SAS Code for Spatial Optimization of Supply Chain Network for Nitrogen Based Fertilizer in North America, by type, by mode of transportation, per county, for all major crops, using Proc OptModel. the code specifies set of random values to run the mixed integer stochastic spatial optimization model repeatedly and collect results for each simulation that are then compiled and exported to be projected in GIS (geographic information systems). Certain supply nodes (fertilizer plants) are specified to work at either 70 percent of their capacities or more. Capacities for nodes of supply (fertilizer plants), demand (county centroids), transhipment nodes (transfer points-mode may change), and actual distance travelled are specified over arcs.

This study aimed to evaluate the influence of initial weight, initial age, average daily gain in initial weight, average daily gain in total weight and genetic group on the probability of pregnancy in primiparous females of the Nellore, 1/2 Simmental + 1/2 Nellore, and 3/4 Nellore + 1/4 Simmental genetic groups. Data were collected from the livestock file of the Farpal Farm, located in the municipality of Jaíba, Minas Gerais State, Brazil. The pregnancy diagnosis results (success = 1 and failure = 0) were used to determine the probability of pregnancy that was modeled using logistic regression by the Proc Logistic procedure available on SAS (Statistical..., 2004) software, from the regressor variables initial weight, average daily gain in initial weight, average daily gain in total weight, and genetic group. Initial weight (IW) was the most important variable in the probability of pregnancy in heifers, and 1-kg increments in IW allowed for increases of 5.8, 9.8 and 3.4% in the probability of pregnancy in Nellore, 1/2 Simmental + 1/2 Nellore and, 3/4 Nellore + 1/4 Simmental heifers, respectively. The initial age influenced the probability of pregnancy in Nellore heifers. From the estimates of the effects of each variable it was possible to determine the minimum initial weights for each genetic group. This information can be used to monitor the development of heifers until the breeding season and increase the pregnancy rate.

We tested whether the probability of a visit was a function oftreatment (dietary N content as a continuous variable) using logistic regression in SAS (PROC GLIMMIX with a binomial distribution and logit link function, SAS 9.4). Day (fixed effect), site (random effect) and feeding station nested within site (random effect) were also included in the model. We then analysed the effect of treatment (dietary N content as a continuous variable) on visit length (min), each behaviour (% of total time) and GUD (count) separately using the generalized linear mixed model (GLMM) procedure in SAS (PROC GLIMMIX with lognormal distribution and identity link function, SAS 9.4). Day (1-4) was included in the models as a fixed effect, and site and feeding station (nested within site) were random effects.To analyse our VOCs data we looked at the odours of the diets using a canonical analysis of principal coordinates(CAP) analysis in the PERMANOVA+ add-on of PRIMER v6to determine whether the multivariate VOC data could differentiate the diets along a continuous (dietary nitrogencontent) gradient, similar to analyses of VOCs from other plant/food material. We applied a dispersion weighting followed by square root transformation to the VOC peak area values, then performed CAP analysis on the Bray-Curtis resemblance matrix of the transformed data. To tease apart the contributing VOCs we then applied the CAPanalysis using diet as a class variable. We also isolated the specific volatile signature of the highest quality diet usingthe Random Forests (RF). We analysed the data with RF, using a one treatment-versus-the rest approach with the VSURF package (version 1.0.3) in R (version 3.1.2; R Core Team, 2015). Before analysis, TQPA data were transformed using the centred log ratio method using CoDaPack v. 2.01.15.

1Estimated using multi-level procedures; students nested within schools, and SAS PROC GLIMMIX Procedure.2Model was adjusted for the following factors: sex, age group, socio-economic status, urban-rural geographic status, years in Canada.

We often rely on the likelihood to obtain estimates of regression parameters but it is not readily available for generalized linear mixed models (GLMMs). Inferences for the regression coefficients and the covariance parameters are key in these models. We presented alternative approaches for analyzing binary data from a hierarchical structure that do not rely on any distributional assumptions: a generalized quasi-likelihood (GQL) approach and a generalized method of moments (GMM) approach. These are alternative approaches to the typical maximum-likelihood approximation approach in Statistical Analysis System (SAS) such as Laplace approximation (LAP). We examined and compared the performance of GQL and GMM approaches with multiple random effects to the LAP approach as used in PROC GLIMMIX, SAS. The GQL approach tends to produce unbiased estimates, whereas the LAP approach can lead to highly biased estimates for certain scenarios. The GQL approach produces more accurate estimates on both the regression coefficients and the covariance parameters with smaller standard errors as compared to the GMM approach. We found that both GQL and GMM approaches are less likely to result in non-convergence as opposed to the LAP approach. A simulation study was conducted and a numerical example was presented for illustrative purposes.

ABSTRACT The aim of the work was to evaluate the adjustment of the logistic and gompertz model with structure of first-order autoregressive errors in the study on the ‘Dwarf green’ coconut fruit growth based on longitudinal and cross-sectional internal cavity diameter data (DLCI and DTCI). Model adjustments showed positive residual autocorrelation, according to the Durbin-Watson test and for both variables, DLCI and DTCI, the residue was modeled according to first-order autoregressive process (AR1). The analysis was performed using the least squares method in the PROC MODEL of the SAS software and results indicated that for both characteristics under study, the logistic model was the most appropriate in describing fruit growth and, according to the model, fully developed ‘Dwarf green’ coconut fruits have longitudinal and cross-sections internal cavity diameter of approximately 7.39 cm and 7.60 cm, respectively.

Volume overload in peritoneal dialysis patients is a common issue that can lead to poor prognosis. We employed a group trajectory model to categorize volume load trajectories and examined the factors associated with each trajectory class to explore the impact of different trajectory groups on clinical prognosis and residual renal function (RRF). This single-center prospective cohort study included 214 patients on maintenance peritoneal dialysis within a tertiary hospital. The ratio of extracellular water to total body water was measured using Bioimpedance analysis. The SAS 9.4 PROC Traj procedure was used to examine the group-based trajectory of the patients. A multivariate logistic regression model was used to calculate the adjusted odds ratios (aOR) of the associated factors to predict the trajectory class of participants. The average age of the included patients was 53.56 (SD: 11.77) years, with a male proportion of 46.7% and a median follow-up time of 6 months. The normal stable group accounted for 35.05% of the total population and maintained a normal and stable level, the moderate stable group accounted for 52.8% of the total population and showed a slightly higher and stable level, and the high fluctuation group accounted for 12.15% of the total population and showed a high and fluctuating level. A multivariate logistic regression analysis revealed that age, diabetes, and albumin levels are significant factors influencing the categorization of volume load trajectories. There were statistically significant differences in both the technical survival rate and the loss of residual renal function among the three trajectory groups.