Getting Started
Pascal Küng
getting-started.RmdInstallation
You can install the development version of interdep from
GitHub with:
# install.packages("pak")
pak::pak("Pascal-Kueng/interdep")About this Vignette
interdep helps researchers prepare cross-sectional and
intensive longitudinal dyadic data for (generalized) multilevel models.
It automatically creates model-ready columns for dyadic multilevel model
parameterizations such as Actor-Partner Interdependence Models (APIM),
Dyad-Individual Models (DIM), and Dyadic Score Models (DSM). DIM
currently supports one exchangeable dyad composition, whereas DSM
supports one distinguishable dyad composition.
This vignette focuses on automatic data preparation for multilevel models (MLMs). For a comparison of MLM and structural equation modeling (SEM) approaches to dyadic data, see Ledermann and Kenny (2017).
For the broader package workflow and an overview of the available model-specific vignettes, including the Actor-Partner Interdependence Model, Mixed-Composition APIM, Dyad-Individual Model, and Dyadic Score Model, see the Overview.
For an in-depth tutorial covering data preparation and model fitting, but also additional steps like diagnostics and assumption checks, see Distinguishable and Exchangeable Dyads: Bayesian Multilevel Modelling.
Prerequisites
The basic data structure needed for interdep is a long
data frame where dyads are stacked on top of each other and both members
of a dyad appear as separate rows.
If your raw data are currently in wide format (for time or dyads or
both), reshape them to this long structure before using
prepare_interdep_data(). See the tidyr pivoting
vignette or the pivot_longer()
reference.
Roughly, the expected structure for interdep is:
- For cross-sectional data: one row per
dyad x member
| dyad | member | x | y |
|---|---|---|---|
| 1 | 1 | 4.2 | 7.1 |
| 1 | 2 | 5.0 | 6.4 |
| 2 | 1 | 3.8 | 5.9 |
| 2 | 2 | 4.5 | 6.8 |
- For intensive longitudinal data: at most one row per
dyad x time x member
| dyad | time | member | x | y |
|---|---|---|---|---|
| 1 | 1 | 1 | 4.2 | 7.1 |
| 1 | 1 | 2 | 5.0 | 6.4 |
| 1 | 2 | 1 | 4.0 | 6.9 |
| 1 | 2 | 2 | 5.3 | 6.6 |
Measured variables may contain missing values. The structural
group, member, and optional time
variables must not contain missing values.
In intensive longitudinal data, missing measurement occasions can be represented by absent rows, as long as the time variable preserves the observed measurement occasions. For example:
| dyad | personID | time | x | y |
|---|---|---|---|---|
| 1 | 1 | 1 | 4.2 | 7.1 |
| 1 | 1 | 3 | 4.0 | 6.9 |
| 1 | 2 | 1 | 5.3 | 6.6 |
| 1 | 2 | 2 | 4.7 | 6.1 |
| 1 | 2 | 3 | 5.1 | 6.4 |
In this example, the row for person 1 at time 2 is absent. The time
variable preserves the observed measurement occasions and skips from
time 1 to time 3 for that person. interdep accepts this
structure without requiring a placeholder row for the missing
occasion.
Data preparation for distinguishable dyads
example_dyadic_crosssectional is a simulated
cross-sectional dataset for distinguishable dyads. Each dyad has two
rows: one for each member.
#> personID coupleID gender communication satisfaction
#> 1 1 1 female 4.789772 4.367824
#> 2 2 1 male 3.803445 2.342890
#> 3 3 2 female 2.914052 2.442250
#> 4 4 2 male 6.508207 6.080428
#> 5 5 3 female 5.696995 5.865494
#> 6 6 3 male 8.215332 9.661295We validate and prepare the data with the function
prepare_interdep_data()
