true discoveries {ctgt} | R Documentation |

## True discoveries

### Description

To count the number of true discoveries within a given pathway or feature set of interest.

### Usage

```
discoveries (y, X, xs, hyps, maxit = 0, alpha = 0.05)
```

### Arguments

`y` |
The response vector (numeric vector). |

`X` |
The full design matrix, whose columns are named by the covariates. |

`xs` |
The name vector of all covariates (character vector). |

`hyps` |
The name vector of the covariates in the pathway of interest (character vector). |

`maxit` |
An optional integer to denote the maximal interations for branch and bound method. The default value 0 means the single-step shortcut without branch and bound method. Note that larger value is more time-consuming. |

`alpha` |
The type I error rate allowed. The default is 0.05. |

### Value

Returns a non-negative interger.

### Author(s)

Ningning Xu

Maintainer: Ningning Xu <n.xu@lumc.nl; xu15263142750@gmail.com>

### References

Ningning Xu, Aldo solari, Jelle Goeman, Clsoed testing with global test, with applications on metabolomics data, arXiv:2001.01541, https://arxiv.org/abs/2001.01541

### Examples

```
#Generate the design matrix and response vector for logistic regression models
n= 100
m = 5
X = matrix(data = 0, nrow = n, ncol = m,byrow = TRUE )
for ( i in 1:n){
set.seed(1234+i)
X[i,] = as.vector(arima.sim(model = list(order = c(1, 0, 0), ar = 0.2), n = m) )
}
y = rbinom(n,1,0.6)
X[which(y==1),1:3] = X[which(y==1),1:3] + 0.8
xs = paste("x",seq(1,m,1),sep="")
colnames(X) = xs
# For standarized data
X = scale(x = X,center = FALSE,scale = TRUE)/sqrt(n-1)
interest = xs
discoveries(y=y, X = X, xs = xs, hyps = interest)
#2
discoveries(y=y, X = X, xs = xs, hyps = interest, maxit=10)
#2
```

*ctgt*version 2.0.1 Index]