overall
Descriptive
version: facescene - counterbalanced stimuli of faces or scenes, learning regime: learning terminates upon reaching criteria (66%) >> unbalanced learning trials between conditions and unbalanced presentation
version: objects - counterbalanced stimuli of objects, learning regime: learning terminates upon reaching criteria (66%), but continues to present stimuli without feedback until 9th block >> unbalanced learning trials between H1 and H2, but balanced presentation between H1 and H2
version: scenes - counterbalanced stimuli of scenes, learning regime: learning terminates upon reaching criteria (66%) for H1, H2 learning fixed in accordance to H1 blockcount, >> balanced learning trials H1 and H2 and balanced presentation between H1 and H2, but unbalanced performance
Exploratory
ggpairs(summary.facescene[-1], aes(color = faceFirst, alpha = 0.5),
lower = list(continuous = "smooth"), progress = FALSE)## Warning: Removed 3 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values## Warning: Removed 3 rows containing non-finite values (stat_boxplot).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing non-finite values (stat_density).## Warning: Removed 3 rows containing non-finite values (stat_boxplot).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 3 rows containing missing values## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 3 rows containing non-finite values (stat_bin).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 3 rows containing non-finite values (stat_bin).## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggpairs(summary.objects[-1], aes(color = set1First, alpha = 0.5),
lower = list(continuous = "smooth"), progress = FALSE)## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 16 rows containing non-finite values (stat_boxplot).## Warning: Removed 14 rows containing non-finite values (stat_boxplot).## Warning: Removed 13 rows containing non-finite values (stat_boxplot).
## Removed 13 rows containing non-finite values (stat_boxplot).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 16 rows containing non-finite values (stat_bin).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 14 rows containing non-finite values (stat_bin).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 13 rows containing non-finite values (stat_bin).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 13 rows containing non-finite values (stat_bin).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).
ggpairs(summary.scenes[-1], aes(alpha = 0.5),
lower = list(continuous = "smooth"), progress = FALSE)
#overall
## objects and scenes together
summary.combined <- dplyr::bind_rows(summary.objects %>% mutate(stimulus="objects"), summary.scenes %>% mutate(stimulus="scenes", timeelapsed=timeelapsed*60)) %>% select(-set1First)
ggpairs(summary.combined[-1], aes(color = stimulus, alpha = 0.5),
lower = list(continuous = "smooth"), progress = FALSE)## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 16 rows containing missing values## Warning: Removed 16 rows containing non-finite values (stat_boxplot).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 14 rows containing missing values## Warning: Removed 14 rows containing non-finite values (stat_boxplot).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values
## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## Warning: Removed 13 rows containing non-finite values (stat_boxplot).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_density).## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 13 rows containing missing values## Warning: Removed 13 rows containing non-finite values (stat_boxplot).## Warning: Removed 16 rows containing non-finite values (stat_smooth).## Warning: Removed 16 rows containing missing values (geom_point).## Warning: Removed 14 rows containing non-finite values (stat_smooth).## Warning: Removed 14 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing non-finite values (stat_smooth).## Warning: Removed 13 rows containing missing values (geom_point).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 16 rows containing non-finite values (stat_bin).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 14 rows containing non-finite values (stat_bin).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 13 rows containing non-finite values (stat_bin).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 13 rows containing non-finite values (stat_bin).## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Exploratory with learning exclusion (Immediate recall performance)
excluded.obj <- summary.objects %>% filter(learningPerf_H1 <= 0.5 || learningPerf_H2 <= 0.5) #6 participants
excluded.facescene <- summary.facescene %>% filter(learningPerf_H1 <= 0.5 || learningPerf_H2 <= 0.5) #4 participants
excluded.scenes <- summary.scenes %>% filter(learningPerf_H1 <= 0.5 || learningPerf_H2 <= 0.5) #3 participants
ggpairs(summary.facescene %>% filter(!participant %in% excluded.facescene$participant) %>%ungroup() %>% select(-participant), aes(color = faceFirst, alpha = 0.5) ,
lower = list(continuous = "smooth"), progress = FALSE)
ggpairs(summary.objects %>% filter(!participant %in% excluded.obj$participant) %>%ungroup() %>% select(-participant), aes(color = set1First, alpha = 0.5),
lower = list(continuous = "smooth"), progress = FALSE)
ggpairs(summary.scenes %>% filter(!participant %in% excluded.scenes$participant) %>%ungroup() %>% select(-participant), aes(alpha = 0.5),
