Description Usage Arguments Details Value Objects from the Class Slots Extends Methods Author(s) References See Also Examples

The `SimThresher`

class is used to simulate `Thresher`

objects.

1 2 | ```
SimThresher(ss, nSample, nm = deparse(substitute(ss)), rho = NULL,
agfun = agDimTwiceMean, ...)
``` |

`ss` |
A covariance matrix. |

`nSample` |
An integer; the number of samples to simulate. |

`nm` |
A character string; the name of this object. |

`rho` |
A numeric vector; the correlation between different variables. If NULL, then these are obtained from the covariance matrix. |

`agfun` |
A function used by the |

`...` |
Parameters to be passed to the |

Basically, given a number of samples and a covariance matrix, simulate a data matrix of the appropriate size and multivariate normal structure by assuming that all of the means are zero. After simulating the data, we apply the Thresher algorithm. The result is an object that combines the simulation parameters, simulated data, and fitted model.

The `SimThresher`

function returns an object of the
`SimThresher`

class.

Objects should be created using the `SimThresher`

constructor.

`nSample`

:An integer; the number of simulated samples.

`covariance`

:A covariance matrix.

`rho`

:A vector of correlation coefficients; essentially the unique values in the upper triangular part of the covariance matrix.

Class `Thresher`

, directly.

- image
`signature(x = "SimThresher")`

: Produces an image of the covariance matrix.- makeFigures
`signature(object = "SimThresher")`

: This is a convenience function to produce a standard set of figures. In addition tot he plots preodcued for`Thresher`

object, this function also produces an image of te covariance matrix used in the simulations. If the`DIR`

argument is non-null, it is treated as the name of an existing directory where the figures are stored as PNG files. Otherwise, the figures are displayed interactively, one at a time, in a window on screen.

Kevin R. Coombes <[email protected]>, Min Wang.

Wang M, Abrams ZB, Kornblau SM, Coombes KR. Thresher: determining the number of clusters while removing outliers. BMC Bioinformatics, 2018; 19(1):1-9. doi://10.1186/s12859-017-1998-9.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
set.seed(250264)
rho <- 0.5
nProtein <- 16
splinter <- sample((nProtein/2) + (-3:3), 1)
sigma1 <- matrix(rho, ncol=nProtein, nrow=nProtein)
diag(sigma1) <- 1
st <- SimThresher(sigma1, nSample=300)
image(st, col=redgreen(64), zlim=c(-1,1))
screeplot(st, col='pink', lcol='red')
plot(st)
scatter(st)
heat(st)
``` |

Thresher documentation built on March 7, 2019, 5:07 p.m.

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