Sas proc glm

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SAS PROC GLM is a statistical procedure within the SAS software suite that performs general linear model analysis. It provides functionality for analyzing data using techniques such as linear regression, analysis of variance (ANOVA), and analysis of covariance (ANCOVA). The procedure offers a flexible framework for model specification and hypothesis testing.

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Sas proc mixed, by SAS Institute (3 mentions) Prism 8, by GraphPad (2 mentions) Sas studio software, by SAS Institute (2 mentions) Sas proc reg, by SAS Institute (2 mentions) Sas proc lifetest, by SAS Institute (2 mentions) Sas proc phreg, by SAS Institute (2 mentions) Sas version 9, by SAS Institute (2 mentions) Sas 9, by SAS Institute (1 mentions) Sas software, by SAS Institute (1 mentions) Sas proc freq, by SAS Institute (1 mentions) Jmp software, by SAS Institute (1 mentions)

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PROC GLM (78 citations) SAS GLM (11 citations) SAS/PROC GLM software (5 citations) SAS 9.4 PROC GLM (4 citations) PROC GLM SAS version 9.4 (4 citations) Proc GLM of SAS, version 9.4 (3 citations) SAS PROC GLM procedure (2 citations)

35 protocols using sas proc glm

Determinants of Depression in Graves' Disease

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All analyses were performed with SAS 9.1. Sample characterization was performed using the procedures SAS PROC FREQ, PROC MEANS, PROC TTEST and PROC NPAR1WAY. Mean levels of depression and anxiety symptoms, as measured by the 0-21 scored scales in the two patient groups, were compared using multiple linear regression (SAS PROC GLM). The analyses were also controlled for age, sex, educational level, comorbidity, cohabitation and time since diagnosis (covariates). Clinical and socio-demographic determinants of symptom severity in patients with Graves' disease were evaluated by multiple linear regression (SAS PROC GLM). The covariates were: age, gender, education, comorbidity, cohabitation, time since diagnosis, TRAb, TPOAb, and thyroid function (represented by fT₄) levels. Sensitivity analyses were conducted by entering the covariates fT₃, TSH, current thyroid dysfunction (mild/overt hypo/hyperthyroidism, cf. table 1) and being untreated, both separately and simultaneously in the regression model.

Bové K.B., Watt T., Vogel A., Hegedüs L., Bjoerner J.B., Groenvold M., Bonnema S.J., Rasmussen Å.K, & Feldt-Rasmussen U. (2014). Anxiety and Depression Are More Prevalent in Patients with Graves' Disease than in Patients with Nodular Goitre. European Thyroid Journal, 3(3), 173-178.

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Investigating Antimalarial Sugar Impacts

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The details of statistical methods are listed in the figure legends. Data were analyzed in GraphPad Prism 8 unless otherwise indicated. The unpaired, two-tailed Student’s t test or Mann-Whitney test were used to compare two groups depending on the normality of the data. The one-way ANOVA with different multiple comparisons tests were used to compare the difference among more than two groups depending on the normality of the data. The statistical analysis of metabolomics data and transcriptome data are described in the corresponding methods. For correlation analysis, we divided the sugar content into groups by their medians. Then ANOVA analysis was applied to evaluate the impact of the different sugar and their combination on A. bogorensis load and P. berghei load. The ANOVA analysis was performed by using SAS proc glm (SAS Institute, USA). The number of biological replicates and the significant differences were shown in the corresponding figure legends.

Wang M., An Y., Gao L., Dong S., Zhou X., Feng Y., Wang P., Dimopoulos G., Tang H, & Wang J. (2021). Glucose-mediated proliferation of a gut commensal bacterium promotes Plasmodium infection by increasing mosquito midgut pH. Cell reports, 35(3), 108992.

