分析数据处理中应用回归分析法的讨论

一.直线回归方程间有无区别的判别问题 1.《理化检验》化学分册第16卷第5期“利用回归分析方法计算吸光度分析结果”-文中提出如下判别式: ?? 并确定t≤2时判两个直线回归方程无区别,可以进行合并应用的看法.我们在应用中曾多次出现一些错判的现象,经研究认为用(1)式来判别分析化学领域中的回归方程之间的区别问题存在局限性,现将(1)式通过关系式转化成     

一类4次OR曲线的几何判别法

针对4次Bézier曲线,探讨其是一类具有精确多项式等距线的平面参数曲线(OR curves)的充要条件.根据4次OR曲线的速端曲线在复平面的分解式,将OR曲线分为PH曲线、第1类普通OR曲线和第2类普通OR曲线3种;针对第1类普通OR曲线或PH曲线,利用其速端曲线和二阶导矢曲线之间的关系给出统一的几何判别充要条件,得到了控制多边形上边角分离的几何条件;给出4次第1类普通OR曲线或PH曲线的速端曲线的统一的分解式及判别这2类曲线的方法.最后给出了4次第1类普通OR曲线与PH曲线的等距线实例.     

东北黑土区土壤肥力变化特征的分析

黑土区土壤肥力一直是人们关心的问题。通过选择在黑土区典型县份-德惠市的调查表明,耕地土壤有机质、全氮、有效磷与1980年相比较,分别增加了3.2g kg-11,0.11 g kg-1和5.70 mg kg-1,但有效钾下降了33.3 mg kg-1。土壤中有机质2003年盈余量48.7kg hm-2,氮15.07 kg hm-2,磷25.35 kg hm-2;钾亏损量170.62 kg hm-2。笔者认为,单纯从土壤营养管理角度看,目前黑土区耕地土壤肥力状况似乎没有想象的那么坏。但已有的研究也表明,黑土区耕地肥力的变化与现行的耕作制度有很大的关系。     

江苏如皋市农田土壤速效钾时空变化与平衡施肥技术研究

通过分析江苏省如皋市1982年~2002年耕层土壤速效钾含量,研究了农田土壤速效钾时空变化及水稻钾肥适宜用量。结果表明,20年间该市耕层土壤速效钾含量已发生明显变化。2002年含量仅为50.51mg kg-1,比1982年降低了5.5mg kg-1,降低9.80%。耕地速效钾含量分级已从20年前的以Ⅳ、Ⅴ为级为主转变为Ⅳ、Ⅴ、Ⅵ级为主,60%以上的耕地面积已发生速效钾缺乏现象。土壤肥力动态监测结果表明,随着秸秆还田和平衡施肥技术的逐步推广,速效钾快速下降势头得到有效遏止,下降速度趋缓。     

被动微波遥感监测土壤冻融界限的研究综述

主要介绍了被动微波遥感数据在研究土壤冻融状态方面的国内外研究进展。目前常用的而且比较成熟的冻融界限判别指标是37GHz亮度温度及负亮温谱梯度,重点对这两个冻融边界判别指标的发展进行了详细讨论,并对冻融判别和积雪判别的关系及其相互影响进行了分析和讨论。发展可靠实用的微波遥感土壤冻融状态的判别算法,以提供区域和全球尺度上的土壤状态信息,对水文学、气象学以及农业科学、工程地质研究与应用都有重要意义。     

土壤通报第38卷总目次(2007)

第1期昆山绰墩古土壤粒度特征及母质判别杨用钊，李福春，曾志洪，等(1)苏州城市边缘带土壤综合肥力质量时空特征周峰，陈杰，李桂林，等(6)农用地定级两种因素法比较研究-以重庆市九龙坡区为例秦静，朱德举，姜广辉，等(11)土壤中可溶性有机氮含量及其影响因素研究杨绒，周建斌，赵满兴(15)土壤无机氮解吸模型及其特征值生物效应研究颜明娟，章明清，陈子聪，等(19)山东主要土类有机质及其与供磷特性的关系孟庆华，李根英(25)红壤丘陵坡地不同利用模式的土壤N矿化特征郝金菊，陈钦华，周卫军，等(29)榨菜叶还田对土壤肥力的影响研究温明霞…     

不同林龄人工林对退化生态系统土壤肥力质量的影响

选择黄土高原地区典型人工林为研究对象,以土壤容重、质地、有机质、养分、矿质全量等为指标,对不同恢复时期人工林改良土壤的效应进行了研究,并采用新复极差法分析了有机质及养分含量的差异显著性,最后应用综合指数法进行检验。结果表明:(1)沙棘林中中龄林土壤肥力质量最高,过熟林较低,幼林最低;(2)油松成林和刺槐成林各项肥力指标与其中幼林和撂荒地都呈极显著性差异;(3)同龄刺槐林土壤肥力质量高于油松林。     

