Description Usage Arguments Details Value Author(s) References See Also Examples
Fits a classifier for matrix data. catch_matrix
is a special case of catch
when each observation \mathbf{X}_i is a matrix. Different from catch
takes list as input, data need to be formed in an array to call the function (see arguments). The function will perform prediction as well.
1 |
x |
Input matrix array. The array should be organized with dimension p1*p2*N, where \eqn{N} is the number of observations. |
z |
Input covariate matrix of dimension N*q, where q<N. |
y |
Class label. For |
testx |
Input testing matrix array. When |
testz |
Input testing covariate matrix. Can be omitted if there is no covariate. |
... |
Other arguments that can be passed to |
The function fits a matrix classifier as a special case of catch
. The fitted model and predictions should be identical to catch
when matrix data is provided. Input matrix should be organized as three-way array where sample size is the last dimension. If the matrix is organized in a list, users can either reorganize it or use catch
directly to fit model, which takes a matrix or tensor list as input and has the same output as catch_matrix
.
beta |
Output variable coefficients for each |
df |
The number of nonzero variables for each value in sequence |
dim |
Dimension of coefficient array. |
lambda |
The actual |
obj |
Objective function value for each value in sequence |
x |
The matrix list after adjustment in training data. If covariate is absent, this is the original input matrix. |
y |
Class label in training data. |
npasses |
Total number of iterations. |
jerr |
Error flag. |
sigma |
Estimated covariance matrix on each mode. |
delta |
Estimated delta matrix (vec(\widehat{\boldsymbol{μ}}_2-\widehat{\boldsymbol{μ}}_1),\cdots,vec(\widehat{\boldsymbol{μ}}_K-\widehat{\boldsymbol{μ}}_1)). |
mu |
Estimated mean array. |
prior |
Prior proportion of observations in each class. |
call |
The call that produces this object. |
pred |
Predicted categorical response for each value in sequence |
Yuqing Pan, Qing Mai, Xin Zhang
Pan, Y., Mai, Q., and Zhang, X. (2018), "Covariate-Adjusted Tensor Classification in High-Dimensions." Journal of the American Statistical Association, accepted.
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