inudge.fit(DIME)
inudge.fit()所属R语言包：DIME
Function for Fitting iNUDGE model parameters
为的配件iNUDGE模型参数的功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function to estimate parameters for NUDGE model, mixture of uniform and k-normal. Parameters are estimated using EM algorithm.
赞扬模型中，混合均匀和k-正常功能的参数估计。使用EM算法的参数估计。
用法----------Usage----------
inudge.fit(data, K = 2, weights = NULL, pi = NULL, mu = NULL,
sigma = NULL, tol = 1e-16, max.iter = 2000, z = NULL)
参数----------Arguments----------
参数：data
an <STRONG>R list</STRONG> of vector of normalized intensities (counts). Each element can correspond to particular chromosome. User can construct their own list containing only the chromosome(s) they want to analyze.
<STRONG> R列表</ STRONG>矢量归一化强度（计数）。每个元素都可以对应于特定染色体。用户可以构建自己的列表，其中包含的染色体（），他们要分析。
参数：K
optional number of normal component that will be fitted in iNUDGE model.
可选的正常组成部分，将被安装在iNUDGE模型。
参数：weights
optional matrix of weights to be used for robust iNUDGE model fitting.
可选的权重矩阵被用于鲁棒iNUDGE模型拟合。
参数：pi
optional vector containing initial estimates for proportion of the iNUDGE mixture components. The first entry is for the uniform component, the middle k entries are for normal components.
可选的向量，初步估计的iNUDGE混合成分的比例。第一项是统一的组件，中间的K表项的正常成分。
参数：mu
optional vector containing initial estimates of the Gaussian means in iNUDGE model.
可选的向量，初步估计的高斯表示iNUDGE模型。
参数：sigma
optional vector containing initial estimates of the Gaussian standard deviation in (i)NUDGE model. Must have K entries.
可选的向量，初步估计高斯分布的标准偏差（我）赞扬模型。必须有K项目。
参数：tol
optional threshold for convergence for EM algorithm to estimate iNUDGE parameters.
可选的阈值收敛EM算法的估计iNUDGE参数的。
参数：max.iter
optional maximum number of iterations for EM algorithm to estimate iNUDGE parameters.
可选的最大迭代次数EM算法的估计iNUDGE参数的。
参数：z
optional 2-column matrix with each row giving initial estimate of probability of the region being non-differential and a starting estimate for the probability of the region being differential. Each row must sum to 1. Number of row must be equal to data length.
可选的2列的矩阵，每行给该区域即非差动和起始预算的区域差的概率的概率的初始估计。每一行必须等于1。的行数必须等于数据长度。
值----------Value----------
A list of object: <table summary="R valueblock"> <tr valign="top"><td>name</td> <td> the name of the model "iNUDGE"</td></tr> <tr valign="top"><td>pi</td> <td> a vector of estimated proportion of each components in the model</td></tr> <tr valign="top"><td>mu</td> <td> a vector of estimated Gaussian means for k-normal components.</td></tr> <tr valign="top"><td>sigma</td> <td> a vector of estimated Gaussian standard deviation for k-normal components.</td></tr> <tr valign="top"><td>K</td> <td> the number of normal components in the corresponding mixture model.</td></tr> <tr valign="top"><td>loglike</td> <td> the log likelihood for the fitted mixture model.</td></tr> <tr valign="top"><td>iter</td> <td> the actual number of iterations run by the EM algorithm.</td></tr> <tr valign="top"><td>fdr</td> <td> the local false discover rate estimated based on iNUDGE model.</td></tr> <tr valign="top"><td>phi</td> <td> a matrix of estimated iNUDGE mixture component function.</td></tr> <tr valign="top"><td>AIC</td> <td> Akaike Information Criteria.</td></tr> <tr valign="top"><td>BIC</td> <td> Bayesian Information Criteria.</td></tr> </table>
对象的列表：<table summary="R valueblock"> <tr valign="top"> <TD> name</ TD> <TD>的模型名称“iNUDGE”</ TD> < / TR> <tr valign="top"> <TD> pi </ TD> <td>一个矢量的估计模型中的各组分的比例</ TD> </ TR> <TR VALIGN =“顶“<TD> mu </ TD> <td>一个矢量的估计高斯手段，正常的K-组件。</ TD> </ TR> <tr valign="top"> <TD> sigma </ TD> <td>一个矢量的估计的高斯标准偏差为正常的K-组件。</ TD> </ TR> <tr valign="top"> <TD>K</ TD> <TD>的数量在相应的混合模型的正常成分。</ TD> </ TR> <tr valign="top"> <TD> loglike</ TD> <TD>的对数似然混合模型的拟合。</ TD> </ TR> <tr valign="top"> <TD>iter</ TD> <TD>实际运行的EM算法的迭代。</ TD> </ TR> <tr valign="top"> <TD>fdr </ TD> <TD>当地假发现率估计iNUDGE模型的基础上，</ TD> </ TR> <TR VALIGN =“”> <TD>phi</ TD> <td>一个矩阵的估计iNUDGE的混合成分功能。</ TD> </ TR> <tr valign="top"> <TD> AIC </ TD> <TD>赤池信息准则。</ TD> </ TR> <tr valign="top"> <TD> BIC </ TD> <TD>贝叶斯信息标准。 </ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Cenny Taslim <a href="mailto:taslim.2@osu.edu">taslim.2@osu.edu</a>, with contributions from Abbas Khalili
<a href="mailto:khalili@stat.ubc.ca">khalili@stat.ubc.ca</a>, Dustin Potter <a href="mailto:potterdp@gmail.com">potterdp@gmail.com</a>,
and Shili Lin <a href="mailto:shili@stat.osu.edu">shili@stat.osu.edu</a>
参见----------See Also----------
DIME, gng.fit, nudge.fit
DIME，gng.fit，nudge.fit
实例----------Examples----------
library(DIME);
# generate simulated datasets with underlying uniform and 2-normal distributions[产生基本均匀，2正态分布的模拟实验]
set.seed(1234);
N1 <- 1500; N2 <- 500; rmu <- c(-2.25,1.5); rsigma <- c(1,1);
rpi <- c(.10,.45,.45); a <- (-6); b <- 6;
chr4 <- list(c(-runif(ceiling(rpi[1]*N1),min = a,max =b),
rnorm(ceiling(rpi[2]*N1),rmu[1],rsigma[1]),
rnorm(ceiling(rpi[3]*N1),rmu[2],rsigma[2])));
chr9 <- list(c(-runif(ceiling(rpi[1]*N2),min = a,max =b),
rnorm(ceiling(rpi[2]*N2),rmu[1],rsigma[1]),
rnorm(ceiling(rpi[3]*N2),rmu[2],rsigma[2])));
# analyzing chromosome 4 and 9[4号和9号染色体分析]
data <- list(chr4,chr9);
# fit iNUDGE model with 2 normal components and maximum iterations = 20[与正常的组件和最大迭代次数= 20适合iNUDGE模型]
set.seed(1234);
test <- inudge.fit(data, K = 2, max.iter=20);
# Getting the best fitted iNUDGE model (parameters)[获取拟合iNUDGE模型（参数）]
test$best$pi # estimated proportion of each component in iNUDGE[比例估计中的每个组件iNUDGE]
test$best$mu # estimated mean of the normal component(s) in iNUDGE[估计平均正常组分（s）在iNUDGE]
# estimated standard deviation of the normal component(s) in iNUDGE[的正常成分的估计的标准偏差（s）在iNUDGE]
test$best$sigma
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 2012-9-16 20:22:47
