）＃预测平均费用为参考年龄]
#Fit a time-dependent covariable representing the instantaneous effect[合身的时间依赖的协变量代表的瞬时效果]
#of an intervening non-fatal event[的非致命性事件]
rm(age)
set.seed(121)
dframe <- data.frame(failure.time=1:10, event=rep(0:1,5),
ie.time=c(NA,1.5,2.5,NA,3,4,NA,5,5,5),
age=sample(40:80,10,rep=TRUE))
z <- ie.setup(dframe$failure.time, dframe$event, dframe$ie.time)
S <- z$S
ie.status <- z$ie.status
attach(dframe[z$subs,]) # replicates all variables[复制所有变量]
f <- cph(S ~ age + ie.status, x=TRUE, y=TRUE)
#Must use x=TRUE,y=TRUE to get survival curves with time-dep. covariables[必须使用X = TRUE，Y = TRUE求生存曲线随着时间的推移-DEP。协变量]
#Get estimated survival curve for a 50-year old who has an intervening[一个50岁的人有干预的估计生存曲线]
#non-fatal event at 5 days[非致命事件在5天的]
new <- data.frame(S=Surv(c(0,5), c(5,999), c(FALSE,FALSE)), age=rep(50,2),
ie.status=c(0,1))
g <- survfit(f, new)
plot(c(0,g$time), c(1,g$surv[,2]), type='s',
xlab='Days', ylab='Survival Prob.')
# Not certain about what columns represent in g$surv for survival5[不能确定哪些列代表在G $存活率为survival5]
# but appears to be for different ie.status[但似乎是不同的ie.status]
#or:[或：]
#g <- survest(f, new)[G < - survest（新）]
#plot(g$time, g$surv, type='s', xlab='Days', ylab='Survival Prob.')[图（G $ G $的时间，存活率，类型=S，xlab =天，ylab的生存概率。）]
#Compare with estimates when there is no intervening event[与预期的比较时，有没有介入事件]
new2 <- data.frame(S=Surv(c(0,5), c(5, 999), c(FALSE,FALSE)), age=rep(50,2),
ie.status=c(0,0))
g2 <- survfit(f, new2)
lines(c(0,g2$time), c(1,g2$surv[,2]), type='s', lty=2)
#or:[或：]
#g2 <- survest(f, new2)[G2 < - survest（F，NEW2）]
#lines(g2$time, g2$surv, type='s', lty=2)[线（G2，G2 $存活率，类型=S，LTY = 2）]
detach("dframe[z$subs, ]")
options(datadist=NULL)
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。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-27 19:09:49
