lgc: General function to specify a general latent growth...

In semnova: Latent Repeated Measures ANOVA

Description

General function to specify a general latent growth components model.

Usage

lgc(
  data,
  mmodel,
  C_matrix,
  hypotheses = NULL,
  covariates = NULL,
  groups = NULL,
  append = NULL,
  verbose = FALSE,
  compound_symmetry = FALSE,
  sphericity = FALSE,
  multiv_tests = c("wilks", "wald"),
  univ_tests = NULL,
  randomization = list(ncores = 1, nsamples = 1000),
  ...
)

Arguments

data	Dataframe. Data object to be passed to lavaan.
mmodel	Object of class mmodel. If not provided, manifest variables from the formula object will be used. Otherwise, use create_mmodel() to specify measurement model.
C_matrix	Contrast matrix. Must be invertible.
hypotheses	List of numeric vectors. Each list element represents a hypothesis. For each hypothesis, the contrasts indicated by the elements of the vectors are tested against zero.
covariates	Not implemented yet.
groups	Not implemented yet.
append	Character. Syntax that is to be appended to lavaan syntax.
verbose	Boolean. Print details during procedure.
compound_symmetry	Boolean. When set to TRUE, compound symmetry is assumed.
sphericity	Boolean or formula. When set to TRUE, sphericity is assumed for all effects.
multiv_tests	Character vector. Multivariate test statistics that are to be computed. Possible statistics are: c("wilks", "wald"). Default is multiv_tests = c("wilks", "wald").
univ_tests	Character vector. Univariate test statistics that are to be computed. Possible statistics are: c("F"). Default is univ_tests = NULL.
randomization	Not yet supported.
...	Additional arguments to be passed to lavaan.

Value

Function returns an lgc object. Use summary(object) to print hypotheses. Otherwise use object@sem_obj to get access to the underlying lavaan object.

Examples

set.seed(323412431)

data("semnova_test_data", package = "semnova")

mmodel <- create_mmodel(
    A1B1 = "var1",
    A2B1 = "var2",
    A3B1 = "var3",
    A1B2 = "var4",
    A2B2 = "var5",
    A3B2 = "var6",
    lv_scaling = "referent"
)

hypotheses <- list(
    Intercept = c(1),
    A        = c(2, 3),
    B        = c(4),
    AB       = c(5, 6)
)

C_matrix <- matrix(
    c(1, 1, 0, 1, 1, 0,
      1, 0, 1, 1, 0, 1,
      1,-1,-1, 1,-1,-1,
      1, 1, 0,-1,-1, 0,
      1, 0, 1,-1, 0,-1,
      1,-1,-1,-1, 1, 1),
    nrow=6
)

fit_lgc <- lgc(data = semnova_test_data, mmodel, C_matrix, hypotheses)
summary(fit_lgc)

semnova documentation built on July 1, 2020, 10:43 p.m.