Linear Discriminant Function Model Research Articles

Respiratory Syncytial Virus Infection in Infants: A Comparative Study Using Discriminant, Probit and Logistic Regression Analysis

In babies, respiratory syncytial virus (RSV) is the most common cause of lung inflammation (pneumonia) or bronchiolitis (inflammation of the lungs' airways). This virus comes with several symptoms such as congested or runny nose, dry cough, low-grade fever, sore throat, sneezing, headache, difficulty in breathing etc. The virus can cause death in babies if not properly managed and therefore calls for immediate investigations to reveal the significant causes. Several research works have been conducted but the idea of investigating more potential predictor variables and the application of both regression and classification models have been grossly understudied. Therefore, unpublished secondary data collected from three different hospitals in Port Harcourt, Rivers State, Nigeria on fifteen predictor variables which are potential causes of RSV are modeled using two categorical regression approaches – logistic and probit regression models and one classification model – discriminant function analysis. The models were compared using misclassification errors, Receiver Operating Characteristic (ROC) plot, concordance, sensitivity, specificity and pseudo R-square values. The linear discriminant function model outperformed both the logit and probit models. The results showed that paternal history of asthma, maternal history of asthma, mother’s occupation, mother’s smoking habit and mother’s education level were the most important variables to linearly classify seropositive and seronegative RSV patients.

Study on Discrimination Model of Water Inrush Source in Binhuang Mining Area of China

Groundwater flow into the mine by coal mining, which threatens the safety of coal mine. Therefore, it is necessary to identify the source of the water flowing into the coal mine. Taking binhuang mining area in Shaanxi Province as the research background, the discrimination models of six water inrush sources had been studied. The composition of 256 water samples was determined and the water quality characteristics of 6 water sources were analyzed. Fisher linear discriminant function model was established by selecting 9 indexes of water quality components. By testing the discriminant effect, it is considered that the probability of K1L aquifer misjudging as Q aquifer and surface water is high, and the misjudgment rate is 30.4%. Combined with the analysis of geological conditions, it is considered that K1L aquifer has a good hydraulic connection with Q aquifer and surface water. Aiming at the problem of misjudgment, the neural network analysis model is used, and the misjudgment rate is reduced to 0%.

Discriminant EEG analysis for differential diagnosis of schizophrenia. Methodological aspects

A search for accurate linear discriminant function (LDF) allowing the diagnosis of schizophrenia and estimation of treatment effectiveness according to EEG is an urgent problem. To develop a methodology for discriminant EEG analysis for minimizing the overlearning effect, selection of optimal LDF model and evaluation of its generalizing ability. Two hundred and twenty patients with schizophrenia and 1400 people without psychiatric diseases, who were comparable in basic characteristics, were enrolled. EEG was recorded using 16 leads, 10-20 system and aural reference electrodes. EEG was processed by spectral and coherent analysis. After linear discriminant analysis, LDF was obtained to differentiate people with schizophrenia from healthy subjects and a formula was selected from LDF that included 8 predictors (spectral and coherent parameters of standard EEG ranges theta, alpha and beta) with 90% sensitivity and 80% specificity, significance level for Wilks' lambda p<3.9E-28 and the Mahalanobis distance between training set centroids 4,6. A method for obtaining optimal LDF models and selection the best one with further LDF generalizing ability assessment is suggested.

Application of Clustering Analysis and Discriminant Analysis on Quality Grade Evaluation of Jadeite-Jades’ Green Color

Based on CIE1976 L*a*b* uniform color space, 381 jadeite-jades’ green color were measured by Color i5 spectrophotometer. By applying clustering analysis and discriminant analysis, a classifying and grading function model of jadeite-jades’ green color is tried to be established according to L*, a* and b* variable factors. The results showed that jadeite-jades’ green color were divided well into six categories by K-Means clustering analysis, and ANOVA showed L*, a* and b* values among various categories were significantly different (all p &lt;0.01). The green color of Category 5 with moderate brightness and high chroma outperformed the others, followed by Category 3. The green color of Category 2 with higher brightness and chroma was appraised to be better than the deep green color of Category 3. Dark green of Category 4 and faint green of Category 6 were in the minimum level of the quality. A linear discriminant function model of the six categories of jadeite-jades’ green color was established by Fisher discriminant analysis, which had excellent performance of discriminant with the model checking accuracy of 95.80%.It is concluded that the method of color measurement used by Color i5 spectrophotometer combined with clustering analysis and discriminant analysis offers an efficient, reasonable and feasible approach to the fast classifying and quality grading evaluation of jadeite-jades’ green color.