cross_distinguishable_data <- prepare_interdep_data(
data = example_dyadic_crosssectional,
group = coupleID,
member = personID,
role = gender,
# In this example, we optionally specify a predictor variable
# and a model type to generate the columns needed for that model type.
predictors = communication,
model_type = "apim"
)
print(cross_distinguishable_data, n = 4)
#> # interdep data
#> # Rows: 190 | Dyads: 95 | Intensive longitudinal: no
#> # Structure: group = coupleID, member = personID, role = gender
#> #
#> # Dyad compositions:
#> # female_x_male distinguishable 95 dyads
#> #
#> # Added columns:
#> # .i_composition inferred dyad composition
#> # .i_composition_role composition-specific member role
#> # .i_is_{comp-role} composition-role indicator columns
#> # .i_{pred}_actor APIM actor predictor: actor's original predictor
#> # values
#> # .i_{pred}_partner APIM partner predictor: partner's original predictor
#> # values
#> #
#> # A tibble: 190 × 11
#> personID coupleID gender communication satisfaction .i_composition
#> <int> <int> <fct> <dbl> <dbl> <fct>
#> 1 1 1 female 4.79 4.37 female_x_male
#> 2 2 1 male 3.80 2.34 female_x_male
#> 3 3 2 female 2.91 2.44 female_x_male
#> 4 4 2 male 6.51 6.08 female_x_male
#> # ℹ 186 more rows
#> # ℹ 5 more variables: .i_composition_role <fct>,
#> # .i_is_female_x_male_female <dbl>, .i_is_female_x_male_male <dbl>,
#> # .i_communication_actor <dbl>, .i_communication_partner <dbl>The function automatically recognized that in this dataset there are
95 female-male dyads and created APIM-relevant variables (Kenny and Cook 1999). These generated
.i_* columns can be used directly in model formulas.
For fitted APIM examples using these columns, see the Actor-Partner Interdependence Model vignette.
Data preparation for exchangeable dyads
For a dataset with only one type of dyad that should be treated as
exchangeable, omit the role argument:
cross_exchangeable_data <- prepare_interdep_data(
data = example_dyadic_crosssectional,
group = coupleID,
member = personID,
seed = 123
)
print(cross_exchangeable_data, n = 4)
#> # interdep data
#> # Rows: 190 | Dyads: 95 | Intensive longitudinal: no
#> # Structure: group = coupleID, member = personID
#> #
#> # Dyad compositions:
#> # assumed_exchangeable exchangeable 95 dyads
#> #
#> # Added columns:
#> # .i_composition inferred dyad composition
#> # .i_composition_role composition-specific member role
#> # .i_is_{comp-role} composition-role indicator columns
#> # .i_diff_{comp} composition-specific sum-diff contrasts with arbitrary
#> # direction; 0 for distinguishable dyads or other
#> # exchangeable compositions
#> #
#> # A tibble: 190 × 9
#> personID coupleID gender communication satisfaction .i_composition
#> <int> <int> <fct> <dbl> <dbl> <fct>
#> 1 1 1 female 4.79 4.37 assumed_exchangeable
#> 2 2 1 male 3.80 2.34 assumed_exchangeable
#> 3 3 2 female 2.91 2.44 assumed_exchangeable
#> 4 4 2 male 6.51 6.08 assumed_exchangeable
#> # ℹ 186 more rows
#> # ℹ 3 more variables: .i_composition_role <fct>,
#> # .i_is_assumed_exchangeable <dbl>,
#> # .i_diff_assumed_exchangeable_arbitrary <dbl>The generated .i_diff_assumed_exchangeable_arbitrary
contrast assigns -1 and 1 to the two members
of each exchangeable dyad. Its direction is arbitrary, and
seed makes the assignment reproducible. When role
compositions are available, each exchangeable composition receives its
own contrast, such as .i_diff_female_x_female_arbitrary,
which is 0 for all other compositions (del Rosario and West 2025). We use a fixed seed
in the examples below for consistent results.
Alternatively, for more control, we can explicitly set dyad types to exchangeable:
cross_exchangeable_data <- prepare_interdep_data(
data = example_dyadic_crosssectional,
group = coupleID,
member = personID,
role = gender,
set_exchangeable_compositions = "male-female",
seed = 123
)
print(cross_exchangeable_data, n = 4)
#> # interdep data
#> # Rows: 190 | Dyads: 95 | Intensive longitudinal: no
#> # Structure: group = coupleID, member = personID, role = gender
#> #
#> # Dyad compositions:
#> # female_x_male exchangeable (set by user) 95 dyads
#> #
#> # Added columns:
#> # .i_composition inferred dyad composition
#> # .i_composition_role composition-specific member role
#> # .i_is_{comp-role} composition-role indicator columns
#> # .i_diff_{comp} composition-specific sum-diff contrasts with arbitrary
#> # direction; 0 for distinguishable dyads or other
#> # exchangeable compositions
#> #
#> # A tibble: 190 × 9
#> personID coupleID gender communication satisfaction .i_composition
#> <int> <int> <fct> <dbl> <dbl> <fct>
#> 1 1 1 female 4.79 4.37 female_x_male
#> 2 2 1 male 3.80 2.34 female_x_male
#> 3 3 2 female 2.91 2.44 female_x_male
#> 4 4 2 male 6.51 6.08 female_x_male
#> # ℹ 186 more rows
#> # ℹ 3 more variables: .i_composition_role <fct>, .i_is_female_x_male <dbl>,
#> # .i_diff_female_x_male_arbitrary <dbl>Note that whenever you need to refer to a dyad type, the
order of members does not matter (e.g., male-female and
female-male will both work), and you can use different
separators like male_female, male_x_female, or
male female.
