quickpsy_ is the standard evaluation SE function associated
to the non-standard evaluation NSE function quickpsy.
SE functions can be more easily called from other functions.
In SE functions, you need to quote the names of the variables.
quickpsy_(d, x = "x", k = "k", n = "n", grouping, random, within, between,
xmin = NULL, xmax = NULL, log = FALSE, fun = "cum_normal_fun",
parini = NULL, guess = 0, lapses = 0, prob = NULL, thresholds = T,
bootstrap = "parametric", B = 100, ci = 0.95, optimization = "optim")Data frame with the results of a Yes-No experiment to fit. It should have a tidy form in which each column corresponds to a variable and each row is an observation.
Name of the explanatory variable.
Name of the response variable. The response variable could be the number of trials in which a yes-type response was given or a vector of 0s (or -1s; no-type response) and 1s (yes-type response) indicating the response on each trial.
Only necessary if k refers to the number of trials
in which a yes-type response was given. It corresponds to the name of the
variable indicating the total number of trials.
Name of the grouping variables. It should be specified as
grouping = .(variable_name1, variable_name2).
Name of the random variable. It should be specified as
random = .(variable_name1, variable_name2). In the current version
of quickpsy, the random variable has not special treatment. It does the
same as grouping.
Name of the within variable. It should be specified as
within = .(variable_name1, variable_name2). In the current version
of quickpsy, the within variable has not special treatment. It does the
same as grouping.
Name of the between variable. It should be specified as
between = .(variable_name1, variable_name2). In the current version
of quickpsy, the between variable has not special treatment. It does the
same as grouping.
Minimum value of the explanatory variable for which the curves should be calculated (the default is the minimum value of the explanatory variable).
Maximum value of the explanatory variable for which the curves should be calculated (the default is the maximum value of the explanatory variable).
If TRUE, the logarithm of the explanatory variable is used
to fit the curves (default is FALSE).
Name of the shape of the curve to fit. It could be a predefined
shape (cum_normal_fun, logistic_fun, weibull_fun)
or the name of a function introduced by the user
(default is cum_normal_fun).
Initial parameters. quickpsy calculates default
initial parameters using probit analysis, but it is also possible to
specify a vector of initial parameters or a list of the form
list(c(par1min, par1max), c(par2min, par2max)) to
constraint the lower and upper bounds of the parameters (when
optimization = 'DE', parini should be also a list).
Value indicating the guess rate \(\gamma\) (default is 0). If
TRUE, the guess rate is estimated as the i + 1 parameter where
i corresponds to the number of parameters of fun. If, for
example, fun is a predefined shape with parameters p1 and p2,
then the guess rate corresponds to parameter p3.
Value indicating the lapse rate \(\lambda\) (default is 0).
If TRUE, the lapse rate is estimated as the i + 1 parameter where
i corresponds to the number of parameters of fun plus one if
the guess rate is estimated. If, for example, fun is a
predefined shape with parameters p1 and p2,
then the lapse rate corresponds to parameter p3. If the guess rate is also
estimated, p3 will be the guess rate and p4 the lapse rate.
Probability to calculate the threshold (default is
guess + .5 * (1 - guess)).
If FALSE, thresholds are not calculated
(default is TRUE).
'parametric' performs parametric bootstrap;
'nonparametric' performs non-parametric bootstrap;
'none' does not perform bootstrap (default is 'parametric').
number of bootstrap samples (default is 100 ONLY).
Confidence intervals level based on percentiles (default is .95).
Method used for optimizization. The default is 'optim' which uses
the optim function. It can also be 'DE' which uses de function
DEoptim from the package DEoptim, which performs differential
evolution optimization. By using DEoptim, it is less likely that the
optimization finishes in a local minimum, but the optimization is slow.
When 'DE' is used, parini should be specified as a list with
lower and upper bounds.