# Function to compute predicted risks for all individuals in the dataset.

### Description

Function to compute predicted risks for all individuals in the (new)dataset.

### Usage

1 |

### Arguments

`riskModel` |
Name of logistic regression model that can be fitted using
the function |

`data` |
Data frame or matrix that includes the ID number and predictor variables. |

`cID` |
Column number of ID variable. The ID number and predicted risks
will be saved under |

`filename` |
Name of the output file in which the ID number and
estimated predicted risks will be saved. The file is saved in the working
directory as a txt file. Example: filename="name.txt". When no |

### Details

The function computes predicted risks from a specified logistic regression model.
The function `fitLogRegModel`

can be used to construct such a model.

### Value

The function returns a vector of predicted risks.

### See Also

`fitLogRegModel`

, `plotCalibration`

,
`plotROC`

, `plotPriorPosteriorRisk`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
# specify dataset with outcome and predictor variables
data(ExampleData)
# specify column number of the outcome variable
cOutcome <- 2
# specify column number of ID variable
cID <- 1
# specify column numbers of non-genetic predictors
cNonGenPred <- c(3:10)
# specify column numbers of non-genetic predictors that are categorical
cNonGenPredCat <- c(6:8)
# specify column numbers of genetic predictors
cGenPred <- c(11,13:16)
# specify column numbers of genetic predictors that are categorical
cGenPredCat <- c(0)
# fit logistic regression model
riskmodel <- fitLogRegModel(data=ExampleData, cOutcome=cOutcome,
cNonGenPreds=cNonGenPred, cNonGenPredsCat=cNonGenPredCat,
cGenPreds=cGenPred, cGenPredsCat=cGenPredCat)
# obtain predicted risks
predRisk <- predRisk(riskModel=riskmodel)
``` |