Discriminant analysis function

Sawle, G. V., Playford, E. D., Burn, D. J. et al. Separating Parkinson s disease from normality. Discriminant function analysis of fluorodopa F-18 positron emission tomography data. Arch. Neurol. 51 237-243,1994. 

Struve FA, Straumanis JJ, Patrick G. (1994). Persistent topographic quantitative EEG sequelae of chronic marihuana use a replication study and initial discriminant function analysis. Clin Electroencephalogr. 25(2) 63-75. 

Roser, B.P. Korsch, R.J. 1988. Provenance signatures of sandstone-mudstone suites determined using discriminant function analysis of major-element data. Chemical Geology, 67, 119-139. 

TABLE III. Summary of cluster analysis and discriminant function analysis of high (> 17 % of theoretical) and low (< 13 X of theoretical) biochemical oxygen demand (BOD) values for 340... 

Neuroimaging techniques assessing cerebral blood flow (CBF] and cerebral metabolic rate provide powerful windows onto the effects of ECT. Nobler et al. [1994] assessed cortical CBE using the planar xenon-133 inhalation technique in 54 patients. The patients were studied just before and 50 minutes after the sixth ECT treatment. At this acute time point, unilateral ECT led to postictal reductions of CBF in the stimulated hemisphere, whereas bilateral ECT led to symmetric anterior frontal CBE reductions. Regardless of electrode placement and stimulus intensity, patients who went on to respond to a course of ECT manifested anterior frontal CBE reductions in this acute postictal period, whereas nonresponders failed to show CBF reductions. Such frontal CBF reductions may reflect functional neural inhibition and may index anticonvulsant properties of ECT. A predictive discriminant function analysis revealed that the CBF changes were sufficiently robust to correctly classify both responders (68% accuracy] and nonresponders (85% accuracy]. More powerful measures of CBF and/or cerebral metabolic rate, as can be obtained with positron-emission tomography, may provide even more sensitive markers of optimal ECT administration. 

The 76 variables derived from the DEC evaluation were first analyzed using stepwise discriminant analysis to determine the variables that best predicted the presence or absence of each drug. This subset of best-predictor variables was then subjected to a discriminant function analysis that predicted and classified whether subjects were dosed or not dosed with drug. The resulting data were classified as true positive, true negative, false positive, or false negative. These parameters were then used to calculate several measures of predictive accuracy of the DEC evaluation, including sensitivity, specificity, and efficiency. 

To evaluate this possibility, we conducted a discriminant function analysis of the two datasets (Figure 6). While there is much overlap—which is not surprising as all the soils derive from the same geological substrate—the plot of the plaza data shows that the northwest and northeast comers have different soil chemical signatures compared to the other soils analyzed from this space. This suggests that different activities were carried out in these locales. In contrast, the plot of the patio data suggests that the chemical signatures of soils in the southeast and northeast comers are similar to each other but different from those in the western half of the patio. Thus, in the plaza, activities can be differentiated by north and south, while those in the patio can be differentiated by east and west. 

Figure 6. Scatterplots of the first two factor scores from a discriminant function analysis for the plaza (top) and patio (bottom) data. The plots show haw the data vary by corner northwest (NW), southwest (SW), southeast (SE), or...

Samples that were veiy low in hematite (<20 wt %), even after heavy mineral separation, should be excluded from or used with caution in the initial discriminant function analysis to avoid skewing the function with samples that may have a significant contribution to the geochemical signature from light minerals. This limitation only excluded one sample, a veiy specularite poor schist. Also, similar samples of such low specularite content are unlikely to be used in the described archaeological contexts. 

Denotes coefficient of variables omitted by stepwise discriminant function analysis. 

Diet and stable isotopes, western Mediterranean prehistory, 118-120 Dietary reconstruction from coprolites, human mtDNA extraction, Hinds Cave, Texas, 81 Dietary research through stable isotopes, principles and interpretation, 115-117 Dikgatlampi workings, Botswana, specularite sourcing, 465 Discriminant function analysis, INAA geochemical data, 466,469-477/... 

Discriminant analysis (DA) performs samples classification with an a priori hypothesis. This hypothesis is based on a previously determined TCA or other CA protocols. DA is also called "discriminant function analysis" and its natural extension is called MDA (multiple discriminant analysis), which sometimes is named "discriminant factor analysis" or CD A (canonical discriminant analysis). Among these type of analyses, linear discriminant analysis (LDA) has been largely used to enforce differences among samples classes. Another classification method is known as QDA (quadratic discriminant analysis) (Frank and Friedman, 1989) an extension of LDA and RDA (regularized discriminant analysis), which works better with various class distribution and in the case of high-dimensional data, being a compromise between LDA and QDA (Friedman, 1989). 

The discriminate function analysis also yields classification functions for each variable (Fe, Pb, or Ni) within each group (TC, MB, TB, NMDB, KVDB, NMSB, and KVSB) and a constant for each group. Once known, the classification functions can be used to classify each of the original sherds into one of the seven possible groups. The classification matrix, obtained by treating data from the 32 original sherds with the classification functions, is given as Table VI. 

Chemometrics is a branch of science and technology dealing with the extraction of useful information from multidimensional measurement data using statistics and mathematics. It is applied in numerous scientific disciplines, including the analysis of food [313-315]. The most common techniques applied to multidimensional analysis include principal components analysis (PCA), factor analysis (FA), linear discriminant analysis (LDA), canonical discriminant function analysis (DA), cluster analysis (CA) and artificial neurone networks (ANN). 

Previous experience has shown that some markers are important, while others are only of minor value. Sensitivity may be improved to over 80% with certain combinations of single tests, in particular when the principle of discriminant functional analysis is applied (D.M. Chalmers el ah, 1981). In order to examine chronic alcohol consumption, a triple test combination would be of advantage, which, in the individual case, could be supported by additional values derived from the set of four tests, (s. tab. 28.6) When determining and differentiating acute alcohol consumption, the values of the triple test combination can be upgraded by additional tests for better clinical reliability. This would also help to determine important, so-called alcohol-typical emergency situations, including Zieve s syndrome, (s. tab. 28.6)... 

Another parametric routine implements a discriminant function by the method commonly called linear discriminant function analysis. It is nearly identical to the linear Bayesian discriminant, except that instead of using the covariance matrix, the sum of cross-products matrix is used. Results obtained with the routine are ordinarily very similar to those obtained using the linear Bayes routine. The routine implemented as LDFA is a highly modified version of program BMD04M taken from the Biomedical Computer Programs Package (47). 

Cluster analysis. In this section, the application of the simple multiscale approach to cluster analysis is demonstrated. The masking method will also be used to localise important features. There are several possible cluster analysis algorithms, however only discriminant function analysis (DFA) will be used here. Before discussing the results from the simple multiscale analysis, this section will first present DFA and how it can applied to both unsupervised and supervised classification, followed by how the cluster properties S are measured at each resolution level. 

Discriminant function analysis. Discriminant function analysis (DFA) which is also referred to as canonical variates analysis is here the chosen cluster analysis method. DFA is usually used in a supervised mode, but can also be used in an unsupervised way. Here, the unsupervised mode is enabled by direct usage of the replicate information of object samples as classes. The effect of using DFA in this way is that it will reduce the within-replicate-group variance. DFA is in many ways similar to PCA. However, the... 

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