Add correlated data to existing data.table
addCorData(
dtOld,
idname,
mu,
sigma,
corMatrix = NULL,
rho,
corstr = "ind",
cnames = NULL
)Data table that is the new columns will be appended to.
Character name of id field, defaults to "id".
A vector of means. The length of mu must be nvars.
Standard deviation of variables. If standard deviation differs for each variable, enter as a vector with the same length as the mean vector mu. If the standard deviation is constant across variables, as single value can be entered.
Correlation matrix can be entered directly. It must be symmetrical and positive semi-definite. It is not a required field; if a matrix is not provided, then a structure and correlation coefficient rho must be specified.
Correlation coefficient, -1 <= rho <= 1. Use if corMatrix is not provided.
Correlation structure of the variance-covariance matrix defined by sigma and rho. Options include "ind" for an independence structure, "cs" for a compound symmetry structure, and "ar1" for an autoregressive structure.
Explicit column names. A single string with names separated by commas. If no string is provided, the default names will be V#, where # represents the column.
The original data table with the additional correlated columns
def <- defData(varname = "xUni", dist = "uniform", formula = "10;20", id = "myID")
def <- defData(def,
varname = "xNorm", formula = "xUni * 2", dist = "normal",
variance = 8
)
dt <- genData(250, def)
mu <- c(3, 8, 15)
sigma <- c(1, 2, 3)
dtAdd <- addCorData(dt, "myID",
mu = mu, sigma = sigma,
rho = .7, corstr = "cs"
)
dtAdd
#> Key: <myID>
#> myID xUni xNorm V1 V2 V3
#> <int> <num> <num> <num> <num> <num>
#> 1: 1 18.15334 35.91999 3.5837828 9.361068 19.606262
#> 2: 2 16.81106 34.98541 0.6595679 3.418403 6.866132
#> 3: 3 16.41371 35.91866 2.9295731 7.656470 14.774557
#> 4: 4 16.31126 28.88397 2.7552943 9.282415 17.152096
#> 5: 5 15.42086 33.82735 2.7521645 7.752415 12.266413
#> ---
#> 246: 246 18.58274 32.27800 4.5926123 7.067914 15.865338
#> 247: 247 11.57482 22.98997 2.8351522 5.202762 15.031914
#> 248: 248 14.99170 33.36374 4.3554301 5.638182 16.781637
#> 249: 249 10.81451 14.51719 3.4863076 8.957330 18.431615
#> 250: 250 11.94921 25.83222 4.0164770 9.961086 19.148114
round(var(dtAdd[, .(V1, V2, V3)]), 3)
#> V1 V2 V3
#> V1 1.100 1.467 2.261
#> V2 1.467 4.216 4.544
#> V3 2.261 4.544 10.209
round(cor(dtAdd[, .(V1, V2, V3)]), 2)
#> V1 V2 V3
#> V1 1.00 0.68 0.67
#> V2 0.68 1.00 0.69
#> V3 0.67 0.69 1.00
dtAdd <- addCorData(dt, "myID",
mu = mu, sigma = sigma,
rho = .7, corstr = "ar1"
)
round(cor(dtAdd[, .(V1, V2, V3)]), 2)
#> V1 V2 V3
#> V1 1.00 0.71 0.48
#> V2 0.71 1.00 0.66
#> V3 0.48 0.66 1.00
corMat <- matrix(c(1, .2, .8, .2, 1, .6, .8, .6, 1), nrow = 3)
dtAdd <- addCorData(dt, "myID",
mu = mu, sigma = sigma,
corMatrix = corMat
)
round(cor(dtAdd[, .(V1, V2, V3)]), 2)
#> V1 V2 V3
#> V1 1.00 0.23 0.80
#> V2 0.23 1.00 0.62
#> V3 0.80 0.62 1.00