In danilinares/quickpsy: Fits Psychometric Functions for Multiple Groups

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Without providing initial parameters

If the function to fit (fun argument) is one of the predefined functions (cum_normal_fun, logistic_fun or weibull_fun), quickpsy calculates the initials parameters for you

library(quickpsy)

fit <- quickpsy(qpdat, phase, resp, grouping = c("participant", "cond"),
                fun = logistic_fun, 
                bootstrap = "none") 

plot(fit)

A vector initial parameters

The initial parameters could be a vector

library(quickpsy)

fit <- quickpsy(qpdat, phase, resp, grouping = c("participant", "cond"),
                parini = c(-100, 70), # the default function is cum_normal_fun
                bootstrap = "none") 

plot(fit)

A list of initial parameters

The initial parameters could be a list in which each component constraints the lower and upper bounds of the parameter (for example, list(c(par1min, par1max), c(par2min, par2max)))

library(quickpsy)

fit <- quickpsy(qpdat, phase, resp, grouping = c("participant", "cond"),
                parini = list(c(-300, 300), c(10, 200)),
                bootstrap = "none") 

plot(fit)

In addition of the upper and lower bound, you can also specify the initial guess of the parameters

library(quickpsy)

fit <- quickpsy(qpdat, phase, resp, grouping = c("participant", "cond"),
                parini = list(c(-300, 300), c(10, 200)),
                parinivector = c(-100, 70), 
                bootstrap = "none") 

plot(fit)

A dataframe of initial parameters

The initial parameters could be a dataframe specifiying the initial parameters for each condition. It should have the same structure that the component par of quickpsy. It is up to you how to build this dataframe. For example:

library(dplyr)
library(tidyr) 

participants <- qpdat %>% distinct(participant)
conditions <- qpdat %>% distinct(cond)

parini <- conditions %>% 
  crossing(participants) %>% 
  crossing(tibble(parn = c("p1", "p2"))) %>% 
  arrange(cond) %>% 
  bind_cols(tibble(par = c(rep(c(-100, 70), 3), 
                           rep(c(-150, 100), 3))))
parini

fit <- quickpsy(qpdat, phase, resp, grouping = c("participant", "cond"),
                parini = parini, bootstrap = "none")

plot(fit)

The dataframe could also contain the constraints of the parameters

parini <- conditions %>% 
  crossing(participants) %>% 
  crossing(tibble(parn = c("p1", "p2"))) %>% 
  arrange(cond) %>% 
  bind_cols(tibble(parmin = c(rep(c(-300, 10), 3), 
                         rep(c(-500, 5), 3)),
                   parmax = c(rep(c(300, 200), 3), 
                         rep(c(500, 400), 3))))
parini

Data frame of functions (dev version of quickpsy)

When the fun argument is a data frame of functions, you should specify all the parameters included in the functions

library(dplyr)

fun_df <- tibble(cond = c("cond1", "cond2"), 
                 fun = c(function(x, p) (1 + exp(-p[2] * (x - p[1])))^(-1), 
                         function(x, p) (1 + exp(-p[2] * (x - p[3])))^(-1)))

fit <- quickpsy(qpdat, phase, resp, grouping = c("participant", "cond"), 
                fun = fun_df, # shared slope parameter
                parini = c(-100, 0.02, -100), 
                bootstrap = "none") 

plot(fit, color = cond)