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Simulate data preserving the correlation structure of an input data frame

Source: R/FUNCTIONS_GENERATE_DATA.R
simulate_correlation_from_sample.Rd

Estimates the covariance matrix and column means from the input data, then draws multivariate normal samples that reproduce the same correlation structure.

Usage

simulate_correlation_from_sample(cordata, nrows = 10)

Arguments

cordata

A numeric data frame or matrix. The source data from which the covariance matrix and means are estimated. Missing values are handled pairwise.

nrows

Integer. Number of observations to simulate. Default is 10.

Value

A data frame with nrows rows and the same number of columns as cordata, containing simulated values with matching correlation structure.

Details

Uses mvrnorm to draw from a multivariate normal distribution parameterised by the sample covariance matrix and column means of cordata. Accuracy of the reproduced correlations improves with larger nrows.

See also

generate_correlation_matrix

Examples

correlation_matrix <- generate_correlation_matrix()
stats::cor(correlation_matrix)
#>           X1       X2       X3     X4       X5        X6      X7        X8       X9       X10
#> X1   1.00000 0.501313  0.18534 0.2085  0.28956  0.656319 -0.4672  0.013719  0.33169  0.184808
#> X2   0.50131 1.000000  0.68518 0.6994  0.48664  0.261138  0.3809  0.623548  0.23253  0.005517
#> X3   0.18534 0.685175  1.00000 0.4973 -0.01293  0.235015  0.2830  0.546300  0.02318  0.010423
#> X4   0.20849 0.699366  0.49726 1.0000  0.46257  0.302368  0.5124  0.379016  0.51796  0.398845
#> X5   0.28956 0.486644 -0.01293 0.4626  1.00000  0.035975  0.1090  0.191652  0.60877  0.564674
#> X6   0.65632 0.261138  0.23502 0.3024  0.03598  1.000000 -0.1892  0.009213 -0.02515  0.169000
#> X7  -0.46716 0.380943  0.28302 0.5124  0.10900 -0.189245  1.0000  0.424804 -0.24659 -0.212387
#> X8   0.01372 0.623548  0.54630 0.3790  0.19165  0.009213  0.4248  1.000000 -0.03675 -0.102624
#> X9   0.33169 0.232525  0.02318 0.5180  0.60877 -0.025150 -0.2466 -0.036749  1.00000  0.826173
#> X10  0.18481 0.005517  0.01042 0.3988  0.56467  0.169000 -0.2124 -0.102624  0.82617  1.000000
stats::cor(simulate_correlation_from_sample(correlation_matrix, nrows = 1000))
#>           X1        X2       X3     X4       X5        X6      X7        X8       X9       X10
#> X1   1.00000  0.496014  0.15907 0.2012  0.25810  0.682100 -0.4642 -0.015474  0.29781  0.151853
#> X2   0.49601  1.000000  0.64986 0.6972  0.47445  0.282054  0.3877  0.615765  0.22078 -0.007062
#> X3   0.15907  0.649858  1.00000 0.4654 -0.06816  0.209322  0.2616  0.497973  0.01875  0.012214
#> X4   0.20117  0.697200  0.46541 1.0000  0.47857  0.309343  0.5229  0.348549  0.52604  0.410248
#> X5   0.25810  0.474454 -0.06816 0.4786  1.00000  0.061123  0.1479  0.191050  0.59065  0.544771
#> X6   0.68210  0.282054  0.20932 0.3093  0.06112  1.000000 -0.1910 -0.008972 -0.02454  0.165468
#> X7  -0.46416  0.387664  0.26162 0.5229  0.14787 -0.191039  1.0000  0.462362 -0.21626 -0.179572
#> X8  -0.01547  0.615765  0.49797 0.3485  0.19105 -0.008972  0.4624  1.000000 -0.08630 -0.144862
#> X9   0.29781  0.220776  0.01875 0.5260  0.59065 -0.024541 -0.2163 -0.086303  1.00000  0.822033
#> X10  0.15185 -0.007062  0.01221 0.4102  0.54477  0.165468 -0.1796 -0.144862  0.82203  1.000000