Additional reference

Author: csoneson

inst/extdata/interface_overview.txt

@@ -14,5 +14,5 @@ element;intro
 #vifs_box;This panel displays the estimated variance inflation factors (VIFs) for the model coefficients, calculated by successively modeling each non-intercept column of the design matrix as a function of the other columns, using a linear model. For each variable, the VIF is obtained as 1/(1-R^2), where R^2 is the coefficient of determination of the linear model. In an ordinary least squares regression analysis, the VIF provides a measure of the increase in a coefficient's variance caused by collinearity with the other predictors in the model. The VIF for a predictor is 1 when the corresponding column of X is orthogonal to all other columns, and larger than 1 otherwise.
 #cooccurrence_matrix_box;The co-occurrence plot displays the number of observations (rows in the sample data table) for each combination of predictor values. It can be useful in order to visualize whether the setup is balanced, or whether some combinations of predictors are not represented by any observations.
 #correlation_matrix_box;This panel shows the correlation among the regression coefficients. In a regular linear model y=Xb+e, the variance-covariance matrix for the vector of regression coefficients b is proportional to (X^TX)^{-1}. This panel shows the matrix (X^TX)^{-1}, converted to a correlation matrix.
-#goodbye;Thank you for taking the tour of ExploreModelMatrix! For more detailed information about (generalized) linear models, design specifications and contrasts, especially in the life sciences field, consider the following references: <br><ul><li>RA Irizarry & MI Love: <a href="https://leanpub.com/dataanalysisforthelifesciences">Data analysis for the life sciences with R.</a></li><li>The <a href="https://www.bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf">limma</a>, <a href="https://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf">edgeR</a> and <a href="https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html">DESeq2</a> vignettes.</li></ul>
+#goodbye;Thank you for taking the tour of ExploreModelMatrix! For more detailed information about (generalized) linear models, design specifications and contrasts, especially in the life sciences field, consider the following references: <br><ul><li>RA Irizarry & MI Love (2015): <a href="https://leanpub.com/dataanalysisforthelifesciences">Data analysis for the life sciences with R.</a></li><li>The <a href="https://www.bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf">limma</a>, <a href="https://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf">edgeR</a> and <a href="https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html">DESeq2</a> vignettes.</li><li>JM Chambers & T Hastie (1992): <a href="https://www.taylorfrancis.com/books/e/9780203738535">Statistical Models in S.</a></li></ul>