Simrel r-package is a versatile tool for simulation of multivariate linear model data. The package consist of four core functions –
simrel for simulation. It also has two more functions – one for plotting covariance and rotation matrices and another for plotting different properties of simulated data. As the name suggests,
unisimrel function is used for simulating univariate linear model data,
bisimrel simulates bivariate linear model data where user can specify the correlation between two responses with and without given X. In addition, this function allows users to get responses (y) having common relevant components.
An extension of
multisimrel, by which user can simulate multivariate linear model data with multiple responses. In this simulation, each response must have exclusive set of predictors and relevant predictors components. Following examples will give a clear picture of these functions. The forth simulation function
simrel wraps around these function and calls them according to what type of data a user is simulating. Following section discusses about the arguments required for each of these simulation function.
A tool for simulating linear model data with single response discussed in sæbø et.al. (2015) and multi-response discussed in rimal et.al. (2018) is the basis for these functions. The function require following arguments which are also parameters for the simulation.
n: Number of training samples (n)
n = 1000simulates 1000 training observations.
p:Number of predictor variables (p)
p = 150gives data with 150 predictor variables.
q:Number of relevant predictors (q)
q = 15results 15 predictors out of
prelevant for the response.
relpos:Position of relevant components
relpos = c(1, 2, 3, 5)will give data with 4 relevant components at position 1, 2, 3 and 5.
R2:Coefficient of determination
R2 = 0.8gives data with 0.8 coefficient of determination.
A numeric value greater than 0. It is a factor controlling exponential decay of eigenvalues of predictor variables. For
gamma: Decay factor for exponential decay of eigenvalues of predictor variables
p predictors, the eigenvalues are computed as
i=1, 2, …, p so that, higher the value of
gamma steeper will be the decay of eigenvalues. Since steeper eigenvalues corresponds to high multicollinearity in data,
gamma also controls the multicollinearity present in the simulated data.
eta:Decay factor for exponential decay of eigenvalues of response variables
gamma, it is a factor controlling exponential decay of eigenvalues of response variables. For
mresponses, the eigenvalues are computed as
j=1, 2, …, mso that, higher the value of
etasteeper will be the decay of eigenvalues.
m:Number of Response Variables (Only applicable for Univariate Simulation)
ypos:Position of response components indices to combine together
yposparameters if specified as
list(c(1, 3), c(2, 4, 5))will mix up the inforamtion in response component 1 with uninformative component 3 so that the response variable 1 and 3 contains the same information that was contained in response component 1. A similar description can be made for the second response component which is mixed with non-informative response components 4 and 5.
rho:Correlation between two response variables (Only applicable for Bivariate Simulation
The package is now available in CRAN and can be installed using
A more recent stable version can be download from GitHub as,
Simulate a univariate linear model data with 100 training samples and 500 test samples having 10 predictors (X) where only 8 of them are relevant for the variation in the response vector. The population model should explain 80% of the variation present in the response. In addition, only 1st and 3rd principal components of X should be relevant for y and the eigenvalues of X decreases exponentially by a factor of 0.7.
library(simrel) sim_obj <- simrel( n = 100, # 100 training samples p = 10, # 10 predictor variables q = 8, # only 8 of them are relevant R2 = 0.8, # 80% of variation is explained by the model relpos = c(1, 3), # First and third principal components are relevant gamma = 0.7, # decay factor of eigenvalue of X is 7 ntest = 500, # 500 Test observations type = "univariate" # Univariate linear model data simulation )
sim_obj is a object with class
simrel and constitue of a list of simulated linear model data along with other relevant properties. Lets use
plot function to overview the situation,
The wrapper function
bisimrel for simulating bivariate linear model data. Lets consider a situation to simulate data from bivariate distribution with 100 training and 500 test samples. The response vectors y1 and y2 have correlation of 0.8 without given X and 0.6 with given X. Among 10 total predictor variables, 5 are relevant for y1 and 5 are relevant for y2. However 3 of them are relevant for both of them. Let the predictors explain 80% and 70% of total variation present in population of y1 and y2 respectively. In addition, let 1, 2 and 3 components are relevant for y1 and 3 and 4 components are relevant for y2. In this case, the third component is relevant for both responses. Let the decay factor of eigenvalues of X be 0.8.
simrel2_obj <- simrel( n = 100, # 100 training samples p = 10, # 10 predictor variables q = c(5, 5, 3), # relevant variables for y1 and y2 relpos = list(c(1, 2, 3), c(3, 4)), # relevant components for y1 and y2 R2 = c(0.8, 0.7), # Coefficient of variation for y1 and y2 rho = c(0.8, 0.6), # correlation between y1 and y2 with and without given X gamma = 0.8, # decay factor of eigenvalues of X ntest = 500, # 500 test samples type = "bivariate" )
Lets look at the plot,
Multivariate simulation uses
multisimrel function and can simulate multiple responses. Lets simulate 100 training samples and 500 test samples. The simulated data has 5 responses and 15 predictors. These 5 responses spans 5 latent space out of which only 3 are related to the predictors. Lets denote them by wi. Let 5, 4 and 4 predictors are relevant for response components w1, w1 and w1 respectively. The position of relevant predictor components for w1 be 1, 2, 3; for w2 be 4 and 5. Similarly, predictor components 6 and 8 are relevant for w3.
Since we need 5 response variables, we mix-up these 3 informative response components with 2 remaining uninformative components so that all simulated response contains information that X are related. Lets combine w1 with w4 and w3 with w5. So that the predictors that are relevant for response components w1 will be relevant for response y1 and y3 and so on.
In addition to these latent space requirements, let X explains 80% variation present in w1, 50% in w2 and 70% in w3. The eigenvalues of X reduces by the factor of 0.8.
simrel_m_obj <- simrel( n = 100, # 100 training samples p = 15, # 15 predictor variables q = c(5, 4, 4), # relevant variables for w1, w2 and w3 relpos = list(c(1, 2, 3), c(4, 5), c(6, 8)), # relevant components for w1, w2 and y3 R2 = c(0.8, 0.5, 0.7), # Coefficient of variation for w1, w2 and y3 ypos = list(c(1, 4), c(2), c(3, 5)), # combining response components together m = 5, # Number of response gamma = 0.8, # decay factor of eigenvalues of X ntest = 500, # 500 test samples type = "multivariate" # multivariate simulation )
Lets look at the
To make the process easier to use, we have created a shiny gadget as an rstudio addons. If you are using Rstuio, you can access this app from Tools > Addins > simulatr. But you can also access this app using
simrel::app_simulatr(). This will open the app in a browser from where you can choose all your parameter, see the true population parametrs you will get from the simulation. When the app is closed, it will give an command output on your R console.