施肥对黑土活性有机碳和碳库管理指数的影响

运用更有效的量化指标来了解和表征土壤碳库的变化是研究土壤碳库动态平衡的基础,也是评价土壤肥力和生态系统的可持续性的必要手段。我们采用土壤碳库管理指数,讨论了国家黑土肥力监测区内的不同施肥情况下土壤碳库的变化。结果显示:施肥与否、施肥种类和数量均对土壤活性有机碳和土壤碳库管理指数有非常显著的影响,施肥尤其高量有机肥与化肥(NPK)配施。更有助于土壤活性有机碳的增加,相应地也就提高了土壤碳库管理指数(CMPI),M2 CK、M4 CK、MO NPK、M1 NPK、1.5M1 NPK、M2 NPK、M4 NPK各施肥处理对土壤活性有机碳提高的贡献率分别高达15.6％、24.8％、63.6％、135.1％、144.2％、185.9％和256.5％,对土壤碳库管理指数的提高系数达0.48、0.72、1.17、3.21、4.70、7.86和10.44。农业生产中必须切实地重视高量有机肥与化肥(NPK)的配施,以求保持土壤肥力,提高土壤质量,使土壤碳库处于良性状态,最终达到维持土壤的可持续利用之目的。     

基于GIS的河南省烤烟土壤肥力适宜性研究

烟草对其生长环境条件的变化敏感。烤烟生产有着严格的地域限制。本研究在分析河南省烤烟种植区各项肥力指标的基础上,建立了烤烟土壤肥力适宜性评价指标体系。运用模糊数学和层次分析法对土壤肥力水平进行了评价、分级。采用地理信息系统软件mapGIS绘制河南省烤烟种植区土壤肥力状况图。结果表明,河南省烤烟种植区以信阳、南阳等地为最适宜,而大部分地区肥力水平处于中等,占整个种植区的78%。对烤烟地力适宜性的主要障碍因子是氯离子含量和pH过高,而有机质过低。     

慈溪市旱地土壤肥力变化的比较研究

2 0 0 2年 3月对慈溪市旱地土壤肥力状况进行了调查分析 ,并与第二次土壤普查 (1981年 3月 )进行了比较 ,结果表明 2 0年来土壤肥力有了很大变化 ,主要问题是土壤有机质含量仍处在较低水平 ,普遍缺氮 ,磷含量不平衡 ,钾含量普遍下降 ,滨海盐土的盐分仍威胁作物生长。针对上述情况 ,提出了改良利用的建议     

Comparison of dimension reduction methods using patient satisfaction data

In this study, we compared classical principal components analysis (PCA), generalized principal components analysis (GPCA), linear principal components analysis using neural networks (PCA-NN), and non-linear principal components analysis using neural networks (NLPCA-NN). Data were extracted from the patient satisfaction query with regard to the satisfaction of patients from hospital staff, which was applied in 2005 at the outpatient clinics of Trakya University Medical Faculty. We found that percentages of explained variance of principal components from PCA-NN and NLPCA-NN were highest for doctor, nurse, radiology technician, laboratory technician, and other staff using a patient satisfaction data set. Results show that methods using NN which have higher percentages of explained variances than classical methods could be used for dimension reduction.     

Principal component analysis of aerial video imagery†

Abstract

Aerial video images of an agricultural test site were analysed using principal component analysis and image processing techniques. The site (six treatments and four replications of cotton, sorghum, cantaloupe, johnsongrass, pigweed and soil) was imaged using blue, yellow-green, red and infrared filters over the lenses of four black-and-white video cameras on 31 May and 24 July 1983. Separate principal component analysis procedures were applied to the May and July data as part of a methodology to assess data dimensionality and structure. Supervisedminimum Euclidean distance classification procedures were conducted on sets of data that consisted of all four principal components, the first three principal components, the first two principal components and the first principal component. Results indicated that the number of components required to represent the four band data sets accurately was three for the May data and two for the July data. Scatter diagrams of plant means for principal components 1 and 2 showed good potential for determining the relative level of plant development.     

主成分提取信息准则的加权规则

并行主成分提取算法在信号特征提取中具有十分重要的作用, 采用加权规则将主子空间(Principal subspace, PS)提取算法转变为并行主成分提取算法是很有效的方式, 但研究加权规则对状态矩阵影响的理论分析非常少. 对加权规则影响的分析不仅可以提供加权规则下的主成分提取算法动力学的详细认知, 而且对于其他子空间跟踪算法转变为并行主成分提取算法的可实现性给出判断条件. 本文通过比较Oja的主子空间跟踪算法和加权Oja并行主成分提取算法, 通过两种算法的差异分析了加权规则对算法提取矩阵方向的影响. 首先, 针对二维输入信号, 研究了提取两个主成分时加权规则的信息准则对状态矩阵方向的作用方式. 进而, 针对大于二维输入信号的情况, 给出加权规则影响多个主成分提取方式的讨论. 最后, MATLAB仿真验证了所提出理论的有效性.     

