getResults: Collect all results from Conquest/TAM analysis into a common...
In eatModel: Educational Assessment Tools for IRT Modeling using Conquest or TAM

Description Usage Arguments Details Value Author(s) Examples

First the IRT model should be defined using defineModel. Afterwards, call runModel with the argument returned by defineModel to start the estimation. The last step then is to create a results frame using getResults.

getResults( runModelObj, overwrite = FALSE, Q3 = TRUE, q3theta = c("pv", "wle", "eap"), 
            q3MinObs = 0, q3MinType = c("singleObs", "marginalSum"), omitFit = FALSE, 
            omitRegr = FALSE, omitWle = FALSE, omitPV = FALSE, abs.dif.bound = 0.6, 
            sig.dif.bound = 0.3, p.value = 0.9, pvMethod = c("regular", "bayesian"), 
            nplausible = NULL, ntheta = 2000, normal.approx = FALSE, samp.regr = FALSE, 
            theta.model=FALSE, np.adj=8, group = NULL, beta_groups = TRUE, level = .95, n.iter = 1000, 
            n.burnin = 500, adj_MH = .5, adj_change_MH = .05, refresh_MH = 50, 
            accrate_bound_MH = c(.45, .55),	print_iter = 20, verbose = TRUE)

`runModelObj`	The object returned by `runModel`.
`overwrite`	Logical. Should result files be overwritten if exist?
`Q3`	Logical. Estimate the Q3 statistic according to Yen (1984)? Note: this is only possible for uni-dimensional models. If `software == "tam"`, Q3 statistic is estimated using the `tam.modelfit` function. If `software == "Conquest"`, Q3 statistic is estimated using the `Q3` function from the `sirt` package.
`q3theta`	Specify whether the Q3 statistic should be estimated using PVs, WLEs or EAPs as the theta variable.
`q3MinObs`	Q3 statistic might be untrustworthy if item covariance estimation is based on very few onservations. Define the minimum number of observation which should be fulfilled for Q3 estimation.
`q3MinType`	If `"singleObs"`, `q3MinObs` argument is based on the least number of observations in the 2x2 0/1 frequency table of item pairs. If `"marginalSum"`, `q3MinObs` argument is based on the sum of marginals in the 2x2 0/1 frequency table of item pairs.
`omitFit`	Logical. Should item fit values be included into the results?
`omitRegr`	Logical. Should regression parameters and their standard errors be included into the results?
`omitWle`	Logical. Should WLE estimates be included into the results?
`omitPV`	Logical. Should plausible values be included into the results?
`abs.dif.bound`	Applies only if DIF analyses are performed before. When DIF-Parameter are evaluated, this specifies the critical value for absolute DIF.
`sig.dif.bound`	Applies only if DIF analyses are performed before. When DIF-Parameter are evaluated, this specifies the critical value for confidence interval DIF.
`p.value`	Applies only if DIF analyses are performed before. When DIF-Parameter are evaluated, this specifies the critical p-value for confidence interval DIF.
`pvMethod`	Applies only if `software = "tam"`: Specifies whether PVs should be drawn regularly or using a Bayesian algorithm (Metropolis Hastings).
`nplausible`	Applies only if `software = "tam"`: Number of plausible values to be drawn. Note: number of plausible values were already defined in `defineModel`, because Conquest needs to know the number of PVs prior to estimation. In `TAM`, it is possible to redefine the number of plausible values and overwrite the definition that was given in `defineModel`.
`ntheta`	Applies only if `software = "tam"`. Following description is borrowed from the help file of `tam.pv` from the `TAM` package: Number of ability nodes for plausible value imputation. Note that in this function ability nodes are simulated for the whole sample, not for every person (contrary to the software ConQuest).
`normal.approx`	Applies only if `software = "tam"`. Following description is borrowed from the help file of `tam.pv` from the `TAM` package: An optional logical indicating whether the individual posterior distributions should be approximated by a normal distribution? The default is `FALSE`. In the case `normal.approx=TRUE` (normal distribution approximation), the number of ability nodes `ntheta` can be substantially smaller than 2000, say 200 or 500. The normal approximation is implemented for unidimensional and multidimensional models.
