Mixed procedure

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The MIXED procedure in SAS is a statistical analysis tool used for fitting linear mixed models. It allows users to model data that exhibit correlated or non-constant variability, such as repeated measurements or clustered data. The MIXED procedure provides a flexible and robust framework for analyzing complex data structures.

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Lab products found in correlation

Sas 9, by SAS Institute (2 mentions) Glimmix, by SAS Institute (1 mentions) Sas software package, by SAS Institute (1 mentions) Univariate procedure, by SAS Institute (1 mentions) General linear model procedure, by SAS Institute (1 mentions) Originpro 8, by OriginLab (1 mentions) Glimmix procedure, by SAS Institute (1 mentions)

41 protocols using mixed procedure

Microbial Growth and Metabolite Analysis

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Data for experiment 1 were analyzed using the MIXED procedure (SAS, version 9.4, SAS Institute Inc., Cary, NC) with period and treatment as fixed effects and fermentor as the random effect. In experiment 2, the count data (cells/mL) were normalized by log 10 transformation before statistical analysis. Volume data by the ellipsoid method (Wenner et al., 2018) were weighted using the inverse of standard deviation to account for variability among measurements. Data for log 10 cells and N (ng/cell) were analyzed using the MIXED procedure (SAS Institute Inc.), with period, treatment, and hour as fixed effects and replicate as a random effect. Data for motility also were analyzed using MIXED procedure (SAS Institute Inc.) in a similar manner except time zero was used as a covariate. The volume data included 0 h as a covariate for both 3 and 6 h postinoculation samples. In both experiments, the main effects of MON and CIN and their interaction were compared using the contrasts of MON, CIN, and MON × CIN (3 df) as reported in the respective tables.
Significance was declared at P ≤ 0.05, and trends were noted at 0.05 < P ≤ 0.15.

Ye D., Karnati S.K.R., Wagner B., Firkins J.L., Eastridge M.L, & Aldrich J.M. (2018). Essential oil and monensin affect ruminal fermentation and the protozoal population in continuous culture. Journal of dairy science, 101(6).

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Dairy Cow Activity and Rumination Analysis

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Data from each herd were analyzed separately; however the statistical model was the same for each herd. For statistical analysis of daily activity and rumination, independent variables were year (2014 to 2017), month (January to December), breed group (HO, H64, MVH, NJV), parity group (primiparous and multiparous), and the interactions of year and breed group, breed group and parity group, and parity group and year. Cow nested within breed group was a random effect. The autoregressive order 1 covariance structure was used because it resulted in the lowest Akaike information criterion (AIC) for repeated measures (Littell et al., 1998 (link)). Although careful consideration was carried out to remove interactions based on P > 0.05, the interactions included in the model helped to reduce the AIC value. For all measurements, the MIXED procedure (SAS Institute, 2014) was used to obtain solutions and conduct the ANOVA. All treatment results were reported as least squares means with significance declared at P < 0.05.

Pereira G.M, & Heins B.J. (2019). Activity and rumination of Holstein and crossbred cows in an organic grazing and low-input conventional dairy herd. Translational Animal Science, 3(4), 1435-1445.

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Randomized Complete Block Design Analysis

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Data were analyzed as a randomized complete block design with pen as experimental unit. Continuous data (e.g., initial BW) were analyzed using the MIXED procedure (SAS 9.4 Inc., Cary, NC), with treatment as a fixed effect and block as a random effect. A generalized linear mixed model (GLIMMIX, SAS 9.4 Inc.) was used to analyze categorical data with the model effects described previously. Model estimation was performed using a logit scale to link events/trials responses to a binomial distribution. Initial estimates of treatment means and respective standard errors are reported on the data scale using an inverse link method (ILINK Option, SAS 9.4 Inc.). When overall treatment effect was significant (P < 0.10), treatment means were partitioned using Tukey’s HSD post hoc analysis.

