Description Usage Arguments Details Note Author(s) Examples

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

function.

1 2 3 4 5 6 7 8 |

`x` |
Path of coefficient Estimates. |

`y` |
Optional parameter inherited from |

`penaltyFun` |
Optional function that when provided results ina plot
of the coefficient estimates verus the corresponding penalty value.
When no |

`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 |

`...` |
Not currently used. |

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`

).

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

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)
``` |

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