Ebbinghaus
ebb_df <- data %>%
filter(task_name == "EBB") %>%
mutate(correct = as.factor(case_when(
resp == "1" ~ "correct",
resp == "0" ~ "incorrect")),
context = task_info,
size_diff = as.numeric(trial_info),
) %>%
select(-resp, -task_info, -trial_info)
ebb_model <- glmer(correct ~ culture * context * size_diff + (size_diff * context | subject),
family = binomial,
data = ebb_df)
## boundary (singular) fit: see ?isSingular
ebb_model
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: correct ~ culture * context * size_diff + (size_diff * context |
## subject)
## Data: ebb_df
## AIC BIC logLik deviance df.resid
## 411.6241 482.2607 -187.8121 375.6241 356
## Random effects:
## Groups Name Std.Dev. Corr
## subject (Intercept) 1.11183
## size_diff 0.09665 -1.00
## contextNC 4.25861 -1.00 1.00
## size_diff:contextNC 0.39340 1.00 -1.00 -1.00
## Number of obs: 374, groups: subject, 11
## Fixed Effects:
## (Intercept) cultureUS
## -0.509653 -0.003713
## contextNC size_diff
## -7.583891 0.131016
## cultureUS:contextNC cultureUS:size_diff
## 2.058857 0.000791
## contextNC:size_diff cultureUS:contextNC:size_diff
## 0.426954 -0.265492
## convergence code 0; 1 optimizer warnings; 0 lme4 warnings
RMTS
rmts_df <- data %>%
filter(task_name == "RMTS") %>%
mutate(choice = as.factor(case_when(
resp == "1" ~ "rel",
resp == "0" ~ "obj"))
) %>%
select(-resp, -task_info, -trial_info)
rmts_model <- glmer(choice ~ culture + (1|subject),
family = binomial,
data = rmts_df)
rmts_model
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: choice ~ culture + (1 | subject)
## Data: rmts_df
## AIC BIC logLik deviance df.resid
## 23.5524 28.9050 -8.7762 17.5524 41
## Random effects:
## Groups Name Std.Dev.
## subject (Intercept) 18.48
## Number of obs: 44, groups: subject, 11
## Fixed Effects:
## (Intercept) cultureUS
## 9.758 -20.425
CP
cp_df <- data %>%
filter(task_name == "CP") %>%
mutate(choice = as.factor(case_when(
resp == "1" ~ "uniq",
resp == "0" ~ "non_uniq"))
) %>%
select(-resp, -task_info, -trial_info)
cp_model <- glm(choice ~ culture,
family=binomial(link="logit"),
data = cp_df)
cp_model
##
## Call: glm(formula = choice ~ culture, family = binomial(link = "logit"),
## data = cp_df)
##
## Coefficients:
## (Intercept) cultureUS
## 19.57 -18.87
##
## Degrees of Freedom: 10 Total (i.e. Null); 9 Residual
## Null Deviance: 10.43
## Residual Deviance: 7.638 AIC: 11.64
Sticker
HZ_height_df <- data %>%
filter(task_name == "HZ", resp_type == "hz_height") %>%
mutate(
height = resp
) %>%
select(-resp, -task_info, -trial_info)
HZ_height_model <- lm(height ~ culture,
data = HZ_height_df)
summary(HZ_height_model)
##
## Call:
## lm(formula = height ~ culture, data = HZ_height_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -129.80 -26.07 10.20 36.93 110.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 204.20 29.80 6.852 7.45e-05 ***
## cultureUS -21.47 40.35 -0.532 0.608
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 66.63 on 9 degrees of freedom
## Multiple R-squared: 0.03049, Adjusted R-squared: -0.07723
## F-statistic: 0.2831 on 1 and 9 DF, p-value: 0.6076
HZ_stkr_area_df <- data %>%
filter(task_name == "HZ", resp_type == "stkr_area") %>%
mutate(
stkr_area = resp
) %>%
select(-resp, -task_info, -trial_info)
HZ_stkr_area_model <- lm(stkr_area ~ culture,
data = HZ_stkr_area_df)
summary(HZ_stkr_area_model)
##
## Call:
## lm(formula = stkr_area ~ culture, data = HZ_stkr_area_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -372502 -117861 5323 192361 223332
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 855654 94371 9.067 8.04e-06 ***
## cultureUS 124759 127778 0.976 0.354
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 211000 on 9 degrees of freedom
## Multiple R-squared: 0.09578, Adjusted R-squared: -0.004693
## F-statistic: 0.9533 on 1 and 9 DF, p-value: 0.3544
HZ_stkr_n_df <- data %>%
filter(task_name == "HZ", resp_type == "stkr_count") %>%
mutate(
stkr_count = resp
) %>%
select(-resp, -task_info, -trial_info)
HZ_stkr_n_model <- lm(stkr_count ~ culture,
data = HZ_stkr_n_df)
summary(HZ_stkr_n_model)
##
## Call:
## lm(formula = stkr_count ~ culture, data = HZ_stkr_n_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3333 -3.9000 -0.3333 2.6667 7.6000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.400 2.100 4.951 0.00079 ***
## cultureUS 3.933 2.844 1.383 0.20000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.697 on 9 degrees of freedom
## Multiple R-squared: 0.1753, Adjusted R-squared: 0.08364
## F-statistic: 1.913 on 1 and 9 DF, p-value: 0.2