hplm: Hierarchical piecewise linear model / piecewise regression

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

Description

The hplm function computes a hierarchical piecewise regression model.

Usage

hplm(
  data,
  dvar,
  pvar,
  mvar,
  model = "B&L-B",
  method = "ML",
  control = list(opt = "optim"),
  random.slopes = FALSE,
  lr.test = FALSE,
  ICC = TRUE,
  trend = TRUE,
  level = TRUE,
  slope = TRUE,
  fixed = NULL,
  random = NULL,
  update.fixed = NULL,
  data.l2 = NULL,
  ...
)

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 slope parameter (see Huitema & McKean, 2000). Default is model = "B&L-B". Possible values are: "B&L-B", "H-M", "Mohr#1", "Mohr#2", "JW", "JW2", and "Manly".
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.
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.

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.

Author(s)

Juergen Wilbert

See Also

Other regression functions: corrected_tauSC(), mplm(), plm()

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
hplm(Leidig2018, data.l2 = Leidig2018_l2, 
     update.fixed = .~. + gender + migration + ITRF_TOTAL*phaseB, 
     slope = FALSE, random.slopes = FALSE, lr.test = FALSE)
     

scan documentation built on Feb. 12, 2021, 3:01 a.m.