Results from PROC MIXED (SAS) analysis of effects of inoculum origin on plant biomass production of mid-successional plant species relative to the sterilized control treatment.

The first block of codes calls PROC MIXED with the QTL effect being treated as a random effect. The second block of codes calls PROC MIXED with the QTL effect being treated as a fixed effect. (SAS)

Example of the code used to account for statistical significances for phenotype and other variables.

Summary data for the studies used in the meta-analysis of local adaptation (Table 1 from the publication)This table contains the data used in this published meta-analysis. The data were originally extracted from the publications listed in the table. The file corresponds to Table 1 in the original publication.tb1.xlsSAS script used to perform meta-analysesThis file contains the essential elements of the SAS script used to perform meta-analyses published in Hoeksema & Forde 2008. Multi-factor models were fit to the data using weighted maximum likelihood estimation of parameters in a mixed model framework, using SAS PROC MIXED, in which the species traits and experimental design factors were considered fixed effects, and a random between-studies variance component was estimated. Significance (at alpha = 0.05) of individual factors in these models was determined using randomization procedures with 10,000 iterations (performed with a combination of macros in SAS), in which effect sizes a...

Multienvironment trials (METs) enable the evaluation of the same genotypes under a v ariety of environments and management conditions. We present META (Multi Environment Trial Analysis), a suite of 31 SAS programs that analyze METs with complete or incomplete block designs, with or without adjustment by a covariate. The entire program is run through a graphical user interface. The program can produce boxplots or histograms for all traits, as well as univariate statistics. It also calculates best linear unbiased estimators (BLUEs) and best linear unbiased predictors for the main response variable and BLUEs for all other traits. For all traits, it calculates variance components by restricted maximum likelihood, least significant difference, coefficient of variation, and broad-sense heritability using PROC MIXED. The program can analyze each location separately, combine the analysis by management conditions, or combine all locations. The flexibility and simplicity of use of this program makes it a valuable tool for analyzing METs in breeding and agronomy. The META program can be used by any researcher who knows only a few fundamental principles of SAS.

Use of SAS proc Mixed.

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.

Models were implemented in the SAS procedure proc mixed [42]. Repeated measures were across time-points, with subject as a random factor. The overall model fit is reported as ChiSquare and p value. For significant overall models (which were all for this data set), variables were tested individually, demonstrating significant time and group-by-time effects in all cases (F statistic and p value reported). Significant within and between group differences at individual time-points are shown in Figs. 1, 3 & 5.

The current study examined how racial/ethnic self-identification combines with gender to shape self-reports of everyday discrimination among youth in the U.S. as they transition to adulthood. Data came from seven waves of the Panel Study of Income Dynamics Transition into Adulthood Supplement (TAS). The sample included individuals with two or more observations who identified as White, Black, or Hispanic (n=2,532). Data includes average everyday discrimination scale scores over 9 time periods (i.e., ages 18 to 27) as well as pattern variables for race/ethnicity and sex groups and family SES proxied by highest level of education in household at baseline. Developmental trajectories of everyday discrimination across ages 18 to 27 were estimated using multilevel longitudinal models with the SAS Proc Mixed procedure.

Results from a Mixed Model Analysis of Variance in SAS PROC GLM to test differences in F1 scores across algorithms and models. The ANOVA model included test sets nested in models as a categorical random factor, and algorithms and models as fixed categorical factors. Scheffé adjustment for multiple comparisons was used to control type I error rate.

The period of early ontogeny constitutes a time when the physical immaturity of an organism is highly susceptible to external stimuli. Thus, early development plays a major role in shaping later adult behavior. The aim of the study was to check whether stimulating puppies at this early stage in life with sound would improve their responsiveness towards unfamiliar noises during the selection process of the police behavioral test for puppies. The cohort comprised 37 puppies from the litters of three mothers. At the commencement of the experiment the dogs were aged 16 days, rising to the age of 32 days at its close. The mothers and litters of the treatment group were either exposed to radio broadcasts, (see below; three litters totaling 19 puppies), while the control group was not exposed to any radio programs (eight litters totaling 18 puppies). All three mothers had previously experienced both auditory circumstances, as described herein. Ordinary radio broadcasts were played to the puppies in the treatment group three times a day for 20 minute periods, always during feeding time. The cohort was subjected to the so-called Puppy Test, i.e. analysis of the potential of each animal, once the dogs had reached the age of 7 weeks. Such tests included exposure to a sudden noise caused by a shovel (100 dB), noise when alone in a room, and response to loud distracting stimuli (the latter two at 70 dB). Said tasks were rated by the same analyst on a scale of 0–5 points; the better the response of the dog, the higher the score given. The differences between the treatment and control groups were analyzed via Mixed Models (PROC MIXED) in SAS. The animals comprising the treatment group responded with a higher score to the sudden noise caused by the shovel than the control dogs (P

