Multivariate Piecewise linear model / piecewise regression

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 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).
contrast	List with contrast definitions.
full.model	Full regression model list.
formula	Formula of the mplm model.

Functions

• print(sc_mplm): Print results

See also

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

Author

Juergen Wilbert

Examples

res <- mplm(Leidig2018$`1a1`,
  dvar = c("academic_engagement", "disruptive_behavior")
)
print(res)
#> Multivariate piecewise linear model
#> 
#> Dummy model: W level = first, slope = first
#> 
#> Coefficients: 
#>               academic_engagement disruptive_behavior
#> (Intercept)                 2.771              0.8493
#> Trend                      -0.216              0.0822
#> Level Phase B               2.340             -1.3898
#> Slope Phase B               0.219             -0.0803
#> 
#> Formula: y ~ 1 + mt + phaseB + interB
#> 
#> Type III MANOVA Tests: Pillai test statistic
#>               Df test stat approx F num Df den Df  Pr(>F)    
#> (Intercept)    1    0.2841    15.48      2     78 2.2e-06 ***
#> Trend          1    0.0317     1.28      2     78 0.28519    
#> Level Phase B  1    0.1905     9.18      2     78 0.00026 ***
#> Slope Phase B  1    0.0315     1.27      2     78 0.28737    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> The following variables were used in this analysis:
#> 'academic_engagement/ disruptive_behavior' as dependent variable, 'phase' as phase variable, and 'mt' as measurement-time variable.
## also report standardized coefficients:
print(res, std = TRUE)
#> Multivariate piecewise linear model
#> 
#> Dummy model: W level = first, slope = first
#> 
#> Coefficients: 
#>               academic_engagement disruptive_behavior
#> (Intercept)                 2.771              0.8493
#> Trend                      -0.216              0.0822
#> Level Phase B               2.340             -1.3898
#> Slope Phase B               0.219             -0.0803
#> 
#> Standardized coefficients: 
#>               academic_engagement disruptive_behavior
#> (Intercept)                 0.000               0.000
#> Trend                      -5.979               5.107
#> Level Phase B               0.576              -0.767
#> Slope Phase B               5.950              -4.897
#> 
#> Formula: y ~ 1 + mt + phaseB + interB
#> 
#> Type III MANOVA Tests: Pillai test statistic
#>               Df test stat approx F num Df den Df  Pr(>F)    
#> (Intercept)    1    0.2841    15.48      2     78 2.2e-06 ***
#> Trend          1    0.0317     1.28      2     78 0.28519    
#> Level Phase B  1    0.1905     9.18      2     78 0.00026 ***
#> Slope Phase B  1    0.0315     1.27      2     78 0.28737    
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> The following variables were used in this analysis:
#> 'academic_engagement/ disruptive_behavior' as dependent variable, 'phase' as phase variable, and 'mt' as measurement-time variable.