lower = list(continuous = "smooth"), progress = FALSE)
#overall
## objects and scenes together
summary.combined <- dplyr::bind_rows(summary.objects %>% mutate(stimulus="objects") %>% filter(!participant %in% excluded.obj$participant), summary.scenes %>% mutate(stimulus="scenes", timeelapsed=timeelapsed*60) %>% filter(!participant %in% excluded.scenes$participant) ) %>% select(-set1First)
ggpairs(summary.combined[-1], aes(color = stimulus, alpha = 0.5),
lower = list(continuous = "smooth"), progress = FALSE)
summary.combined_all <- dplyr::bind_rows(summary.objects %>% mutate(stimulus="objects") %>% filter(!participant %in% excluded.obj$participant), summary.scenes %>% mutate(stimulus="scenes", timeelapsed=timeelapsed*60) %>% filter(!participant %in% excluded.scenes$participant), summary.facescene%>% mutate(stimulus="facescene") %>% filter(!participant %in% excluded.facescene$participant)) %>% select(-set1First,-faceFirst)
ggpairs(summary.combined_all[-1], aes(color = stimulus, alpha = 0.5),
lower = list(continuous = "smooth"), progress = FALSE)
summary.facescene.raw <- session3.pretty.filtered.facescene %>% pivot_wider(id_cols = c("participant","awareness","timeelapsed"), names_from = pairType, values_from = c("H1","H2")) %>% select(-H1_16,-H2_16) %>% left_join(learning.facescene) %>% mutate(H1_inf=(H1_24 +H1_25 +H1_35)/3,H2_inf=(H2_24 +H2_25 +H2_35)/3 )
summary.obj.noshape.raw <- session3.pretty.filtered.noshape %>% pivot_wider(id_cols = c("participant","awareness","timeelapsed","set1First"), names_from = pairType, values_from = c("H1","H2")) %>% select(-H1_16,-H2_16) %>% left_join(learning.objnoshape) %>% mutate(H1_inf=(H1_24 +H1_25 +H1_35)/3,H2_inf=(H2_24 +H2_25 +H2_35)/3 )
summary.obj.shape.raw <- session3.pretty.filtered.shape %>% pivot_wider(id_cols = c("participant","awareness","timeelapsed","set1First"), names_from = pairType, values_from = c("H1","H2")) %>% select(-H1_16,-H2_16) %>% left_join(learning.objwshape) %>% mutate(H1_inf=(H1_24 +H1_25 +H1_35)/3,H2_inf=(H2_24 +H2_25 +H2_35)/3 )
summary.scenes.raw <- session3.pretty.filtered.scenes %>% pivot_wider(id_cols = c("participant","awareness","timeelapsed"), names_from = pairType, values_from = c("H1","H2")) %>% select(-H1_16,-H2_16) %>% left_join(learning.scenes) %>% mutate(H1_inf=(H1_24 +H1_25 +H1_35)/3,H2_inf=(H2_24 +H2_25 +H2_35)/3 )
summary.obj.combined.raw <- bind_rows(summary.obj.noshape.raw, summary.obj.shape.raw)
summary.combined.raw <- bind_rows(summary.obj.combined.raw %>% mutate(stimulus="objects"),summary.scenes.raw %>%mutate(stimulus="scenes")) %>% select(-set1First,-timeelapsed,-awareness)
ggpairs(summary.combined.raw[-1], aes(color = stimulus, alpha = 0.5),
lower = list(continuous = "smooth"), progress = FALSE)
summary.obj.noshape.raw <- session3.pretty.filtered.noshape %>% pivot_wider(id_cols = c("participant","awareness","timeelapsed","set1First"), names_from = pairType, values_from = c("H1","H2")) %>% select(-H1_16,-H2_16) %>% left_join(learning.objnoshape) %>% mutate(H1_inf=(H1_24 +H1_25 +H1_35)/3,H2_inf=(H2_24 +H2_25 +H2_35)/3 ) %>% mutate(H1_inf=(H1_24 +H1_25 +H1_35)/3,H2_inf=(H2_24 +H2_25 +H2_35)/3, H1_inf_1d = (H1_24 +H1_35)/2, H2_inf_1d = (H2_24 +H2_35)/2 )
summary.obj.shape.raw <- session3.pretty.filtered.shape %>% pivot_wider(id_cols = c("participant","awareness","timeelapsed","set1First"), names_from = pairType, values_from = c("H1","H2")) %>% select(-H1_16,-H2_16) %>% left_join(learning.objwshape) %>% mutate(H1_inf=(H1_24 +H1_25 +H1_35)/3,H2_inf=(H2_24 +H2_25 +H2_35)/3 ) %>% mutate(H1_inf=(H1_24 +H1_25 +H1_35)/3,H2_inf=(H2_24 +H2_25 +H2_35)/3, H1_inf_1d = (H1_24 +H1_35)/2, H2_inf_1d = (H2_24 +H2_35)/2 )
summary.scenes.raw <- session3.pretty.filtered.scenes %>% pivot_wider(id_cols = c("participant","awareness","timeelapsed"), names_from = pairType, values_from = c("H1","H2")) %>% select(-H1_16,-H2_16) %>% left_join(learning.scenes) %>% mutate(H1_inf=(H1_24 +H1_25 +H1_35)/3,H2_inf=(H2_24 +H2_25 +H2_35)/3, H1_inf_1d = (H1_24 +H1_35)/2, H2_inf_1d = (H2_24 +H2_35)/2 )
summary.obj.combined.raw <- bind_rows(summary.obj.noshape.raw, summary.obj.shape.raw)
summary.combined.raw <- bind_rows(summary.obj.combined.raw %>% mutate(stimulus="objects"),summary.scenes.raw %>%mutate(stimulus="scenes")) %>% select(-set1First,-timeelapsed,-awareness, -H1_24, -H1_35, -H2_24, -H2_35 )
ggpairs(summary.combined.raw[-1], aes(color = stimulus, alpha = 0.5),
lower = list(continuous = "smooth"), progress = FALSE)
library(afex) ## to run anova## Loading required package: lme4## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack## ************
## Welcome to afex. For support visit: http://afex.singmann.science/## - Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()
## - Methods for calculating p-values with mixed(): 'S', 'KR', 'LRT', and 'PB'
## - 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests
## - NEWS: emmeans() for ANOVA models now uses model = 'multivariate' as default.
## - Get and set global package options with: afex_options()
## - Set orthogonal sum-to-zero contrasts globally: set_sum_contrasts()
## - For example analyses see: browseVignettes("afex")
## ************##
## Attaching package: 'afex'## The following object is masked from 'package:lme4':
##
## lmerlibrary(ggstatsplot)## You can cite this package as:
## Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach.
