hplm: Hierarchical piecewise linear model / piecewise regression

In scan: Single-Case Data Analyses for Single and Multiple Baseline Designs

Hierarchical piecewise linear model / piecewise regression

Description

The hplm() function computes a hierarchical piecewise regression model.

Usage

hplm(
  data,
  dvar,
  pvar,
  mvar,
  model = c("W", "H-M", "B&L-B", "JW"),
  contrast = c("first", "preceding"),
  contrast_level = NA,
  contrast_slope = NA,
  method = c("ML", "REML"),
  control = list(opt = "optim"),
  random.slopes = FALSE,
  lr.test = FALSE,
  ICC = TRUE,
  trend = TRUE,
  level = TRUE,
  slope = TRUE,
  random_trend = FALSE,
  random_level = FALSE,
  random_slope = FALSE,
  fixed = NULL,
  random = NULL,
  update.fixed = NULL,
  data.l2 = NULL,
  ...
)

## S3 method for class 'sc_hplm'
print(x, ..., casewise = FALSE)

## S3 method for class 'sc_hplm'
export(
  object,
  caption = NA,
  footnote = NA,
  filename = NA,
  kable_styling_options = list(),
  kable_options = list(),
  round = 2,
  nice = TRUE,
  casewise = FALSE,
  ...
)

## S3 method for class 'sc_hplm'
coef(object, casewise = FALSE, ...)

Arguments

data	A single-case data frame. See scdf() to learn about this format.
dvar	Character string with the name of the dependent variable. Defaults to the attributes in the scdf file.
pvar	Character string with the name of the phase variable. Defaults to the attributes in the scdf file.
mvar	Character string with the name of the measurement time variable. Defaults to the attributes in the scdf file.
model	Model used for calculating the dummy parameters (see Huitema & McKean, 2000). Default is model = "W". Possible values are: "B&L-B", "H-M", "W", and deprecated "JW".
contrast	Sets contrast_level and contrast_slope. Either "first", "preceding" or a contrast matrix.
contrast_level	Either "first", "preceding" or a contrast matrix. If NA contrast_level is a copy of contrast.
contrast_slope	Either "first", "preceding" or a contrast matrix. If NA contrast_level is a copy of contrast.
method	Method used to fit your model. Pass "REML" to maximize the restricted log-likelihood or "ML" for maximized log-likelihood. Default is "ML".
control	A list of settings for the estimation algorithm, replacing the default values passed to the function lmeControl of the nlme package.
random.slopes	If random.slopes = TRUE random slope effects of the level, trend, and treatment parameter are estimated.
lr.test	If set TRUE likelihood ratio tests are calculated comparing model with vs. without random slope parameters.
ICC	If ICC = TRUE an intraclass-correlation is estimated.
trend	A logical indicating if a trend parameters is included in the model.
level	A logical indicating if a level parameters is included in the model.
slope	A logical indicating if a slope parameters is included in the model.
random_trend	If TRUE, includes a random trend trend effect.
random_level	If TRUE, includes a random level trend effect.
random_slope	If TRUE, includes a random slope trend effect.
fixed	Defaults to the fixed part of the standard piecewise regression model. The parameter phase followed by the phase name (e.g., phaseB) indicates the level effect of the corresponding phase. The parameter 'inter' followed by the phase name (e.g., interB) adresses the slope effect based on the method provide in the model argument (e.g., "B&L-B"). The formula can be changed for example to include further L1 or L2 variables into the regression model.
random	The random part of the model.
update.fixed	An easier way to change the fixed model part (e.g., . ~ . + newvariable).
data.l2	A dataframe providing additional variables at Level 2. The scdf File has to have names for all cases and the Level 2 dataframe has to have a column named 'cases' with the names of the cases the Level 2 variables belong to.
...	Further arguments passed to the lme function.
x	An object returned by hplm()
casewise	Returns the estimations for each case
object	An scdf or an object exported from a scan function.
caption	Character string with table caption. If left NA (default) a caption will be created based on the exported object.
footnote	Character string with table footnote. If left NA (default) a footnote will be created based on the exported object.
filename	String containing the file name. If a filename is given the output will be written to that file.
kable_styling_options	list with arguments passed to the kable_styling function.
kable_options	list with arguments passed to the kable function.
round	Integer passed to the digits argument internally used to round values.
nice	If set TRUE (default) output values are rounded and optimized for publication tables.

Value

model	List containing infromation about the applied model
N	Number of single-cases.
formla	A list containing the fixed and the random formulas of the hplm model.
hplm	Object of class lme contaning the multilevel model
model.0	Object of class lme containing the Zero Model.
ICC	List containing intraclass correlation and test parameters.
model.without	Object of class gls containing the fixed effect model.

Functions

• print(sc_hplm): Print results

• export(sc_hplm): Export results as html table (see export())

• coef(sc_hplm): Extract model coefficients

Author(s)

Juergen Wilbert

See Also

Other regression functions: autocorr(), corrected_tau(), mplm(), plm(), trend()

Examples

## Compute hplm model on a MBD over fifty cases (restricted log-likelihood)
hplm(exampleAB_50, method = "REML", random.slopes = FALSE)

## Analyzing with additional L2 variables
Leidig2018 %>%
  add_l2(Leidig2018_l2) %>%
  hplm(update.fixed = .~. + gender + migration + ITRF_TOTAL*phaseB,
       slope = FALSE, random.slopes = FALSE, lr.test = FALSE
  )

scan documentation built on July 1, 2024, 9:07 a.m.