Some Classification Problems with Multivariate Qualitative Data

Cochran, William G.; Hopkins, Carl E.

doi:10.2307/2527493

Cited by 101 publications

(31 citation statements)

References 5 publications

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“…The ccSSR phenotypes were expressed as binary variables with 0/1 values. Binary variables need not disqualify the various discriminant analyses (Rao 1952;Cochrane and Hopkins 1961;Gilbert 1968;Krzanowski 1975). Canonical discriminant analysis (Kshirsagar 1972), an ordination method, was used to assess whether a classification into five haplome groups is justified by the ccSSR data.…”

Section: Discriminant Analysesmentioning

confidence: 99%

Relationships among Avena species as revealed by consensus chloroplast simple sequence repeat (ccSSR) markers

Peng

Wei

et al. 2008

Genet Resour Crop Evol

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Consensus chloroplast simple sequence repeat (ccSSR) makers were used to assess the genetic variation and genetic relationships of 80 accessions from 25 taxa of the genus Avena. Fifteen out of 16 ccSSR markers (93.75%) were polymorphic. A total of 51 alleles were detected at the 16 ccSSR loci. The number of alleles per locus ranged from 1 to 6, with an average of 3.2 alleles. Among these ccSSR loci, the highest polymorphism information content (PIC) value was 0.754, while the lowest PIC value was 0. The mean genetic similarity index among the 80 Avena accessions was 0.545, ranging from 0.188 to 1.000. To assess the usefulness of ccSSRs in separating and distinguishing between haplome (genome) groups, we used ordination by canonical discriminant analysis and classificatory discriminant analysis. Although discriminant analysis separated the haplome groups unequivocally, it was up to 69% predictive of correctly classifying an individual plant whose haplome(s) is unknown in the case where it belonged to the A haplome group, 75% where it belonged in the AC group, and almost 80% where it belonged in the ACD group. The analysis of genetic similarity showed that diploid species with the A haplome were more diverse than other species, and that the species with the As haplome were more divergent than other diploid species with the A haplome. Among the species with the C haplome, A. clauda was more diverse than A. eriantha and A. ventricosa. In the cluster analysis, we found that the Avena accessions with the same genomes and/or belonging to the same species had the tendency to cluster together. As for the maternal donors of polyploid species based on this maternally inherited marker, A. strigosa served as the maternal donor of some Avena polyploidy species such as A. sativa, A. sterilis and A. occidentalis from Morocco. A. fatua is genetically distinct from other hexaploid Avena species, and A. damascena might be the A genome donor of A. fatua. Avena lusitanica served as the maternal parents during the polyploid formation of the AACC tetraploids and some AACCDD hexaploids. These results suggested that different diploid species were the putative A haplome donors of the tetraploid and hexaploid species. The C genome

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Section: Discriminant Analysesmentioning

confidence: 99%

Relationships among Avena species as revealed by consensus chloroplast simple sequence repeat (ccSSR) markers

Peng

Wei

et al. 2008

Genet Resour Crop Evol

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“…For example, if the continuous variable is normally distributed, the Pitman efficiency of sign test compared with the t-test is about 64% 2 (Cochran and Hopkins, 1961). This is deemed to be the optimistic analogy to the loss in practice in dichotomizing when the median was used as the cutoff point.…”

Section: Discussionmentioning

confidence: 99%

Determining the Cutoff Based on a Continuous Variable to Define Two Populations with Application to Vaccines

Parnes

Chan

2013

Journal of Biopharmaceutical Statistics

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In clinical research, it is sometimes desirable to dichotomize a continuous variable so that the information expressed using a dichotomous variable is more straightforward for clinicians to interpret and communicate with patients. The distribution of the continuous variable can differ between two populations defined by a disease case status. Under such a scenario, the dichotomization process can be based on distributions of the continuous variable in two distinct populations. The resulting dichotomous variable can be used as an endpoint in future studies. Even though dichotomization has not been extensively studied, dichotomization has been commonly carried out in clinical trials. We developed a methodology for determining the optimal cutoff point based on maximizing the correlation between the two populations and the dichotomous variable. In some real-world scenarios where outcome status in samples from two populations is not completely identified, we recommend using EM method to first estimate the parameters associated with the two populations before applying the proposed method to find the optimal cutoff point. In addition, we have investigated the performance of the proposed method for several common distributions (e.g., normal, log-normal and exponential distribution) of the continuous variable. Finally, we applied the proposed methods to a varicella vaccine example.

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“…The random variation in a new dataset is unlikely to match the random peCUliarities of the "training" dataset. As a result, knowing only a fi nite sample, but thinking of it as a universe, the system will be surprised a little more (16). Second, if one has enough candidate predictors to choose fr om, one is bound to fi nd some coincidences.…”

Section: Overoptim Ism Regardin G Fu Tu Re Pe Rformancementioning

confidence: 99%

Appropriate uses of Multivariate Analysis

Hanley¹

1983

Annu. Rev. Public Health

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Some Classification Problems with Multivariate Qualitative Data

Cited by 101 publications

References 5 publications

Relationships among Avena species as revealed by consensus chloroplast simple sequence repeat (ccSSR) markers

Relationships among Avena species as revealed by consensus chloroplast simple sequence repeat (ccSSR) markers

Determining the Cutoff Based on a Continuous Variable to Define Two Populations with Application to Vaccines

Appropriate uses of Multivariate Analysis

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