Question
Answer and Explanation
In R, the abbreviation "adj" typically refers to the "adjusted" value or result, especially within the context of statistical modeling and hypothesis testing. Its specific meaning can vary based on the function or method being used, but it generally indicates a correction or modification to account for some factor.
Here are common scenarios where you'll encounter "adj" in R:
1. p-value adjustments: When performing multiple hypothesis tests (for example, when doing many comparisons at the same time), there's a high chance of committing a type I error (false positive). To address this, many statistical procedures apply p-value adjustments, aiming to control the family-wise error rate or the false discovery rate. If you see something like p.adj it is most likely that this is a p value after adjustment. Methods such as the Bonferroni, Holm, Benjamini-Hochberg (BH), or Benjamini-Yekutieli methods are frequently used for these adjustments. If you see output from a statistical test with p.adj, this usually means that it is the adjusted p-value.
For Example: If you are running p.adjust(p, method = "bonferroni"), the p values will be adjusted using the Bonferroni method.
2. Adjusted R-squared: In regression models, the adjusted R-squared is a modification of the regular R-squared. R-squared measures the proportion of the variance in the dependent variable that is explained by the independent variables. However, R-squared increases as you add more predictors, even if those predictors are not necessarily helpful in explaining the response variable. The adjusted R-squared penalizes for the number of predictors and the sample size, providing a more accurate measure of model fit. When you see output from the summary of a linear model with something like Adjusted R-squared, it is the R-squared value that is adjusted.
3. Adjusted Means: In analysis of variance (ANOVA) and analysis of covariance (ANCOVA), "adjusted means" may refer to means that have been adjusted for the effect of some covariate. For example, if you're testing the effect of a treatment group with different starting levels, the adjusted means would help in comparing the treatment effect, removing the effect of the differences in the initial level. In this context, adj would refer to these adjusted means.
4. Specific Function or Package Context: Sometimes, in the documentation for R packages, the use of adj might be specific to that function. It is important always to read documentation carefully to ensure the meaning of specific abbreviations are fully understood. However, it will commonly indicate some kind of adjusted or corrected result.
In summary, adj in R is a common abbreviation for "adjusted," and it implies that the result or value has been modified for some specific reason, usually related to controlling for some kind of effect or error. Always refer to the documentation for the specific functions to understand exactly how and why the value has been adjusted.