Computes a bayesian (hierarchical) piecewise linear model based on a Markov chain Monte Carlo sampler.
Usage
bplm(
data,
dvar,
pvar,
mvar,
model = c("W", "H-M", "B&L-B"),
contrast_level = c("first", "preceding"),
contrast_slope = c("first", "preceding"),
trend = TRUE,
level = TRUE,
slope = TRUE,
random_trend = FALSE,
random_level = FALSE,
random_slope = FALSE,
fixed = NULL,
random = NULL,
update_fixed = NULL,
...
)
# S3 method for class 'sc_bplm'
print(x, digits = 3, ...)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_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.
- 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).- ...
Further arguments passed to the mcmcglmm function.
- x
An object returned by
bplm()- digits
The minimum number of significant digits to be use. If set to "auto" (default), values are predefined.