## Journal of Open Source Software, 6(61), 3167, doi:10.21105/joss.03167grouped_ggwithinstats(
data = session3.pretty.filtered.facescene %>%
mutate(across(pairType, recode, '24'= "inf", '25'= "inf", '35'="inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")),
subject.id = participant,
x = hierarchy,
y = meanPerf,
type = "parametric",
xlab = "Condition",
grouping.var = pairType,
outlier.label = NULL,
bf.message = FALSE)## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.
grouped_ggwithinstats(
data = rbind(session3.pretty.filtered.shape,session3.pretty.filtered.noshape) %>%
mutate(across(pairType, recode, '24'= "inf", '25'= "inf", '35'="inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")),
subject.id = participant,
x = hierarchy,
y = meanPerf,
type = "parametric",
xlab = "Condition",
grouping.var = pairType,
outlier.label = NULL,
bf.message = FALSE)## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.
# what if we filter out people who..
excluded.obj <- summary.objects %>% filter(learningPerf_H1 <= 0.5 || learningPerf_H2 <= 0.5)
excluded.obj.belowchanceinf <- rbind(session3.pretty.filtered.shape,session3.pretty.filtered.noshape) %>%
mutate(across(pairType, recode, '24'= "inf", '25'= "inf", '35'="inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% filter(pairType=="inf" & hierarchy=="H1" & meanPerf<=0.5)## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.#subgroup analysis for above chance inferencers
grouped_ggwithinstats(
data = rbind(session3.pretty.filtered.shape,session3.pretty.filtered.noshape) %>%
mutate(across(pairType, recode, '24'= "inf", '25'= "inf", '35'="inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% filter(!participant %in% excluded.obj$participant)%>% filter(!participant %in% excluded.obj.belowchanceinf$participant),
subject.id = participant,
x = hierarchy,
y = meanPerf,
type = "parametric",
xlab = "Condition",
grouping.var = pairType,
outlier.label = NULL,
bf.message = FALSE)## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.
excluded.scn <- summary.scenes %>% filter(learningPerf_H1 <= 0.5 || learningPerf_H2 <= 0.5)
excluded.scn.belowchanceinf <- session3.pretty.filtered.scenes %>%
mutate(across(pairType, recode, '24'= "inf", '25'= "inf", '35'="inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% filter(pairType=="inf" & hierarchy=="H1" & meanPerf<=0.5)## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.grouped_ggwithinstats(
data = session3.pretty.filtered.scenes %>%
mutate(across(pairType, recode, '24'= "inf", '25'= "inf", '35'="inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% filter(!participant %in% excluded.scn$participant),
subject.id = participant,
x = hierarchy,
y = meanPerf,
type = "parametric",
xlab = "Condition",
grouping.var = pairType,
outlier.label = NULL,
bf.message = FALSE,
annotation.args = list(
title = "Scenes experiment (w/ImRecall performance exclusion)"))## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.
grouped_ggwithinstats(
data = session3.pretty.filtered.scenes %>%
mutate(across(pairType, recode, '24'= "inf", '25'= "inf", '35'="inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% filter(!participant %in% excluded.scn$participant) %>% filter(!participant %in% excluded.scn.belowchanceinf$participant),
subject.id = participant,
x = hierarchy,
y = meanPerf,
type = "parametric",
xlab = "Condition",
grouping.var = pairType,
outlier.label = NULL,
bf.message = FALSE,
annotation.args = list(
title = "Scenes experiment (w/H1-below chance inference exclusion)"))## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.
grouped_ggwithinstats(
data = session3.pretty.filtered.scenes %>%
mutate(across(pairType, dplyr::recode, '24'= "inf", '25'= "inf", '35'="inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% filter(!participant %in% excluded.scn$participant) %>% filter(!participant %in% excluded.scn.belowchanceinf$participant) %>% rbind(.,rbind(session3.pretty.filtered.shape,session3.pretty.filtered.noshape) %>%
mutate(across(pairType, dplyr::recode, '24'= "inf", '25'= "inf", '35'="inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% filter(!participant %in% excluded.obj$participant)%>% filter(!participant %in% excluded.obj.belowchanceinf$participant)),
subject.id = participant,
x = hierarchy,
y = meanPerf,
type = "parametric",
xlab = "Condition",
grouping.var = pairType,
outlier.label = NULL,
bf.message = FALSE,
annotation.args = list(
title = "Scenenes and Objects experiment combined (w/H1-below chance inference exclusion)"))## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.