Szasz J.I., McMurphy C.P., Bryant T.C., Luque J., Barcelo C., Sepulveda G., Blood K.S., Bernhard B.C, & Hughes H.D. (2018). Influence of therapeutic use of feedgrade tetracyclines in combination with tulathromycin metaphylaxis on animal health and performance of Holstein steer calves. Translational Animal Science, 3(1), 185-194.

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Cattle Digestion Characteristics Analysis

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Treatment effects on characteristics of digestion in cattle were analyzed as a replicated 3×3 Latin square design using the MIXED procedure (SAS Inst. Inc., Cary, NC, USA). Treatments effects on digestion and fermentation variables were tested by means of polynomial contrasts (SAS Inst.; Version 9.3). Contrasts were considered significant when the p-value was ≤0.05, and tendencies were identified when the p-value was >0.05 and ≤0.10.

Ortiz J., Montaño M., Plascencia A., Salinas J., Torrentera N, & Zinn R.A. (2016). Influence of Kaolinite Clay Supplementation on Growth Performance and Digestive Function in Finishing Calf-fed Holstein Steers. Asian-Australasian Journal of Animal Sciences, 29(11), 1569-1575.

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Livestock Growth Performance Analysis

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All data were analyzed using the SAS ‘MIXED procedure’ (SAS Inst. Inc., Cary, NC, USA; version 9.4), with the Satterthwaite approximation to determine denominator degrees of freedom for the fixed effects test. All variables were subjected to the normality test (Shapiro–Wilk). Body weight (BW), weight gain, concentrate intake, and blood date were tested for fixed treatment effect using pen (repetition) as the random effect, as well as treatment × day interaction to BW. Day 1 results were included as an independent covariate. Means were separated using the PDIFF method (t-test), and all results were expressed as LSMEANS followed by the standard error of the mean. Significance was defined when p ≤ 0.05; a trend was when p > 0.05 or ≤0.10.

Ficagna C.A., Galli G.M., Zatti E., Zago I., do Amaral M.A., de Vitt M.G., Paiano D, & da Silva A.S. (2024). Addition of Butyric Acid and Lauric Acid Glycerides in Nursery Pig Feed to Replace Conventional Growth Promoters. Animals : an Open Access Journal from MDPI, 14(8), 1174.

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Goat Enzyme Treatment Effects

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Data were analyzed using the MIXED procedure (SAS Institute Inc., Cary, NC, USA; Littell et al., 1996 ). Goat was the experimental unit for all variables. The full general linear model (GLM) model included the fixed effects of square, period nested within square, treatment (control, UC and CC enzyme), sampling time (day), and the interaction of treatment and sampling time. For all data, if the interaction of treatment by sampling time was not significant (p>0.05), the fixed effect of sampling time and its related interaction were removed from the full GLM model. Sampling time was a repeated effect in the model. Goat nested within square was used in the random statement. Differences were declared significant at p≤0.05, and a tendency to significance was declared at 0.05

Wang L, & Xue B. (2015). Effects of Cellulase Supplementation on Nutrient Digestibility, Energy Utilization and Methane Emission by Boer Crossbred Goats. Asian-Australasian Journal of Animal Sciences, 29(2), 204-210.

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Digestibility and Excretion Parameters of Protein Diets

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Differences in digestibility and excretion parameters between the diets were statistically analysed using the SAS (SAS 9.4, 2014) Mixed procedure (SAS Institute, Cary, NC, USA) applying the following statistical model: Y_ijk = µ_ijk + a_i + p_j + d_k + e_ijk, where µ_ijk is the overall mean, a_i is the random effect of the animal (i = 1…6), p_j is the fixed effect of the period (j = 1…4), d_k is the fixed effect of the diet (k = 1…6) and e_ijk is the normally distributed error, with a mean of 0 and variance δ². Statistical reliability was confirmed according to the Shapiro–Wilk-test (p > 0.05) and the normal distribution of residuals. The differences between the diets were tested with orthogonal contrasts: (1) A vs. B and C–F; (2) B vs. C–F; (3) C vs. D, E and F; (4) D vs. E and F and (5) E vs. F. Concerning the protein supplements, contrasts were selected to compare the diets containing domestic feedstuffs (linseed, rapeseed by products) with imported protein feed (soya).

