This function returns diagnostics for a fitted MUDFOLD scale. Specifically, it returns the iso statistic (see ISO) the max statistic (see MAX), the matrix with stars at the maximum of each row, as well as a test for conditional independence.
diagnostics(x, boot, nlambda, lambda.crit, type, k, which, plot)a list of length six where each component is a diagnostic when which="all". A list equal to length(which) when which != "all".
: A fitted object of class "mdf" obtained from the mudfold function.
: logical argument that controls if bootstrap confidence intervals and summary for the H coefficients and the ISO and MAX statistics will be returned. If boot=FALSE (default) no information for bootstrap is returned. When boot=TRUE, confidence intervals, standard errors, biases, calculated from the bootstrap iterations for each diagnostic are given with the output.
: The number of regularization parameters to be used in cv.glmnet() function when testing local independence.
: String that specifies the criterion to be used by cross-validation for choosing the optimal regularization parameter. Available options are "class" (default), "deviance", "auc", "mse", "mae". See the argument 'type.measure' in the cv.glmnet() function for more details.
: The type of bootstrap confidence intervals to be calculated if the argumnet boot=TRUE. Available options are "norm", "basic", "perc" (deafult), and "bca". See the argument type of the boot.CI() for details.
: The dimension of the basis in the thin plate regression spline that is used when testing for IRF unimodality. The default value of k is four.
: Which diagnostic should be returned by the function. Available options are "H", "LI", "UM", "ISO", "MAX", "STAR", "all" (default).
: Logical. Should plots be returned for the diagnostics that can be plotted? Default value is plot=TRUE.
Spyros E. Balafas (s.balafas@rug.nl)
W.J. Post. (1992). Nonparametric Unfolding Models: A Latent Structure Approach. M & T series. DSWO Press.
W.J. Post. and T.AB. Snijders. (1993). Nonparametric unfolding models for dichotomous data. Methodika.
## load the ANDRICH data
data("ANDRICH")
## Fit a MUDFOLD scale to the ANDRICH data
mudf_andrich <- mudfold(ANDRICH)
## Get the diagnostics
diagnostics(mudf_andrich, which = "UM")
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