For exchangeable dyads, we can request DIM predictor columns. This
works here because omitting role treats all dyads as a
single exchangeable composition.
cross_dim_data <- prepare_interdep_data(
data = example_dyadic_crosssectional,
group = coupleID,
member = personID,
predictors = communication,
model_type = "dim",
seed = 123
)
print(cross_dim_data, n = 4)
#> # interdep data
#> # Rows: 190 | Dyads: 95 | Intensive longitudinal: no
#> # Structure: group = coupleID, member = personID
#> #
#> # Dyad compositions:
#> # assumed_exchangeable exchangeable 95 dyads
#> #
#> # Added columns:
#> # .i_composition inferred dyad composition
#> # .i_composition_role composition-specific member role
#> # .i_is_{comp-role} composition-role indicator columns
#> # .i_diff_{comp} composition-specific sum-diff contrasts with
#> # arbitrary direction; 0 for distinguishable dyads
#> # or other exchangeable compositions
#> # .i_{pred}_dyad_mean_gmc dyad-mean predictor: dyad's average predictor
#> # level, grand-mean centered
#> # .i_{pred}_within_dyad_dev DIM within-dyad member-deviation predictor:
#> # member's difference from the dyad mean
#> #
#> # A tibble: 190 × 11
#> personID coupleID gender communication satisfaction .i_composition
#> <int> <int> <fct> <dbl> <dbl> <fct>
#> 1 1 1 female 4.79 4.37 assumed_exchangeable
#> 2 2 1 male 3.80 2.34 assumed_exchangeable
#> 3 3 2 female 2.91 2.44 assumed_exchangeable
#> 4 4 2 male 6.51 6.08 assumed_exchangeable
#> # ℹ 186 more rows
#> # ℹ 5 more variables: .i_composition_role <fct>,
#> # .i_is_assumed_exchangeable <dbl>,
#> # .i_diff_assumed_exchangeable_arbitrary <dbl>,
#> # .i_communication_dyad_mean_gmc <dbl>,
#> # .i_communication_within_dyad_dev <dbl>For distinguishable dyads, DSM columns can be requested. These additionally require an explicit role order. The role order defines the direction of all DSM predictor differences and the DSM role contrast (Iida et al. 2018).
cross_dsm_data <- prepare_interdep_data(
data = example_dyadic_crosssectional,
group = coupleID,
member = personID,
role = gender,
predictors = communication,
model_type = "dsm",
dsm_role_order = c("female", "male")
)
print(cross_dsm_data, n = 4)
#> # interdep data
#> # Rows: 190 | Dyads: 95 | Intensive longitudinal: no
#> # Structure: group = coupleID, member = personID, role = gender
#> # DSM direction: female - male
#> #
#> # Dyad compositions:
#> # female_x_male distinguishable 95 dyads
#> #
#> # Added columns:
#> # .i_composition inferred dyad composition
#> # .i_composition_role composition-specific member role
#> # .i_is_{comp-role} composition-role indicator columns
#> # .i_dsm_role_contrast DSM role contrast: +0.5 for the first declared
#> # role and -0.5 for the second declared role
#> # .i_{pred}_dyad_mean_gmc dyad-mean predictor: dyad's average predictor
#> # level, grand-mean centered
#> # .i_{pred}_within_dyad_diff DSM signed predictor difference: first declared
#> # role minus second declared role
#> #
#> # A tibble: 190 × 12
#> personID coupleID gender communication satisfaction .i_composition
#> <int> <int> <fct> <dbl> <dbl> <fct>
#> 1 1 1 female 4.79 4.37 female_x_male
#> 2 2 1 male 3.80 2.34 female_x_male
#> 3 3 2 female 2.91 2.44 female_x_male
#> 4 4 2 male 6.51 6.08 female_x_male
#> # ℹ 186 more rows
#> # ℹ 6 more variables: .i_composition_role <fct>,
#> # .i_is_female_x_male_female <dbl>, .i_is_female_x_male_male <dbl>,
#> # .i_dsm_role_contrast <dbl>, .i_communication_dyad_mean_gmc <dbl>,
#> # .i_communication_within_dyad_diff <dbl>Incomplete dyads and missing roles
By default, prepare_interdep_data() stops when a dyad
has only one observed member or when a member’s role cannot be resolved
from the observed rows. These cases can also be dropped before
validation continues.
