Simulation Functions

This function simulates regression and classification data with truly important predictors and irrelevant predictions.

twoClassSim(n = 100, intercept = -5, linearVars = 10, 
	        noiseVars = 0, corrVars = 0, 
            corrType = "AR1", corrValue = 0, mislabel = 0)

SLC14_1(n = 100, noiseVars = 0, corrVars = 0, corrType = "AR1", corrValue = 0)

SLC14_2(n = 100, noiseVars = 0, corrVars = 0, corrType = "AR1", corrValue = 0)

LPH07_1(n = 100, noiseVars = 0, corrVars = 0, corrType = "AR1", corrValue = 0)

LPH07_2(n = 100, noiseVars = 0, corrVars = 0, corrType = "AR1", corrValue = 0)

The number of simulated data points
The intercept, which controls the class balance. The default value produces a roughly balanced data set when the other defaults are used.
The number of linearly important effects. See Details below.
The number of uncorrelated irrelevant predictors to be included.
The number of correlated irrelevant predictors to be included.
The correlation structure of the correlated irrelevant predictors. Values of "AR1" and "exch" are available (see Details below)
The correlation value.
The proportion of data that is possibly mislabeled. See Details below.

The first function (twoClassSim) generates two class data. The data are simulated in different sets. First, two multivariate normal predictors (denoted here as A and B) are created with a correlation our about 0.65. They change the log-odds using main effects and an interaction:

intercept - 4A + 4B + 2AB

The intercept is a parameter for the simulation and can be used to control the amount of class imbalance.

The second set of effects are linear with coefficients that alternate signs and have values between 2.5 and 0.025. For example, if there were six predictors in this set, their contribution to the log-odds would be

-2.50C + 2.05D -1.60E + 1.15F -0.70G + 0.25H

The third set is a nonlinear function of a single predictor ranging between [0, 1] called J here:

(J^3) + 2exp(-6(J-0.3)^2)

The fourth set of informative predictors are copied from one of Friedman's systems and use two more predictors (K and L):


All of these effects are added up to model the log-odds. This is used to calculate the probability of a sample being in the first class and a random uniform number is used to actually make the assignment of the actual class. To mislabel the data, the probability is reversed (i.e. p = 1 - p) before the random number generation.

The remaining functions simulate regression data sets. LPH07_1 and LPH07_2 are from van der Laan et al (2007). The first function uses random Bernoulli variables that have a 40% probability of being a value of 1. The true regression equation is:

2*w_1*w_10 + 4*w_2*w_7 + 3*w_4*w_5 - 5*w_6*w_10 + 3*w_8*w_9 + w_1*w_2*w_4 - 2*w_7*(1-w_6)*w_2*w_9 - 4*(1 - w_10)*w_1*(1-w_4)

The simulated error term is a standard normal (i.e. Gaussian). The noise variables are simulated in the same manner as described above but are made binary based on whether the normal random variable is above or below 0. The second function (LPH07_2) uses 20 independent Gaussians with mean zero and variance 16. The functional form here is:

x_1*x_2 + x_10^2 - x_3*x_17 - x_15*x_4 + x_9*x_5 + x_19 - x_20^2 + x_9*x_8

The error term is also Gaussian with mean zero and variance 16.

The function SLC14_1 simulates a system from Sapp et al (2014). All informative predictors are independent Gaussian random variables with mean zero and a variance of 9. The prediction equation is:

x_1 + sin(x_2) + log(abs(x_3)) + x_4^2 + x_5*x_6 + I(x_7*x_8*x_9 < 0) + I(x_10 > 0) + x_11*I(x_11 > 0) + sqrt(abs(x_12)) + cos(x_13) + 2*x_14 + abs(x_15) + I(x_16 < -1) + x_17*I(x_17 < -1) - 2 * x_18 - x_19*x_20

The random error here is also Gaussian with mean zero and a variance of 9.

SLC14_2 is also from Sapp et al (2014). Two hundred independent Gaussian variables are generated, each having mean zero and variance 16. The functional form is

-1 + log(abs(x_1)) + ... + log(abs(x_200))

and the error term is Gaussian with mean zero and a variance of 25.

For each simulation, the user can also add non-informative predictors to the data. These are random standard normal predictors and can be optionally added to the data in two ways: a specified number of independent predictors or a set number of predictors that follow a particular correlation structure. The only two correlation structure that have been implemented are

  • compound-symmetry (aka exchangeable) where there is a constant correlation between all the predictors
  • auto-regressive 1 [AR(1)]. While there is no time component to these data, this structure can be used to add predictors of varying levels of correlation. For example, if there were 4 predictors andrwas the correlation parameter, the between predictor correlation matrix would be

| 1 sym | | r 1 | | r^2 r 1 | | r^3 r^2 r 1 | | r^4 r^3 r^2 r 1 |


  • a data frame with columns:
  • ClassA factor with levels "Class1" and "Class2"
  • TwoFactor1, TwoFactor2Correlated multivariate normal predictors (denoted as A and B above)
  • Nonlinear1, Nonlinear2, Nonlinear3Uncorrelated random uniform predictors (J, K and L above).
  • Linear1, ...Optional uncorrelated standard normal predictors (C through H above)
  • Noise1, ...Optional uncorrelated standard normal predictions
  • Corr1, ...Optional correlated multivariate normal predictors (each with unit variances)
  • .


van der Laan, M. J., & Polley Eric, C. (2007). Super learner. Statistical Applications in Genetics and Molecular Biology, 6(1), 1-23.

Sapp, S., van der Laan, M. J., & Canny, J. (2014). Subsemble: an ensemble method for combining subset-specific algorithm fits. Journal of Applied Statistics, 41(6), 1247-1259.

  • twoClassSim
  • SLC14_1
  • SLC14_2
  • LPH07_1
  • LPH07_2
example <- twoClassSim(100, linearVars = 1)
splom(~example[, 1:6], groups = example$Class)
Documentation reproduced from package caret, version 6.0-37, License: GPL-2

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