require(car) # get the right sums of squares calculations## Loading required package: car## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## somerequire(dplyr) # for manipulating our data
require(ggplot2) # for plotting and for our dataset
require(sjstats) # save us time computing key ANOVA stats beyond car## Loading required package: sjstats## Warning: package 'sjstats' was built under R version 4.1.3require(broom) # nice for making our results in neat tibbles## Loading required package: broom##
## Attaching package: 'broom'## The following object is masked from 'package:sjstats':
##
## bootstraprequire(emmeans) # for marginal means calculations## Loading required package: emmeans## Warning: package 'emmeans' was built under R version 4.1.3##
## Attaching package: 'emmeans'## The following object is masked from 'package:GGally':
##
## pigslibrary(sjPlot)## Warning: package 'sjPlot' was built under R version 4.1.3## #refugeeswelcomelibrary(lme4)
library(afex)
library(nlme)##
## Attaching package: 'nlme'## The following object is masked from 'package:lme4':
##
## lmList## The following object is masked from 'package:dplyr':
##
## collapse# a shameless plug for a function I wrote called Plot2WayANOVA
# optional for you
# devtools::install_github("ibecav/CGPfunctions")
theme_set(theme_bw()) # set theme
aov_data <- session3.pretty.filtered.scenes %>%
mutate(across(pairType, dplyr::recode, '24'= "inf", '25'= "inf", '35'="inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% filter(!participant %in% excluded.scenes$participant) %>% rbind(.,rbind(session3.pretty.filtered.shape,session3.pretty.filtered.noshape) %>%
mutate(across(pairType, dplyr::recode, '24'= "inf", '25'= "inf", '35'="inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% filter(!participant %in% excluded.obj$participant)) %>% pivot_wider(id_cols = c("participant","hierarchy"), names_from = pairType, values_from = meanPerf)## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.learning.summary <- bind_rows(rbind(session1.avgs.shape, session2.avgs.shape) %>% mutate(version="obj", participant=paste0('s', participant)) %>% filter(participant %in% session3.pretty.filtered.shape$participant) %>% group_by(participant,session,blockcount, version) %>% summarise(learningPerf=mean(key_resp_im.corr)), rbind(session1.avgs.obj, session2.avgs.obj) %>% mutate(version="obj", participant=paste0('ns', participant)) %>% filter(participant %in% session3.pretty.filtered.noshape$participant) %>% group_by(participant,session,blockcount,version) %>% summarise(learningPerf=mean(key_resp_im.corr)), rbind(session1.avgs.scenes, session2.avgs.scenes) %>% mutate(version="scenes") %>% filter(participant %in% session3.pretty.filtered.scenes$participant) %>% group_by(participant,session,blockcount,version) %>% summarise(learningPerf=mean(key_resp_im.corr))) %>% mutate(session=paste0("H",session))## `summarise()` has grouped output by 'participant', 'session', 'blockcount'. You
## can override using the `.groups` argument.
## `summarise()` has grouped output by 'participant', 'session', 'blockcount'. You
## can override using the `.groups` argument.
## `summarise()` has grouped output by 'participant', 'session', 'blockcount'. You
## can override using the `.groups` argument.aov_data %>% left_join(learning.summary %>% pivot_wider(id_cols=c("participant","version"), names_from="session", values_from=c("learningPerf","blockcount"))) -> aov_data.withlearning## Joining, by = "participant"aov_data %>% left_join(learning.summary %>% pivot_wider(id_cols=c("participant","version"), names_from="session", values_from=c("learningPerf","blockcount"))) -> aov_data.alignedlearning## Joining, by = "participant"aw <- aov_ez("participant", "inf", aov_data.withlearning, within = c("hierarchy"))
aw_cov <- aov_ez("participant", "inf", aov_data.withlearning, within = c("hierarchy"), covariate = "learningPerf_H1", factorize = FALSE)## Warning: Numerical variables NOT centered on 0 (i.e., likely bogus results): NA## Warning: Missing values for following ID(s):
## AKOXYS, BNDVJA, CIEVJX, DWVMBJ, EPDHMW, ERQGID, FESLMP, GNIVLK, NZUAGE, OVBKSD, QPUJGB, SLNBDX, TKFYJA
## Removing those cases from the analysis.afex_plot(aw, x = "hierarchy", dv= "inf", id = "participant")## Warning: Additional arguments ignored: dv, id## Warning: Panel(s) show within-subjects factors, but not within-subjects error bars.
## For within-subjects error bars use: error = "within"
afex_plot(aw_cov, x = "hierarchy", id="participant")## Warning: Additional arguments ignored: id
## Warning: Panel(s) show within-subjects factors, but not within-subjects error bars.
## For within-subjects error bars use: error = "within"
#paired t-test
m1 <- lme(inf ~ hierarchy, random= ~1|participant, data = aov_data.withlearning)
#Conditional growth model
#We now want to estimate the (fixed) effect of the Day (aka Hiearchy) of learning on Inference performance, while allowing each subject to have his/hers own effect. Put differently, we want to estimate a random slope for the effect of Day. The fixed Day (Hierarchy) effect can be thought of as the average slope over subjects.
m2 <- lme(inf ~ hierarchy, random= ~hierarchy|participant, aov_data.withlearning)
tab_model(m1,m2)| inf | inf | |||||
|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.57 | 0.49 – 0.66 | <0.001 | 0.57 | 0.49 – 0.65 | <0.001 |
| hierarchy [H2] | -0.02 | -0.12 – 0.09 | 0.743 | -0.02 | -0.12 – 0.09 | 0.743 |
| Random Effects | ||||||
| σ2 | 0.06 | 0.01 | ||||
| τ00 | 0.02 participant | 0.06 participant | ||||
| τ11 | 0.09 participant.hierarchyH2 | |||||
| ρ01 | -0.48 participant | |||||
| ICC | 0.29 | 0.83 | ||||
| N | 45 participant | 45 participant | ||||
| Observations | 90 | 90 | ||||
| Marginal R2 / Conditional R2 | 0.001 / 0.295 | 0.001 / 0.826 | ||||
# box-plots of residuals by Subject
plot(m2, participant ~ resid(.))
