Description Usage Arguments Value Author(s) See Also Examples

View source: R/nudge.classify.R

Classifies observed data into differential and non-differential groups based on NUDGE model.

1 2 | ```
nudge.classify(data, obj, obj.cutoff = 0.1, obj.sigma.diff.cutoff = NULL,
obj.mu.diff.cutoff = NULL)
``` |

`data` |
an |

`obj` |
a list object returned by |

`obj.cutoff` |
optional local |

`obj.sigma.diff.cutoff` |
optional cut-off for standard deviation of the normal component in NUDGE model to be designated as representing differential. |

`obj.mu.diff.cutoff` |
optional cut-off for standard deviation of the normal component in NUDGE model to be designated as representing differential. |

A list object passed as input with additional element $class containing vector of classifications for all the observations in data. A classification of 1 denotes that the data is classified as differential with fdr < obj.cutoff.

`mu.diff.cutoff` |
normal component with mean > mu.diff.cutoff was used to represent differential component. |

`sigma.diff.cutoff` |
normal component with standard deviation > sigma.diff.cutoff was used to represent differential component. |

Cenny Taslim [email protected], with contributions from Abbas Khalili [email protected], Dustin Potter [email protected], and Shili Lin [email protected]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
library(DIME);
# generate simulated datasets with underlying uniform and 1-normal components
set.seed(1234);
N1 <- 1500; N2 <- 500; rmu <- c(1.5); rsigma <- c(1);
rpi <- c(.10,.90); a <- (-6); b <- 6;
chr1 <- c(-runif(ceiling(rpi[1]*N1),min = a,max =b),
rnorm(ceiling(rpi[2]*N1),rmu[1],rsigma[1]));
chr4 <- c(-runif(ceiling(rpi[1]*N2),min = a,max =b),
rnorm(ceiling(rpi[2]*N2),rmu[1],rsigma[1]));
# analyzing chromosome 1 and 4
data <- list(chr1,chr4);
# fit NUDGE model with maximum iterations = 20 only
set.seed(1234);
test <- nudge.fit(data, max.iter=20)
# vector of classification. 1 represents differential, 0 denotes non-differential
nudgeClass <- test$class;
``` |

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