incomplete_data <- tibble::tribble(
~coupleID, ~personID, ~gender, ~satisfaction,
1, 1, "female", 5.2,
# Note missing row
2, 3, "female", 4.8,
2, 4, NA, 4.9,
3, 5, "female", 5.1,
3, 6, "female", 5.0,
4, 7, "female", 4.7,
4, 8, "male", 4.6,
# Note missing row
5, 10, NA, 3.0
)
incomplete_dropped_data <- prepare_interdep_data(
incomplete_data,
group = coupleID,
member = personID,
role = gender,
incomplete_dyads = "drop",
missing_role = "drop",
seed = 123
)
#> Dropped 2 incomplete dyads, with IDs: 1, 5.
#> Dropped 1 dyad with incomplete role information, with ID: 2.
print(incomplete_dropped_data)
#> # interdep data
#> # Rows: 4 | Dyads: 2 | Intensive longitudinal: no
#> # Structure: group = coupleID, member = personID, role = gender
#> #
#> # Dropped incomplete dyads: 2 dyads, with IDs: 1, 5
#> #
#> # Dropped dyads with incomplete role information: 1 dyad, with ID: 2
#> #
#> # Dyad compositions:
#> # female_x_female exchangeable 1 dyad
#> # female_x_male distinguishable 1 dyad
#> #
#> # Added columns:
#> # .i_composition inferred dyad composition
#> # .i_composition_role composition-specific member role
#> # .i_is_{comp-role} composition-role indicator columns
#> # .i_diff_{comp} composition-specific sum-diff contrasts with arbitrary
#> # direction; 0 for distinguishable dyads or other
#> # exchangeable compositions
#> #
#> # A tibble: 4 × 10
#> coupleID personID gender satisfaction .i_composition .i_composition_role
#> <dbl> <dbl> <chr> <dbl> <fct> <fct>
#> 1 3 5 female 5.1 female_x_female female_x_female
#> 2 3 6 female 5 female_x_female female_x_female
#> 3 4 7 female 4.7 female_x_male female_x_male_female
#> 4 4 8 male 4.6 female_x_male female_x_male_male
#> # ℹ 4 more variables: .i_is_female_x_female <dbl>,
#> # .i_is_female_x_male_female <dbl>, .i_is_female_x_male_male <dbl>,
#> # .i_diff_female_x_female_arbitrary <dbl>Intensive longitudinal dyadic data
example_dyadic_ILD is a simulated intensive longitudinal
dyadic dataset. Each dyad has repeated observations over
diaryday, with one row per person-day.
To prepare intensive longitudinal data, pass the time
variable to prepare_interdep_data().
ild_apim_data <- prepare_interdep_data(
example_dyadic_ILD,
group = coupleID,
member = personID,
role = gender,
time = diaryday,
predictors = provided_support,
model_type = "apim",
seed = 123
)
print(ild_apim_data, n = 6)
#> # interdep data
#> # Rows: 1120 | Dyads: 40 | Intensive longitudinal: yes
#> # Structure: group = coupleID, member = personID, role = gender, time =
#> # diaryday
#> #
#> # Dyad compositions:
#> # female_x_male distinguishable 40 dyads
#> #
#> # Added columns:
#> # .i_composition inferred dyad composition
#> # .i_composition_role composition-specific member role
#> # .i_is_{comp-role} composition-role indicator columns
#> # .i_{pred}_cwp within-person predictor: momentary deviations from
#> # each person's usual level
#> # .i_{pred}_cbp between-person predictor: stable differences from
#> # the average person's usual level
#> # .i_{pred}_actor APIM actor predictor: actor's original predictor
#> # values
#> # .i_{pred}_partner APIM partner predictor: partner's original predictor
#> # values
#> # .i_{pred}_cwp_actor APIM within-person actor predictor: actor's
#> # momentary deviations from their usual level
#> # .i_{pred}_cwp_partner APIM within-person partner predictor: partner's
#> # momentary deviations from their usual level
#> # .i_{pred}_cbp_actor APIM between-person actor predictor: actor's stable
#> # difference from the average person's usual level
#> # .i_{pred}_cbp_partner APIM between-person partner predictor: partner's
#> # stable difference from the average person's usual
