Description Usage Arguments Value Author(s) References See Also Examples
Creates an object simulating ARIMA random variables. The created object contains data in the format required to use other methods of this package. Makes a simulation similar to EEG data to test the capabilities those methods. NOTE: The only purpose of the simulated data is to test the package features, it is not to be used to study properties of real EEG data!
1 2 |
n.classes |
number of different classes. |
n.rec |
number of recordings of each class. |
n.channels |
number of channels in the simulation. |
n.signals |
number of signals, observations or samples for each recording of each channel. |
ar |
is a matrix with AR coefficients of the ARIMA model. Each row contains the AR parameters for each class and each channel. One can make the number of rows in |
ma |
is a matrix with MA coefficients, defined the same way as |
order |
is a matrix with the same number of rows of |
vars |
a vector with length equal to the number of rows in |
data |
the simulated data frame. The data frame is organized as follows: each column represents a different channel. Thus, each signal collected by each channel is represented in each row. To identify the class and recording represented by each line, the vectors |
classes.Id |
the vector indicating the class id of each row of |
rec.Id |
the vector indicating the recording id of each row of |
n.classes |
number of different classes. |
n.rec |
number of recordings of each class. |
n.channels |
number of channels in the simulation. |
n.signals |
number of signals, observations or samples for each recording of each channel. |
vars |
a vector with length equal to the number of rows in |
Murilo Coutinho Silva (coutinho.stat@gmail.com), George Freitas von Borries
Brockwell, P.J., Davis, R.A. (2002) Introduction to Time Series and Forecasting. 2nd ed. Colorado: Springer. Cap. 4.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | library(eegAnalysis)
#### Simulating EEG data
X<-randEEG(n.classes = 2, n.rec = 10, n.channels = 20, n.signals = 250,
vars = c(1, 2))
#### What do we have here?
names(X)
#### length of classes.Id and rec.Id are equal to the number of rows of data:
length(X$classes.Id)==nrow(X$data)
length(X$rec.Id)==nrow(X$data)
#### We have two classes:
unique(X$classes.Id)
#### We have 10 recordings for each class:
unique(X$rec.Id[which(X$classes.Id==1)])
unique(X$rec.Id[which(X$classes.Id==2)])
#### We have 20 channels:
ncol(X$data)
#### We have 250 signals for each class, recording and channel:
length(X$data[which(X$classes.Id==1 & X$rec.Id==1),1])
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