# jacobian: Jacobian Matrix for Basic Local Independence Model In pks: Probabilistic Knowledge Structures

## Description

Computes the Jacobian matrix for a basic local independence model (BLIM).

## Usage

 ```1 2 3``` ```jacobian(object, P.K = rep(1/nstates, nstates), beta = rep(0.1, nitems), eta = rep(0.1, nitems), errtype = c("both", "error", "guessing")) ```

## Arguments

 `object` an object of class `blim`, typically the result of a call to `blim`. `P.K` the vector of parameter values for probabilities of knowledge states. `beta` the vector of parameter values for probabilities of a careless error. `eta` the vector of parameter values for probabilities of a lucky guess. `errtype` type of response errors that can occur: `error` for careless errors only, `guessing` for lucky guesses only, and `both` for both error types.

## Details

This is a draft version. It may change in future releases.

## Value

The Jacobian matrix. The number of rows equals 2^(number of items) - 1, the number of columns equals the number of independent parameters in the model.

## References

Heller, J. (2016). Identifiability in probabilistic knowledge structures. Manuscript under revision.

Stefanutti, L., Heller, J., Anselmi, P., & Robusto, E. (2012). Assessing the local identifiability of probabilistic knowledge structures. Behavior Research Methods, 44, 1197–1211.

## See Also

`blim`, `simulate.blim`, `gradedness`.

## Examples

 ```1 2 3 4 5 6 7``` ```data(endm) blim1 <- blim(endm\$K2, endm\$N.R) ## Test of identifiability J <- jacobian(blim1) dim(J) qr(J)\$rank ```

### Example output

```Loading required package: sets
[1] 15 13
[1] 13
```

pks documentation built on May 29, 2017, 11:26 p.m.