ctFitCovCheck: Visual lagged covariance or correlation diagnostics for ctsem...

In ctsem: Continuous Time Structural Equation Modelling

Visual lagged covariance or correlation diagnostics for ctsem fits.

Description

Compares empirical lagged covariances or correlations with the same quantity computed from data generated by the fitted model. Correlations are computed directly from paired observations within each generated data set, then summarised across generated data sets.

Usage

ctFitCovCheck(
  fit,
  cor = TRUE,
  plot = TRUE,
  splitby = NULL,
  data = NULL,
  lags = 0:10,
  variables = NULL,
  splitdata = data,
  split = NULL,
  breaks = 2,
  nsamples = NULL,
  minpairn = 10,
  cores = 1,
  keep = c("summary", "samples")
)

Arguments

fit	A 'ctStanFit' object.
cor	Logical; if 'TRUE' correlations are analysed instead of covariances.
plot	Logical; if 'TRUE' (default) a 'ggplot2' object (or list of plots) is returned. If 'FALSE', the raw 'data.table' with diagnostics is returned.
splitby	Optional character string giving the variable name on which to split subjects or observations. Numeric variables are split at the subject mean and then at the median by default; non-numeric variables are treated as groups and may vary within subject.
data	Optional long-format data to use for the empirical lagged covariances or correlations. If omitted, the fitted data are used.
lags	Non-negative integer vector giving observation lags to compare.
variables	Optional character vector of manifest variables to include. Defaults to all manifest variables in the model.
splitdata	Optional data containing 'splitby', useful when the split variable was not in the fitted model. If omitted, 'data' is used when supplied, otherwise the fitted data are used.
split	Split rule for 'splitby': '"median"', '"mean"', '"quantile"', '"factor"', or '"none"'. If 'NULL', numeric split variables use '"median"' and non-numeric split variables use '"factor"'.
breaks	Number of quantile groups when 'split = "quantile"'.
nsamples	Optional number of generated data sets to use. Existing generated data are reused when present; otherwise 'ctGenerateFromFit' is called.
minpairn	Minimum number of complete paired observations required for an empirical or generated lagged covariance/correlation.
cores	Number of cores to use for generating data and summarising generated samples.
keep	If '"samples"', draw-level model diagnostics are attached as the '"samples"' attribute of the returned data.

Value

Either a plot/list of plots (default, given by 'ctFitCovCheckPlot') or a 'data.table'.

Examples

# Lagged correlations for all manifest variables, lags 0 to 3.
ctFitCovCheck(ctstantestfit, cor = TRUE, lags = 0:3, plot = TRUE)

# Lagged covariances for one manifest variable.
ctFitCovCheck(ctstantestfit, cor = FALSE, variables = "Y1", lags = 0:5,
  plot = FALSE)

# Split the diagnostic by a time independent predictor in the fitted data.
gg <- ctFitCovCheck(ctstantestfit, cor = TRUE, splitby = "TI1", lags = 0:3)
print(gg[[1]])

# Use an external subject-level variable for the split.
splitdat <- unique(data.frame(ctstantestdat)[, "id", drop = FALSE])
splitdat$group <- rep(c("a", "b"), length.out = nrow(splitdat))
ctFitCovCheck(ctstantestfit, splitby = "group", splitdata = splitdat,
  lags = 0:2)

# Split observations into early and late periods within each subject.
dat <- data.table::as.data.table(data.frame(ctstantestdat))
dat[, period := ifelse(time <= stats::median(time, na.rm = TRUE),
  "early", "late"), by = id]
ctFitCovCheck(ctstantestfit, data = dat, splitby = "period",
  split = "factor", lags = 0:2)

ctsem documentation built on June 30, 2026, 5:07 p.m.