Note: For each categorical variable, one category was chosen as a reference category (RC, e.g., RC = Social Sciences for the categorical variable discipline). For categorical variables, effect for each predictor variable (a dummy variable representing one of the categories) is a regression coefficient (Coeff) that should be interpreted in relation to its standard error (SE) and the effect of the reference category. Variance components for level 1 are derived from the data, but variance components at level 2 and level 3 indicate the amount of variance that can be explained by differences between studies (level 3) and differences between single reliability coefficients nested within studies (level 2). The loglikelihood test provided by SAS/proc mixed (−2LL) can be used to compare different models, as can also the Bayes Information Criteria (BIC). The smaller the BIC, the better the model is.*p

Causal inference with observational longitudinal data and time-varying exposures is complicated due to the potential for time-dependent confounding and unmeasured confounding. Most causal inference methods that handle time-dependent confounding rely on either the assumption of no unmeasured confounders or the availability of an unconfounded variable that is associated with the exposure (e.g., an instrumental variable). Furthermore, when data are incomplete, validity of many methods often depends on the assumption of missing at random. We propose an approach that combines a parametric joint mixed-effects model for the study outcome and the exposure with g-computation to identify and estimate causal effects in the presence of time-dependent confounding and unmeasured confounding. G-computation can estimate participant-specific or population-average causal effects using parameters of the joint model. The joint model is a type of shared parameter model where the outcome and exposure-selection models share common random effect(s). We also extend the joint model to handle missing data and truncation by death when missingness is possibly not at random. We evaluate the performance of the proposed method using simulation studies and compare the method to both linear mixed-effects models and fixed-effects models combined with g-computation as well as to targeted maximum likelihood estimation. We apply the method to an epidemiologic study of vitamin D and depressive symptoms in older adults and include code using SAS PROC NLMIXED software to enhance the accessibility of the method to applied researchers.

Chi Square statistics and p value are reported for repeated measures ANOVA, implemented as a mixed linear model with group, time and group by time as effects of interest (SAS proc mixed).Statistics for mixed models.

The two gardens were analyzed separately using Proc MIXED with repeated measures on seedlings and an unstructured (UN) covariance structure. Log transformations were performed as needed to improve the normality of residuals. n = 52 plants for Kirstenbosch (except SLA with 51) and 161 for Jonaskop. Sixteen missing values for Aa and g were imputed with proc MI and MIANALYZE in SAS 9.3. Log transformations were used on stomatal density, pore index, and conductance. Asterisk indicates theta values from MIANALYZE rather than F-values.

Adjusted for BMI, fruit and bean consumption at baseline.*Coefficient associated with group*time based on proc mixed procedure in SAS.

Arterial hypertension is influenced by the intestinal microbiota and its metabolites, which play a crucial role in host health. Dietary peptides are multifunctional molecules with therapeutic potential for managing hypertension. This study aimed to evaluate the impact of incorporating enzymatically hydrolyzed poultry byproduct meal (EHPM-c) into extruded dry diets on the fecal microbiota and blood pressure parameters of elderly obese cats. Eighteen owners of neutered, clinically healthy male and female cats of various breeds were randomly assigned to two groups: control (30.8%, conventional poultry byproduct meal—CPM-c) and test (17.07%, CPM-c + 12.0% EHPM-c). Clinical values of systolic blood pressure, serum aldosterone concentrations, angiotensin-converting enzyme I activity, and fecal microbiota using 16S rRNA were measured. Data were processed using SAS software (PROC MIXED, PROC GLIMMIX, and PROC CORR; p 

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.

Composition of the control diet and its ingredients1.

Guaranteed analysis and ingredients of the weight loss diet of the present study.