group.plot {rare} | R Documentation |
The function plots an hclust
tree with branches and leaves colored
based on group membership. The groups span the covariate indices {1, ..., nvars
}.
Covariates from the same group share equal coefficient (beta
), and sibling
groups have different coefficients. The function determines groups based on
the sparsity in gamma
. In an hclust
tree with beta[i]
on the
i
th leaf, the branch and leaf are colored in blue, red or gray according to beta[i]
being positive, negative or zero, respectively. The larger the magnitude of beta[i]
is,
the darker the color will be. So branches and leaves from the same group will have the
same color.
group.plot(beta, gamma, A, hc, nbreaks = 20)
beta |
Length- |
gamma |
Length- |
A |
|
hc |
An |
nbreaks |
Number of breaks in binning |
## Not run: # See vignette for more details. set.seed(100) ts <- sample(1:length(data.rating), 400) # Train set indices # Fit the model on train set ourfit <- rarefit(y = data.rating[ts], X = data.dtm[ts, ], hc = data.hc, lam.min.ratio = 1e-6, nlam = 20, nalpha = 10, rho = 0.01, eps1 = 1e-5, eps2 = 1e-5, maxite = 1e4) # Cross validation ourfit.cv <- rarefit.cv(ourfit, y = data.rating[ts], X = data.dtm[ts, ], rho = 0.01, eps1 = 1e-5, eps2 = 1e-5, maxite = 1e4) # Visualize the groups at optimal beta and gamma ibest.lambda <- ourfit.cv$ibest[1] ibest.alpha <- ourfit.cv$ibest[2] beta.opt <- ourfit$beta[[ibest.alpha]][, ibest.lambda] gamma.opt <- ourfit$gamma[[ibest.alpha]][, ibest.lambda] # works if ibest.alpha > 1 # Visualize the groups at optimal beta and gamma group.plot(beta.opt, gamma.opt, ourfit$A, data.hc) ## End(Not run)