Zscore {BayesLCA} | R Documentation |

## Evaluating Class Membership of Binary Data

### Description

For a fitted model of class `blca`

, and binary data `X`

, the probability of class membership for each data point is provided.

### Usage

```
Zscore(X, fit = NULL, itemprob = NULL, classprob = NULL)
```

### Arguments

`X` |
A binary data matrix. |

`fit` |
An object of class |

`itemprob` |
A matrix of item probabilities, conditional on class membership. |

`classprob` |
A vector denoting class membership probability. |

### Details

Calculation of the probability of class membership for a data point relies on two parameters, class membership and item probability. These may be supplied directly to Zscore, or alternatively, a `blca`

object containing both parameters can be used instead.

### Value

A matrix of equal rows to `X`

and with G, the number of classes, columns, where each row is a score denoting the probability of class membership. Each row should therefore sum to 1.

### Note

`Zscore.internal`

has the same functionality as `Zscore`

, but is only intended for internal use.

### Author(s)

Arthur White

### Examples

```
set.seed(1)
type1 <- c(0.8, 0.8, 0.05, 0.2)
type2 <- c(0.2, 0.2, 0.05, 0.8)
x<- rlca(250, rbind(type1,type2), c(0.5,0.5))
fit <- blca.em(x, 2)
fit$Z ## Unique data types
Zscore(x, fit=fit) ## Whole data set
Zscore(c(0, 1, 1, 0), fit=fit) ## Not in data set
Zscore(x, itemprob=rbind(type1,type2), classprob=c(0.5,0.5))
```

*BayesLCA*version 1.9 Index]