# Shiny variance inflation factor sandbox

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In multiple regression, strong correlation among covariates increases the uncertainty or variance in estimated regression coefficients. Variance inflation factors (VIFs) are one tool that has been used as an indicator of problematic covariate collinearity. In teaching students about VIFs, it may be useful to have some interactive supplementary material so that they can manipulate factors affecting the uncertainty in slope terms in real-time.

Here’s a little R shiny app that could be used as a starting point for such a supplement. Currently it only includes two covariates for simplicity, and gives the user control over the covariate $R^2$ value, the residual variance, and the variance of both covariates.

As usual, the file `server.R`

defines what you want to actually do in R:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | # interactive variance inflation factor module library(shiny) library(car) library(mvtnorm) library(gridExtra) library(ggplot2) shinyServer(function(input, output){ output$plot <- renderPlot({ r2 <- input$r2 var_error <- input$resid_var var_x1 <- input$var_x1 var_x2 <- input$var_x2 beta <- c(0, 1, 1) # users enter R^2, this backcalculates covariance cov_x1x2 <- sqrt(var_x1 * var_x2 * r2) sigma <- matrix(c(var_x1, cov_x1x2, cov_x1x2, var_x2), nrow=2) X <- array(1, dim=c(input$n, 3)) X[, c(2, 3)] <- rmvnorm(n=input$n, sigma=sigma, method="chol") mu <- X %*% beta epsilon <- rnorm(input$n, 0, sqrt(var_error)) y <- mu + epsilon X1 <- X[, 2] X2 <- X[, 3] model <- lm(y ~ 1 + X1 + X2) # thanks to Ben Bolker for the next 3 lines # https://groups.google.com/forum/#!topic/ggplot2/4-l3dUT-h2I l <- list(vif = round(vif(model)[1], digits=2)) eq <- substitute(italic(VIF) == vif, l) eqstr <- as.character(as.expression(eq)) l2 <- list(vif = round(vif(model)[2], digits=2)) eq2 <- substitute(italic(VIF) == vif, l2) eqstr2 <- as.character(as.expression(eq2)) # plot 1: parameter recovery df3 <- data.frame(truth = beta, lci = confint(model)[, 1], uci = confint(model)[, 2], est = coef(model), y=0:(length(beta) - 1)) cip <- ggplot(df3, aes(x=truth, y=y)) + geom_point(col="blue", size=5, alpha=.5) + theme_bw() + geom_segment(aes(x=lci, y=y, xend=uci, yend=y)) + geom_point(aes(x=est, y=y), size=3, pch=1) + ggtitle("Coefficient recovery") + xlab("Value") + ylab(expression(beta)) + scale_y_continuous(breaks = c(0, 1, 2)) + theme(axis.title.y = element_text(angle = 0, hjust = 0)) + annotate(geom="text", x=0, y=2, label=eqstr, parse=TRUE, vjust=1.5, hjust=-1) + annotate(geom="text", x=0, y=1, label=eqstr2, parse=TRUE, vjust=1.5, hjust=-1) # plot 2: X1 & X2 correlation plot xcorp <- ggplot(data.frame(X1, X2), aes(x=X1, y=X2)) + geom_point(pch=1) + theme_bw() + ggtitle("Covariate scatterplot") grid.arrange(cip, xcorp, ncol=2) }) }) |

The file `ui.R`

defines the user interface:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | library(shiny) shinyUI(pageWithSidebar( headerPanel("Variance inflation factor sandbox"), sidebarPanel( sliderInput("r2", "Covariate R-squared:", min=0, max=.99, value=0), sliderInput("resid_var", "Residual variance:", min=1, max=10, value=1), sliderInput("var_x1", "Variance of X1:", min=1, max=10, value=1), sliderInput("var_x2", "Variance of X2:", min=1, max=10, value=1) ), mainPanel(plotOutput("plot") ) )) |

Everything’s ready to fork or clone on Github.

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**Ecology in silico**.

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