addmtoolbox: addmtoolbox

# RUN PRIOR TO TUTORIAL ----------------------------------------------------------------------
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# Prepare multi-item data --------------------------------------------------------------------

# Read in my addm project data ---------------------------------------------------------------
afdat_eye8 = as.data.table(read.table(file = 'temp/data/eye8.txt',
                                      header = TRUE,
                                      sep = ' '))

afdat_choice8 = as.data.table(read.table(file = 'temp/data/choice8.txt',
                                         header = TRUE,
                                         sep = ' '))
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# Take Subset --------------------------------------------------------------------------------
rows.usable = createDataPartition(1:length(afdat_choice8$id),times=1, p=0.05)
afdat_choice = afdat_choice8[rows.usable[[1]],]

setkey(afdat_choice, id)
setkey(afdat_eye8, id)

afdat_eye = afdat_choice[afdat_eye8]
afdat_eye = afdat_eye[complete.cases(afdat_eye)] %>% select(id,fixloc,fixdur,fixnr)

# Prepare multiattribute data ----------------------------------------------------------------
ma_eye = as.data.table(read.table(file = 'temp/data/addm_data_eye_ma.txt',
                                  header = TRUE,
                                  sep = ' '))

ma_choice = as.data.table(read.table(file = 'temp/data/addm_data_choice_ma.txt',
                                     header = TRUE,
                                     sep = ' '))

ma_choice$decision = ma_choice$decision + 1
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rm(rows.usable)
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########################### START TUTORIAL ###################################################

# INSTALL PACKAGE IF NECESSARY ---------------------------------------------------------------
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devtools::install_github('AlexanderFengler/addmtoolbox',build_vignette=TRUE)
library("addmtoolbox")
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# INPUT DATA ---------------------------------------------------------------------------------
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# STANDARD 2 ITEM ----------------------------------------------------------------------------
# Choice Data
View(head(addm_data_choice))

# Eyetracking Data
View(head(addm_data_eye))
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# MORE ITEMS ---------------------------------------------------------------------------------
# Choice Data
View(head(afdat_choice))

# Eyetracking Data
View(head(afdat_eye))
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# MULTIATTRIBUTE -----------------------------------------------------------------------------
# Choice Data
View(head(ma_choice))

# Eyetracking
View(head(ma_eye))
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# PREPROCESS DATA FOR USAGE WITH ADDMTOOLBOX -------------------------------------------------
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# Preprocess supplied data-frame -------------------------------------------------------------
addm.dat = addm_preprocess(choice.dat = addm_data_choice,
                           eye.dat = addm_data_eye,
                           rtbinsize = 100,
                           timestep = 10)
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# Look at what we get ------------------------------------------------------------------------
addm.dat
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# DO A GRID SEARCH ---------------------------------------------------------------------------
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# Perform grid search by trial:
addm_loglik_trial = addm_fit_grid(addm.dat,
                                 nr.reps = 1000,
                                 drifts = c(0.001,0.003,0.005),
                                 thetas = c(0, 0.5, 1),
                                 sds = c(0.02,0.04,0.06,0.08,0.1),
                                 fit.type = 'trial')

addm_plot_loglik(addm_loglik_trial)

# Perform grid search by condition
addm_loglik_condition = addm_fit_grid(addm.dat,
                                      nr.reps = 1000,
                                      drifts = c(0.001,0.003,0.005),
                                      thetas = c(0,0.5,1),
                                      sds = c(0.06,0.08,0.1),
                                      fit.type = 'condition',
                                      fixation.model = 'random') # can be 'fixedpath' or 'user'

addm_plot_loglik(addm_loglik_condition)


# EXPERIMENTAL
addm_loglik_dyn = addm_fit_grid(addm.dat,
                                drifts = c(0.001,0.003,0.005),
                                thetas = c(0,0.5,1),
                                sds = c(0.06,0.08,0.1),
                                fit.type = 'dyn')

addm_plot_loglik(addm_loglik_dyn)
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# GENERATE DETAILED OUTPUT -------------------------------------------------------------------
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# Generate
addm_output = addm_run_by_condition(choice.dat = addm.dat$choice.dat,
                                    conditions.dat = addm.dat$conditions.dat,
                                    model.parameters = c(0,0.0014,0.071,1), # mention order
                                    nr.reps = 500,
                                    fixation.model = 'random', # can be 'fixedpath' or 'user'
                                    output.type = 'full')

# Take a look at whats included
View(addm_output)
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# PLOT PSYCHOMETRICS -------------------------------------------------------------------------
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addm2_plot_family(choice.dat = addm.dat$choice.dat,
                  addm.output = addm_output)

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# GENERATE FAKE DATA -------------------------------------------------------------------------

addm.fake = addm_generate_artificial(set.size = 6,
                                     possible.valuations = c(0,1,2,3),
                                     model.parameters = c(0,0.006,0.06,0.5),
                                     nr.attributes = 1,
                                     nr.condition = 20,
                                     nr.reps = 1000,
                                     timestep = 10,
                                     rtbinsize = 100,
                                     model.type = 'standard')

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# MULTIPLE -----------------------------------------------------------------------------------
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# SET SIZE 8 ---------------------------------------------------------------------------------
# Preprocess my data
afdat = addm_preprocess(choice.dat = afdat_choice, eye.dat = afdat_eye)

# Run Grid Search
afloglik = addm_fit_grid(afdat,
                         drifts = c(0.005,0.006,0.007),
                         sds = c(0.08, 0.06, 0.04),
                         theta = c(0,0.5,1),
                         fit.type = 'trial')
addm_plot_loglik(afloglik)
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