scholarly journals Possible methods of Norway spruce (Picea abies[L.] Karst.) stem shape description

2012 ◽  
Vol 51 (No. 6) ◽  
pp. 244-255 ◽  
Author(s):  
M. Křepela ◽  
D. Zahradník ◽  
J. Sequens
Keyword(s):  
Picea Abies ◽  
Covariance Matrix ◽  
Norway Spruce ◽  
Principal Components ◽  
Principal Component ◽  
Shape Description ◽  
Tree Class ◽  
Mean Shape ◽  
Class Representation ◽  
Stem Shape

The paper shows a possibility of using Bookstein coordinates for stem shape studies. Bookstein coordinates are simplified to stem shape diameters, for which tests of multidimensional normality, variance-covariance matrix homogeneity, equality of mean shape vectors and principal component calculation are carried out in sample plots Doubravčice 1 and Štíhlice. Principal components are also calculated for Procrustes tangent coordinates, presented in graphs, and the plots are compared. Doubravčice 1 and Štíhlice plots differ especially in age (70 and 30 years) while they do not differ in tree class representation.

scholarly journals The use of principal components and univariate charts to control multivariate processes

2008 ◽  
Vol 28 (1) ◽  
pp. 173-196 ◽  
Author(s):  
Marcela A. G. Machado ◽  
Antonio F. B. Costa
Keyword(s):  
Dimensionality Reduction ◽  
Covariance Matrix ◽  
Principal Components ◽  
Control Charts ◽  
Principal Component ◽  
Multivariate Processes ◽  
Generalized Variance ◽  
Overall Performance ◽  
The Way

In this article, we evaluate the performance of the T² chart based on the principal components (PC X chart) and the simultaneous univariate control charts based on the original variables (SU charts) or based on the principal components (SUPC charts). The main reason to consider the PC chart lies on the dimensionality reduction. However, depending on the disturbance and on the way the original variables are related, the chart is very slow in signaling, except when all variables are negatively correlated and the principal component is wisely selected. Comparing the SU , the SUPC and the T² charts we conclude that the SU X charts (SUPC charts) have a better overall performance when the variables are positively (negatively) correlated. We also develop the expression to obtain the power of two S² charts designed for monitoring the covariance matrix. These joint S² charts are, in the majority of the cases, more efficient than the generalized variance chart.

A Multivariate Analysis of Human Promoter Sequences.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 4249-4249
Author(s):  
Mario-Antoine Dicato ◽  
Garry Mahon
Keyword(s):  
Principal Components Analysis ◽  
Covariance Matrix ◽  
Transcription Start Site ◽  
Principal Components ◽  
Consensus Sequence ◽  
Principal Component ◽  
Start Site ◽  
Transcription Start ◽  
Promoter Sequences ◽  
Components Analysis

Abstract The human genome has been estimated to contain tens of thousands of genes. Of these, the promoters have been experimentally verified for almost two thousand. We have examined the DNA sequences just up-stream of the transcription start site, a region which includes the TATA box. Genetic control sites, such as promoters, often have a characteristic consensus sequence, but the variation about a given consensus sequence has received little attention. Sequence variations may be related to functional differences amongst the control sites. Principal components analysis has been chosen because of its generality and the variety of phenomena which it reveals. Promoter sequences were considered because of the large number available and their importance in gene expression. The sequences of the 1977 promoters recognised by human RNA polymerase II were obtained from the Eukaryotic Promoter Database. Many of these promoters are of interest in oncology and the database includes sequences for growth factors (e.g. GM-CSF, interleukins), oncogenes and tumour viruses among others. Sub-sequences of 25 bases centred on position −13 relative to the transcription start site were extracted. Two bits were used to encode each base (a=11, c=00, g=10 and t=01) and the covariance matrix of the resulting 50 variables was determined. The eigenvalues and eigenvectors of the covariance matrix were calculated. All calculations were carried out by computer using MS-Excel and SYSTAT 11. The eigenvalues of the covariance matrix ranged from 0.571 down to 0.133. The eigenvectors were used to calculate principal components. Thus 50 more or less correlated variables were transformed into 50 uncorrelated variables with the same total variance. The sequences were sorted according to the principal components to reveal which features were associated with the most variation amongst the sequences. When the covariances among the coded sequences were calculated many associations were found, for example, a purine at position 15 was associated with a purine at position 16, and a purine at position 19 with a G or C at position 20. Although these correlations individually were not especially strong, together they were a notable feature of the set of sequences. The consensus sequence was observed to be agggg ggggg ggc(g/c)c ggggg gcgcc. A principal components analysis enabled the promoters to be identified which differed most (in opposite directions) from the consensus sequence, taking account of the correlations. Nearly all the elements of the first eigenvector were of alternating sign; thus the first principal component separated promoters which were rich in G from those rich in T. Almost all elements of the second eigenvector were positive, so the second principal component distinguished promoters rich in A from those rich in C. There was a remarkable concentration of promoters from genes for interleukins or IL repressors with large values for the second principal component:- IL1A, IL2, IL4, IL6-2, IL2RA1, IL2RA2 and IL8RB were in positions 160, 43, 14, 158, 131, 101 and 158 (out of 1977) respectively. The variation in the sequence of promoters about their consensus sequence is seen not to be random but to display detectable patterns. Correlations were found to be frequent within the promoter sequences considered here; in the absence of correlations all the eigenvalues would have been equal. The major principal components separated promoters with markedly different sequences. It is to be expected that the other principal components would yield further separations.