基于改进的主成分分析法的目标威胁评估

为提高防空作战指挥员对空中来袭目标的主成分分析实施火力分配的量化决策水平,提出了改进的主成分分析法的目标威胁评估模型及算法。改进法不仅消除了指标量纲和数量级的影响,还能包含原始数据的全部信息,在原始变量转变为主成分过程中,反映了主成分权数的计算综合评价值,在指标权重选择上克服了主观因素的影响,客观反映了各指标因素间的现实关系。并通过引入实例,对来袭空中目标进行多指标综合评价,根据综合评估值对各批目标威胁进行排序。     

Subjective principal component analysis

In this study we deal with the problem of finding subjective principal components for a given set of variables in a data matrix. The principal components are not determined by maximizing their variances; they are specified by a user, who can maximize the absolute values of the correlations between principal components and the variables important to him. The correlation matrix of the variables is the basic information needed in the analysis.The problem is formulated as a multiple criteria problem and solved by using an interactive procedure. The procedure is convenient to use and easy to implement. We have implemented an experimental version on an APPLE III microcomputer. A graphical display is used as an aid in finding the principal components. An illustrative application is presented, too.     

A new and practical influence measure for subsets of covariance matrix sample principal components with applications to high dimensional datasets

Principal Component Analysis (PCA) is an important tool in multivariate analysis, in particular when faced with high dimensional data. There has been much done with regard to sensitivity analysis and the development of influence diagnostics for the eigenvector estimators that define the sample principal components. However, little, if any, has been done in this setting with regard to the sample principal components themselves. In this paper we develop a sensitivity measure for principal components associated with the covariance matrix that is very much related to the influence function (Hampel, 1974). This influence measure is based on the average squared canonical correlation and differs from the existing measures in that it assesses the influence of certain observational types on the sample principal components. We use this measure to derive an influence diagnostic that satisfies two key criteria being (i) it detects influential observations with respect to subsets of sample principal components and (ii) is efficient to calculate even in high dimensions. We use several microarray datasets to show that our measure satisfies both criteria.     

基于故障检测率的主元个数确定方法

主元个数是PCA模型的关键参数,其选取直接决定PCA的故障诊断性能;针对传统主元个数选取方法主观性较大,且不考虑故障诊断要求的缺点,提出一种改进的主元个数确定方法;该方法将传统的累积方差贡献率与故障检测率相结合,首先利用累积方差贡献率初步确定主元个数,然后确定满足故障检测率要求的主元个数,将两个主元个数进行比较,从而获得最佳主元个数;与单纯累积方差贡献率方法相比,提高了主元模型的精度,减少了以往方法中人为因素的影响;通过对卫星控制系统的故障检测,证实了该方法可大大提高故障检测准确率。     

KPCA与GRNN在含能化合物QSAR中的应用研究

使用KPCA(核主成分分析)对含能化合物的结构参数进行参数选择,在保持原有数据主要信息的情形下,得到数据的主成分.将降维后的特征信息作为GRNN(广义回归神经网络)的输入,含能化合物的性能数据作为输出,建立非线性的定量含能化合物结构性能关系预测模型.与PCA_GRNN模型的比较表明,该模型能很好地反映含能化合物结构和性能之间的关系,具有较高的预测正确率.     

A variable selection method in principal canonical correlation analysis

We propose a variable selection procedure for the canonical correlation analysis (CCA) between two sets of principal components. We attempt to create predictive models for selecting such variables by combining principal component analysis (PCA) and CCA, and we refer to them collectively as principal canonical correlation analysis (PCCA). We derive a model selection criterion of one set of principal components, based on the selection of a covariance structure analysis within the framework of the PCCA. Compared to the variable selection procedure used in the CCA, the procedure used in the PCCA return a smaller number of variables. This is because the principal components derived from a PCA descend in order of the amount of information that they contain. The principal components with the smallest variance contributions are disregarded because their information contribution becomes negligible. Herein, we demonstrate the effectiveness of this criterion by using an example. Moreover, we investigate the properties of a variable selection criterion using the bootstrap resampling. The variable selection procedure used with the PCCA is compared to that used for the CCA.     

R软件在主成分分析中的应用研究

在研究复杂问题时,主成分分析方法可以抓住问题的主要矛盾,揭示其内部各因素之间的规律性,提高分析的效率。R软件是一款免费且功能强大的软件,研究表明R软件可以方便快捷地完成主成分分析的计算,且具有很高的计算精度。