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Extract report-ready evidence tables

Source: R/nomo_table.R
nomo_table.Rd

nomo_table() returns compact tibbles intended for manuscripts, audit appendices, teaching materials, and nomo_report(). It never rounds away the underlying object: full engine results remain stored in the original nomologR object.

Usage

nomo_table(x, ...)

Arguments

x

A supported nomologR result object.

...

Additional arguments passed to methods, usually type.

Value

A tibble.

Details

Supported objects and type values. The first value listed is the default.

  • nomo_screen: "items", "distribution", "cases", "relationships", "effort", "decision_log"; see nomo_screen().

  • nomo_factors: "evidence", "criteria", "adequacy", "concordance", "decision_log"; see nomo_factors().

  • nomo_efa: "items", "pattern", "factor_correlations", "residuals", "decision_log". The pattern and factor-correlation matrices are returned as tables with the item or factor in the first column; see nomo_efa().

  • nomo_cfa: "fit", "loadings", "factor_correlations", "heywood", "residuals", "modification_indices", "decision_log"; see nomo_cfa().

  • nomo_reliability: "coefficients", "alpha_status", "ci_status", "decision_log"; see nomo_reliability().

  • nomo_validity: "convergent", "discriminant", "htmt_status", "decision_log". "discriminant" has one row per construct pair, with the latent correlation and the HTMT-family values together; see nomo_validity().

  • nomo_scores: "scores", "diagnostics", "unit_weighting", "notes"; see nomo_scores().

  • nomo_hypotheses: the machine-readable hypothesis table (no type).

  • nomo_network: "hypotheses" (default), "fit", "measurement", "relations", "replication", "decision_log".

  • nomo_invariance: "fit" (default), "categories", "partial", "local_strain", "decision_log". The local-strain table keeps lavaan's internal constraint label and adds a human-readable constraint_display column (for example, Intercept: ag3 (online vs. paper)).

  • nomo_compare: "comparisons" (default), "models", "loadings", "evidence", "decision_log"; see nomo_compare().

  • nomo_missing: "strategies" (default), "estimates", "fit", "reliability", "pattern", "variables", "decision_log"; see nomo_missing().

  • nomo_hierarchical: "indices" (default), "subscales", "loadings", "notes", "decision_log"; see nomo_hierarchical().

  • nomo_run: "stages" (default), "requests", "decisions", "component_log", "scales", "recipe", "settings", "lineage", "methods"; see nomo_run() and nomo_revise(). "lineage" returns one row per revision, or no rows for a workflow that was never revised. "methods" returns the registry entries for the methods the run actually used, and is equivalent to nomo_methods(x); see nomo_methods().

Examples

h <- nomo_hypotheses(
  "Agency -> Persistence" = positive(min = .20),
  "Agency <-> SocialDesirability" = negligible(within = c(-.15, .15))
)
nomo_table(h)
#> # A tibble: 2 × 16
#>   id    relation    source target operator relation_type prediction lower  upper
#>   <chr> <chr>       <chr>  <chr>  <chr>    <chr>         <chr>      <dbl>  <dbl>
#> 1 H1    Agency -> … Agency Persi… ->       directed      positive    0.2  Inf   
#> 2 H2    Agency <->… Agency Socia… <->      association   negligible -0.15   0.15
#> # ℹ 7 more variables: lower_inclusive <lgl>, upper_inclusive <lgl>,
#> #   region <chr>, scale <chr>, origin <chr>, magnitude_specified <lgl>,
#> #   confirmable <lgl>

inv <- nomo_invariance(
  "Agency =~ ag1 + ag2 + ag3 + ag4",
  data = nomo_demo_network,
  group = "group",
  levels = c("configural", "metric", "scalar")
)
nomo_table(inv, "fit")
#> # A tibble: 3 × 19
#>   level     constraints partial_requested status converged  chisq    df   pvalue
#>   <chr>     <chr>       <chr>             <chr>  <lgl>      <dbl> <dbl>    <dbl>
#> 1 configur… none        ""                estim… TRUE       0.327     4 9.88e- 1
#> 2 metric    loadings    ""                estim… TRUE       5.71      7 5.75e- 1
#> 3 scalar    loadings, … ""                estim… TRUE      68.9      10 7.12e-11
#> # ℹ 11 more variables: cfi <dbl>, rmsea <dbl>, srmr <dbl>, delta_cfi <dbl>,
#> #   delta_rmsea <dbl>, delta_srmr <dbl>, lrt_chisq <dbl>, lrt_df <dbl>,
#> #   lrt_p <dbl>, warnings <chr>, error <chr>
head(nomo_table(inv, "local_strain"))
#> # A tibble: 6 × 8
#>   level  constraint_index constraint    score_x2    df  p_value diagnostic_only
#>   <chr>             <int> <chr>            <dbl> <dbl>    <dbl> <lgl>          
#> 1 metric                3 .p3. == .p17.   4.92       1 2.66e- 2 TRUE           
#> 2 metric                2 .p2. == .p16.   1.03       1 3.10e- 1 TRUE           
#> 3 metric                4 .p4. == .p18.   0.690      1 4.06e- 1 TRUE           
#> 4 metric                1 .p1. == .p15.   0.0273     1 8.69e- 1 TRUE           
#> 5 scalar                7 .p7. == .p21.  61.1        1 5.33e-15 TRUE           
#> 6 scalar                5 .p5. == .p19.  11.3        1 7.68e- 4 TRUE           
#> # ℹ 1 more variable: constraint_display <chr>