mplm: Multivariate Piecewise linear model / piecewise regression

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

Multivariate Piecewise linear model / piecewise regression

Description

The mplm() function computes a multivariate piecewise regression model.

Usage

mplm(
  data,
  dvar,
  mvar,
  pvar,
  model = c("W", "H-M", "B&L-B", "JW"),
  contrast = c("first", "preceding"),
  contrast_level = c(NA, "first", "preceding"),
  contrast_slope = c(NA, "first", "preceding"),
  trend = TRUE,
  level = TRUE,
  slope = TRUE,
  formula = NULL,
  update = NULL,
  na.action = na.omit,
  ...
)

## S3 method for class 'sc_mplm'
print(x, digits = "auto", std = 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.
mvar	Character string with the name of the measurement time 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.
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.
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.
formula	Defaults to 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 variables into the regression model.
update	An easier way to change the regression formula (e.g., . ~ . + newvariable).
na.action	Defines how to deal with missing values
...	Further arguments passed to the lm function.
x	Object returned from mplm().
digits	The minimum number of significant digits to be use. If set to "auto" (default), values are predefined.
std	If TRUE, a table with standardized estimates is included.

Value

model	Character string from function call (see arguments above).
full.model	Full regression model list.

Functions

• print(sc_mplm): Print results

Author(s)

Juergen Wilbert

See Also

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

Examples

res <- mplm(Leidig2018$`1a1`,
  dvar = c("academic_engagement", "disruptive_behavior")
)
print(res)
## also report standardized coefficients:
print(res, std = TRUE)

scan documentation built on Aug. 8, 2023, 5:07 p.m.