# Classification Based on iNUDGE Model

### Description

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

### Usage

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

### Arguments

`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 iNUDGE model to be designated as representing differential. |

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

### Value

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. |

### Author(s)

Cenny Taslim taslim.2@osu.edu, with contributions from Abbas Khalili khalili@stat.ubc.ca, Dustin Potter potterdp@gmail.com, and Shili Lin shili@stat.osu.edu

### See Also

`inudge.fit`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
library(DIME);
# generate simulated datasets with underlying uniform and 2-normal distributions
set.seed(1234);
N1 <- 1500; N2 <- 500; rmu <- c(-2.25,1.5); rsigma <- c(1,1);
rpi <- c(.10,.45,.45); a <- (-6); b <- 6;
chr4 <- list(c(-runif(ceiling(rpi[1]*N1),min = a,max =b),
rnorm(ceiling(rpi[2]*N1),rmu[1],rsigma[1]),
rnorm(ceiling(rpi[3]*N1),rmu[2],rsigma[2])));
chr9 <- list(c(-runif(ceiling(rpi[1]*N2),min = a,max =b),
rnorm(ceiling(rpi[2]*N2),rmu[1],rsigma[1]),
rnorm(ceiling(rpi[3]*N2),rmu[2],rsigma[2])));
# analyzing chromosome 4 and 9
data <- list(chr4,chr9);
# fit iNUDGE model with 2 normal components and maximum iterations = 20
set.seed(1234);
test <- inudge.fit(data, K = 2, max.iter=20);
# vector of classification. 1 represents differential, 0 denotes non-differential
inudgeClass <- test$class;
``` |