# observed versus fitted values by Subject
plot(m1, inf ~ fitted(.) | participant, abline = c(0,1))
plot(m2, inf ~ fitted(.) | participant, abline = c(0,1))
plot(m2, resid(., type = "p") ~ fitted(.),type = c("p", "smooth"))
qqnorm(m2, ~ resid(., type = "p"))
pairs(ranef(m2))
cmp.1 <- compareFits(coef(m1), coef(m2))
plot(cmp.1, mark = fixef(m1))
F0.so<-fitted(m2, level=0)
F1.so<-fitted(m2, level=1)
I.so<-order(aov_data.withlearning$hierarchy)
hiers<-sort(aov_data.withlearning$hierarchy)
plot(hiers, F0.so[I.so], lwd=4, type="l",xlim=c(-2,2),ylim=c(5,25))## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercionfor (i in unique(aov_data.withlearning$participant)){
print(i)
x1.so <- aov_data.withlearning$hierarchy[aov_data.withlearning$participant== i]
y1.so <- F1.so[aov_data.withlearning$participant==i]
K.so <- order(x1.so)
lines(sort(x1.so), y1.so[K.so], type="l")
}## [1] "BIHKJQY"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "GYEDWMN"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "IFSAQWK"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "KWLNBQD"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "PLIKUZD"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "PNAIOQU"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "PONDGRB"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "PVMAFRS"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "QABSUFL"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "UTQPXAC"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "VDGNBTR"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "VJLRZNB"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "XPDGHBF"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "AKOXYS"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "BNDVJA"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "CIEVJX"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "DWVMBJ"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "EPDHMW"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "ERQGID"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "FESLMP"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "GNIVLK"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsADPOMZ"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsEHDFYG"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsEPDHMW"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsERQGID"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsFESLMP"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsFLQHYG"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsHNITKW"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsIJGAUW"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsJYVUIN"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsKMUZPQ"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsLMIUZF"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsNRZWQT"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsNZUAGE"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsOVBKSD"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsRJYUNC"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsSNYVLZ"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsXSOEBL"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsZVBSDC"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "nsZWLXBM"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "NZUAGE"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "OVBKSD"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "QPUJGB"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "SLNBDX"## Warning in xy.coords(x, y): NAs introduced by coercion## [1] "TKFYJA"## Warning in xy.coords(x, y): NAs introduced by coercion
# extract the fitted values to make a best fit line
F0<-fitted(m2, level = 0) # fitted across sites
F1<-fitted(m2, level = 1) # fitted between sites
# create an array of unique sites
pars<-unique(aov_data.withlearning$participant)
plot(aov_data.withlearning$hierarchy, aov_data.withlearning$inf, xlab = 'hierarchy', ylab = 'inf', xlim=c(1,2))## Warning in xy.coords(x, y, xlabel, ylabel, log): NAs introduced by coercion# plot the best line with the random intercepts
for (i in 1:length(pars)){
x1<-aov_data.withlearning$hierarchy[aov_data.withlearning$participant==pars[i]] # x value at site i
y1<-F1[aov_data.withlearning$participant==pars[i]] # y value at site i
ind<-order(x1) # reorder the data
lines(c(1,2),y1[ind], col =i) # make the plot
}
# add a legend
legend("topleft",legend= pars, col=c(1,2,3,4), pch=21, bty="n")
# save(awareness_score.scenes, file = "awareness_score_scenes.Rdata")
# save(awareness_score.facescene, file = "awareness_score_facescene.Rdata")
# save(awareness_score.shape, file = "awareness_score_shape.Rdata")
# save(awareness_score.obj, file = "awareness_score_obj.Rdata")
load("awareness_score_scenes.Rdata")
load("awareness_score_facescene.Rdata")
load("awareness_score_shape.Rdata")
load("awareness_score_obj.Rdata")
lmm.scenes <- bind_rows(session3.pretty.filtered.scenes %>%
mutate(across(pairType, dplyr::recode, '24'= "inf", '25'= "inf", '35'="inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% mutate(session=3) ,rbind(session1.avgs.scenes, session2.avgs.scenes) %>% filter(participant %in% session3.pretty.filtered.scenes$participant) %>% group_by(participant,session,blockcount) %>% summarise(premise=mean(key_resp_im.corr)) %>% gather(pairType, meanPerf, premise) %>% mutate(hierarchy=paste0("H",session))) %>% mutate(stimuli="scene") %>% select(-awareness) %>% left_join(awareness_score.scenes, c("participant"="code")) %>% mutate(participant=paste0("scn",participant), timeelapsed = timeelapsed*60)## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'participant', 'session'. You can override
