Datasets for bayes4psy examples Example datasets for use in rstanarm examples and vignettes. The datasets were extracted from the internal MBLab http://www.mblab.si repository. MBLab is a research lab at the Faculty of Arts, Department of Psychology, University of Ljubljana, Slovenia.
rstanarm examples and vignettes. The datasets were extracted from the internal MBLab http://www.mblab.si repository. MBLab is a research lab at the Faculty of Arts, Department of Psychology, University of Ljubljana, Slovenia.adaptation_level_small: Small dataset on subjects picking up weights and determining their weights from 1..10.
Source: Internal MBLab repository.
50 obs. of 3 variables
* `sequence` sequence index.
* `weight` actual weight of the object.
* `response` subject's estimation of weight.
adaptation_level: Data on subjects picking up weights and determining their weights from 1..10.
Source: Internal MBLab repository.
2900 obs. of 6 variables
* `subject` subject index.
* `group` group index.
* `part` first or second part of the experiment.
* `sequence` sequence index.
* `weight` actual weight of the object.
* `response` subject's estimation of weight.
after_images_opponent_process: Colors predicted by the opponent process theory.
Source: Internal MBLab repository.
6 obs. of 7 variables
* `stimuli` name of the color stimuli.
* `r` value of the R component in the RGB model.
* `g` value of the G component in the RGB model.
* `b` value of the B component in the RGB model.
* `h` value of the H component in the HSV model.
* `s` value of the S component in the HSV model.
* `v` value of the V component in the HSV model.
after_images_opponent_stimuli: Stimuli used in the after images experiment.
Source: Internal MBLab repository.
6 obs. of 7 variables
* `r_s` value of the R component in the RGB model.
* `g_s` value of the G component in the RGB model.
* `b_s` value of the B component in the RGB model.
* `stimuli` name of the color stimuli.
* `h_s` value of the H component in the HSV model.
* `s_s` value of the S component in the HSV model.
* `v_s` value of the V component in the HSV model.
after_images_trichromatic: Colors predicted by the trichromatic theory.
Source: Internal MBLab repository.
6 obs. of 7 variables
* `stimuli` name of the color stimuli.
* `r` value of the R component in the RGB model.
* `g` value of the G component in the RGB model.
* `b` value of the B component in the RGB model.
* `h` value of the H component in the HSV model.
* `s` value of the S component in the HSV model.
* `v` value of the V component in the HSV model.
after_images: Data gathered by the after images experiment.
Source: Internal MBLab repository.
1311 obs. of 12 variables
* `subject` subject index.
* `rt` reaction time.
* `r` value of the R component in the RGB model of subject's response.
* `g` value of the G component in the RGB model of subject's response.
* `b` value of the B component in the RGB model of subject's response.
* `stimuli` name of the color stimuli.
* `r_s` value of the R component in the RGB model of the shown stimulus
* `g_s` value of the G component in the RGB model of the shown stimulus
* `b_s` value of the B component in the RGB model of the shown stimulus
* `h_s` value of the H component in the HSV model of the shown stimulus
* `s_s` value of the S component in the HSV model of the shown stimulus
* `v_s` value of the V component in the HSV model of the shown stimulus
flanker: Data gathered by the flanker experiment.
Source: Internal MBLab repository.
8256 obs. of 5 variables
* `subject` subject index.
* `group` group index.
* `congruencty` type of stimulus.
* `result` was subject's reponse correct or wrong?
* `rt` reaction time.
stroop_extended: All the data gathered by the Stroop experiment.
Source: Internal MBLab repository.
41068 obs. of 5 variables
* `subject` subject ID.
* `cond` type of condition.
* `rt` reaction time.
* `acc` was subject's reponse correct or wrong?
* `age` age of subject.
stroop_simple: All the data gathered by the Stroop experiment.
Source: Internal MBLab repository.
61 obs. of 5 variables
* `subject` subject ID.
* `reading_neutral` average response time for reading neutral stimuli.
* `naming_neutral` average response time for naming neutral stimuli.
* `reading_incongruent` average response time for reading incongruent stimuli.
* `naming_incongruent` average response time for naming incongruent stimuli.
# Example of Bayesian bootstraping on 'adaptation_level_small' dataset # linear function of seqence vs. response lm_statistic <- function(data) { lm(sequence ~ response, data)$coef } # load data data <- adaptation_level_small # bootstrap data_bootstrap <- b_bootstrap(data, lm_statistic, n1=1000, n2=1000)