plot.cv.customizedGlmnet {customizedTraining} | R Documentation |

Produces a plot, with a row for each customized training submodel, showing the variables selected in the subset, with variables along the horizonal axis. The lambda used is the one which minimizes CV error.

```
## S3 method for class 'cv.customizedGlmnet'
plot(x, ...)
```

`x` |
a fitted |

`...` |
ignored |

Scott Powers, Trevor Hastie, Robert Tibshirani

```
require(glmnet)
# Simulate synthetic data
n = m = 150
p = 50
q = 5
K = 3
sigmaC = 10
sigmaX = sigmaY = 1
set.seed(5914)
beta = matrix(0, nrow = p, ncol = K)
for (k in 1:K) beta[sample(1:p, q), k] = 1
c = matrix(rnorm(K*p, 0, sigmaC), K, p)
eta = rnorm(K)
pi = (exp(eta)+1)/sum(exp(eta)+1)
z = t(rmultinom(m + n, 1, pi))
x = crossprod(t(z), c) + matrix(rnorm((m + n)*p, 0, sigmaX), m + n, p)
y = rowSums(z*(crossprod(t(x), beta))) + rnorm(m + n, 0, sigmaY)
x.train = x[1:n, ]
y.train = y[1:n]
x.test = x[n + 1:m, ]
y.test = y[n + 1:m]
foldid = sample(rep(1:10, length = nrow(x.train)))
# Example 1: Use clustering to fit the customized training model to training
# and test data with no predefined test-set blocks
fit1 = cv.customizedGlmnet(x.train, y.train, x.test, Gs = c(1, 2, 3, 5),
family = "gaussian", foldid = foldid)
# Print the optimal number of groups and value of lambda:
fit1$G.min
fit1$lambda.min
# Print the customized training model fit:
fit1
# Compute test error using the predict function:
mean((y[n + 1:m] - predict(fit1))^2)
# Plot nonzero coefficients by group:
plot(fit1)
# Example 2: If the test set has predefined blocks, use these blocks to define
# the customized training sets, instead of using clustering.
foldid = apply(z == 1, 1, which)[1:n]
group.id = apply(z == 1, 1, which)[n + 1:m]
fit2 = cv.customizedGlmnet(x.train, y.train, x.test, group.id, foldid = foldid)
# Print the optimal value of lambda:
fit2$lambda.min
# Print the customized training model fit:
fit2
# Compute test error using the predict function:
mean((y[n + 1:m] - predict(fit2))^2)
# Plot nonzero coefficients by group:
plot(fit2)
# Example 3: If there is no test set, but the training set is organized into
# blocks, you can do cross validation with these blocks as the basis for the
# customized training sets.
fit3 = cv.customizedGlmnet(x.train, y.train, foldid = foldid)
# Print the optimal value of lambda:
fit3$lambda.min
# Print the customized training model fit:
fit3
# Compute test error using the predict function:
mean((y[n + 1:m] - predict(fit3))^2)
# Plot nonzero coefficients by group:
plot(fit3)
```

[Package *customizedTraining* version 1.2 Index]