Rainfall and Temperature Distinguish Between Karnal Bunt Positive and Negative Years in Wheat Fields in Texas

Karnal bunt of wheat, caused by the fungus Tilletia indica, is an internationally regulated disease. Since its first detection in central Texas in 1997, regions in which the disease was detected have been under strict federal quarantine regulations resulting in significant economic losses. A study was conducted to determine the effect of weather factors on incidence of the disease since its first detection in Texas. Weather variables (temperature and rainfall amount and frequency) were collected and used as predictors in discriminant analysis for classifying bunt-positive and -negative fields using incidence data for 1997 and 2000 to 2003 in San Saba County. Rainfall amount and frequency were obtained from radar (Doppler radar) measurements. The three weather variables correctly classified 100% of the cases into bunt-positive or -negative fields during the specific period overlapping the stage of wheat susceptibility (boot to soft dough) in the region. A linear discriminant-function model then was developed for use in classification of new weather variables into the bunt occurrence groups (+ or -). The model was evaluated using weather data for 2004 to 2006 for San Saba area (central Texas), and data for 2001 and 2002 for Olney area (north-central Texas). The model correctly predicted bunt occurrence in all cases except for the year 2004. The model was also evaluated for site-specific prediction of the disease using radar rainfall data and in most cases provided similar results as the regional level evaluation. The humid thermal index (HTI) model (widely used for assessing risk of Karnal bunt) agreed with our model in all cases in the regional level evaluation, including the year 2004 for the San Saba area, except for the Olney area where it incorrectly predicted weather conditions in 2001 as unfavorable. The current model has a potential to be used in a spray advisory program in regulated wheat fields.

A new strategy to filter out false positive identifications of peptides in SEQUEST database search results

Based on the randomized database method and a linear discriminant function (LDF) model, a new strategy to filter out false positive matches in SEQUEST database search results is proposed. Given an experiment MS/MS dataset and a protein sequence database, a randomized database is constructed and merged with the original database. Then, all MS/MS spectra are searched against the combined database. For each expected false positive rate (FPR), LDFs are constructed for different charge states and used to filter out the false positive matches from the normal database. In order to investigate the error of FPR estimation, the new strategy was applied to a reference dataset. As a result, the estimated FPR was very close to the actual FPR. While applied to a human K562 cell line dataset, which is a complicated dataset from real sample, more matches could be confirmed than the traditional cutoff-based methods at the same estimated FPR. Also, though most of the results confirmed by the LDF model were consistent with those of PeptideProphet, the LDF model could still provide complementary information. These results indicate that the new method can reliably control the FPR of peptide identifications and is more sensitive than traditional cutoff-based methods.

A piecewise linear programming approach to the two-group discriminant problem – an adaptation to Fisher’s linear discriminant function model

Most existing linear programming (LP) models have optimization objectives that are very different from Fisher’s linear discriminant function (FLDF). An LP technique that adapts to FLDF to solve the two-group classification problem is desirable, as FLDF is one of the most popular classification rules. Therefore, this paper introduces a piecewise linear programming (PLP- p) approach that has an optimization objective very similar to that of FLDF to solve the two-group classification problem in discriminant analysis. Moreover, the paper compares the classificatory performance between FLDF and the new PLP- p model, and shows that the results from both approaches are as good as each other when applied to three published data sets. However, the new PLP- p is more flexible than FLDF in terms of adding different types of constraints and weighting individual observations. The results of a simulation experiment confirm the value of our proposed approach.

