Interference of sample size on multicollinearity diagnosis in path analysis

Sari, Bruno Giacomini; Lúcio, Alessandro Dal’Col; Olivoto, Tiago; Krysczun, Dionatan Ketzer; Tischler, André Luís; Drebes, Lucas

doi:10.1590/s0100-204x2018000600014

Cited by 14 publications

(7 citation statements)

References 8 publications

(9 reference statements)

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“…However, due to the small number of genotypes in the research, the analysis was considered unnecessary. Path analysis with fewer samples can cause multicollinearity (Olivoto et al 2017;Sari et al 2018), hence, this index application could increase the selection effectiveness of synthetic maize under drought stress. Based on the selection index, productivity does not dominate index variance but is the main character, as a result, the index requires adjustment.…”

Section: Resultsmentioning

confidence: 99%

Drought-adapted maize line based on morphophysiological selection index

et al. 2021

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Abstract. Padjung R, Farid M, Musa Y, Anshori MF, Nur A, Masnenong A. 2021. Drought-adapted maize line based on morphophysiological selection index. Biodiversitas 22: 4028-4035. Synthetic line formation is an effort to increase maize productivity in drought-stressed areas. This process requires systematic selection in determining adaptability levels involving important secondary characters formulated in the selection index. Furthermore, the principal component index had been widely reported, both in normal and stressed conditions. The selection index development on morpho-physiological characters based on multivariate analysis was expected to increase drought stress tolerance and maize lines selection precision. Therefore, the purpose of this research was to form a selection index based on morpho-physiological characters and selecting synthetic lines adaptive under drought stress. This research was designed using a split-plot with 3 replications, where the main plot consisted of normal and stressed irrigation, while the subplot was genotype consisting of 6 lines and 3 check varieties namely Bisma, Lamuru, and Sukamarga. Observations were performed on 18 morphological and 4 physiological characters, and the results showed that the selection index was formed based on multivariate analysis from 9 characters. Through index selection, Syn_2-2 (0.79), Syn_2-15 (0.85) and Syn_2-16 (0.97) were considered as drought stress adaptive lines. Therefore, the 3 lines can be recommended in the synthetic variety release process.

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Section: Resultsmentioning

confidence: 99%

Drought-adapted maize line based on morphophysiological selection index

et al. 2021

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“…Some methods could be used to analysis multicollinearity, for instance, Pearson’s correlation coefficients and diagnostics like variance inflation factors and eigenvalues of the correlation matrix (Sari et al , 2018; Sun et al , 2018; Chen et al , 2018). In order to ensure the overall performance of the algorithm, Pearson coefficients are selected to detect collinear factors.…”

Section: Regression Pattern Model and Rp-minermentioning

confidence: 99%

A regression-based algorithm for frequent itemsets mining

Jia

Wang

2019

DTA

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Purpose Frequent itemset mining (FIM) is a basic topic in data mining. Most FIM methods build itemset database containing all possible itemsets, and use predefined thresholds to determine whether an itemset is frequent. However, the algorithm has some deficiencies. It is more fit for discrete data rather than ordinal/continuous data, which may result in computational redundancy, and some of the results are difficult to be interpreted. The purpose of this paper is to shed light on this gap by proposing a new data mining method. Design/methodology/approach Regression pattern (RP) model will be introduced, in which the regression model and FIM method will be combined to solve the existing problems. Using a survey data of computer technology and software professional qualification examination, the multiple linear regression model is selected to mine associations between items. Findings Some interesting associations mined by the proposed algorithm and the results show that the proposed method can be applied in ordinal/continuous data mining area. The experiment of RP model shows that, compared to FIM, the computational redundancy decreased and the results contain more information. Research limitations/implications The proposed algorithm is designed for ordinal/continuous data and is expected to provide inspiration for data stream mining and unstructured data mining. Practical implications Compared to FIM, which mines associations between discrete items, RP model could mine associations between ordinal/continuous data sets. Importantly, RP model performs well in saving computational resource and mining meaningful associations. Originality/value The proposed algorithms provide a novelty view to define and mine association.

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“…Inferências na área agronômica quanto ao tamanho de amostra necessário para o diagnóstico do grau de multicolinearidade foram realizadas em dados de genótipos de milho (OLIVOTO et al, 2017). A interferência da multicolinearidade em análise de trilha, em tomate cereja, foi estudada por Sari et al (2018). Esses autores constataram haver interferência de amostras pequenas no diagnóstico do grau de multicolinearidade.…”

Section: Introductionunclassified

“…Os maiores níveis de muticolinearidade Já em estudos a partir de banco de dados de milho e tomate cereja, foram realizadas observações sobre o tamanho de amostra. Nestes há indicações de que tamanhos de amostra insuficientes podem estimar incorretamente o grau de multicolinearidade(OLIVOTO et al, 2017;SARI et al, 2018). Em estudo realizado com a cultura de tomate cereja, seriam necessários tamanhos de amostra superiores a 45 plantas, com 5% de probabilidade de erro, para estimar o indicadorDET (SARI et al, 2018).…”

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Tamanho de amostra para avaliação do grau de multicolinearidade em caracteres produtivos de centeio

Neu

Filho

et al. 2021

CeN

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The objectives of this work were to determine the sample size (number of plants) necessary to estimate the indicators of the of multicollinearity degree - condition number (CN), determinant of the correlation matrix (DET), and variance inflation factor (VIF) - in productive traits of rye and to verify the variability of the sample size between the indicators. Five and three uniformity trials were conducted with the cultivars BRS Progresso and Temprano, respectively, and seven productive traits were evaluated in 780 plants. Twenty-one cases were obtained from seven traits, combined five to five. In each case, 197 sample sizes were planned (20, 25, 30, ..., 1,000 plants) and in each size 2,000 resampling were performed, with replacement. For each resample the CN, DET and FIV were determined and the average among 2,000 estimates of each indicator of the multicollinearity degree was calculated. Then, for each case and indicator, the sample size was determined through three models: models of maximum modified curvature, segmented linear with plateau response, and segmented quadratic with plateau response. There was superiority the quadratic model segmented with plateau in adjusting the degree of multicollinearity according to the sample size for all indicators. There is a need greater sample size to detect multicollinearity when diagnosed by DET and for sizes larger than 101, 258 and 102 plants when diagnosing for the number of conditions, determinant and inflation factor performed, respectively.

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Interference of sample size on multicollinearity diagnosis in path analysis

Cited by 14 publications

References 8 publications

Drought-adapted maize line based on morphophysiological selection index

Drought-adapted maize line based on morphophysiological selection index

A regression-based algorithm for frequent itemsets mining

Tamanho de amostra para avaliação do grau de multicolinearidade em caracteres produtivos de centeio

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