Simulate experiment-like data with independent binary Xs.

Simulate experiment-like data with independent binary Xs.

Usage

sim_data_exp(
  n,
  r.xy,
  approx = TRUE,
  tol = 0.01,
  max.iter = 30,
  verbose = FALSE,
  seed = NULL
)

Arguments

n

Number of observations (cases).

r.xy

A vector of expected correlations of each X (binary independent variable: 0 or 1) with Y.

approx

Make the sample correlation matrix approximate more to values as specified in r.xy, using the method of orthogonal decomposition of residuals (i.e., making residuals more independent of Xs). Defaults to TRUE.

tol

Tolerance of absolute difference between specified and empirical correlations. Defaults to 0.01.

max.iter

Maximum iterations for approximation. More iterations produce more approximate correlations, but the absolute differences will be convergent after about 30 iterations. Defaults to 30.

verbose

Print information about iterations that satisfy tolerance. Defaults to FALSE.

seed

Random seed for replicable results. Defaults to NULL.

Value

Return a data.frame of simulated data.

See also

sim_data()

Examples

data = sim_data_exp(n=1000, r.xy=c(0.5, 0.3), seed=1)
cor(data)  # tol = 0.01
#>             X1          X2         Y
#> X1 1.000000000 0.004486829 0.5053679
#> X2 0.004486829 1.000000000 0.3073449
#> Y  0.505367897 0.307344860 1.0000000

data = sim_data_exp(n=1000, r.xy=c(0.5, 0.3), seed=1,
                    verbose=TRUE)
#> 1 iterations done: abs(cor.diff.) = 0.01087 and 0.01431
#> 2 iterations done: abs(cor.diff.) = 0.00754 and 0.0101
#> 3 iterations satisfied tolerance of 0.01
cor(data)  # print iteration information
#>             X1          X2         Y
#> X1 1.000000000 0.004486829 0.5053679
#> X2 0.004486829 1.000000000 0.3073449
#> Y  0.505367897 0.307344860 1.0000000

data = sim_data_exp(n=1000, r.xy=c(0.5, 0.3), seed=1,
                    verbose=TRUE, tol=0.001)
#> 1 iterations done: abs(cor.diff.) = 0.010872 and 0.014311
#> 2 iterations done: abs(cor.diff.) = 0.007539 and 0.010095
#> 3 iterations done: abs(cor.diff.) = 0.005368 and 0.007345
#> 4 iterations done: abs(cor.diff.) = 0.003956 and 0.005555
#> 5 iterations done: abs(cor.diff.) = 0.003039 and 0.004392
#> 6 iterations done: abs(cor.diff.) = 0.002444 and 0.003637
#> 7 iterations done: abs(cor.diff.) = 0.002058 and 0.003147
#> 8 iterations done: abs(cor.diff.) = 0.001807 and 0.002829
#> 9 iterations done: abs(cor.diff.) = 0.001645 and 0.002623
#> 10 iterations done: abs(cor.diff.) = 0.00154 and 0.002489
#> 11 iterations done: abs(cor.diff.) = 0.001472 and 0.002403
#> 12 iterations done: abs(cor.diff.) = 0.001427 and 0.002347
#> 13 iterations done: abs(cor.diff.) = 0.001399 and 0.00231
#> 14 iterations done: abs(cor.diff.) = 0.00138 and 0.002287
#> 15 iterations done: abs(cor.diff.) = 0.001368 and 0.002272
#> 16 iterations done: abs(cor.diff.) = 0.00136 and 0.002262
#> 17 iterations done: abs(cor.diff.) = 0.001355 and 0.002255
#> 18 iterations done: abs(cor.diff.) = 0.001352 and 0.002251
#> 19 iterations done: abs(cor.diff.) = 0.00135 and 0.002248
#> 20 iterations done: abs(cor.diff.) = 0.001349 and 0.002247
#> 21 iterations done: abs(cor.diff.) = 0.001348 and 0.002245
#> 22 iterations done: abs(cor.diff.) = 0.001347 and 0.002245
#> 23 iterations done: abs(cor.diff.) = 0.001347 and 0.002244
#> 24 iterations done: abs(cor.diff.) = 0.001346 and 0.002244
#> 25 iterations done: abs(cor.diff.) = 0.001346 and 0.002244
#> 26 iterations done: abs(cor.diff.) = 0.001346 and 0.002244
#> 27 iterations done: abs(cor.diff.) = 0.001346 and 0.002244
#> 28 iterations done: abs(cor.diff.) = 0.001346 and 0.002244
#> 29 iterations done: abs(cor.diff.) = 0.001346 and 0.002243
#> 30 iterations done: abs(cor.diff.) = 0.001346 and 0.002243
cor(data)  # more approximate, though not exact
#>             X1          X2         Y
#> X1 1.000000000 0.004486829 0.5013461
#> X2 0.004486829 1.000000000 0.3022435
#> Y  0.501346082 0.302243457 1.0000000

data = sim_data_exp(n=1000, r.xy=c(0.5, 0.3), seed=1,
                    approx=FALSE)
cor(data)  # far less exact
#>             X1          X2         Y
#> X1 1.000000000 0.004486829 0.4793269
#> X2 0.004486829 1.000000000 0.3322344
#> Y  0.479326865 0.332234387 1.0000000