#> # level
#> #
#> # A tibble: 1,120 × 18
#> personID coupleID diaryday gender closeness provided_support .i_composition
#> <int> <int> <int> <fct> <dbl> <dbl> <fct>
#> 1 1 1 0 female 5.03 4.30 female_x_male
#> 2 2 1 0 male 4.68 4.45 female_x_male
#> 3 1 1 1 female 5.64 4.24 female_x_male
#> 4 2 1 1 male 4.52 5.84 female_x_male
#> 5 1 1 2 female 5.49 3.54 female_x_male
#> 6 2 1 2 male NA NA female_x_male
#> # ℹ 1,114 more rows
#> # ℹ 11 more variables: .i_composition_role <fct>,
#> # .i_is_female_x_male_female <dbl>, .i_is_female_x_male_male <dbl>,
#> # .i_provided_support_cwp <dbl>, .i_provided_support_cbp <dbl>,
#> # .i_provided_support_actor <dbl>, .i_provided_support_partner <dbl>,
#> # .i_provided_support_cwp_actor <dbl>, .i_provided_support_cwp_partner <dbl>,
#> # .i_provided_support_cbp_actor <dbl>, …By default, numeric predictors in longitudinal APIM preparation are
decomposed into within-person and between-person components (Bolger and Laurenceau 2013). This temporal
predictor decomposition is controlled by
temporal_predictor_decomposition. The default
"auto" setting selects "time_2l" for this
longitudinal setup and retains raw actor and partner columns alongside
both components.
Note that observed person means used to construct the between-person
(cbp) predictors can be unreliable when each member
contributes few occasions, which can bias between-person estimates (Gottfredson 2019).
Preparing lagged predictors
Lagged versions of variables, including an outcome that is also
passed to predictors, can be obtained through the
lag_predictors argument.
prepare_interdep_data() then returns lag-1 raw and
within-person-centered actor and partner columns alongside their
contemporaneous versions. Lagging respects the dyad and member
structure, matches observations at exactly time - 1, and
does not bridge missing occasions.
Brief example:
ild_apim_data_dynamic <- prepare_interdep_data(
example_dyadic_ILD,
group = coupleID,
member = personID,
time = diaryday,
predictors = closeness,
lag_predictors = closeness,
model_type = "apim",
seed = 123
)
print(ild_apim_data_dynamic, n = 6)
#> # interdep data
#> # Rows: 1120 | Dyads: 40 | Intensive longitudinal: yes
#> # Structure: group = coupleID, member = personID, time = diaryday
#> #
#> # Dyad compositions:
#> # assumed_exchangeable exchangeable 40 dyads
#> #
#> # Added columns:
#> # .i_composition inferred dyad composition
#> # .i_composition_role composition-specific member role
#> # .i_is_{comp-role} composition-role indicator columns
#> # .i_diff_{comp} composition-specific sum-diff contrasts with
#> # arbitrary direction; 0 for distinguishable
#> # dyads or other exchangeable compositions
#> # .i_{pred}_lag1 lag-1 raw predictor values
#> # .i_{pred}_cwp within-person predictor: momentary deviations
#> # from each person's usual level
#> # .i_{pred}_cwp_lag1 lag-1 within-person predictor: momentary
#> # deviations from each person's usual level
#> # .i_{pred}_cbp between-person predictor: stable differences
#> # from the average person's usual level
#> # .i_{pred}_actor APIM actor predictor: actor's original
#> # predictor values
#> # .i_{pred}_actor_lag1 lag-1 APIM actor predictor: actor's original
#> # predictor values
#> # .i_{pred}_partner APIM partner predictor: partner's original
#> # predictor values
#> # .i_{pred}_partner_lag1 lag-1 APIM partner predictor: partner's
#> # original predictor values
#> # .i_{pred}_cwp_actor APIM within-person actor predictor: actor's
#> # momentary deviations from their usual level
#> # .i_{pred}_cwp_actor_lag1 lag-1 APIM within-person actor predictor:
#> # actor's momentary deviations from their usual
