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- The Iman-Conover Method — aggregate 0. 30. 0 documentation
Here is the basic idea of the Iman-Conover method Given samples of n values from two known marginal distributions X and Y and a desired correlation ρ between them, re-order the samples to have the same rank order as a reference distribution, of size n × 2, with linear correlation ρ
- Simulate correlated variables by using the Iman-Conover transformation
To use the Iman-Conover transformation, you must specify two things: A set of data and a target correlation matrix The transformation will induce the specified correlation in the data by rearranging (or permuting) the columns of the data
- flexIC: Flexible Rank-Preserving Correlation Engine
Applies a rank-based correlation structure to a numeric matrix using a flexible, iterative variant of the Iman–Conover algorithm The method reorders each column of x based on the rank structure of a multivariate normal draw whose correlation matrix matches target_r
- Risk aggregation with empirical margins: Latin hypercubes, empirical . . .
The original description of the Iman–Conover method uses random reordering of marginal samples, and the intention there was to control the rank correlations in the synthetic multivariate sample
- 2. 12. Working With Samples — aggregate 0. 22. 0 documentation
It is also easy to apply Iman-Conover to a dataframe using the method aggregate utilities iman_conover() It reorders the input dataframe to have the same rank correlation as a multivariate normal reference sample with the desired linear correlation
- The geometry of the Iman-Conover transformation - The DO Loop
But that's okay: Iman and Conover recognized that any two matrices whose columns have the same ranks also have the same rank correlation Thus, the last step uses the column ranks of Y to reorder the values in the columns of X
- Help for package flexIC - The Comprehensive R Archive Network
Applies a rank-based correlation structure to a numeric matrix using a flexible, iterative variant of the Iman–Conover algorithm The method reorders each column of x based on the rank structure of a multivariate normal draw whose correlation matrix matches target_r
- CRAN: Package flexIC
Implements a fast, flexible method for simulating continuous variables with specified rank correlations using the Iman–Conover transformation (Iman Conover, 1982 < doi:10 1080 03610918208812265 >) and back-ranking Includes plotting tools and error-diagnostics
- Rank Transformations as a Bridge Between Parametric and . . . - JSTOR
This new procedure is conditionally distribution free given the ranks in the blocks and asymptotically distribution free by virtue of the central limit theorem Properties of this test are reported by Iman and Conover (1980a)
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