simex(simex)
simex()所属R语言包：simex
Measurement error in models using SIMEX
测量误差模型中使用SIMEX
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Implementation of the SIMEX algorithm for measurement error models according
的SIMEX测量误差模型算法的实现
用法----------Usage----------
simex(model, SIMEXvariable, measurement.error, lambda = c(0.5, 1, 1.5, 2),
B = 100, fitting.method = "quadratic", jackknife.estimation = "quadratic",
asymptotic = TRUE)
## S3 method for class 'simex':
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'simex':
summary(object,...)
## S3 method for class 'simex':
plot(x, xlab = expression((1 + lambda)), ylab = colnames(b[, -1]),
ask = FALSE, show = rep(TRUE, NCOL(b) - 1), ...)
## S3 method for class 'simex':
predict(object, newdata, ...)
refit(object, ...)
## S3 method for class 'simex':
refit(object, fitting.method = "quadratic",
jackknife.estimation = "quadratic", asymptotic = TRUE, ...)
参数----------Arguments----------
参数：model
the naive model
天真的模型
参数：SIMEXvariable
character or vector of characters containing the names of the variables with measurement error
含有与测量误差的变量的名称的字符的字符或矢量
参数：measurement.error
vector of standard deviations of the known measurement errors
向量的已知的测量误差的标准偏差的
参数：lambda
vector of lambdas for which the simulation step should be done (without 0)
矢量应该做的仿真步长的lambda表达式（不加0）
参数：B
number of iterations for each lambda
每个lambda的迭代次数
参数：fitting.method
fitting method linear, quadratic, nonlinear are implemented. (first 4 letters are enough)
拟合方法linear，quadratic，nonlinear的实施。 （第4个字母是足够的）
参数：jackknife.estimation
specifying the extrapolation method for jackknife variance estimation. Can be set to FALSE if it should not be performed
指定的刀切法方差估计，外推法。如果它不应该被执行，可以设置为FALSE
参数：asymptotic
logical, indicating if asymptotic variance estimation should be done, in the naive model the option x = TRUE has to be set
逻辑，如果渐近方差估计应该做的，天真的模型，设置选项“x = TRUE有
参数：x
object of class 'simex'
对象类的新加坡国际金融交易所“
参数：digits
number of digits to be printed
要打印的数字位数
参数：object
object of class 'simex'
对象类的新加坡国际金融交易所“
参数：xlab
optional name for the X-Axis
X-轴的名称（可选）
参数：ylab
vector containing the names for the Y-Axis
向量含有的Y-轴的名称
参数：ask
logical. If TRUE, the user is asked for input, before a new figure is drawn
逻辑。如果TRUE，用户被要求输入，绘制在一个新的数字
参数：show
vector of logicals indicating for wich variables a plot should be produced
向量，逻辑值至极的变量，一个图，应
参数：newdata
optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used
可选的数据框中寻找变量，用以预测。如果省略该参数，拟合的线性预测
参数：...
arguments passed to other functions
参数传递给其他函数
Details
详细信息----------Details----------
Nonlinear is implemented as described in Cook and Stefanski, but is numerically instable. It is not advisable to use this feature. If a nonlinear extrapolation is desired please use the refit() method.
非线性是在库克和斯蒂范斯基实施，但在数值上的不稳定。要使用此功能，这是不可取的。如果所需的非线性外推法，请使用refit()方法。
Asymptotic is only implemented for naive models of class lm or glm.
渐近只实现类lm或glm天真的模型。
refit() refits the object with a different extrapolation function.
refit()改装一个不同的外推功能的对象。
值----------Value----------
An object of class 'simex' which contains: <table summary="R valueblock"> <tr valign="top"><td>coefficients</td> <td> the corrected coefficients of the SIMEX model,</td></tr> <tr valign="top"><td>SIMEX.estimates</td> <td> the estimates for every lambda,</td></tr> <tr valign="top"><td>model</td> <td> the naive model,</td></tr> <tr valign="top"><td>measurement.error</td> <td> the known error standard deviations,</td></tr> <tr valign="top"><td>B</td> <td> the number of iterations,</td></tr> <tr valign="top"><td>extrapolation</td> <td> the model object of the extrapolation step,</td></tr> <tr valign="top"><td>fitting.method</td> <td> the fitting method used in the extrapolation step,</td></tr> <tr valign="top"><td>residuals</td> <td> the residuals,</td></tr> <tr valign="top"><td>fitted.values</td> <td> the fitted values,</td></tr> <tr valign="top"><td>call</td> <td> the function call,</td></tr> <tr valign="top"><td>variance.jackknife</td> <td> the jackknife variance estimate,</td></tr> <tr valign="top"><td>extrapolation.variance</td> <td> the model object of the variance extrapolation,</td></tr> <tr valign="top"><td>variance.jackknife.lambda</td> <td> the data set for the extrapolation,</td></tr> <tr valign="top"><td>variance.asymptotic</td> <td> the asymptotic variance estimates,</td></tr> <tr valign="top"><td>theta</td> <td> the estimates for every B and lambda,</td></tr> </table> ...