#> # level
#> # .i_{pred}_cwp_partner APIM within-person partner predictor: partner's
#> # momentary deviations from their usual level
#> # .i_{pred}_cwp_partner_lag1 lag-1 APIM within-person partner predictor:
#> # partner's momentary deviations from their usual
#> # level
#> # .i_{pred}_cbp_actor APIM between-person actor predictor: actor's
#> # stable difference from the average person's
#> # usual level
#> # .i_{pred}_cbp_partner APIM between-person partner predictor:
#> # partner's stable difference from the average
#> # person's usual level
#> #
#> # A tibble: 1,120 × 24
#> personID coupleID diaryday gender closeness provided_support .i_composition
#> <int> <int> <int> <fct> <dbl> <dbl> <fct>
#> 1 1 1 0 female 5.03 4.30 assumed_exchange…
#> 2 2 1 0 male 4.68 4.45 assumed_exchange…
#> 3 1 1 1 female 5.64 4.24 assumed_exchange…
#> 4 2 1 1 male 4.52 5.84 assumed_exchange…
#> 5 1 1 2 female 5.49 3.54 assumed_exchange…
#> 6 2 1 2 male NA NA assumed_exchange…
#> # ℹ 1,114 more rows
#> # ℹ 17 more variables: .i_composition_role <fct>,
#> # .i_is_assumed_exchangeable <dbl>,
#> # .i_diff_assumed_exchangeable_arbitrary <dbl>, .i_closeness_cwp <dbl>,
#> # .i_closeness_cbp <dbl>, .i_closeness_lag1 <dbl>,
#> # .i_closeness_cwp_lag1 <dbl>, .i_closeness_actor <dbl>,
#> # .i_closeness_partner <dbl>, .i_closeness_cwp_actor <dbl>, …Note: Whether to use the raw or within-person-centered lagged outcome depends on the research question and the data. Including a lagged outcome in dynamic models can introduce bias, especially in shorter time series (Hamaker and Grasman 2015; Nickell 1981; Gistelinck et al. 2021). See the APIM vignette for a more detailed discussion and guidance.
Data with multiple and mixed-composition dyads
example_dyadic_crosssectional_mixed contains three dyad
compositions in the same data object: distinguishable female-male dyads
and exchangeable female-female and male-male dyads (Bolger et al. 2025).
Let’s have interdep infer the compositions
automatically:
mixed_cross_data <- prepare_interdep_data(
example_dyadic_crosssectional_mixed,
group = coupleID,
member = personID,
role = gender,
seed = 123
)
print(mixed_cross_data, n = 4)
#> # interdep data
#> # Rows: 640 | Dyads: 320 | Intensive longitudinal: no
#> # Structure: group = coupleID, member = personID, role = gender
#> #
#> # Dyad compositions:
#> # female_x_female exchangeable 100 dyads
#> # female_x_male distinguishable 120 dyads
#> # male_x_male exchangeable 100 dyads
#> #
#> # Added columns:
#> # .i_composition inferred dyad composition
#> # .i_composition_role composition-specific member role
#> # .i_is_{comp-role} composition-role indicator columns
#> # .i_diff_{comp} composition-specific sum-diff contrasts with arbitrary
#> # direction; 0 for distinguishable dyads or other
#> # exchangeable compositions
#> #
#> # A tibble: 640 × 12
#> personID coupleID gender satisfaction .i_composition .i_composition_role
#> <int> <int> <fct> <dbl> <fct> <fct>
#> 1 1 1 female 4.95 female_x_male female_x_male_female
#> 2 2 1 male 5.26 female_x_male female_x_male_male
#> 3 3 2 female 5.14 female_x_male female_x_male_female
#> 4 4 2 male 3.11 female_x_male female_x_male_male
#> # ℹ 636 more rows
#> # ℹ 6 more variables: .i_is_female_x_female <dbl>,
#> # .i_is_female_x_male_female <dbl>, .i_is_female_x_male_male <dbl>,
#> # .i_is_male_x_male <dbl>, .i_diff_female_x_female_arbitrary <dbl>,
#> # .i_diff_male_x_male_arbitrary <dbl>We can use this data to model these dyad types as separate or in the same model. The APIMs with Mixed Dyad Compositions vignette shows both mixed-composition formulas and practical convergence notes.