## using the `.groups` argument.lmm.facescene <- bind_rows(session3.pretty.filtered.facescene %>%
mutate(across(pairType, dplyr::recode, '24'= "inf", '25'= "inf", '35'="inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% mutate(session=3), rbind(session1.avgs.facescene, session2.avgs.facescene) %>% filter(participant %in% session3.pretty.filtered.facescene$participant) %>% group_by(participant,session,blockcount) %>% summarise(premise=mean(key_resp_im.corr)) %>% gather(pairType, meanPerf, premise) %>% mutate(hierarchy=paste0("H",session))) %>% mutate(stimuli="facescene") %>% select(-awareness) %>% left_join(awareness_score.facescene, c("participant"="code")) %>% mutate(participant=paste0("fsc",participant))## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'participant', 'session'. You can override
## using the `.groups` argument.lmm.shape <- bind_rows(session3.pretty.filtered.shape %>%
mutate(across(pairType, dplyr::recode, '24'= "inf", '25'= "inf", '35'="inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% mutate(session=3), rbind(session1.avgs.shape, session2.avgs.shape) %>% filter(participant %in% session3.pretty.filtered.shape$participant) %>% group_by(participant,session,blockcount) %>% summarise(premise=mean(key_resp_im.corr)) %>% gather(pairType, meanPerf, premise) %>% mutate(hierarchy=paste0("H",session))) %>% mutate(stimuli="obj") %>% select(-awareness) %>% left_join(awareness_score.shape, c("participant"="code")) %>% mutate(participant=paste0("o_s",participant))## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'participant', 'session'. You can override
## using the `.groups` argument.# save(session3.pretty.filtered.obj, file = "session3_pretty_filtered_obj.Rdata")
load("session3_pretty_filtered_obj.Rdata")
lmm.noshape <- bind_rows(session3.pretty.filtered.obj %>%
mutate(across(pairType, dplyr::recode, '24'= "inf", '25'= "inf", '35'="inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% mutate(session=3), rbind(session1.avgs.obj, session2.avgs.obj) %>% filter(participant %in% session3.pretty.filtered.obj$participant) %>% group_by(participant,session,blockcount) %>% summarise(premise=mean(key_resp_im.corr)) %>% gather(pairType, meanPerf, premise) %>% mutate(hierarchy=paste0("H",session))) %>% mutate(stimuli="obj") %>% select(-awareness) %>% left_join(awareness_score.obj, c("participant"="code")) %>% mutate(participant=paste0("o_ns",participant))## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'participant', 'session'. You can override
## using the `.groups` argument.lmm.combined <- bind_rows(lmm.scenes,lmm.facescene,lmm.shape,lmm.noshape)
# length(unique(lmm.combined$participant)) #73 people
belowchance.exclusion <- lmm.combined %>% filter(session %in% c(1,2), meanPerf <= 0.5)
lmm.combined.filtered <- lmm.combined %>% filter(!participant %in% belowchance.exclusion$participant)
# length(unique(lmm.combined$participant)) #61 people
lmm.combined.filtered.wide <-lmm.combined.filtered %>% filter(session==3) %>% pivot_wider(id_cols = c("participant","hierarchy","stimuli","awareness","timeelapsed","blockcount"), names_from = c("pairType","session"),values_from = meanPerf) %>% select(-blockcount) %>% left_join(.,lmm.combined.filtered %>% filter(session %in% c(1,2)) %>% pivot_wider(id_cols = c("participant","stimuli","hierarchy"), names_from = c("pairType"),values_from = meanPerf) %>% rename(learningPerf=premise), c("participant","hierarchy","stimuli")) %>% left_join(.,lmm.combined.filtered %>% filter(session %in% c(1,2)) %>% ungroup() %>% select(participant,hierarchy,blockcount), c("participant","hierarchy"))
ggplot(lmm.combined %>% filter(pairType !="anchor"), aes(x = interaction(hierarchy,pairType), y = meanPerf, color=pairType, shape=as.factor(session))) +
geom_point() +
facet_wrap(~participant)
ggplot(lmm.combined %>% filter(pairType !="anchor"), aes(x = interaction(session, hierarchy,pairType), y = meanPerf, color=pairType, shape=as.factor(session))) +
geom_point() +
facet_wrap(~participant)
library(lme4)
library(lmerTest)## Warning: package 'lmerTest' was built under R version 4.1.3##
## Attaching package: 'lmerTest'
##
## The following object is masked from 'package:lme4':
##
## lmer
##
## The following object is masked from 'package:stats':
##
## step#random intercepts
m1 <- lmer(inf_3 ~ hierarchy + (1 | participant), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene"))
m2 <- lmer(inf_3 ~ hierarchy + learningPerf + (1 | participant), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene"))
m3 <- lmer(inf_3 ~ hierarchy * learningPerf + (1 | participant), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene"))
m4 <- lmer(inf_3 ~ hierarchy + premise_3 + (1 | participant), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene"))
m5 <- lmer(inf_3 ~ hierarchy + learningPerf + (1 | participant) + (1 | stimuli), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene"))
#model shows main effect of learning performance
m5 <- lmer(inf_3 ~ hierarchy + learningPerf + (1 | participant) + (1 | stimuli), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene"))
