Probit or Logit? Which is the better model to predict the longevity of seeds?

Author(s)

de Faria, Rute Q. ^[1] ; dos Santos, Amanda R. P. ^[2] ; Amorim, Deoclecio J. ^[2] ; Cantao, Renato F. ^[3] ; da Silva, Edvaldo A. A. ^[2] ; Sartori, Maria M. P. ^[2]

Affiliation

^[1] Inst Fed Educ Ciencia & Tecnol Goiano, Dept Agr Engn, Campus Urutai, Km 2, 5, BR-75790000 Urutai, Go - Brazil

^[2] Univ Estadual Paulista, Sch Agr, Dept Prod & Plant Breeding, UNESP, Botucatu Av Univ 3780, BR-18610034 Botucatu, SP - Brazil

^[3] Univ Fed Sao Carlos, Campus Sorocaba UFSCar, BR-18052780 Sorocaba, SP - Brazil

• Document type: Journal article
• Source: SEED SCIENCE RESEARCH; v. 30, n. 1, p. 49-58, MAR 2020.
• Web of Science Citations: 0

Abstract

The prediction of seed longevity (P50) is traditionally performed by the use of the Probit model. However, due to the fact that the survival data are of binary origin (0,1), the fit of the model can be compromised by the non-normality of the residues. Consequently, this leads to prediction losses, despite the data being partially smoothed by Probit and Logit models. A possibility to reduce the effect of non-normality of the data would be to apply the principles of the central limit theorem, which states that non-normal residues tend to be normal as thensample is increased. The Logit and Probit models differ in their normal and logistic distribution. Therefore, we developed a new estimation procedure by using a small increase of thensample and tested it in the Probit and Logit functions to improve the prediction of P50. The results showed that the calculation of P50 by increasing thensamples from 4 to 6 replicates improved the index of correctness of the prediction. The Logit model presented better performance when compared with the Probit model, indicating that the estimation of P50 is more adequate when the adjustment of the data is performed by the Logit function. (AU)

• FAPESP's process: 16/13126-0 - Software for predicting physiological quality of seeds of agricultural crops
• Grantee: Maria Márcia Pereira Sartori
• Support Opportunities: Regular Research Grants