lmPerGene(GSEAlm)
lmPerGene()所属R语言包：GSEAlm
Fit linear model for each gene
适合每一个基因的线性模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
For each gene, lmPerGene fits the same, user-specified linear model. It returns the estimates of the model parameters and their variances for each fitted model. The function uses matrix algebra so it is much faster than repeated calls to lm.
对于每一个基因，lmPerGene适合相同的，用户指定的线性模型。它返回每个拟合模型的模型参数及其方差的估计。该功能使用矩阵代数，所以它的速度远远超过千呼万唤lm。
用法----------Usage----------
lmPerGene(eSet, formula, na.rm=TRUE,pooled=FALSE)
参数----------Arguments----------
参数：eSet
An ExpressionSet object.
ExpressionSet对象。
参数：formula
an object of class formula (or one that can be coerced to that class), specifying only the right-hand side starting with the '~' symbol. The LHS is automatically set as the expression levels provided in eSet. The names of all predictors must exist in the phenotypic data of eSet.
一个类的对象formula（或可以强制该级），指定只有右侧的~符号开始。 LHS是自动设置为eSet提供的表达水平。所有预测的名称必须存在于eSet表型数据。
参数：na.rm
Whether to remove missing observations.
是否要删除丢失的意见。
参数：pooled
Whether to pool the variance calculation across all genes.
是否池中的所有基因的差异计算。
Details
详情----------Details----------
This function efficiently computes the least squares fit of a linear regression to a set of gene expression values. We assume that there are G genes, on n samples, and that there are p variables in the regression equation. So the result is that G different regressions are computed, and various summary statistics are returned.
此功能有效地计算线性回归最小二乘拟合一组基因的表达值。我们假设有G基因，，n样品，并有p回归方程中的变量。这样的结果是G不同的回归计算，各种汇总统计返回。
Since the independent variables are the same in each model fitting, instead of repeatedly fitting linear model for each gene, lmPerGene accelarates the fitting process by calculating the hat matrix X(X'X)^(-1)X' first. Then matrix multiplication, and solve are to compute estimates of the model parameters.
自独立的变量是相同的，而不是每一个基因的反复拟合线性模型，在每个模型拟合，lmPerGeneaccelarates装修过程中，通过计算的帽子矩阵X(X'X)^(-1)X'第一。矩阵乘法和solve计算模型参数的估计。
Leaving the formula blank (the default) will calculate an intercept-only model, useful for generic pattern and outlier identification.
留下的公式空白（默认），将计算截取的唯一模式，通用模式和孤立点识别非常有用。
值----------Value----------
A list with components:
与组件列表：
参数：eS
The ExpressionSet used in the model fitting.
ExpressionSet模型拟合中使用。
参数：x
The design matrix of the coded predictor variables.
编码的预测变量的设计矩阵。
参数：Hmat
The Hat matrix.
帽子矩阵。
参数：coefficients
A matrix of dimension p by G containing the estimated model parameters.
维矩阵pG包含估计模型参数。
参数：pooled
Whether the variance was pooled (this affects “coef.var” and “tstat”, but not “sigmaSqr”).
是否方差汇集（这会影响“coef.var”和“TSTAT”，而不是“sigmaSqr”）。
参数：sigmaSqr
A vector of length $G$ containing the mean square error for that model, the sum of the residuals squared divided by n - p.
长度的向量图$ G $包含该模型的平均平方误差，残差总和平方分为n - p。
参数：coef.var
A matrix of dimension p by G containing the estimated variances for the model parameters, for each regression.
维矩阵pG包含每个回归模型参数的估计方差，。
参数：tstat
A matrix of the same dimension as coefficients, containing the $t$-statistics for each model estimate. This is simply coefficients divided by the square root of coef.var, and is provided for convenience.
一个的coefficients的同一维度的矩阵，包含每个模型估计$ T $统计。这简直是coefficients的coef.var平方根除以，并提供了方便。
作者（S）----------Author(s)----------
Robert Gentleman, Assaf Oron
参见----------See Also----------
getResidPerGene to extract row-by-row residuals; gsealmPerm for code that utilizes 'lmPerGene' for gene-set-enrichment analysis (GSEA); and CooksDPerGene for diagnostic functions on an object produced by 'lmPerGene'. Applying a by-gene regression in the manner performed here is a special case of a more generic linear-model framework available in the GlobalAncova package; our assumption here is equivalent to a diagonal covariance structure
getResidPerGene行由行残差提取;gsealmPerm代码，利用“lmPerGene”基因组富集分析（GSEA）;CooksDPerGene对象的诊断功能生产由“lmPerGene。申请由基因回归的方式在这里进行的是一个更通用的线性模型框架GlobalAncova包中可用的特殊情况，我们这里假设是相当于对角协方差结构
举例----------Examples----------
data(sample.ExpressionSet)
layout(1)
lm1 = lmPerGene(sample.ExpressionSet,~sex)
qqnorm(lm1$coefficients[2,]/sqrt(lm1$coef.var[2,]),main="Sample Dataset: Sex Effect by Gene",ylab="Individual Gene t-statistic",xlab="Normal Quantile")
abline(0,1,col=2)
lm2 = lmPerGene(sample.ExpressionSet,~type+sex)
qqnorm(lm2$coefficients[2,]/sqrt(lm2$coef.var[2,]),main="Sample Dataset: Case vs. Control Effect by Gene, Adjusted for Sex",ylab="Individual Gene t-statistic",xlab="Normal Quantile")
abline(0,1,col=2)
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 2012-2-25 21:18:24