类的新加坡国际金融交易所“，其中包含的对象：表summary="R valueblock"> <tr valign="top"> <TD> coefficients </ TD> <TD>的交易模型的修正系数， </ TD> </ TR> <tr valign="top"> <TD> SIMEX.estimates</ TD> <TD>每拉姆达的估计，</ TD> </ TR> <TR VALIGN =“顶“<TD> model </ TD> <TD>天真的模型，</ TD> </ TR> <tr valign="top"> <TD> measurement.error </ TD> <TD>已知错误的标准偏差，</ TD> </ TR> <tr valign="top"> <TD>B </ TD> <TD>的迭代次数，</ TD> < / TR> <tr valign="top"> <TD> extrapolation </ TD> <TD>模型对象外推步骤，</ TD> </ TR> <tr valign="top"> <TD> fitting.method </ TD> <TD>的拟合方法用于外推步骤，</ TD> </ TR> <tr valign="top"> <TD>residuals</ TD> <TD>的残差，</ TD> </ TR> <tr valign="top"> <TD>fitted.values </ TD> <TD>的拟合值，</ TD> </ TR > <tr valign="top"> <TD> call </ TD> <TD>的函数调用，</ TD> </ TR> <tr valign="top"> <TD><X > </ TD> <TD>刀切法方差估计，</ TD> </ TR> <tr valign="top"> <TD>variance.jackknife </ TD> <TD>的模型对象方差推断，</ TD> </ TR> <tr valign="top"> <TD>extrapolation.variance </ TD> <TD>的数据外推，</ TD> </ TR> < TR VALIGN =“”> <TD>variance.jackknife.lambda </ TD> <TD>渐近方差的估计，</ TD> </ TR> <tr valign="top"> <TD>variance.asymptotic </ TD> <TD>估计每个B和λ</ TD> </ TR> </ TABLE> ...
（作者）----------Author(s)----------
Wolfgang Lederer,<a href="mailto:wolfgang.lederer@googlemail.com">wolfgang.lederer@googlemail.com</a>
参考文献----------References----------
Cook, J.R. and Stefanski, L.A. (1994) Simulation-extrapolation estimation in parametric measurement error models. Journal of American Statistical Association, 89, 1314 – 1328
Carroll, R.J., K眉chenhoff, H., Lombard, F. and Stefanski L.A. (1996) Asymptotics for the SIMEX estimator in nonlinear measurement error models. Journal of the American Statistical Association, 91, 242 – 250
Carrol, R.J., Ruppert, D., Stefanski, L.A. and Crainiceanu, C. (2006). Measurement error in nonlinear models: A modern perspective., Second Edition. London: Chapman and Hall.
Lederer, W. and K眉chenhoff, H. (2006) A short introduction to the SIMEX and MCSIMEX. R News, 6(4), 26–31
参见----------See Also----------
mcsimex for discrete data with misclassification,
mcsimex离散数据分类错误，
实例----------Examples----------
# to test nonlinear extrapolation[测试非线性外推]
#set.seed(3)[set.seed（3）]
x <- rnorm(200, 0, 100)
u <- rnorm(200, 0, 25)
w <- x + u
y <- x + rnorm(200, 0, 9)
true.model <- lm(y ~ x)
naive.model <- lm(y ~ w, x = TRUE)
simex.model <- simex(model = naive.model, SIMEXvariable = "w",
measurement.error = 25)
plot(x, y)
abline(true.model, col = "darkblue")
abline(simex.model, col = "red")
abline(naive.model, col = "green")
legend(min(x), max(y), legend = c("True Model", "SIMEX model", "Naive Model"),
col = c("darkblue", "red", "green"), lty = 1)
plot(simex.model, mfrow = c(2, 2))
simex.model2 <- refit(simex.model, "line")
plot(simex.model2, mfrow = c(2, 2))
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注：
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发表于 2012-9-30 02:40:20
