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Item Diagnostics From a Psychometric MCML Fit

Source: R/mcml_pc.R
item_loadings.Rd

The item table behind build_mcml_pc: one row per node, reporting how strongly the item connects to its own cluster, the weight it carries into that cluster's composite, and whether it connects more strongly to some other cluster.

Reading the table is item_loadings(fit) - never a reach into the fit's internals. The name says item: these are network loadings of items on their own cluster, not factor loadings.

Usage

item_loadings(x, ...)

# S3 method for class 'mcml_pc'
item_loadings(x, misfit = NULL, ...)

Arguments

x

An mcml_pc object from build_mcml_pc.

...

Ignored.

misfit

Logical or NULL. NULL (default) returns every item; TRUE returns only the items whose strongest cross-cluster connection exceeds their own-cluster loading; FALSE returns only the items that fit where they were assigned.

Value

A data frame with one row per node (one row per misfitting or fitting node when misfit is set) and columns node, cluster, loading (signed mean connection to its own cluster), weight (its composite weight), sign (+1, or -1 for a reverse-keyed item), max_cross (strongest connection to any other cluster), cross_cluster (which cluster that is), and misfit (logical).

See also

build_mcml_pc to create the fit, loading_stability for the weights' sampling uncertainty, composites for the scores these weights produce.

Examples

set.seed(1)
f <- stats::rnorm(200)
g <- stats::rnorm(200)
df <- data.frame(a1 = f + stats::rnorm(200), a2 = f + stats::rnorm(200),
                 a3 = f + stats::rnorm(200), b1 = g + stats::rnorm(200),
                 b2 = g + stats::rnorm(200), b3 = g + stats::rnorm(200))
clusters <- list(A = c("a1", "a2", "a3"), B = c("b1", "b2", "b3"))
fit <- build_mcml_pc(df, clusters, aggregation = "loadings",
                     method = "cor")
item_loadings(fit)
#>   node cluster   loading    weight sign  max_cross cross_cluster misfit
#> 1   a1       A 0.4535516 0.3288962    1 0.02471784             B  FALSE
#> 2   a2       A 0.4572043 0.3315450    1 0.04824045             B  FALSE
#> 3   a3       A 0.4682554 0.3395588    1 0.05652080             B  FALSE
#> 4   b1       B 0.5363571 0.3493775    1 0.05405353             A  FALSE
#> 5   b2       B 0.4977645 0.3242387    1 0.01233061             A  FALSE
#> 6   b3       B 0.5010576 0.3263838    1 0.06309494             A  FALSE
item_loadings(fit, misfit = TRUE)
#> [1] node          cluster       loading       weight        sign         
#> [6] max_cross     cross_cluster misfit       
#> <0 rows> (or 0-length row.names)