A logistic regression was performed to evaluate the risk of CMD+ occurrence based on subject status (i.e., control, NAF, AF and proband) while controlling for age, sex, race, site and SES differences. A type-1 error rate of 0.05 was applied. Adequacy of the regression model fit was evaluated using the Hosmer-Lemeshow calibration test (Hosmer et al., 2013 ). All statistical analysis was performed using the R statistical programming package (Vienna, Austria; 2013, http://www.R-project.org , version 2.15.3). As a post-hoc comparison, a test of independence was performed to compare CMD+ prevalence in relatives of SZ, SZA and BP probands to identify if any specific group influenced results. Similar post-hoc tests of independence were performed across diagnostic groups in probands.
R statistical programming package
Manufactured by R Project for Statistical Computing
Sourced in Austria
R is an open-source software environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and more. R is widely used in academic and research communities for data analysis and visualization.
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Lab products found in correlation
2 protocols using r statistical programming package
1
Logistic Regression for CMD+ Risk
2
Analyzing Student Achievement Outcomes
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For each aspect of student achievement, descriptive statistics were compiled and inferential analysis was run comparing treatment groups using the R statistical programming package (R Project for Statistical Computing, 2015 ). Normalized gain scores (G = (post score % − prescore %)/ (100 − prescore %)) were calculated for each student who completed all aspects of the study (Hake, 1998 ). Multiple linear regression analysis was used to investigate the effect of possible explanatory variables on normalized gain scores. In addition to linear regression, we looked at individual demographic variables and analyzed treatment results across each factor. Using independent t tests, we calculated p values comparing treatment groups and calculated 95% confidence intervals for improvement differences between treatments. Cohen’s d, a mean difference effect size, was reported when significant results were found. Two-way analysis of variance (ANOVA) was used to investigate possible interactions between treatment conditions and demographic variables.
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