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Generate lavaan syntax for longitudinal CFA MIC estimation

Source: R/lcfa_model.R
lcfa_model.Rd

lcfa_model() generates lavaan model syntax for estimating anchor-based minimal important change (MIC) using longitudinal confirmatory factor analysis.

Usage

lcfa_model(
  data,
  trt,
  pair_by = c("position", "suffix"),
  t1_suffix = NULL,
  t2_suffix = NULL,
  pair_map = NULL,
  factor_t1 = "F1",
  factor_t2 = "F2",
  loading_prefix = "a",
  threshold_prefix = "b",
  trt_loading_t1 = "f1",
  trt_loading_t2 = "f2",
  trt_threshold_label = "thr.trt",
  psb_label = "psb",
  mic_label = "b_param",
  correlated_errors = TRUE,
  threshold_invariance = TRUE,
  include_residual_variances_t2 = TRUE,
  include_factor_structure = TRUE,
  include_comments = TRUE,
  print_model = TRUE
)

Arguments

data

A data frame containing paired Time 1 and Time 2 PROM items and a transition rating variable.

trt

Character. Name of the transition rating variable.

pair_by

Pairing method. "position" assumes the first half of item columns are Time 1 items and the second half are Time 2 items. "suffix" detects item pairs using t1_suffix and t2_suffix.

t1_suffix

regex suffix identifying Time 1 items when pair_by = "suffix". Use "" when Time 1 items have no suffix.

t2_suffix

regex suffix identifying Time 2 items when pair_by = "suffix".

pair_map

Optional data frame describing item pairs, typically from equalize_levels()$pair_map. If supplied, it takes precedence over pair_by, t1_suffix, and t2_suffix.

factor_t1

Character. Name of the Time 1 latent factor.

factor_t2

Character. Name of the Time 2 latent factor.

loading_prefix

Character. Prefix for equality-constrained item loading labels.

threshold_prefix

Character. Prefix for equality-constrained item threshold labels.

trt_loading_t1

Character. Label for the transition rating loading on the Time 1 factor.

trt_loading_t2

Character. Label for the transition rating loading on the Time 2 factor.

trt_threshold_label

Character. Label for the transition rating threshold.

psb_label

Character. Name of the defined present-state-bias parameter.

mic_label

Character. Name of the defined MIC parameter on the latent theta scale.

correlated_errors

Logical. If TRUE, add correlated residuals between corresponding Time 1 and Time 2 items.

threshold_invariance

Logical. If TRUE, constrain thresholds equal across Time 1 and Time 2 within each item pair.

include_residual_variances_t2

Logical. If TRUE, frees residual variances of Time 2 items using item_t2 ~~ NA*item_t2.

include_factor_structure

Logical. If TRUE, adds factor variances, covariance, and latent means/intercepts sections.

include_comments

Logical. If TRUE, include section comments in the generated lavaan syntax.

print_model

Logical. If TRUE, prints the generated lavaan syntax for easy inspection.

Value

An object of class lcfa_model, invisibly. The generated lavaan syntax can be accessed using $model.

Details

The generated model is intended for ordinal PROM items and a binary transition rating item. Item thresholds are constrained equal across Time 1 and Time 2 within each item pair. The number of thresholds is determined automatically from the observed response levels in the supplied data.

Item pairs can be detected either by column position or by suffix patterns, using the same logic as equalize_levels().

The model defines:


psb := (f1/f2) + 1
b_param := thr.trt/f2

Examples

set.seed(123)
sim <- simdat(N = 100)
dat <- sim$datw

mod <- lcfa_model(
  data = dat[, c(sim$item_names$t1_items,
                 sim$item_names$t2_items,
                 "trat")],
  trt = "trat",
  pair_by = "suffix",
  t1_suffix = "",
  t2_suffix = "\\.1",
  print_model = FALSE
)

mod$model
#> [1] "\n# Factors\nF1 =~ a1*item1 + a2*item2 + a3*item3 + a4*item4 + a5*item5 + a6*item6 + a7*item7 + a8*item8 + a9*item9 + a10*item10 + f1*trat\nF2 =~ a1*item1.1 + a2*item2.1 + a3*item3.1 + a4*item4.1 + a5*item5.1 + a6*item6.1 + a7*item7.1 + a8*item8.1 + a9*item9.1 + a10*item10.1 + f2*trat\n\n# Correlated errors over time\nitem1 ~~ item1.1\nitem2 ~~ item2.1\nitem3 ~~ item3.1\nitem4 ~~ item4.1\nitem5 ~~ item5.1\nitem6 ~~ item6.1\nitem7 ~~ item7.1\nitem8 ~~ item8.1\nitem9 ~~ item9.1\nitem10 ~~ item10.1\n\n# Thresholds\nitem1 + item1.1 | b1_1*t1 + b1_2*t2 + b1_3*t3\nitem2 + item2.1 | b2_1*t1 + b2_2*t2 + b2_3*t3\nitem3 + item3.1 | b3_1*t1 + b3_2*t2 + b3_3*t3\nitem4 + item4.1 | b4_1*t1 + b4_2*t2 + b4_3*t3\nitem5 + item5.1 | b5_1*t1 + b5_2*t2 + b5_3*t3\nitem6 + item6.1 | b6_1*t1 + b6_2*t2 + b6_3*t3\nitem7 + item7.1 | b7_1*t1 + b7_2*t2 + b7_3*t3\nitem8 + item8.1 | b8_1*t1 + b8_2*t2 + b8_3*t3\nitem9 + item9.1 | b9_1*t1 + b9_2*t2 + b9_3*t3\nitem10 + item10.1 | b10_1*t1 + b10_2*t2 + b10_3*t3\ntrat | thr.trt*t1\n\n# Variances/covariances\nF1 ~~ 1*F1\nF2 ~~ NA*F2\nF1 ~~ NA*F2\nitem1.1 ~~ NA*item1.1\nitem2.1 ~~ NA*item2.1\nitem3.1 ~~ NA*item3.1\nitem4.1 ~~ NA*item4.1\nitem5.1 ~~ NA*item5.1\nitem6.1 ~~ NA*item6.1\nitem7.1 ~~ NA*item7.1\nitem8.1 ~~ NA*item8.1\nitem9.1 ~~ NA*item9.1\nitem10.1 ~~ NA*item10.1\n\n# Means/intercepts\nF1 ~ 0*1\nF2 ~ NA*1\n\n# Derived parameters\npsb := (f1/f2) + 1\nb_param := thr.trt/f2\n"