This correlation matrix was published in Jeffers (1967) and was calculated from 180 observations. The 13 variables were used as explanatory variables in a regression problem which arised from a study on the strength of
该相关矩阵被出版杰弗斯(1967)和计算从180观测。 13个变量作为解释变量的回归问题相比发生了从研究的力量
用法----------Usage----------
data(pitpropC)
格式----------Format----------
Its a correlation matrix of 13 variables which have the following meaning:
它的一个相关矩阵的13个变量具有以下含义:
TOPDIAM
TOPDIAM
LENGTH
LENGTH
MOIST
MOIST
TESTSG
TESTSG
OVENSG
OVENSG
RINGTOP
RINGTOP
RINGBUT
RINGBUT
BOWMAX
BOWMAX
BOWDIST
BOWDIST
WHORLS
旋涡
CLEAR
CLEAR
KNOTS
KNOTS
DIAKNOT
DIAKNOT
Details
详细信息----------Details----------
Jeffers (1967) replaced these 13 variables by their first six principal components. As noted by Vines (2000), this is an example where simple structure has proven difficult to detect in the past.
杰弗斯(1967)取代了前6个主成分,这13个变量。正如藤本植物(2000)所指出的,这是一个例子,结构简单,在过去已被证明难以察觉。
参考文献----------References----------
Two case studies in the application of principal components analysis. Appl. Statist. 16, 225–236.
Simple principal components. Appl. Statist. 49, 441–451.