m5_adjusted <- lmer(inf_3 ~ hierarchy + learningPerf + (1 | participant) + (1 | stimuli), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene", !is.na(blockcount)))
m6 <- lmer(inf_3 ~ hierarchy + learningPerf * premise_3 + (1 | participant) + (1 | stimuli), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene"))
m7_adjusted <- lmer(inf_3 ~ hierarchy + learningPerf + blockcount + (1 | participant) + (1 | stimuli), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene", !is.na(blockcount)))
m1_adjusted <- lmer(inf_3 ~ hierarchy + (1 | participant), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene", !is.na(blockcount)))
m8_adjusted <- lmer(inf_3 ~ hierarchy + learningPerf + blockcount + hierarchy:blockcount + (1 | participant) + (1 | stimuli), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene", !is.na(blockcount)))
m9_adjusted <- lmer(inf_3 ~ hierarchy + learningPerf + hierarchy:blockcount + (1 | participant) + (1 | stimuli), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene", !is.na(blockcount)))
m10 <- lmer(inf_3 ~ hierarchy + learningPerf + stimuli+ hierarchy:blockcount + (1 | participant), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene"))
m9_adjusted_obj <- lmer(inf_3 ~ hierarchy + learningPerf + hierarchy:blockcount + (1 | participant), data = lmm.combined.filtered.wide %>% filter(stimuli == "obj", !is.na(blockcount)))
#core take home message
plot_model(m8_adjusted,"int")
tab_model(m1_adjusted,m5_adjusted,m7_adjusted,m8_adjusted,m9_adjusted,m9_adjusted_obj)| inf_3 | inf_3 | inf_3 | inf_3 | inf_3 | inf_3 | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.56 | 0.47 – 0.65 | <0.001 | 0.11 | -0.29 – 0.51 | 0.584 | -0.04 | -0.45 – 0.36 | 0.826 | 0.10 | -0.31 – 0.51 | 0.630 | 0.10 | -0.31 – 0.51 | 0.630 | 0.11 | -0.41 – 0.63 | 0.666 |
| hierarchy [H2] | -0.01 | -0.11 – 0.10 | 0.916 | -0.05 | -0.15 – 0.05 | 0.315 | -0.04 | -0.14 – 0.06 | 0.387 | -0.22 | -0.41 – -0.03 | 0.026 | -0.22 | -0.41 – -0.03 | 0.026 | -0.27 | -0.53 – -0.02 | 0.037 |
| learningPerf | 0.61 | 0.12 – 1.11 | 0.015 | 0.62 | 0.14 – 1.10 | 0.012 | 0.53 | 0.05 – 1.00 | 0.029 | 0.53 | 0.05 – 1.00 | 0.029 | 0.40 | -0.15 – 0.96 | 0.151 | |||
| blockcount | 0.03 | 0.00 – 0.06 | 0.023 | 0.01 | -0.02 – 0.05 | 0.381 | ||||||||||||
|
hierarchy [H2] * blockcount |
0.05 | 0.00 – 0.09 | 0.037 | 0.06 | 0.02 – 0.10 | 0.002 | 0.10 | 0.04 – 0.15 | 0.001 | |||||||||
|
hierarchy [H1] * blockcount |
0.01 | -0.02 – 0.05 | 0.381 | 0.03 | -0.02 – 0.08 | 0.255 | ||||||||||||
| Random Effects | ||||||||||||||||||
| σ2 | 0.05 | 0.04 | 0.04 | 0.04 | 0.04 | 0.03 | ||||||||||||
| τ00 | 0.03 participant | 0.03 participant | 0.03 participant | 0.03 participant | 0.03 participant | 0.03 participant | ||||||||||||
| 0.01 stimuli | 0.00 stimuli | 0.00 stimuli | 0.00 stimuli | |||||||||||||||
| ICC | 0.37 | 0.50 | 0.48 | 0.46 | 0.46 | 0.49 | ||||||||||||
| N | 42 participant | 42 participant | 42 participant | 42 participant | 42 participant | 29 participant | ||||||||||||
| 2 stimuli | 2 stimuli | 2 stimuli | 2 stimuli | |||||||||||||||
| Observations | 80 | 80 | 80 | 80 | 80 | 54 | ||||||||||||
| Marginal R2 / Conditional R2 | 0.000 / 0.368 | 0.071 / 0.533 | 0.119 / 0.538 | 0.152 / 0.546 | 0.152 / 0.546 | 0.195 / 0.586 | ||||||||||||
# plot_model(m9_adjusted,"int", show.data = TRUE)
library(afex)
library(sjPlot)
plot_model(m1,type="pred", show.values = TRUE) ## $hierarchy
plot_model(m3,type="int", show.values = TRUE) 
plot_model(m2, type = "eff", terms = c("learningPerf","hierarchy"), show.data = TRUE)
plot_model(m7_adjusted, type = "pred", terms = c("learningPerf","hierarchy","blockcount [3,10]"))
#core take home message
plot_model(m8_adjusted, type = "pred", terms = c("learningPerf","hierarchy","blockcount [3,5,9]"))
plot_model(m8_adjusted, type = "pred", terms = c("hierarchy"), show.p = TRUE)
#core take home message
plot_model(m8_adjusted, show.values = TRUE)
plot_model(m9_adjusted, show.values = TRUE)
plot_model(m9_adjusted_obj, show.values = TRUE)
plot_model(m9_adjusted_obj, type = "pred", terms = c("learningPerf","hierarchy","blockcount [3,5,9]"))
# #unrelated histogram
# x.a <- lmm.combined.filtered.wide %>% filter(stimuli != "facescene") %>% group_by(participant) %>% summarise(maxBlock=max(blockcount))
# plot.ecdf(x.a$maxBlock)
#
# x.a <- lmm.combined.filtered.wide %>% filter(stimuli == "obj") %>% group_by(participant) %>% summarise(maxBlock=max(blockcount))
# plot.ecdf(x.a$maxBlock)
#
# #looking at cutoffs in more details
# x.b <- lmm.combined.filtered.wide %>% group_by(participant,stimuli) %>% summarise(maxBlock=max(blockcount))
# plot.ecdf(x.b$maxBlock)
#
#
# x.c <- session1.avgs.obj %>% group_by(participant) %>% summarise(maxBlock=max(blockcount)) %>% mutate(stimuli="all_obj")
# plot.ecdf(x.c$maxBlock)
#
#
# ggplot(rbind(x.b,x.c), aes(maxBlock,color=stimuli)) +
# stat_ecdf(geom = "step")
#
#
# #which stimuli is better
# lmm.combined.filtered.wide %>% group_by(stimuli, hierarchy) %>% summarise(meanAwarness=mean(awareness, na.rm=TRUE),
# meanLearning=mean(learningPerf, na.rm=TRUE),
# meanBlockcount=mean(blockcount, na.rm=TRUE))Split by degree
lmm.scenes <- bind_rows(session3.pretty.filtered.scenes %>%
