exogenize: Structural Mapping for an IRTree Model
In irtrees: Estimation of Tree-Based Item Response Models

Description Usage Arguments Value Author(s) References See Also Examples

Expands a wide-form file of item responses to a long-form file supporting structural mappings among latent variables

1	exogenize(mat, cmx, items=seq_len(ncol(mat)), endnode, crossitem=NULL)

`mat`	An integer matrix of IRT responses (columns represent items, rows represent respondents)
`cmx`	The mapping matrix.
`items`	A numeric vector with the column positions of all items (measurement variables) in `mat`. Defaults to all columns of `mat`.
`endnode`	A factor with the same length as `items` indicating the latent variable to which the item is attached, and compatible with the mapping matrix.
`crossitem`	A factor with the same length as `items` indicating the original items that have been crossed with `endnode` (e.g., given at various occasions over time), thus producing the actual items. This is not meaningful when the design is nested, and should better be omitted in such cases.

A data frame with columns:

`person`	a factor identifying the respondent;
`item`	a factor identifying the items;

`exo1 to exoS`	dummy variables identifying the internal nodes: these play the same role as `exofactor` but are necessary when the model must include independent random effects;
`endnode`	a factor identifying the end nodes and thus the endogeneous latent variables;
`crossitem`	a factor produced only for crossed designs, identifying the items that are crossed with `endnode`, for example, in the case when the same items are repeated over time in a longitudinal design;
`value`	the binary item responses.

Ivailo Partchev

De Boeck, P. and Partchev, I. (2012). IRTrees: Tree-Based Item Response Models of the GLMM Family, Journal of Statistical Software – Code Snippets, 48(1), 1–28. https://www.jstatsoft.org/v48/c01/.

dendrify

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str(linlatT <- exogenize(linlat, 
   cbind(c(1,0,0), c(1,1,0), c(0,1,0), c(0,1,1), c(0,0,1)),
   endnode = rep(1:3, each=10), cross = rep(1:10, 3)))

'data.frame':	9000 obs. of  10 variables:
 $ value    : num  1 1 1 1 0 0 0 0 1 1 ...
 $ item     : Factor w/ 30 levels "i01","i02","i03",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ person   : Factor w/ 300 levels "p001","p002",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ endnode  : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
 $ crossitem: Factor w/ 10 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ exo1     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ exo2     : num  1 1 1 1 1 1 1 1 1 1 ...
 $ exo3     : num  0 0 0 0 0 0 0 0 0 0 ...
 $ exo4     : num  0 0 0 0 0 0 0 0 0 0 ...
 $ exo5     : num  0 0 0 0 0 0 0 0 0 0 ...