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Computes modality_gravity on two conditional modality graphs and returns a side-by-side comparison of dMGI, OS, and role for every modality present in either graph. Optionally plots a dot-chart of dMGI differences.

Source: R/gravity_plots.R
compare_gravity.Rd

Computes modality_gravity on two conditional modality graphs and returns a side-by-side comparison of dMGI, OS, and role for every modality present in either graph. Optionally plots a dot-chart of dMGI differences.

Usage

compare_gravity(x, plot = TRUE, top_n = 20L, ...)

Arguments

x

A named list of exactly two catmodgraph objects, e.g. list(women = mg_f, men = mg_m). Names are used as group labels.

plot

Logical. Whether to draw a comparison dot-chart. Default TRUE.

top_n

Integer. Number of modalities to show in the plot (those with the largest absolute dMGI difference). Default 20L.

...

Additional arguments passed to modality_gravity.

Value

A data frame with columns node, variable, modality, and for each group g1/g2: prevalence_g1, delta_mgi_g1, os_g1, role_g1 (and equivalents for g2), plus delta_mgi_diff (g1 - g2). Rows are ordered by abs(delta_mgi_diff) descending.

See also

modality_gravity, plot_gravity, compare_modality_graphs

Examples

# \donttest{
data(survey_health)
mg_f <- build_conditional_modality_graph(survey_health,
          given = list(sex = "female"))
mg_m <- build_conditional_modality_graph(survey_health,
          given = list(sex = "male"))
mg_f <- prune_modality_edges(mg_f, min_weight = 0.10, max_p = 0.05)
mg_m <- prune_modality_edges(mg_m, min_weight = 0.10, max_p = 0.05)
cmp  <- compare_gravity(list(female = mg_f, male = mg_m))

print(head(cmp, 10))
#>                      node         variable modality prevalence_female
#> 1   smoking_status=former   smoking_status   former            0.3107
#> 2      exercise_freq=high    exercise_freq     high            0.2913
#> 3         age_group=35-54        age_group    35-54            0.3252
#> 4     health_insurance=no health_insurance       no            0.2233
#> 5       diet_quality=poor     diet_quality     poor            0.3107
#> 6  exercise_freq=moderate    exercise_freq moderate            0.3252
#> 7    smoking_status=never   smoking_status    never            0.4612
#> 8         age_group=18-34        age_group    18-34            0.3932
#> 9    health_insurance=yes health_insurance      yes            0.7767
#> 10 smoking_status=current   smoking_status  current            0.2282
#>    delta_mgi_female os_female role_female prevalence_male delta_mgi_male
#> 1            3.9140    0.6739   attractor          0.2359         0.1131
#> 2            0.0661    0.4820   attractor          0.2769         1.2373
#> 3           -0.1331    0.1537   satellite          0.4410         0.7921
#> 4           -0.5221    1.1880   satellite          0.2103         0.0118
#> 5           -0.1160    0.1858   satellite          0.3128         0.3539
#> 6            0.4428    0.7460   attractor          0.4051         0.0000
#> 7            1.6641    1.0736   attractor          0.4769         1.2346
#> 8            1.0068    1.3410   attractor          0.3641         1.3715
#> 9            1.8159    0.3416   attractor          0.7897         1.4981
#> 10           0.5249    2.0601   attractor          0.2872         0.2449
#>    os_male role_male delta_mgi_diff
#> 1   0.4135 attractor         3.8009
#> 2   1.3554 attractor        -1.1712
#> 3   0.6004 attractor        -0.9252
#> 4   0.9104 attractor        -0.5339
#> 5   1.1123 attractor        -0.4699
#> 6   0.0000   neutral         0.4428
#> 7   1.1081 attractor         0.4295
#> 8   1.8771 attractor        -0.3647
#> 9   0.2424 attractor         0.3178
#> 10  1.0714 attractor         0.2800
# }