Keeping only selected dyad compositions (filtering)
Sometimes a mixed dataset contains dyad compositions that should not
be part of a given analysis. Use include_compositions to
keep only dyads whose observed composition matches the
requested labels. The filtering happens before exchangeability
constraints and pooling, so set_exchangeable_compositions
and pool_compositions arguments can only refer to retained
types of dyads.
mixed_cross_data_included <- prepare_interdep_data(
example_dyadic_crosssectional_mixed,
group = coupleID,
member = personID,
role = gender,
include_compositions = c("female-female", "male-male"),
seed = 123
)
print(mixed_cross_data_included, n = 4)
#> # interdep data
#> # Rows: 400 | Dyads: 200 | Intensive longitudinal: no
#> # Structure: group = coupleID, member = personID, role = gender
#> #
#> # Dyad compositions:
#> # female_x_female exchangeable 100 dyads
#> # male_x_male exchangeable 100 dyads
#> #
#> # Added columns:
#> # .i_composition inferred dyad composition
#> # .i_composition_role composition-specific member role
#> # .i_is_{comp-role} composition-role indicator columns
#> # .i_diff_{comp} composition-specific sum-diff contrasts with arbitrary
#> # direction; 0 for distinguishable dyads or other
#> # exchangeable compositions
#> #
#> # A tibble: 400 × 10
#> personID coupleID gender satisfaction .i_composition .i_composition_role
#> <int> <int> <fct> <dbl> <fct> <fct>
#> 1 241 121 female 5.32 female_x_female female_x_female
#> 2 242 121 female 5.37 female_x_female female_x_female
#> 3 243 122 female 5.99 female_x_female female_x_female
#> 4 244 122 female 6.93 female_x_female female_x_female
#> # ℹ 396 more rows
#> # ℹ 4 more variables: .i_is_female_x_female <dbl>, .i_is_male_x_male <dbl>,
#> # .i_diff_female_x_female_arbitrary <dbl>,
#> # .i_diff_male_x_male_arbitrary <dbl>Setting distinguishable dyads to be treated as exchangeable
As mentioned earlier, omitting the role argument treated
all dyads as one exchangeable composition, effectively pooling them.
For more control, a distinguishable dyad composition in a mixed
dataset can be treated as exchangeable. This specification keeps the
differentiation between the kinds of dyads (e.g.,
male-male, female-female, and
male-female).
mixed_cross_exchangeable_data <- prepare_interdep_data(
example_dyadic_crosssectional_mixed,
group = coupleID,
member = personID,
role = gender,
set_exchangeable_compositions = c("male-female"),
seed = 123
)
print(mixed_cross_exchangeable_data, n = 4)
#> # interdep data
#> # Rows: 640 | Dyads: 320 | Intensive longitudinal: no
#> # Structure: group = coupleID, member = personID, role = gender
#> #
#> # Dyad compositions:
#> # female_x_female exchangeable 100 dyads
#> # female_x_male exchangeable (set by user) 120 dyads
#> # male_x_male exchangeable 100 dyads
#> #
#> # Added columns:
#> # .i_composition inferred dyad composition
#> # .i_composition_role composition-specific member role
#> # .i_is_{comp-role} composition-role indicator columns
#> # .i_diff_{comp} composition-specific sum-diff contrasts with arbitrary
#> # direction; 0 for distinguishable dyads or other
#> # exchangeable compositions
#> #
#> # A tibble: 640 × 12
#> personID coupleID gender satisfaction .i_composition .i_composition_role
#> <int> <int> <fct> <dbl> <fct> <fct>
#> 1 1 1 female 4.95 female_x_male female_x_male
#> 2 2 1 male 5.26 female_x_male female_x_male
#> 3 3 2 female 5.14 female_x_male female_x_male
#> 4 4 2 male 3.11 female_x_male female_x_male
#> # ℹ 636 more rows
#> # ℹ 6 more variables: .i_is_female_x_female <dbl>, .i_is_female_x_male <dbl>,
#> # .i_is_male_x_male <dbl>, .i_diff_female_x_female_arbitrary <dbl>,
#> # .i_diff_female_x_male_arbitrary <dbl>, .i_diff_male_x_male_arbitrary <dbl>Pooling different dyad compositions
Sometimes for theoretical or practical reasons, we may want to pool different exchangeable dyad compositions and analyze them as if they were one. This also allows testing various constraints via model comparisons.
