lrm.fit(rms)
lrm.fit()所属R语言包：rms
Logistic Model Fitter
Logistic模型钳工
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fits a binary or ordinal logistic model for a given design matrix and response vector with no missing values in either. Ordinary or penalized maximum likelihood estimation is used.
适合一个二进制或没有缺失值在任一给定的设计矩阵和响应向量序逻辑模型。使用普通或惩罚最大似然估计。
用法----------Usage----------
lrm.fit(x, y, offset, initial, est, maxit=12, eps=.025,
tol=1E-7, trace=FALSE, penalty.matrix, weights, normwt)
参数----------Arguments----------
参数：x
design matrix with no column for an intercept
截距无柱设计矩阵
参数：y
response vector, numeric, categorical, or character
响应向量，数字，分类，或字符
参数：offset
optional numeric vector containing an offset on the logit scale
可选的数字向量上的偏移logit的规模
参数：initial
vector of initial parameter estimates, beginning with the intercept
向量的初始参数估计，开始与拦截
参数：est
indexes of x to fit in the model (default is all columns of x). Specifying est=c(1,2,5) causes columns 1,2, and 5 to have parameters estimated. The score vector u and covariance matrix var can be used to obtain score statistics for other columns
指标x，以适应在模型中（默认情况下是所有x列）。指定est=c(1,2,5)导致列1,2，和5的参数估计。得分向量u和协方差矩阵var可以使用其他列中获得的得分统计
参数：maxit
maximum no. iterations (default=12). Specifying maxit=1 causes logist to compute statistics at initial estimates.
最大的没有。迭代（默认值=12）。 maxit=1会导致逻辑对计算统计初步估计。
参数：eps
difference in <PRE>-2 log</PRE> likelihood for declaring convergence. Default is .025. If the <PRE>-2 log</PRE> likelihood gets worse by eps/10 while the maximum absolute first directive of <PRE>-2 log</PRE> likelihood is below 1E-9, convergence is still declared. This handles the case where the initial estimates are MLEs, to prevent endless step-halving.
差异在<PRE> -2log</ PRE>为声明收敛的可能性。默认是.025。如果的<PRE> -2log</ pre>的可能性变得更糟eps/10，而最大绝对第一个指令的<PRE> -2log</ PRE>，可能低于1E-9，收敛宣布。这样处理的初步估计最大似然估计的情况下，为避免无尽的步骤减少一半。
参数：tol
Singularity criterion. Default is 1E-7
奇异的标准。默认为1E-7
参数：trace
set to TRUE to print -2 log likelihood, step-halving fraction, change in -2 log likelihood, maximum absolute value of first derivative, and vector of first derivatives at each iteration.
设置为TRUE打印-2对数似然，步骤减半馏分，在-2对数似然的变化，一阶导数的绝对值的最大值，和在每次迭代时的一阶导数的向量。
参数：penalty.matrix
a self-contained ready-to-use penalty matrix - see lrm
一个独立的准备使用的刑罚矩阵 - lrm
参数：weights
a vector (same length as y) of possibly fractional case weights
一个向量（y）可能是部分的情况下，权重相同的长度，
参数：normwt
set to TRUE to scale weights so they sum to the length of y; useful for sample surveys as opposed to the default of frequency weighting
设置为TRUE规模weights所以他们总结的长度y;有用的抽样调查，而不是默认的频率加权
值----------Value----------
a list with the following components:
与以下组件的列表：
参数：call
calling expression
调用表达式
参数：freq
table of frequencies for y in order of increasing y
表的频率为y为了增加y
参数：stats
vector with the following elements: number of observations used in the fit, maximum absolute value of first derivative of log likelihood, model likelihood ratio chi-square, d.f., P-value, c index (area under ROC curve), Somers' D_{xy}, Goodman-Kruskal gamma, and Kendall's tau-a rank correlations between predicted probabilities and observed response, the Nagelkerke R^2 index, the Brier probability score with respect to computing the probability that y > the mid level less one, the g-index, gr (the g-index on the odds ratio scale), and gp (the g-index on the probability scale using the same cutoff used for the Brier score). Probabilities are rounded to the nearest 0.002 in the computations or rank correlation indexes. When penalty.matrix is present, the chi-square, d.f., and P-value are not corrected for the effective d.f.
矢量包含下列元素：数的配合，最大绝对值的一阶导数的对数似然的观测，模型似然比卡方，DF，P-值，c指数（ROC曲线下面积），萨默斯D_{xy}，古德曼-克鲁斯卡gamma，和Kendall的tau-a排名之间的相关性预测的概率和观测到的响应，Nagelkerke R^2指数，的蒺藜概率得分就计算的概率y >中期的水平少一个，指数g，gr（指数g的几率比规模），和gp （g指数的概率规模的石南木得分相同的截止使用的）。概率四舍五入至最接近的0.002的计算或等级的相关指标。当penalty.matrix存在，chi-square，DF，P-值不纠正的有效DF
参数：fail
set to TRUE if convergence failed (and maxiter>1)
如果融合失败（TRUE设置为maxiter>1）
参数：coefficients
estimated parameters
估计参数
参数：var
estimated variance-covariance matrix (inverse of information matrix). Note that in the case of penalized estimation, var is not the improved sandwich-type estimator (which lrm does compute).
估计的方差 - 协方差矩阵的（信息矩阵逆）。请注意，在处罚的估计的情况下，var是不是改进的夹心型估计（其中lrm不计算）。
参数：u
vector of first derivatives of log-likelihood
矢量的一阶导数的对数似然
参数：deviance
-2 log likelihoods. When an offset variable is present, three deviances are computed: for intercept(s) only, for intercepts+offset, and for intercepts+offset+predictors. When there is no offset variable, the vector contains deviances for the intercept(s)-only model and the model with intercept(s) and predictors.
-2对数似然。当偏移变量是存在的，三个deviances计算：仅截距（S），拦截+偏移量，拦截+偏移+的预测。当不存在偏移变量，该向量包含deviances的截距（）的唯一模式和模型的截距（s）和预测因子。
参数：est
vector of column numbers of X fitted (intercepts are not counted)
矢量X装的列数（不计入拦截）
参数：non.slopes
number of intercepts in model
数截距模型
参数：penalty.matrix
see above </table>
看到上面的</ TABLE>
（作者）----------Author(s)----------
Frank Harrell<br>
Department of Biostatistics, Vanderbilt University<br>
f.harrell@vanderbilt.edu
参见----------See Also----------
lrm, glm, matinv, solvet, cr.setup, gIndex
lrm，glm，matinv，solvet，cr.setup，gIndex
实例----------Examples----------
#Fit an additive logistic model containing numeric predictors age, [包含数字的预测年龄适合的添加剂logistic回归模型，]
#blood.pressure, and sex, assumed to be already properly coded and [blood.pressure，性别，假设已被正确编码，]
#transformed[转化]
#[]
# fit <- lrm.fit(cbind(age,blood.pressure,sex), death)[符合< - lrm.fit（CBIND（的年龄，blood.pressure，性别），死亡）]
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。
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发表于 2012-9-27 19:11:51
