Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/customizedGlmnet.R

Fit a regularized lasso model using customized training

1 2 3 | ```
customizedGlmnet(xTrain, yTrain, xTest, groupid = NULL, G = NULL,
family = c("gaussian", "binomial", "multinomial"), dendrogram = NULL,
dendrogramTestIndices = NULL)
``` |

`xTrain` |
an n-by-p matrix of training covariates |

`yTrain` |
a length-n vector of training responses. Numeric for family = |

`xTest` |
an m-by-p matrix of test covariates |

`groupid` |
an optional length-m vector of group memberships for the test set. If
specified, customized training subsets are identified using the union of
nearest neighbor sets for each test group. Either |

`G` |
a positive integer indicating the number of clusters for the joint clustering
of the test and training data. Ignored if |

`family` |
response type |

`dendrogram` |
optional output from |

`dendrogramTestIndices` |
optional set of indices (corresponding to dendrogram) held out in
cross-validation. Used by |

Identify customized training subsets of the training data through one of two methods: (1) If groupid is specified, grouping the test data, then for each test group find the 10 nearest neighbors of each observation in the group and use the union of these nearest neighbor sets as the customized training set or (2) If G is specified, jointly cluster the test and training data using hierarchical clustering with complete linkage. Within each cluster, the training data are used as the customized training subset for the test data. Once the customized training subsets have been identified, use glmnet to fit an l1-regularized regression model to each.

an object with class `customizedGlmnet`

`call` |
the call that produced this object |

`CTset` |
a list containing the customized training subsets for each test group |

`fit` |
a list containing the glmnet fit for each test group |

`groupid` |
a length-m vector containing the group memberships of the test data |

`x` |
a list containing |

`y` |
training response vector (specified in function call) |

`family` |
response type (specified in function call) |

`standard` |
the fit of |

Scott Powers, Trevor Hastie, Robert Tibshirani

Scott Powers, Trevor Hastie and Robert Tibshirani (2015) "Customized training with an application to mass specrometric imaging of gastric cancer data." Annals of Applied Statistics 9, 4:1709-1725.

`print.customizedGlmnet`

, `predict.customizedGlmnet`

,
`plot.customizedGlmnet`

, `cv.customizedGlmnet`

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 | ```
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]
# Example 1: Use clustering to fit the customized training model to training
# and test data with no predefined test-set blocks
fit1 = customizedGlmnet(x.train, y.train, x.test, G = 3,
family = "gaussian")
# Print the customized training model fit:
fit1
# Extract nonzero regression coefficients for each group:
nonzero(fit1, lambda = 10)
# Compute test error using the predict function:
mean((y.test - predict(fit1, lambda = 10))^2)
# Plot nonzero coefficients by group:
plot(fit1, lambda = 10)
# Example 2: If the test set has predefined blocks, use these blocks to define
# the customized training sets, instead of using clustering.
group.id = apply(z == 1, 1, which)[n + 1:m]
fit2 = customizedGlmnet(x.train, y.train, x.test, group.id)
# Print the customized training model fit:
fit2
# Extract nonzero regression coefficients for each group:
nonzero(fit2, lambda = 10)
# Compute test error using the predict function:
mean((y.test - predict(fit2, lambda = 10))^2)
# Plot nonzero coefficients by group:
plot(fit2, lambda = 10)
``` |

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