`samp.regr`	Applies only if `software = "tam"`. Following description is borrowed from the help file of `tam.pv` from the `TAM` package: An optional logical indicating whether regression coefficients should be fixed in the plausible value imputation or also sampled from their posterior distribution? The default is `FALSE`. Sampled regression coefficients are obtained by nonparametric bootstrap.
`theta.model`	Applies only if `software = "tam"`. Following description is borrowed from the help file of `tam.pv` from the `TAM` package: Logical indicating whether the theta grid from the `tamobj` object should be used for plausible value imputation. In case of `normal.approx=TRUE`, this should be sufficient in many applications.
`np.adj`	Applies only if `software = "tam"`. Following description is borrowed from the help file of `tam.pv` from the `TAM` package: This parameter defines the “spread” of the random theta values for drawing plausible values when `normal.approx=FALSE`. If s_{EAP} denotes the standard deviation of the posterior distribution of theta (in the one-dimensional case), then theta is simulated from a normal distribution with standard deviation `np.adj` times s_{EAP}.
`group`	Applies only if `software = "tam"` and `pvMethod = "bayesian"`. Optional vector of group identifiers. See the help page of `tam.pv.mcmc` for further details.
`beta_groups`	Applies only if `software = "tam"` and `pvMethod = "bayesian"`. See the help page of `tam.pv.mcmc` for further details.
`level`	Applies only if `software = "tam"` and `pvMethod = "bayesian"`. Confidence level in bayesian approach. See the help page of `tam.pv.mcmc` for further details.
`n.iter`	Applies only if `software = "tam"` and `pvMethod = "bayesian"`. Number of iterations in the bayesian approach. See the help page of `tam.pv.mcmc` for further details.
`n.burnin`	Applies only if `software = "tam"` and `pvMethod = "bayesian"`. Number of burn-in iterations in the bayesian approach. See the help page of `tam.pv.mcmc` for further details.
`adj_MH`	Applies only if `software = "tam"` and `pvMethod = "bayesian"`. See the help page of `tam.pv.mcmc` for further details.
`adj_change_MH`	Applies only if `software = "tam"` and `pvMethod = "bayesian"`. See the help page of `tam.pv.mcmc` for further details.
`refresh_MH`	Applies only if `software = "tam"` and `pvMethod = "bayesian"`. See the help page of `tam.pv.mcmc` for further details.
`accrate_bound_MH`	Applies only if `software = "tam"` and `pvMethod = "bayesian"`. See the help page of `tam.pv.mcmc` for further details.
`print_iter`	Applies only if `software = "tam"` and `pvMethod = "bayesian"`. See the help page of `tam.pv.mcmc` for further details.
`verbose`	Applies only if `software = "tam"` and `pvMethod = "bayesian"`. See the help page of `tam.pv.mcmc` for further details.

If defineModel was run with software Conquest, a path argument ('dir') is necessary. The path argument is optional for software TAM. If 'dir' was specified, getResults additionally writes its output into the specified folder, using the 'analysis.name' argument for file naming. Otherwise, getResults only returnes the result data frame.

A data frame in the long format with ten columns.

`model`	The name of the model (as specified by the user in `analysis.name`.
`source`	The estimation software (i.e, conquest or TAM)
`var1`	The variable name for which the corresponding value is given, i.e. its indicator.
`var2`	Additional variable information if necessary.
`type`	Type of coefficient (for example, random or fixed).
`indicator.group`	The type of the group the corresponding variable belongs to.
`group`	The group the corresponding variable belongs to. Note: group is nested within `indicator.group`.
`par`	The type of the parameter.
`derived.par`	Optionally: The derived parameter.
`value`	The value of the corresponding estimate.