# plot.sgee: Coefficient Traceplot Function In sgee: Stagewise Generalized Estimating Equations

## Description

Function to produce the coefficent traceplot, with capabilities to account for covariate groups. Used in place of the `plot` function.

## Usage

 ```1 2 3 4 5 6 7 8``` ```## S3 method for class 'sgee' plot(x, y, penaltyFun = NULL, main = NULL, xlab = "Iterations", ylab = expression(beta), dropIntercept = FALSE, trueBeta = NULL, color = TRUE, manualLineColors = NULL, pointSpacing = 3, cutOff = NULL, ...) ## S3 method for class 'sgeeSummary' plot(x, y, ...) ```

## Arguments

 `x` Path of coefficient Estimates. `y` Optional parameter inherited from `plot(x,y,...)`; not used with sgee. `penaltyFun` Optional function that when provided results ina plot of the coefficient estimates verus the corresponding penalty value. When no `penaltyFun` value is given, the plot generated is of the coefficent estimates versus the iteration number. `main` Optional title of plot. `xlab` Label of x axis; default value is 'Iterations'. `ylab` Label of y axis; default value is the beta symbol. `dropIntercept` Logical parameter indicating whether the intercept estimates should be dropped from the plot (i.e. not plotted). The default is FALSE. `trueBeta` The true coefficient values. If the true coefficient values can be provided, then coefficient estimates that are false positive identifications as non-zero are marked in the plot. `color` Logical parameter indicating that a plot using colors to differentiate coefficients is desired. `manualLineColors` Vector of desired line colors; must match dimension of line colors needed (i.e. same number of colors as there are groups if grouped covariates are sharing a color). `pointSpacing` Space between marks used to indicate a coefficient is a false positive. Spacing is measured in terms of number of indices of the path matrix between marks. `cutOff` Integer value indicating that only the first `cutOff` steps are to be plotted. Default value is `NULL`, indicating all steps are to be plotted. `...` Not currently used.

## Details

plot.sgee is meant to allow for easy visualization of paths of stagewise (or regularized) coefficient estimates. A great deal of flexibility is provided in terms of how the plot is presented. The poenaltyFun paramter allows for a penalty function to be provided (such as the \$L_1\$ norm) to plot the coefficietn estimates against. When given the trueBeta parameter, the plot marks the paths of coefficient estimates that are falsely identified as being non zero. Finally, a switch for black and white versus color plots is provided (`color`).

## Note

Function is intended to give a visual representation of the coefficient estimates. Which x values to compare the estimates to can depend on the situation, but typically the most versatile measure to use is the sum of absolute values, the \$L_1\$ norm; especially when comparing different coefficient paths from different techniques.

Gregory Vaughan

## Examples

 ``` 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``` ```##################### ## Generate test data ##################### ## Initialize covariate values p <- 50 beta <- c(rep(2.4,5), c(1.3, 0, 1.7, 0, .5), rep(0.5,5), rep(0,p-15)) groupSize <- 1 numGroups <- length(beta)/groupSize generatedData <- genData(numClusters = 50, clusterSize = 4, clusterRho = 0.6, clusterCorstr = "exchangeable", yVariance = 1, xVariance = 1, numGroups = numGroups, groupSize = groupSize, groupRho = 0.3, beta = beta, family = gaussian(), intercept = 0) genDF <- data.frame(generatedData\$y, generatedData\$x) coefMat <- bisee(formula(genDF), data = genDF, lambda1 = 0, ##effectively see lambda2 = 1, family = gaussian(), clusterID = generatedData\$clusterID, corstr="exchangeable", maxIt = 200, epsilon = .1) ############################ ## Various options for plots ############################ par(mfrow = c(2,2)) ## plain useage plot(coefMat, main = "Plain Usage") ## With penalty plot(coefMat, penaltyFun = function(x){sum(abs(x))}, xlab = expression(abs(abs(beta))[1]), main = "With Penalty") ## using true beta value to highlight misclassifications plot(coefMat, trueBeta = beta, main = "ID Missclassification") ## black and white option plot(coefMat, trueBeta = beta, color = FALSE, main = "Black and White", pointSpacing = 5) ```

sgee documentation built on May 1, 2019, 7:10 p.m.