mutate(across(pairType, dplyr::recode, '24'= "close_inf", '25'= "dist_inf", '35'="close_inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% mutate(session=3) ,rbind(session1.avgs.scenes, session2.avgs.scenes) %>% filter(participant %in% session3.pretty.filtered.scenes$participant) %>% group_by(participant,session,blockcount) %>% summarise(premise=mean(key_resp_im.corr)) %>% gather(pairType, meanPerf, premise) %>% mutate(hierarchy=paste0("H",session))) %>% mutate(stimuli="scene") %>% select(-awareness) %>% left_join(awareness_score.scenes, c("participant"="code")) %>% mutate(participant=paste0("scn",participant), timeelapsed = timeelapsed*60)## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'participant', 'session'. You can override
## using the `.groups` argument.lmm.facescene <- bind_rows(session3.pretty.filtered.facescene %>%
mutate(across(pairType, dplyr::recode, '24'= "close_inf", '25'= "dist_inf", '35'="close_inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% mutate(session=3), rbind(session1.avgs.facescene, session2.avgs.facescene) %>% filter(participant %in% session3.pretty.filtered.facescene$participant) %>% group_by(participant,session,blockcount) %>% summarise(premise=mean(key_resp_im.corr)) %>% gather(pairType, meanPerf, premise) %>% mutate(hierarchy=paste0("H",session))) %>% mutate(stimuli="facescene") %>% select(-awareness) %>% left_join(awareness_score.facescene, c("participant"="code")) %>% mutate(participant=paste0("fsc",participant))## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'participant', 'session'. You can override
## using the `.groups` argument.lmm.shape <- bind_rows(session3.pretty.filtered.shape %>%
mutate(across(pairType, dplyr::recode, '24'= "close_inf", '25'= "dist_inf", '35'="close_inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% mutate(session=3), rbind(session1.avgs.shape, session2.avgs.shape) %>% filter(participant %in% session3.pretty.filtered.shape$participant) %>% group_by(participant,session,blockcount) %>% summarise(premise=mean(key_resp_im.corr)) %>% gather(pairType, meanPerf, premise) %>% mutate(hierarchy=paste0("H",session))) %>% mutate(stimuli="obj") %>% select(-awareness) %>% left_join(awareness_score.shape, c("participant"="code")) %>% mutate(participant=paste0("o_s",participant))## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'participant', 'session'. You can override
## using the `.groups` argument.lmm.noshape <- bind_rows(session3.pretty.filtered.obj %>%
mutate(across(pairType, dplyr::recode, '24'= "close_inf", '25'= "dist_inf", '35'="close_inf",'16'= "anchor")) %>% group_by(participant,pairType, awareness) %>% summarise(H1 = mean(H1), H2 = mean(H2)) %>% gather(hierarchy, meanPerf, c("H1","H2")) %>% mutate(session=3), rbind(session1.avgs.obj, session2.avgs.obj) %>% filter(participant %in% session3.pretty.filtered.obj$participant) %>% group_by(participant,session,blockcount) %>% summarise(premise=mean(key_resp_im.corr)) %>% gather(pairType, meanPerf, premise) %>% mutate(hierarchy=paste0("H",session))) %>% mutate(stimuli="obj") %>% select(-awareness) %>% left_join(awareness_score.obj, c("participant"="code")) %>% mutate(participant=paste0("o_ns",participant))## `summarise()` has grouped output by 'participant', 'pairType'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'participant', 'session'. You can override
## using the `.groups` argument.lmm.combined <- bind_rows(lmm.scenes,lmm.facescene,lmm.shape,lmm.noshape)
# length(unique(lmm.combined$participant)) #73 people
belowchance.exclusion <- lmm.combined %>% filter(session %in% c(1,2), meanPerf <= 0.5)
lmm.combined.filtered <- lmm.combined %>% filter(!participant %in% belowchance.exclusion$participant)
# length(unique(lmm.combined$participant)) #61 people
lmm.combined.filtered.wide <-lmm.combined.filtered %>% filter(session==3) %>% pivot_wider(id_cols = c("participant","hierarchy","stimuli","awareness","timeelapsed","blockcount"), names_from = c("pairType","session"),values_from = meanPerf) %>% select(-blockcount) %>% left_join(.,lmm.combined.filtered %>% filter(session %in% c(1,2)) %>% pivot_wider(id_cols = c("participant","stimuli","hierarchy"), names_from = c("pairType"),values_from = meanPerf) %>% rename(learningPerf=premise), c("participant","hierarchy","stimuli")) %>% left_join(.,lmm.combined.filtered %>% filter(session %in% c(1,2)) %>% ungroup() %>% select(participant,hierarchy,blockcount), c("participant","hierarchy"))
library(lme4)
library(lmerTest)
#random intercepts
m1 <- lmer(dist_inf_3 ~ hierarchy + (1 | participant), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene"))
m2 <- lmer(dist_inf_3 ~ hierarchy + learningPerf + (1 | participant), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene"))
m3 <- lmer(dist_inf_3 ~ hierarchy * learningPerf + (1 | participant), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene"))
m4 <- lmer(dist_inf_3 ~ hierarchy + premise_3 + (1 | participant), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene")) #important
m5 <- lmer(dist_inf_3 ~ hierarchy + learningPerf + (1 | participant) + (1 | stimuli), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene"))## boundary (singular) fit: see help('isSingular')m5 <- lmer(dist_inf_3 ~ hierarchy + learningPerf + (1 | participant) + (1 | stimuli), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene"))## boundary (singular) fit: see help('isSingular')m6 <- lmer(dist_inf_3 ~ hierarchy + learningPerf * premise_3 + (1 | participant) + (1 | stimuli), data = lmm.combined.filtered.wide %>% filter(stimuli != "facescene"))## boundary (singular) fit: see help('isSingular')library(afex)
library(sjPlot)
plot_model(m1,type="pred", show.values = TRUE) ## $hierarchy
plot_model(m4, type = "eff", terms = c("premise_3","hierarchy"), show.data = TRUE)