# Generate the artificial dataset This function generates simulated datasets with different attributes ```r 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 ) ``` ## Arguments - `responseType`: The type of the dataset. The types include `multidim.nocorrel`, `multidim.withcorrel`, `discriminate`, `multidim.within`, and `testlets`. - `theta`: A vector of the ability parameters range value, `c(min.theta,max.theta)`. It applies when the `randtype = "uniform"`. - `sdtheta`: Standard deviation which is used to generate theta values using `stats::rnorm()` with `n = ntheta`, `mean = 0`, and `sd = sdtheta`.It applies when the `randtype = "normal"`. - `ntheta`: The number of the observations. - `beta`: A vector of the item difficulty parameters range value, `c(min.beta,max.beta)`. It applies when the `randtype = "uniform"`. - `sdbeta`: Standard deviation which is used to generate item location values using `stats::rnorm()` with `n = nitem`, `mean = 0`, and `sd = sdbeta`.It applies when the `randtype = "normal"`. - `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 `stats::rnorm()` with `n = ntheta`, `mean = 0`, and `sd = sdlambda` - `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 `uniform` and `normal`. - `corLevel`: The correlation between the two dimensions. - `dim.members`: The list of item members in each dimension. - `seed`: Integer seed for reproducibility. ## Returns The generated dataset as a `data.frame`. ## Examples ```r # 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 ) ```

generate_data function