For instance, let’s pool male-male and
female-female dyads and name them same-sex
dyads:
mixed_cross_data_pooled <- prepare_interdep_data(
example_dyadic_crosssectional_mixed,
group = coupleID,
member = personID,
role = gender,
pool_compositions = list(
"same-sex" = c("male-male", "female_female")
),
seed = 123
)
print(mixed_cross_data_pooled)
#> # interdep data
#> # Rows: 640 | Dyads: 320 | Intensive longitudinal: no
#> # Structure: group = coupleID, member = personID, role = gender
#> #
#> # Dyad compositions:
#> # female_x_male distinguishable 120 dyads
#> # same-sex (pooled) exchangeable 200 dyads
#> # female_x_female
#> # male_x_male
#> #
#> # Added columns:
#> # .i_composition inferred dyad composition
#> # .i_composition_role composition-specific member role
#> # .i_is_{comp-role} composition-role indicator columns
#> # .i_diff_{comp} composition-specific sum-diff contrasts with arbitrary
#> # direction; 0 for distinguishable dyads or other
#> # exchangeable compositions
#> #
#> # A tibble: 640 × 10
#> personID coupleID gender satisfaction .i_composition .i_composition_role
#> <int> <int> <fct> <dbl> <fct> <fct>
#> 1 1 1 female 4.95 female_x_male female_x_male_female
#> 2 2 1 male 5.26 female_x_male female_x_male_male
#> 3 3 2 female 5.14 female_x_male female_x_male_female
#> 4 4 2 male 3.11 female_x_male female_x_male_male
#> 5 5 3 female 6.40 female_x_male female_x_male_female
#> 6 6 3 male 3.45 female_x_male female_x_male_male
#> 7 7 4 female 4.16 female_x_male female_x_male_female
#> 8 8 4 male 6.47 female_x_male female_x_male_male
#> 9 9 5 female 5.97 female_x_male female_x_male_female
#> 10 10 5 male 5.44 female_x_male female_x_male_male
#> # ℹ 630 more rows
#> # ℹ 4 more variables: .i_is_female_x_male_female <dbl>,
#> # .i_is_female_x_male_male <dbl>, .i_is_same_sex <dbl>,
#> # .i_diff_same_sex_arbitrary <dbl>Note that you cannot pool distinguishable dyads. To pool
female-male with male-male, we first have to
treat female-male as exchangeable:
mixed_cross_data_pooled_constrained <- prepare_interdep_data(
example_dyadic_crosssectional_mixed,
group = coupleID,
member = personID,
role = gender,
set_exchangeable_compositions = "male female",
pool_compositions = list(
"pooled_exchangeable" = c("male-male", "male_female")
),
seed = 123
)
print(mixed_cross_data_pooled_constrained)
#> # interdep data
#> # Rows: 640 | Dyads: 320 | Intensive longitudinal: no
#> # Structure: group = coupleID, member = personID, role = gender
#> #
#> # Dyad compositions:
#> # female_x_female exchangeable 100 dyads
#> # pooled_exchangeable (pooled) exchangeable 220 dyads
#> # female_x_male
#> # male_x_male
#> #
#> # Added columns:
#> # .i_composition inferred dyad composition
#> # .i_composition_role composition-specific member role
#> # .i_is_{comp-role} composition-role indicator columns
#> # .i_diff_{comp} composition-specific sum-diff contrasts with arbitrary
#> # direction; 0 for distinguishable dyads or other
#> # exchangeable compositions
#> #
#> # A tibble: 640 × 10
#> personID coupleID gender satisfaction .i_composition .i_composition_role
#> <int> <int> <fct> <dbl> <fct> <fct>
#> 1 1 1 female 4.95 pooled_exchangeable pooled_exchangeable
#> 2 2 1 male 5.26 pooled_exchangeable pooled_exchangeable
#> 3 3 2 female 5.14 pooled_exchangeable pooled_exchangeable
#> 4 4 2 male 3.11 pooled_exchangeable pooled_exchangeable
#> 5 5 3 female 6.40 pooled_exchangeable pooled_exchangeable
#> 6 6 3 male 3.45 pooled_exchangeable pooled_exchangeable
#> 7 7 4 female 4.16 pooled_exchangeable pooled_exchangeable
#> 8 8 4 male 6.47 pooled_exchangeable pooled_exchangeable
#> 9 9 5 female 5.97 pooled_exchangeable pooled_exchangeable
#> 10 10 5 male 5.44 pooled_exchangeable pooled_exchangeable
#> # ℹ 630 more rows
#> # ℹ 4 more variables: .i_is_female_x_female <dbl>,
#> # .i_is_pooled_exchangeable <dbl>, .i_diff_female_x_female_arbitrary <dbl>,
#> # .i_diff_pooled_exchangeable_arbitrary <dbl>Continue with the Actor-Partner Interdependence Model (APIM) vignette.
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