scholarly journals Comparison of Norway spruce (Picea abies [L.] Karst.), Scots pine (Pinus sylvestris L.) and European larch (Larix decidua Mill.) stem shape by means of geometrical methods

2012 ◽  
Vol 52 (No. 8) ◽  
pp. 337-347
Author(s):  
M. Křepela ◽  
R. Petráš
Keyword(s):  
Norway Spruce ◽  
Scots Pine ◽  
Principal Components ◽  
Tree Species ◽  
European Larch ◽  
Inflection Points ◽  
Sample Test ◽  
Coniferous Tree Species ◽  
The Relationship ◽  
Stem Shape

In this article the stem shape is compared in three coniferous tree species: Norway spruce, Scots pine and European larch. Stem is investigated by means of geometrical methods. Simplified Bookstein coordinates (stem shape diameters) and Procrustes coordinates were used for variability investigation. The material, originating from the Czech and Slovak territories, involved in total 3,346 spruce stems, 3,082 pine stems and 1,403 larch stems. The accordance of mean stem vectors was assessed by means of Hotelling&rsquo;s T<sup>2</sup> two-sample test. For stem shape diameters and Procrustes tangent coordinates, the variability was examined using the method of principal components analysis. The three most important principal components were diagrammatized and described. The relationship between the stem shape and its size was also investigated, and inflection points of morphological stem curve were described for all three tree species.

Different cone colours pay off: lessons learnt from European larch (Larix decidua) and Norway spruce (Picea abies)

2007 ◽  
Vol 85 (2) ◽  
pp. 132-140 ◽  
Author(s):  
Thomas Geburek ◽  
Karin Robitschek ◽  
Norbert Milasowszky ◽  
Klemens Schadauer
Keyword(s):  
Picea Abies ◽  
Spatial Pattern ◽  
Norway Spruce ◽  
Spatial Genetic Structure ◽  
Principal Component ◽  
Binary Logistic Regression ◽  
Larix Decidua ◽  
Systematic Sampling ◽  
European Larch ◽  
Colour Morphs

The colour morphs of immature female cones in European larch (Larix decidua Mill.) and Norway spruce (Picea abies (L.) Karst.) are green, red, and intermediate in colour. For the first time, these three colour morphs were studied to verify the thermoregulatory hypothesis and to investigate its underlying genetic spatial pattern. The study was based on an extensive systematic sampling, and data were analysed using principal component analysis (PCA), binary logistic regression (BLR), and spatial autocorrelation. Correlations between the nontransformed environmental variables and PC scores revealed two main ecological gradients, (i) altitude–temperate and (ii) annual precipitation. Loadings of the first two principal components exceeded 85% in both species. BLR was used to test the effect of the altitude–temperature gradient on the probability of occurrence of a specific cone colour. In both species, the occurrence of red cones was significantly positively related to high altitude with low temperatures, while green cones were significantly negatively correlated with decreasing temperature and increasing altitude. In both species the spatial pattern based on a putative Mendelian gene was nonrandom as indicated by significantly high Moran’s I values based on altitudinal distance. Spatial genetic structure was probably maintained by limited gene flow and balanced selection that maintained short-distance genetic differentiation.

scholarly journals Multivariate statistical approach to comparison of the nutrient status of Norway spruce (Picea abies [L.] Karst.) and top-soil properties in differently managed forest stands

2008 ◽  
Vol 53 (No. 3) ◽  
pp. 101-112 ◽  
Author(s):  
P. Samec ◽  
D. Vavříček ◽  
P. Šimková ◽  
J. Pňáček
Keyword(s):  
Picea Abies ◽  
Soil Properties ◽  
Norway Spruce ◽  
Principal Component ◽  
Nutrient Status ◽  
Element Cycling ◽  
Multivariate Statistical ◽  
Plant Soil ◽  
Multivariate Statistical Approach ◽  
Altitudinal Zone

