C. B. Ammerman

Bio: C. B. Ammerman is an academic researcher from University of Florida. The author has contributed to research in topics: Bioavailability & Phosphorus. The author has an hindex of 32, co-authored 103 publications receiving 5872 citations. Previous affiliations of C. B. Ammerman include University of Minnesota & Ohio Agricultural Research and Development Center.

Statistical analysis of repeated measures data using SAS procedures.

Abstract: Mixed linear models were developed by animal breeders to evaluate genetic potential of bulls. Application of mixed models has recently spread to all areas of research, spurred by availability of advanced computer software. Previously, mixed model analyses were implemented by adapting fixed-effect methods to models with random effects. This imposed limitations on applicability because the covariance structure was not modeled. This is the case with PROC GLM in the SAS® System. Recent versions of the SAS System include PROC MIXED. This procedure implements random effects in the statistical model and permits modeling the covariance structure of the data. Thereby, PROC MIXED can compute efficient estimates of fixed effects and valid standard errors of the estimates. Modeling the covariance structure is especially important for analysis of repeated measures data because measurements taken close in time are potentially more highly correlated than those taken far apart in time.

Chemical characteristics and relative bioavailability of supplemental organic zinc sources for poultry and ruminants

Abstract: Eight commercially available organic Zn products and reagent-grade ZnSO4 x 7H2O (Zn Sulf) were evaluated by polarographic analysis, and solubility in 1 M K2HPO4-KH2PO4 buffer (pH 5), 2 M HCl-KCl buffer (pH 2), and deionized water Fractions from these solubility tests were evaluated by gel filtration chromatography for structural integrity Degree of chelation was generally positively related to chelation effectiveness determined by polarography The organic sources were Zn methionine complex A (Zn MetA), Zn methionine complex B (Zn MetB), Zn polysaccharide complex (Zn Poly), Zn lysine complex (Zn Lys), Zn amino acid chelate (Zn AA), Zn proteinate A (Zn ProA), Zn proteinate B (Zn ProB), and Zn proteinate C (Zn ProC) Three experiments were conducted to estimate the relative bioavailability of Zn from the organic Zn supplements for chicks and lambs when added at high dietary levels to practical diets Bone Zn concentration increased (P < 001) as dietary Zn increased in both experiments When Zn Sulf was assigned a value of 100% as the standard, multiple linear regression slope ratios of bone Zn from chicks fed 3 wk regressed on dietary Zn intake gave estimated relative bioavailability values of 83 +/- 146 and 139 +/- 169 for Zn AA and Zn ProA, respectively, in Exp 1 and 94 +/- 116, 99 +/- 88, and 108 +/- 114 for Zn Poly, Zn ProB, and Zn ProC, respectively, in Exp 2 In Exp 3, 42 lambs were fed diets containing Zn Sulf, Zn ProA, Zn AA, or Zn MetB for 21 d Based on multiple linear regression slope ratios of liver, kidney, and pancreas Zn and liver metallothionein concentrations on added dietary Zn, bioavailability estimates relative to 100% for Zn Sulf were 130, 110, and 113 for Zn ProA, Zn AA, and Zn MetB, respectively Except for Zn ProA, which was greater, the organic Zn supplements had bioavailability values similar to that of Zn Sulf for chicks and lambs Bioavailability of organic Zn products was inversely related to solubility of Zn in pH 5 buffer in chicks (r2 = 91) and pH 2 buffer in lambs (r2 = 91), but not to an estimate of degree of chelation

Estimation of relative bioavailability of nutrients using SAS procedures

Abstract: The General Linear Models procedure (PROC GLM) in SAS/STAT software can be programmed to perform the standard statistical analyses used for relative bioavailability studies. The first steps are validity checks to test for statistical validity (linearity), fundamental validity (intersection of regression lines at 0 supplemental level), and equality of the basal diet mean to the point of intersection. The CLASS variable capabilities of PROC GLM can be exploited to expedite these tests. After the validity checks, the GLM procedure can be used to obtain parameter estimates for calculation of relative bioavailability. Optional output provides an inverse matrix to calculate standard errors of slopes and slope ratios. Logarithmic and other transformations of the dependent variable to reduce variance heterogeneity or achieve linearity for subsequent calculation of appropriate bioavailability values also can be accomplished within the SAS System. When nonlinear regression models are more appropriate than linear models, the NLIN procedure can be used.

Chemical characteristics and relative bioavailability of supplemental organic copper sources for poultry.