Hormonally active agents in the environment and children's behavior: assessing effects on children's gender-dimorphic outcomes.

Early sex hormone exposure contributes to gender-dimorphic behavioral development in mammals, including humans. Environmental toxicants concentrated in contaminated sport fish can interfere with the actions of sex steroids. This study developed an outcome variable by combining gender-dimorphic behaviors that differentiates boys and girls. Offspring of participants in the New York State Angler Cohort Study (NYSACS) were targeted in a parent-report postal survey. Instruments were selected based on findings of gender differences in the general population. A linear discriminant function model incorporating three gender behavior scales correctly classified the sex of 97.7% of children (252 boys and 234 girls) from a random NYSACS sample. The discriminant function was cross-validated by correctly classifying the sex of 98.4% of children (457 boys and 425 girls) from the remaining NYSACS cases and 97.6% of children (154 boys and 142 girls) from an independent school sample. Within-sex stepwise multiple regression analyses revealed that masculine behavior increased among boys with age and with the number of years of maternal sport fish consumption. In girls, older age and previous live-born siblings were associated with more masculine behavior, whereas feminine behavior increased with the duration of breast feeding. These associations were replicated in an independent sample. A linear discriminant function effectively transformed the binary classification of sex (male-female) to a bipolar continuum of gender (masculinity-femininity). Findings from this study are consistent with the hypothesis that environmental contaminants contribute to shifts in gender-role behavior. Future investigations will need to account for competing explanations of this effect.

ECONOMIES OF SCALE FOR SMALL AND LARGE SUGAR CANE FARMS IN KWAZULU-NATAL / EKONOMIE VAN SKAAL VIR GROOT EN KLEIN SUIKERRIETPLASE IN KWAZULU-NATAL

This paper gives a profile of a sample of a small and large sugar cane farms in the North coast region of the KwaZulu-Natal sugar cane belt. The survey was conducted during May 1995. Farms studied varied between one and 600 hectares. Values for small farms were significantly lower than large farms for human capital resources, farm resource utilisation, rate of search and utilisation of farm information, and adoption of appropriate and improved cultural farm practices. Such differences may account for the differences in farm productivity between small and large farms that exist in the South African sugar industry. A linear discriminant function model shows that small and large farms studied differ significantly on lines of human resources and cost of borrowed capital (market related). The findings of the study show that large cane farms face lower market related interest rates, are relatively better equipped in human resource capital, and are in a position to implement appropriate and recommendable farm practices (soil analysis and use of certified seedcane) compared to small farmers.

Predicting the Response of Potatoes to Post-storage Handling Stress

The potato processing industry needs to predict responses of potatoes to handling so that off-color processed products can be avoided. We developed a system for evaluating and classifying potatoes based on their physiological responses to post-storage handling. Six cultivars (handling characteristics known in the industry) and a selection were treated in a handling simulator and changes in their respiration (CO2), ion conductivity (CND), and sugar development (AGT) were analyzed. A linear discriminant function model was developed to indicate the classification of the selection in relation to the known cultivars.

FEXPAC: a program for linear discriminant classification

A new program for feature extraction and two-class classification utilizing the Fisher linear discriminant function model has been developed. It is intended as a tool in multivariate data analysis, and is especially suited for the design and storage of linear classifiers from training data sets.

Visual-manual commonality in size judgments

Visual-manual commonality was studied in terms of commonality of process and commonality of achievement. Fifty-four Ss madelarge-small categorical judgments about a set of stimuli presented in counterbalanced order through both the visual modality and the haptic, touch, or kinesthetic modality. Using a linear discriminant function model, analyses of the several visual-manual pairings were made on the basis of intra-S absolute differences in the weighting of the physical dimensions (process) and the degree of category separation (achievement). A correlational analysis revealed the independence of these measures, and analyses of variance revealed greater visual-haptic commonality for weighting systems and similar visual-haptic and visual-touch commonality for category separation. The results were interpreted in terms of similarities and differences in information gathering and processing characteristics.