A comparison between Poisson and zero-inflated Poisson regression models with an application to number of black spots in Corriedale sheep

Mastitis in cows can be defined as a binary trait, reflecting presence or absence of clinical mastitis (CM), or as a count variable, number of mastitis cases (NCM), within a defined time interval. Many different models have been proposed for genetic analyses of mastitis, and the objective of this study was to evaluate the predictive ability and sire predictions of a set of models for genetic evaluation of CM or NCM. Linear- and threshold liability models for CM, and linear, censored ordinal threshold, and zero-inflated Poisson (ZIP) models for NCM were compared in a cross-validation study. To assess the ability of these models to predict future data, records from 620492 first-lactation Norwegian Red cows, which were daughters of 3064 sires, were evaluated in a fourfold cross-validation scheme. The mean squared error of prediction was used for model comparison. All models but ordinal threshold model equally performed when comparing the overall predictive ability. This result was on average, across sick and healthy cows; however, the models behaved differently for each category of animals. For example, healthy cows were predicted better by the threshold and linear models for binary data and ZIP model, whereas for mastitic cows, the ordinal threshold model was by far the best model. Predicted sire effects and rankings of sires were highly correlated across all models. For practical purposes, the linear models are very competitive with the nonlinear models.

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“…2007, 2010); the latter has been used successfully in animal breeding studies in other species (e.g. Naya et al. 2008; Penagaricano et al.…”

Section: Introductionmentioning

confidence: 99%

Predictive ability of alternative models for genetic analysis of clinical mastitis

Vázquez

Pérez-Cabal

Heringstad

et al. 2011

J Animal Breeding Genetics

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“…This study only addressed the presence of black wool spots at weaning and at yearling ages. Naya et al. (2008) reported estimates of the posterior median of heritability for NUMBS of 0.246 and 0.166, for zero‐inflated Poisson and Poisson models, respectively; however, the distribution was very skewed, presumably because of the few rams used.…”

Section: Resultsmentioning

confidence: 99%

“…For NUMBS, the Poisson model fitted the data markedly better than the linear model both in terms of MSE and COR (13.88 versus 19.24 and 0.86 versus 0.81, respectively). Fitting a Poisson model with a residual term allowed capturing overdispersion, a feature that may arise with count data, resulting in much better performance than the Poisson model without such residual (Naya et al. 2008) or the linear model.…”

Section: Resultsmentioning

confidence: 99%

Assessment of Poisson, Probit and linear models for genetic analysis of presence and number of black spots in Corriedale sheep

Peñagaricano

Urioste

Naya

et al. 2010

Journal of Animal Breeding and Genetics

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Black skin spots are associated with pigmented fibres in wool, an important quality fault. Our objective was to assess alternative models for genetic analysis of presence (BINBS) and number (NUMBS) of black spots in Corriedale sheep. During 2002-08, 5624 records from 2839 animals in two flocks, aged 1 through 6 years, were taken at shearing. Four models were considered: linear and probit for BINBS and linear and Poisson for NUMBS. All models included flock-year and age as fixed effects and animal and permanent environmental as random effects. Models were fitted to the whole data set and were also compared based on their predictive ability in cross-validation. Estimates of heritability ranged from 0.154 to 0.230 for BINBS and 0.269 to 0.474 for NUMBS. For BINBS, the probit model fitted slightly better to the data than the linear model. Predictions of random effects from these models were highly correlated, and both models exhibited similar predictive ability. For NUMBS, the Poisson model, with a residual term to account for overdispersion, performed better than the linear model in goodness of fit and predictive ability. Predictions of random effects from the Poisson model were more strongly correlated with those from BINBS models than those from the linear model. Overall, the use of probit or linear models for BINBS and of a Poisson model with a residual for NUMBS seems a reasonable choice for genetic selection purposes in Corriedale sheep.

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“…Moreover, Naya et al. () and Peñagaricano, Urioste, Naya, de los Campos, and Gianola () compared, also in sheep, the performance of Poisson and zero‐inflated Poisson (ZIP) models for analysis of count traits while introducing the effect of residuals in the predictor. These models have also been applied in pigs (Perez‐Enciso, Tempelman, & Gianola, ), dairy (e.g., Vazquez, Gianola, Bates, Weigel, & Heringstad, ) and beef cattle (Ayres et al., ).…”

Section: Introductionmentioning

confidence: 99%

Modelling female fertility traits in beef cattle using linear and non‐linear models

Naya

Peñagaricano

Urioste

2017

J Animal Breeding Genetics

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SummaryFemale fertility traits are key components of the profitability of beef cattle production. However, these traits are difficult and expensive to measure, particularly under extensive pastoral conditions, and consequently, fertility records are in general scarce and somehow incomplete. Moreover, fertility traits are usually dominated by the effects of herd-year environment, and it is generally assumed that relatively small margins are kept for genetic improvement. New ways of modelling genetic variation in these traits are needed. Inspired in the methodological developments made by Prof. Daniel Gianola and co-workers, we assayed linear (Gaussian), Poisson, probit (threshold), censored Poisson and censored Gaussian models to three different kinds of endpoints, namely calving success (CS), number of days from first calving (CD) and number of failed oestrus (FE). For models involving FE and CS, non-linear models overperformed their linear counterparts. For models derived from CD, linear versions displayed better adjustment than the non-linear counterparts. Non-linear models showed consistently higher estimates of heritability and repeatability in all cases (h 2 < 0.08 and r < 0.13, for linear models; h 2 > 0.23 and r > 0.24, for non-linear models).While additive and permanent environment effects showed highly favourable correlations between all models (>0.789), consistency in selecting the 10% best sires showed important differences, mainly amongst the considered endpoints (FE, CS and CD). In consequence, endpoints should be considered as modelling different underlying genetic effects, with linear models more appropriate to describe CD and non-linear models better for FE and CS. K E Y W O R D Sbeef cattle, fertility, non-linear models

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A comparison between Poisson and zero-inflated Poisson regression models with an application to number of black spots in Corriedale sheep

Cited by 16 publications

References 16 publications

Predictive ability of alternative models for genetic analysis of clinical mastitis

Predictive ability of alternative models for genetic analysis of clinical mastitis

Assessment of Poisson, Probit and linear models for genetic analysis of presence and number of black spots in Corriedale sheep

Modelling female fertility traits in beef cattle using linear and non‐linear models

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