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ANOVA-based Association Analysis of Yield Traits

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The single-factor ANOVA method of association analysis was used to test the association between constructed SNPLBD markers and GCA effect values of 11 yield-related traits. The linear model is calculated as follows:
where yij is the jth observation of the ith allele at the SNPLDB under testing, μ is population mean, a_i is the effect of i-th allele and εij is random error.
All the computations of association analysis were performed using SAS PROC GLM (Release 9.1.3; SAS Institute, Cary, NC). The significant SNPLDBs on the chromosomal region were selected based on the least P-value (α = 0.01 probability level). The coefficient of determination (R²) was estimated to determine the percentage of phenotypic variation explained by each associated SNPLDB marker. Further, the genes that lie within the intervals of associated SNPLDBs were searched and annotated using the Rice Genome Annotation Project (http://rice.plantbiology.msu.edu/) database for a detailed investigation of their biological and molecular functions.

Ullah Zaid I., Tang W., He J., Ullah Khan S, & Hong D. (2018). Association analysis uncovers the genetic basis of general combining ability of 11 yield-related traits in parents of hybrid rice. AoB Plants, 11(1), ply077.

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Analyzing Recombinant Inbred Line Traits

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Least square means (lsmeans) were calculated for each RIL over all 3 environments in SAS PROC GLM (SAS Institute, 2004), using RIL and environment (three environments: the two replications from Arlington were considered as distinct environments) as fixed effects. Least square means for each RIL were used as phenotypic data for correlation and QTL analysis. Phenotypic correlations were calculated as Pearson product-moment coefficients using SAS PROC CORR. Coefficients of variation (CV) were calculated by dividing square root of the mean square error by mean (multiplied by 100). Phenotypic data for B73 and Mo17 were compared against each other using SAS PROC GLM, with genotype and environment as fixed effects.
Heritabilities were calculated according to Holland et al. [30 ] on an entry mean basis using SAS PROC MIXED by fitting lines, locations, lines x locations (G x E), and field replications as random effects.

Jeffrey B., Kuzhiyil N., de Leon N, & Lübberstedt T. (2016). Genetic and Quantitative Trait Locus Analysis for Bio-Oil Compounds after Fast Pyrolysis in Maize Cobs. PLoS ONE, 11(1), e0145845.

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Analyzing Objective Quality and Volatiles

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The data were analyzed using a two-way analysis of variance. Using SAS PROC GLM
(SAS Institute, USA), we assessed the main effects of the variables in the two
experiments and injection treatments in regard to their objective quality traits
and volatiles. The means of the measurements were compared using Duncan’s
multiple-range test at a significance level of 0.05.

Yang J., Dashdorj D, & Hwang I. (2018). Effect of the Calpain System on Volatile Flavor Compounds in the Beef Longissimus lumborum Muscle. Korean Journal for Food Science of Animal Resources, 38(3), 515-529.

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Regression Diagnostics for Warmth Assessment

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Thorough regression diagnostics were performed on the warmth and dominance scores to check for outliers, leverage, influential data, heteroscedasticity, and multicollinearity of residuals, and confirmed that our data met the assumptions for linear regression analysis. Mixed effects general linear models (SAS Proc Mixed) were used to analyze group differences in change from premorbid to current warmth. The threshold for statistical significance was set at p < .013, Benjamini-Yekutieli (B-Y) corrected (Narum, 2006 ) for n = 25 multiple comparisons. General linear models (SAS Proc GLM) were performed to examine group differences in RCIwarmth score, and Dunnett-Hsu post-hoc tests were used to compare patient groups' least-square RCIwarmth score to the NC group.

Toller G., Yang W.F., Brown J.A., Ranasinghe K.G., Shdo S.M., Kramer J.H., Seeley W.W., Miller B.L, & Rankin K.P. (2019). Divergent patterns of loss of interpersonal warmth in frontotemporal dementia syndromes are predicted by altered intrinsic network connectivity. NeuroImage : Clinical, 22, 101729.

