Applies a canonical correlation transformation to the data
应用典型相关数据的转换
译者:生物统计家园网 机器人LoveR
描述----------Description----------
Applies a canonical correlation transformation to the combination of the raw signal intensities with an initial set of posterior probabilities.
适用于典型相关转换原始信号强度的组合,后验概率的初始设置。
用法----------Usage----------
apply.ldf(full.signal, posterior)
参数----------Arguments----------
参数:full.signal
A matrix with the raw signal intensity. One row per data point or sample in the data, and one column for the probability of each call. The matrix MUST have row names.
一个矩阵与原始信号强度。每个数据点或样品中的数据,和一列,为每个呼叫的概率一行。矩阵必须有行名。
参数:posterior
A matrix of posterior distribution for the calls. This matrix must have row names that match the signal intensity. The ordering does not have to be the same as the matrix of signals but each data point in “full.signal” must have a corresponding set of posterior probabilities.
来电后验分布矩阵。这个矩阵必须具有行名称相匹配的信号强度。没有排序矩阵的信号,但在“full.signal”必须有一个相应的后验概率,每个数据点是相同的。
Details
详情----------Details----------
Do not forget to add row names to both matrices.
不要忘了添加两个矩阵的行名。
值----------Value----------
A one-dimensional vector with the transformed canonical corelation transformed values.
转化的相关分析与一维的向量转换值。
作者(S)----------Author(s)----------
Vincent Plagnol <a href="mailto:vincent.plagnol@cimr.cam.ac.uk">vincent.plagnol@cimr.cam.ac.uk</a> and Chris Barnes <a href="mailto:christopher.barnes@imperial.ac.uk">christopher.barnes@imperial.ac.uk</a>