gendataIRT {lazy.irt} | R Documentation |

Generation of Simulated Item Response Data

Generation of Simulated Item Response Data

gendataIRT( Ntot, param, DinP = 1, Nmat = NULL, zero = 1, theta = NULL, npoints = 31, thmin = -3, thmax = 3, thd = NULL, thdist = "NORMAL", thmean = 0, thstd = 1, nomiss = 0, compress = 0, sort = 1, thetaname = NULL ) gendataIRT( Ntot, param, DinP = 1, Nmat = NULL, zero = 1, theta = NULL, npoints = 31, thmin = -3, thmax = 3, thd = NULL, thdist = "NORMAL", thmean = 0, thstd = 1, nomiss = 0, compress = 0, sort = 1, thetaname = NULL )

`Ntot` |
# of total observations to be generated
If Ntot == 1, one observation per each theta point
will be generated, |

`param` |
Item Parameter Data Frame |

`DinP` |
= 1 to include D=1.7 in logistic function |

`Nmat` |
npoints x nitems matrix consisting of # of trials, or NULL. This will be used only when Ntot=1 and compress=0. |

`zero` |
= 0 to exclude zero-th category when not compressed. |

`theta` |
Discrete theta values |

`npoints` |
# of discrete points for theta, or # of random variables |

`thmin` |
Minimum value of discrete thata value |

`thmax` |
Maximum value of discrete thata value |

`thd` |
theta distribution probability vector |

`thdist` |
= "NORMAL" or "UNIFORM" or "rnorm" or "runif" |

`thmean` |
mean of theta distribution |

`thstd` |
std of theta distribution |

`nomiss` |
= 1 to exclude those theta points with N = 0. |

`compress` |
= 1 to compress the data when Ntot = 1. |

`sort` |
= 1 to sort the result according to theta. |

`thetaname` |
= Name of the variable which will contain theta value or NULL. |

A list of

U, N, npoints, theta, thd, Ntot, fromP, toP, type, ncat, thmean, thstd
, compress, sort

where

If compress = 0 and Ntot is large,

U is npoints x sum of ncat[j]

U[,fromP[j]:toP[j]] is npoints x ncat[j]

U[,fromP[j]:toP[j]][i,k] contains # of responses to

the k-th category (k=0,1,...,ncat[j]) at theta[i]

to which N[i]=round(Ntot*thd[i]) observations belong.

If compress = 1 and Ntot = 1,

U is npoints x nitems

U[i,j] contains the response to the j-th item at theta[i]

where 0 <= U[i,j] <= ncat[j]-1.

In this case, fromP and toP are not compressed.

(theta, thd, N) are npoints x 1

N[i] is the # of observations at theta[i]

A list of

U, N, npoints, theta, thd, Ntot, fromP, toP, type, ncat, thmean, thstd
, compress, sort

where

If compress = 0 and Ntot is large,

U is npoints x sum of ncat[j]

U[,fromP[j]:toP[j]] is npoints x ncat[j]

U[,fromP[j]:toP[j]][i,k] contains # of responses to

the k-th category (k=0,1,...,ncat[j]) at theta[i]

to which N[i]=round(Ntot*thd[i]) observations belong.

If compress = 1 and Ntot = 1,

U is npoints x nitems

U[i,j] contains the response to the j-th item at theta[i]

where 0 <= U[i,j] <= ncat[j]-1.

In this case, fromP and toP are not compressed.

(theta, thd, N) are npoints x 1

N[i] is the # of observations at theta[i]

# 10 observations with theta from N( 0, 1 ): not compressed set.seed(1702) res1 <- gendataIRT( 1, paramS1, npoints=10, thdist="rnorm" ) Print(res1$N,res1$U,res1$theta) # # 10 observations with theta from N( 0, 1 ): compressed set.seed(1702) res1 <- gendataIRT( 1, paramS1, npoints=10, thdist="rnorm", compress=1 ) Print(res1$N,res1$U,res1$theta) # # 10 observations with theta from N( 0, 1 ): no zero category set.seed(1702) res1 <- gendataIRT( 1, paramS1, npoints=10, thdist="rnorm", zero=0 ) Print(res1$N,res1$U,res1$theta) # # # # seven theta points in [-3,3] set.seed(1701) res1 <- gendataIRT( 1, paramS1, npoints=7, thmin=-3, thmax=3 ) Print(res1$N,res1$U,res1$theta) # # seven theta points in [-3,3] and compressed set.seed(1701) res1 <- gendataIRT( 1, paramS1, npoints=7, thmin=-3, thmax=3, compress=1 ) Print(res1$N,res1$U,res1$theta) # set.seed(1701) # 100 observations tabulated at seven theta points in [-3,3] res1 <- gendataIRT( 1000, paramS1, npoints=7, thmin=-3, thmax=3 ) Print(res1$N,res1$U,res1$theta) # # # Using Nmat argument set.seed(1701) npoints=20 param=paramS1 nitems=nrow(param) Nmat=matrix(100,npoints,nitems) res1 <- gendataIRT( 1, param, Nmat=Nmat, npoints=npoints , thdist="rnorm", compress=1 ) Print(res1$N,res1$U,res1$theta) # 10 observations with theta from N( 0, 1 ): not compressed set.seed(1702) res1 <- gendataIRT( 1, paramS1, npoints=10, thdist="rnorm" ) Print(res1$N,res1$U,res1$theta) # # 10 observations with theta from N( 0, 1 ): compressed set.seed(1702) res1 <- gendataIRT( 1, paramS1, npoints=10, thdist="rnorm", compress=1 ) Print(res1$N,res1$U,res1$theta) # # 10 observations with theta from N( 0, 1 ): no zero category set.seed(1702) res1 <- gendataIRT( 1, paramS1, npoints=10, thdist="rnorm", zero=0 ) Print(res1$N,res1$U,res1$theta) # # # # seven theta points in [-3,3] set.seed(1701) res1 <- gendataIRT( 1, paramS1, npoints=7, thmin=-3, thmax=3 ) Print(res1$N,res1$U,res1$theta) # # seven theta points in [-3,3] and compressed set.seed(1701) res1 <- gendataIRT( 1, paramS1, npoints=7, thmin=-3, thmax=3, compress=1 ) Print(res1$N,res1$U,res1$theta) # set.seed(1701) # 100 observations tabulated at seven theta points in [-3,3] res1 <- gendataIRT( 1000, paramS1, npoints=7, thmin=-3, thmax=3 ) Print(res1$N,res1$U,res1$theta) # # # Using Nmat argument set.seed(1701) npoints=20 param=paramS1 nitems=nrow(param) Nmat=matrix(100,npoints,nitems) res1 <- gendataIRT( 1, param, Nmat=Nmat, npoints=npoints , thdist="rnorm", compress=1 ) Print(res1$N,res1$U,res1$theta)

[Package *lazy.irt* version 0.1.3 Index]