Saastamoinen M., Särkijärvi S, & Suomala H. (2021). Protein Source and Intake Effects on Diet Digestibility and N Excretion in Horses—A Risk of Environmental N Load of Horses. Animals : an Open Access Journal from MDPI, 11(12), 3568.

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Evaluating Breed and Dietary Impacts

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The data from this study were evaluated using a completely randomized design framework, incorporating a factorial arrangement for the treatments. This analysis was performed using the SAS Institute’s MIXED procedure (Cary, NC, USA), treating each animal as an individual experimental unit. The impact of breed (purebred and crossbred) and dietary supplementation (pasture alone, soy hull, and corn gluten feed) was examined, along with their interactive effects. The significance of the differences between the means was determined using the least squares means method, applying the PDIFF option in SAS for assessing both the main and interactive effects, with the significance set at p < 0.05 and trends noted at p < 0.1.

Lee J.H., Wildeus S., O’Brien D, & Kouakou B. (2024). Impact of Agro-Byproduct Supplementation on Carcass Traits and Meat Quality of Hair Sheep and Wool × Hair Crossbreds Grazing on Fescue Pasture. Animals : an Open Access Journal from MDPI, 14(8), 1217.

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NE Intake Prediction in Growing Pigs

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The data obtained from Exp. 1 were analyzed using the MIXED procedure (SAS Institute Inc., Cary, NC, USA) with the individual pig as the experimental unit. Homogeneity of the variances was verified using the UNIVARIATE procedure of SAS. Dietary treatment was the main effect in the model, whereas pig and period were random effects. However, no significant random effects were observed, and therefore, the random effects were removed from the final model. The LSMEANS procedure was used to calculate mean values. Orthogonal polynomial contrast tests were used to determine the linear and quadratic effects of increasing NE concentrations of diets.
The data obtained from Exp. 2 were analyzed in a manner similar to that of Exp. 1, but in a completely randomized design with the individual pig as the experimental unit. The model included dietary treatment as the main effect with no random variables in the model. In addition, regression analysis was performed to develop prediction equations for daily NE intake (MJ/d) as a function of the BW of pigs. Average BW of pigs between weeks was used to represent the independent variables in the analysis. A probability of p<0.05 was considered significant for all data analyses.

Lee G.I., Kim J.H., Han G.P., Koo D.Y., Choi H.S, & Kil D.Y. (2017). Effect of dietary net energy concentrations on growth performance and net energy intake of growing gilts. Asian-Australasian Journal of Animal Sciences, 30(9), 1314-1322.

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Gestational Feeding Intake and Behavior

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All cow gestational feed intake and behavior data were analyzed with repeated measures by weekly averages of daily observations using generalized least squares (MIXED procedure; SAS Institute Inc., Cary, NC). Gestational metabolite data were also analyzed with repeated measures (day of gestation) using generalized least squares. The model statements included cow, maternal diet, day of gestation, and a diet by day of gestation interaction. Sire (n = 4) was treated as a random variable. Means were separated using the least significant difference approach (PDIFF option of LSMEANS).

Tanner A.R., Kennedy V.C., Bauer M.L., Swanson K.C., Kirsch J.D., Gaspers J., Negrin-Pereira N., Fontoura A.B., Perry G.A., Stokka G., Rodas-Gonzalez A., Ward A., Dahlen C.R., Neville B., Reynolds L.P., Ominski K.H, & Vonnahme K.A. (2018). Corn supplementation as a winter-feeding strategy alters maternal feeding behavior and endocrine profiles in mid- to late-gestating beef cows. Translational Animal Science, 2(Suppl 1), S106-S111.

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