The soil is an irreplaceable component of forest ecosystems. Soil-forming processes directly influence element cycling (EC). Plant-soil interaction is a specific part of EC. Plant-soil interactions were observed on an example of natural spruce stand (NSS), semi-natural spruce stand (SNSS) and allochthonous spruce stand (ASS) in conditions of the spruce forest altitudinal zone (1,140&minus;1,260 m a.s.l.; +3.0&deg;C; 1,200 mm) of the Hrubý Jeseník Mts. (Czech Republic, Central Europe), where Norway spruce (<i>Picea abies</i> [L.] Karst.) is the main edificator and stand-forming tree species. We evaluated the soil properties of H- and Ep-horizons at selected sites with Haplic and Skeletic Podzols and they were compared with the nutrient status of spruce. A method of the principal component analysis was used for definition of the basic hypotheses: (1) each forest stand is in specific and topically individual interactions with soil and these interactions influence its state, (2) the influence of forest management reflects in humification and in the nutrient status in plant assimilatory tissues. Cluster analysis calculated results comparable with the multivariate analysis of variance. The results show that the continuity of linear and multivariate statistical methods gives the approach to detection of the forest stage based on soil and plant tissue data.

scholarly journals A contribution to the knowledge of Scots pine (Pinus sylvestris L.) stem shape

2012 ◽  
Vol 50 (No. 5) ◽  
pp. 211-218
Author(s):  
M. Křepela ◽  
J. Sequens ◽  
D. Zahradník
Keyword(s):  
Scots Pine ◽  
Principal Components ◽  
Statistical Tests ◽  
Procrustes Analysis ◽  
Individual Sample ◽  
The Czech Republic ◽  
Generalized Procrustes Analysis ◽  
Method Of Principal Components ◽  
Mean Shape ◽  
Stem Shape

This is a study on the evaluation of stem shape in Scots pine. The experiments were carried out on 430 stems without bark, taken from 30 fully analysed sample trees that grew in 5 different locations in the Czech Republic. Generalized Procrustes analysis was used as the method of study. The stems were described by the use of landmarks. Full Procrustes coordinates were calculated for all stems, and the full Procrustes mean shape was set for individual sample trees, locations and all stems. For Procrustes tangent coordinates, variability was examined using the method of principal components. The two most important principal components were diagrammatized and described. Furthermore, statistical tests of mean shape vectors for individual locations were carried out.

Structuring Assessments of Psychopathology

2006 ◽  
Vol 27 (2) ◽  
pp. 87-92 ◽  
Author(s):  
Willem K.B. Hofstee ◽  
Dick P.H. Barelds ◽  
Jos M.F. Ten Berge
Keyword(s):  
Principal Components ◽  
Personality Assessment ◽  
Clinical Sample ◽  
Principal Component ◽  
Normal Sample ◽  
Normative Sample ◽  
Assessment Data ◽  
Obsessive Compulsive ◽  
Oblique Rotation ◽  
Two Samples

Hofstee and Ten Berge (2004a) have proposed a new look at personality assessment data, based on a bipolar proportional (-1, .. . 0, .. . +1) scale, a corresponding coefficient of raw-scores likeness L = ΢XY/N, and raw-scores principal component analysis. In a normal sample, the approach resulted in a structure dominated by a first principal component, according to which most people are faintly to mildly socially desirable. We hypothesized that a more differentiated structure would arise in a clinical sample. We analyzed the scores of 775 psychiatric clients on the 132 items of the Dutch Personality Questionnaire (NPV). In comparison to a normative sample (N = 3140), the eigenvalue for the first principal component appeared to be 1.7 times as small, indicating that such clients have less personality (social desirability) in common. Still, the match between the structures in the two samples was excellent after oblique rotation of the loadings. We applied the abridged m-dimensional circumplex design, by which persons are typed by their two highest scores on the principal components, to the scores on the first four principal components. We identified five types: Indignant (1-), Resilient (1-2+), Nervous (1-2-), Obsessive-Compulsive (1-3-), and Introverted (1-4-), covering 40% of the psychiatric sample. Some 26% of the individuals had negligible scores on all type vectors. We discuss the potential and the limitations of our approach in a clinical context.

Comparison of Principal Component Solutions in Two Populations

Methodology ◽  
2016 ◽  
Vol 12 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Gregor Sočan
Keyword(s):  
Simulation Study ◽  
Principal Components ◽  
Common Factor ◽  
Principal Component ◽  
Component Model ◽  
Bootstrap Procedure ◽  
Factor Loadings ◽  
Component Loadings ◽  
Principal Component Model ◽  
Two Populations

Abstract. When principal component solutions are compared across two groups, a question arises whether the extracted components have the same interpretation in both populations. The problem can be approached by testing null hypotheses stating that the congruence coefficients between pairs of vectors of component loadings are equal to 1. Chan, Leung, Chan, Ho, and Yung (1999) proposed a bootstrap procedure for testing the hypothesis of perfect congruence between vectors of common factor loadings. We demonstrate that the procedure by Chan et al. is both theoretically and empirically inadequate for the application on principal components. We propose a modification of their procedure, which constructs the resampling space according to the characteristics of the principal component model. The results of a simulation study show satisfactory empirical properties of the modified procedure.

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