SAS for Mixed Models

By S. Wulff. 2007

1,185 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/34623dd30353d0eb3da13c3c6cb5a3996a5201c8

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In Artificial Intelligence

Citations in the corpus

Evaluation and application of the CPM Dairy Nutrition model (2007, methodology): “A random coefficients model (Littell et al. 2006) was used to combine model predictions of the five studies using PROC MIXED of SAS (Littell et al. 2006).”
Mixed-effects basal area increment models for tree species in the boreal forest of Ontario, Canada using an ecological land classification approach to incorporate site effects (2012, methodology): “If models are fitted to datasets that contain spatial and temporal autocorrelation using the ordinary least squares technique, biased standard error of parameter estimates are produced (Littell et al., 2006).”
Alfalfa Nitrogen Credit to First‐Year Corn: Potassium, Regrowth, and Tillage Timing Effects (2012, methodology): “05), best linear unbiased predictors (BLUPs) were used to determine the significance of the fixed effect for each location (Littell et al., 2006).”
Consequences of propagule dispersal and river fragmentation for riparian plant community diversity and turnover (2010, methodology): “The Kenward-Roger correction was used to obtain appropriate degrees of freedom as is recommended for repeated-measures analysis (Littell et al. 2006).”
GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction (2020, methodology): “Same as the extension of single-marker tests using GLM to stepwise regression (e.g. GLMSelect Procedure in SAS)[12,13], single-locus tests using MLM were also extended to multiple loci tests, named multiple loci mixed linear model (MLMM) [14]The most significant maker is fitted as a covariate in…”
Modeling Heterogeneous Variance–Covariance Components in Two-Level Models (2014, methodology): “Restricted versions of the model with no random effects entering the Level-1 variance function can be fitted in SAS (SAS Institute Inc., 2013) using the PROC MIXED procedure (Littell, 2006, chap.”
Root biomass, root/shoot ratio, and soil water content under perennial grasses with different nitrogen rates (2017, methodology): “Data for root biomass, root/shoot ratio, and soil water content at a depth and shoot biomass for grasses were analyzed using the MIXED procedure of SAS after testing for homogeneity of variance (Littell et al., 2006).”
Repeatability for Gaussian and non‐Gaussian data: a practical guide for biologists (2010, methodology): “However, in some software packages, it is possible to allow unconstrained variance estimation (e.g. Littell et al., 2006), which will allow for negative repeatability estimates if this is required.”
Impact of Nighttime Temperature on Physiology and Growth of Spring Wheat (2008, methodology): “Data Analyses All data were statistically analyzed using PROC MIXED in the SAS software (SAS Institute, 2003) as described by Littell et al. (2006). The experimental design was a randomized complete block with a split plot treatment structure; nighttime temperatures were used as main plots and culti”
Rethinking the Analysis of Non-Normal Data in Plant and Soil Science (2015, methodology): “Many researchers still use PROC GLM for certain mixed models but this is emphatically discouraged—see Littell et al. (2006) for a full treatment of this subject.”