trendSC {scan} | R Documentation |
The trendSC
function provides an overview of linear trends in
single-case data. By default, it gives you the intercept and slope of a
linear and a squared regression of measurement-time on scores. Models are
computed separately for each phase and across all phases. For a
more advanced application, you can add regression models using the R
specific formula class.
trendSC(data, dvar, pvar, mvar, offset = -1, model = NULL)
data |
A single-case data frame. See |
dvar |
Character string with the name of the independend variable. |
pvar |
Character string with the name of the phase variable. |
mvar |
Character string with the name of the measurement time variable. |
offset |
An offset for the first measurement-time of each phase (MT). If
set |
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 |
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 |
Juergen Wilbert
## Compute the linear and squared regression for a random single-case design <- design_rSC(slope = 0.5) matthea <- rSC(design) trendSC(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_rSC(slope = 0.3) ben <- rSC(design) trendSC(ben, offset = 0, model = c("Cubic" = values ~ I(mt^3), "Log Time" = values ~ log(mt)))