Abstract: Five commercially available organic Cu products and reagent-grade CuSO4 x 5H2O (Cu Sulf) were evaluated by polarographic analysis and solubility in 0.1 M K2HPO4-KH2PO4 buffer (pH 5), 0.2 M HCl-KCl buffer (pH 2), or deionized water. Fractions from these solubility tests were evaluated by gel filtration chromatography for structural integrity. The organic sources were Cu lysine complex (Cu Lys), Cu amino acid chelate (Cu AA), Cu proteinate A (Cu ProA), Cu proteinate B (Cu ProB), and Cu proteinate C (Cu ProC). Separation of peaks in the chromatograms for the soluble Cu fraction from deionized water indicated that 77, 31, 69, 94, and 16% of the Cu remained chelated for the above sources, respectively. Two experiments were conducted to estimate the relative bioavailability of Cu from the organic Cu supplements for chicks when added at high dietary concentrations to practical corn-soybean meal diets. Liver Cu concentration increased (P < 0.0001) as dietary Cu increased in both experiments. When Cu Sulf was assigned a value of 100% as the standard, linear regression slope ratios of log10 liver Cu concentration regressed on added dietary Cu concentration gave estimated relative bioavailability values of 124 +/- 5.1, 122 +/- 5.3, and 111 +/- 6.0 for Cu Lys, Cu AA, and Cu ProC, respectively, in Exp. 1. The bioavailability estimates for Cu Lys and Cu AA were greater (P < 0.05) than that for Cu Sulf. Values in Exp. 2 were 111 +/- 7.6, 109 +/- 8.4, and 105 +/- 7.5 for Cu Lys, Cu ProA, and Cu ProB, respectively, and all sources were similar in value for chicks. Solubility of Cu in pH 2 buffer provided the best prediction of bioavailability (r2 = 0.924). Other indicators of chelation integrity and solubility had little value as predictors of bioavailability (r2 < or = 0.445).

Statistical analysis of repeated measures data using SAS procedures.

Abstract: Mixed linear models were developed by animal breeders to evaluate genetic potential of bulls. Application of mixed models has recently spread to all areas of research, spurred by availability of advanced computer software. Previously, mixed model analyses were implemented by adapting fixed-effect methods to models with random effects. This imposed limitations on applicability because the covariance structure was not modeled. This is the case with PROC GLM in the SAS® System. Recent versions of the SAS System include PROC MIXED. This procedure implements random effects in the statistical model and permits modeling the covariance structure of the data. Thereby, PROC MIXED can compute efficient estimates of fixed effects and valid standard errors of the estimates. Modeling the covariance structure is especially important for analysis of repeated measures data because measurements taken close in time are potentially more highly correlated than those taken far apart in time.

The aligned rank transform for nonparametric factorial analyses using only anova procedures

Abstract: Nonparametric data from multi-factor experiments arise often in human-computer interaction (HCI). Examples may include error counts, Likert responses, and preference tallies. But because multiple factors are involved, common nonparametric tests (e.g., Friedman) are inadequate, as they are unable to examine interaction effects. While some statistical techniques exist to handle such data, these techniques are not widely available and are complex. To address these concerns, we present the Aligned Rank Transform (ART) for nonparametric factorial data analysis in HCI. The ART relies on a preprocessing step that "aligns" data before applying averaged ranks, after which point common ANOVA procedures can be used, making the ART accessible to anyone familiar with the F-test. Unlike most articles on the ART, which only address two factors, we generalize the ART to N factors. We also provide ARTool and ARTweb, desktop and Web-based programs for aligning and ranking data. Our re-examination of some published HCI results exhibits advantages of the ART.

Estimation of nutrient requirements using broken-line regression analysis.

Abstract: We evaluated and compared various broken-line regression models and SAS (SAS Inst. Inc., Cary, NC) procedures for estimating nutrient requirements from nutrient dose response data. We used the SAS (Version 9) procedures NLIN and NLMixed and the response data of Parr et al. (2003), who evaluated the isoleucine requirement of growing swine. The SAS NLIN was used to fit 2 different broken-line regression models: a simple 2 straight-line, one-breakpoint model and a quadratic broken-line model in which the response below the single breakpoint was quadratic; there was a plateau above the breakpoint. The latter was fit using 2 different approaches in NLIN. We also used SAS NLMixed to fit 3 different broken-line models: the 2 straight-line, one-breakpoint model that included a random component for the plateau; the quadratic broken-line model that included a random component for the plateau; and the quadratic broken-line model that included random components for both the plateau and the slope of the curve below the requirement. The best fit (greater adjusted R2; least log likelihood) was achieved using SAS NLMixed and the quadratic model with a random component for asymptote included in the model. Model descriptions, SAS code, and output are presented and discussed. Additionally, we provide other examples of possible models and discuss approaches to handling difficult-to-fit data.