trend {scan}

Trend analysis for single-cases data

Description

The trend() function provides an overview of linear trends in single case data. By default, it provides the intercept and slope of a linear and quadratic regression of measurement time on scores. Models are calculated separately for each phase and across all phases. For more advanced use, you can add regression models using the R-specific formula class.

Usage

trend(
  data,
  dvar,
  pvar,
  mvar,
  offset = "deprecated",
  first_mt = 0,
  model = 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.

offset

(Deprecated. Please use first_mt). An offset for the first measurement-time of each phase. If offset = 0, the phase measurement is handled as MT 1. Default is offset = -1, setting the first value of MT to 0.

first_mt

A numeric setting the value for the first measurement-time. Default = 0.

model

A string or a list of (named) strings each depicting one regression model. This is a formula expression of the standard R class. The parameters of the model are values, mt and phase.

Value

trend

A matrix containing the results (Intercept, B and beta) of separate regression models for phase A, phase B, and the whole data.

offset

Numeric argument from function call (see arguments section).

Author(s)

Juergen Wilbert

See Also

describe()

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

Examples

## Compute the linear and squared regression for a random single-case
design <- design(slope = 0.5)
matthea <- random_scdf(design)
trend(matthea)

## Besides the linear and squared regression models compute two custom models:
## a) a cubic model, and b) the values predicted by the natural logarithm of the
## measurement time.
design <- design(slope = 0.3)
ben <- random_scdf(design)
trend(ben, offset = 0, model = c("Cubic" = values ~ I(mt^3), "Log Time" = values ~ log(mt)))