knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
We are going to analize the data set qpdat
, which is included in quickpsy
. It has a tidy form: each column corresponds to a variable and each row to an observation.
To fit the psychometric functions (the default shape is the cumulative normal function), we use the main function of quickpsy, which has the name of the package. We introduce the data, the name of the explanatory variable, the name of the response variable and the name of the grouping variables.
library(quickpsy) fit <- quickpsy(qpdat, phase, resp, grouping = c("participant", "cond"))
quickpsy
creates a object of class quickpsy with a list of data frames with all the results. We can use names
to see the names of the output data frames
names(fit)
To obtain the thresholds, for example, we can do
fit$thresholds
and to obtain the parameters
fit$par
We can use plot
to plot the psychometric curves
plot(fit)
or if we want to used different colors for the different conditions
plot(fit, color = cond)
The output plot is a ggplot and we can modify it accordingly. For example
library(ggplot2) plot(fit, color = cond) + labs(y = "Proportion") + theme_minimal()
Of course, we can also use the output data frames to create the plot ourselves
ggplot() + facet_wrap(~ participant) + geom_line(data = fit$curvesbootstrap, aes(x = x, y = y, color = cond, group=paste(sample, cond)), lwd = .2, alpha = .4) + geom_line(data = fit$curves, aes(x = x, y = y, group = cond)) + geom_point(data = fit$averages, aes(x = phase, y = prob), size = 2) + geom_point(data = fit$averages, aes(x = phase, y = prob, color = cond))
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