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Statistical Analysis Methods for Biological Research

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Statistical analyses were performed using GraphPad Prism version 7.0 (GraphPad Software, Inc, CA) and SAS Proc GLM (SAS 9.4). All the data were assessed for normality and equal variance using GraphPad Prism and SAS Proc GLM. Continuous data were examined by Shapiro-Wilk test for normality. Unpaired 2-tailed Student t test was used to determine statistical difference between 2 groups for normally distributed continuous variables. For comparison of multiple groups, ANOVA followed by Tukey multiple comparison analysis or 2-way ANOVA followed by Bonferroni post hoc tests were used. For data without normal distribution, nonparametric Mann-Whitney U test or Kruskal-Wallis test were applied. In data that revealed unequal variance, Kolmogorov-Smirnov test was applied. For incidence of AAA, Fisher exact test was performed. In the case of blood pressure, repeated measure 2-way ANOVA was used to determine between and within group differences, with time as the repeating factor. All data are presented as mean±SEM. P<0.05 was considered statistically significant for all tests.

Sharma N., Dev R., Belenchia A.M., Aroor A.R., Whaley-Connell A., Pulakat L, & Hans C.P. (2019). Deficiency of IL12p40 (Interleukin 12 p40) Promotes Ang II (Angiotensin II)-Induced Abdominal Aortic Aneurysm. Arteriosclerosis, thrombosis, and vascular biology, 39(2), 212-223.

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Seed Germination and Vigor Analysis

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Analysis of variance and T-tests for germination and vigor testing were conducted using SAS PROC GLM (SAS Institute Inc., 2012) to analyze seed treatment effects. Two-sample T-tests were conducted to compare treated versus untreated samples in disease control experiments, and polymer film drying using SAS PROC GLM. Equal variance was determined using Hartley’s F-max test, thus a pooled variance was used. Data was log transformed for disease control and polymer film t-tests and transformed back for presentation. Statistical analysis of storage stability studies was done using SAS PROC GLM.

Kimmelshue C., Goggi A.S, & Cademartiri R. (2019). The use of biological seed coatings based on bacteriophages and polymers against Clavibacter michiganensis subsp. nebraskensis in maize seeds. Scientific Reports, 9, 17950.

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Ecogeographical Analysis of Teosinte Races

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Differences among races and species for the various environmental variables were analyzed by one-way analysis of variance using SAS proc GLM [40 ] with race treated as a class variable. It should be noted that there was high collinearity within groups of variables, therefore F values among races and correlation coefficients were used to select variables for further analysis. At this stage, variable selection for clustering and classification (VSCC) technique was used; it is intended to find the variables that simultaneously minimize the ‘within-group’ variance and maximize the ‘between-group’ variance [41 ]. Principal components analysis was conducted to synthetically analyze ecogeographical data; using the first two principal components, a biplot graph was built and visualized with NTSyS 2.2 [42 ]. In addition, linear discriminant analysis was used to verify if the recorded sites of teosinte were correctly assigned to “geographic races” and species.

Sánchez González J.D., Ruiz Corral J.A., García G.M., Ojeda G.R., Larios L.D., Holland J.B., Medrano R.M, & García Romero G.E. (2018). Ecogeography of teosinte. PLoS ONE, 13(2), e0192676.

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Analyzing Group Differences in Neuropsychological Measures

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Group differences on potentially confounding covariates (age, sex, education, MMSE) were analyzed using general linear models in SAS (SAS Proc GLM). GLMs were also used to analyze group differences in RSMS and BFD scores, controlling for age, sex, education, and MMSE score (as a proxy for disease severity). Prior to GLM analysis, RSMS and BDF scores were evaluated in order to determine the presence of outliers. Data points considered inappropriately influential were removed after statistical examination of leverage, distance, influence, and collinearity, resulting in exclusion of one aberrant observation from the original data set. Dunnett-Hsu post-hoc tests were performed to compare each patient group’s least-square mean RSMS and BFD scores to those of NCs.

Toller G., Brown J., Sollberger M., Shdo S., Bouvet L., Sukhanov P., Seeley W.W., Miller B.L, & Rankin K.P. (2018). Individual differences in socioemotional sensitivity are an index of salience network function. Cortex; a journal devoted to the study of the nervous system and behavior, 103, 211-223.

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