View source: R/post_hocChange.R

post_hocChange | R Documentation |

Returns post hoc power of Wald (W), likelihood ratio (LR), Rao score (RS)
and gradient (GR) test given data and probability of error of first kind `\alpha`

.
The hypothesis to be tested states that the shift parameter quantifying the constant change
for all items between time points 1 and 2 equals 0. The alternative states that the
shift parameter is not equal to 0. It is assumed that the same items are presented at both
time points. See function `change_test`

.

```
post_hocChange(alpha = 0.05, data)
```

`alpha` |
Probability of error of first kind. |

`data` |
Data matrix as required for function |

The power of the tests (Wald, LR, score, and gradient) is determined from the assumption that the
approximate distributions of the four test statistics are from the family of noncentral `\chi^2`

distributions with `df = 1`

and noncentrality parameter `\lambda`

. In case of evaluating the post hoc power,
`\lambda`

is assumed to be given by the observed value of the test statistic. Given the probability of the
error of the first kind `\alpha`

the post hoc power of the tests can be determined from `\lambda`

.
More details about the distributions of the test statistics and the relationship between `\lambda`

, power, and
sample size can be found in Draxler and Alexandrowicz (2015).

In particular, let `q_{\alpha}`

be the `1- \alpha`

quantile of the central `\chi^2`

distribution with df = 1. Then,

`power = 1 - F_{df, \lambda} (q_{\alpha}),`

where `F_{df, \lambda}`

is the cumulative distribution function of the noncentral `\chi^2`

distribution with
`df = 1`

and `\lambda`

equal to the observed value of the test statistic.

A list of results.

`test` |
A numeric vector of Wald (W), likelihood ratio (LR), Rao score (RS), and gradient (GR) test statistics. |

`power` |
Posthoc power value for each test. |

`observed deviation` |
CML estimate of shift parameter expressing observed deviation from hypothesis to be tested. |

`person score distribution` |
Relative frequencies of person scores. Uninformative scores, i.e., minimum and maximum score, are omitted. Note that the person score distribution does also have an influence on the power of the tests. |

`degrees of freedom` |
Degrees of freedom |

`noncentrality parameter` |
Noncentrality parameter |

`call` |
The matched call. |

Draxler, C., & Alexandrowicz, R. W. (2015). Sample size determination within the scope of conditional maximum likelihood estimation with special focus on testing the Rasch model. Psychometrika, 80(4), 897-919.

Fischer, G. H. (1995). The Linear Logistic Test Model. In G. H. Fischer & I. W. Molenaar (Eds.), Rasch models: Foundations, Recent Developments, and Applications (pp. 131-155). New York: Springer.

Fischer, G. H. (1983). Logistic Latent Trait Models with Linear Constraints. Psychometrika, 48(1), 3-26.

`sa_sizeChange`

, and `powerChange`

.

```
## Not run:
# Numerical example with 200 persons and 4 items
# presented twice, thus 8 virtual items
# Data y generated under the assumption that shift parameter equals 0.5
# (change from time point 1 to 2)
# design matrix W used only for exmaple data generation
# (not used for estimating in change_test function)
W <- rbind(c(1,0,0,0,0), c(0,1,0,0,0), c(0,0,1,0,0), c(0,0,0,1,0),
c(1,0,0,0,1), c(0,1,0,0,1), c(0,0,1,0,1), c(0,0,0,1,1))
# eta parameter vector, first 4 are nuisance, i.e., item parameters at time point 1.
# (easiness parameters of the 4 items at time point 1),
# last one is the shift parameter
eta <- c(-2,-1,1,2,0.5)
y <- eRm::sim.rasch(persons=rnorm(150), items=colSums(-eta*t(W)))
res <- post_hocChange(alpha = 0.05, data = y)
# > res
# $test
# W LR RS GR
# 9.822 10.021 9.955 10.088
#
# $power
# W LR RS GR
# 0.880 0.886 0.884 0.888
#
# $`observed deviation (estimate of shift parameter)`
# [1] 0.504
#
# $`person score distribution`
#
# 1 2 3 4 5 6 7
# 0.047 0.047 0.236 0.277 0.236 0.108 0.047
#
# $`degrees of freedom`
# [1] 1
#
# $`noncentrality parameter`
# W LR RS GR
# 9.822 10.021 9.955 10.088
#
# $call
# post_hocChange(alpha = 0.05, data = y)
## End(Not run)
```

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