# EMMsim

From rEMM v1.0-11
by Michael Hahsler

##### Synthetic Data to Demonstrate EMMs

A simulated data set with four clusters in $R^2$. Each cluster is represented by a bivariate normally distributed random variable.

$\mu$ are the coordinates of the means of the distributions and $\Sigma$ contains the covariance matrices. Two data stream are created using a fixed sequence $<1,2,1,3,4>$ through the four clusters. For the training data, the sequence is repeated 40 times (200 data points) and for the test data five times (25 data points).

The code to generate the data is shown in the Examples section below.

- Keywords
- datasets

##### Usage

`data(EMMsim)`

##### Format

`EMMsim_train`

and `EMMsim_test`

are matrices containing the
data. `EMMsim_sequence_train`

and `EMMsim_sequence_test`

contain the sequence of the data through the four clusters.

##### Examples

```
## the data was generated by
## Not run:
# set.seed(1234)
#
# ## simulated data
# mu <- cbind(
# x = c(0, 0.2,1,0.9),
# y = c(0, 0.7,1,0.2)
# )
#
# sd_rho <- cbind(
# x = c(0.2, 0.15, 0.05, 0.02),
# y = c(0.1, 0.04, 0.03, 0.05),
# rho = c(0, 0.7, 0.3, -0.4)
# )
#
# Sigma <- lapply(1:nrow(sd_rho), FUN = function(i) rbind(
# c(sd_rho[i,"x"]^2, sd_rho[i,"rho"]*sd_rho[i,"x"]*sd_rho[i,"y"]),
# c(sd_rho[i,"rho"]*sd_rho[i,"x"]*sd_rho[i,"y"], sd_rho[i,"y"]^2)))
#
#
# sequence <- c(1,2,1,3,4)
#
# EMMsim_sequence_train <- rep(sequence, 40)
# EMMsim_sequence_test <- rep(sequence, 5)
#
# library("MASS")
# EMMsim_train <- t(sapply(EMMsim_sequence_train, FUN = function(i)
# mvrnorm(1, mu=mu[i,], Sigma=Sigma[[i]])))
# EMMsim_test <- t(sapply(rep(EMMsim_sequence_test), FUN = function(i)
# mvrnorm(1, mu=mu[i,], Sigma=Sigma[[i]])))
# ## End(Not run)
```

*Documentation reproduced from package rEMM, version 1.0-11, License: GPL-2*

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