generate_data {autoRasch} | R Documentation |

This function generates simulated datasets with different attributes

```
generate_data(
responseType = "multidim.nocorrel",
theta = c(-3, 3),
sdtheta = 6,
ntheta = 301,
beta = c(-2.5, 2.5),
sdbeta = 4,
nitem = 6,
alpha = c(1),
sdlambda = 1,
ncat = 5,
thGap = 0.8,
ndim = 3,
randtype = "uniform",
corLevel = 0,
dim.members = c(),
seed = NULL
)
```

`responseType` |
The type of the dataset. The types include |

`theta` |
A vector of the ability parameters range value, |

`sdtheta` |
Standard deviation which is used to generate theta values using |

`ntheta` |
The number of the observations. |

`beta` |
A vector of the item difficulty parameters range value, |

`sdbeta` |
Standard deviation which is used to generate item location values using |

`nitem` |
The number of the items in each subgroup. |

`alpha` |
A vector of the discrimination parameters apply to each items. |

`sdlambda` |
A vector of the standard deviation to simulate the testlet (local dependency) effect. The effect is added using |

`ncat` |
The number of the response categories |

`thGap` |
The difference between adjacent threshold. |

`ndim` |
The number of subgroups (dimensions/testlets) created. |

`randtype` |
The randomize type. This includes |

`corLevel` |
The correlation between the two dimensions. |

`dim.members` |
The list of item members in each dimension. |

`seed` |
Integer seed for reproducibility. |

The generated dataset as a `data.frame`

.

```
# 1. Multidimensional Polytomous Dataset with 0.2 Correlation
# Generate multidimensional dataset which having correlation of 0.2 between the dimensions
correl02_multidim <- generate_data(
responseType = "multidim.withcorrel", corLevel = 0.2, seed = 2021
)
# 2. Within-item Multidimensional Polytomous Dataset
# Generate multidimensional dataset with some items relate to more than one
# dimension.
withinItem_multidim <- generate_data(
responseType = "multidim.within", ndim = 3,
dim.members = list(c(1:6,13),c(3,7:12),c(5,13:18)), seed = 2021
)
# 3. Multi-testlets Polytomous Dataset
# Generate dataset which consist of two bundle items with different level of
# local dependency effect.
testlets_dataset <- generate_data(
responseType = "testlets", ndim = 2, sdlambda = c(0,4), seed = 2021
)
# 4a. Inhomogenous Dichotomous Dataset
# Generate dataset with binary type responses containing three subsets
# with different discrimination values.
dicho_inh_dset <- generate_data(
responseType = "discriminate", ncat = 2, seed = 2021,
alpha = c(0.04,0.045,0.05,0.055,0.06,0.065,0.2,0.25,0.3,0.35,0.4,0.45,
2.6,2.65,2.7,2.75,2.8,2.85)
)
# 4b. Inhomogenous Polytomous Dataset
# Generate dataset with polytomous responses (five categories) containing
# three subsets with different discrimination values.
poly_inh_dset <- generate_data(
responseType = "discriminate", ncat = 5, seed = 2021,
alpha = c(0.04,0.045,0.05,0.055,0.06,0.065,0.2,0.25,0.3,0.35,0.4,0.45,
2.6,2.65,2.7,2.75,2.8,2.85)
)
# 4c. Shorter Inhomogenous Polytomous Dataset
short_poly_data <- generate_data(
alpha = c(0.02,0.5,2), nitem = 3, ndim = 3, ncat = 5,
theta = c(-6,6), beta = c(-4,4), ntheta = 151, seed = 2021
)
# 4d. Short Dataset containing DIF items
# Generate dataset with polytomous responses (five categories) containing
# three subsets with different discrimination values and two DIF-items.
seed <- c(54748,96765)
difset_short1 <- generate_data(responseType = "discriminate", ncat = 3,
ntheta = 50, nitem = 3, ndim = 1,
seed = seed[1], alpha = c(2))
difset_short2 <- generate_data(responseType = "discriminate", ncat = 3,
ntheta = 50, nitem = 2, ndim = 1,
seed = seed[2], alpha = c(0.8),
beta = c(-2.5,2.5))
shortDIF <- cbind(rbind(difset_short1,difset_short1),
c(difset_short2[,1],difset_short2[,2]))
# 5a. Uncorrelated Multidimensional Dichotomous Dataset
# Generate dataset with binary type responses containing three subsets which
# represent different uncorrelated dimensions.
dicho_md_dset <- generate_data(
responseType = "multidim.nocorrel", ncat = 2, seed = 2021
)
# 5b. Uncorrelated Multidimensional Polytomous Dataset
# Generate dataset with polytomous responses (five categories) containing
# three subsets which represent different uncorrelated dimensions.
poly_md_dset <- generate_data(
responseType = "multidim.nocorrel", ncat = 5, seed = 2021
)
```

[Package *autoRasch* version 0.2.2 Index]