Description Usage Arguments Examples

This function evaluates the inference performance of the sparse factor analysis model by using different cut-off values to calculate the sensitivity and specificity of the result of the Gibbs sampling algorithm. In this way, it can give the AUC (area under curve) value and plot the receiver-operating-curve.

1 2 | ```
ROC_plot(matrixZ1, matrixZ2, matrixZ_chain,
plot_name, result_file_name, burn)
``` |

`matrixZ1,matrixZ2` |
The binary matrix indicating whether each entry of loading matrix W1 and W2 is non-zero. For example, matrixZ1[g,k]=1 indicates that matrixW1[g,k] is non-zero, and vice versa. Dim(matrixZ1)=G1*K. Dim(matrixZ2)=G2*K. |

`matrixZ_chain` |
The updated matrixZ in each recorded iteration. A list of length 2, for matrixZ1 and matrixZ2 respectively. matrixZ_chain[[1]] and matrixZ2[[2]] are both matrices with dimension A*B, whereas A is the number of recorded iterations and B the length of matrixZ1/Z2. Each row of the matrix corresponds to the vectorized matrixZ1 or matrixZ2 in each iteration. |

`plot_name` |
The name of the ".pdf" file of the plot |

`result_file_name` |
The name of the ".RData" file which stores the result of the performance evaluation. |

`burn` |
The number of iteration taken as the starting point for the inference of the unknown values in the model. For example,if burn=101, samples from the 101-th iteration to the final iteration of the Gibbs sampling process will be averaged for the inference. |

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iFad documentation built on May 29, 2017, 8:31 p.m.

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