Molly_DATA_PATH <- here("data/processed/syntactic_bootstrapping_tidy_data_molly.csv")
Anjie_DATA_PATH <- here("data/processed/syntactic_bootstrapping_tidy_data.csv")
ma_data_Molly <- read_csv(Molly_DATA_PATH) %>%
mutate(row_id = 1:n()) %>%
mutate(agent_argument_type2 = case_when(str_detect(agent_argument_type, "pronoun") ~ "pronoun",
TRUE ~ "noun"),
transitive_event_type2 = case_when(transitive_event_type == "direct_caused_action" ~ "direct_caused_action",
TRUE ~ "indirect_caused_action"))
## Parsed with column specification:
## cols(
## .default = col_character(),
## se = col_double(),
## mean_age = col_double(),
## productive_vocab_mean = col_double(),
## productive_vocab_median = col_double(),
## n_train_test_pair = col_double(),
## n_test_trial_per_pair = col_double(),
## n_repetitions_sentence = col_double(),
## n_repetitions_video = col_double(),
## inclusion_certainty = col_double(),
## n_1 = col_double(),
## x_1 = col_double(),
## x_2 = col_double(),
## x_2_raw = col_double(),
## sd_1 = col_double(),
## sd_2 = col_double(),
## sd_2_raw = col_double(),
## t = col_double(),
## d = col_double(),
## d_calc = col_double(),
## d_var_calc = col_double()
## # ... with 1 more columns
## )
## See spec(...) for full column specifications.
ma_data_Anjie <- read_csv(Anjie_DATA_PATH) %>%
mutate(row_id = 1:n()) %>%
mutate(agent_argument_type2 = case_when(str_detect(agent_argument_type, "pronoun") ~ "pronoun",
TRUE ~ "noun"),
transitive_event_type2 = case_when(transitive_event_type == "direct_caused_action" ~ "direct_caused_action",
TRUE ~ "indirect_caused_action"))
## Parsed with column specification:
## cols(
## .default = col_character(),
## se = col_double(),
## mean_age = col_double(),
## productive_vocab_mean = col_double(),
## productive_vocab_median = col_double(),
## n_train_test_pair = col_double(),
## n_test_trial_per_pair = col_double(),
## n_repetitions_sentence = col_double(),
## n_repetitions_video = col_double(),
## inclusion_certainty = col_double(),
## n_1 = col_double(),
## x_1 = col_double(),
## x_2 = col_double(),
## x_2_raw = col_double(),
## sd_1 = col_double(),
## sd_2 = col_double(),
## sd_2_raw = col_double(),
## t = col_double(),
## d = col_double(),
## d_calc = col_double(),
## d_var_calc = col_double()
## # ... with 1 more columns
## )
## See spec(...) for full column specifications.
summary(comparedf(data.frame(ma_data_Molly$d_calc), data.frame(ma_data_Anjie$d_var_calc)))
##
##
## Table: Summary of data.frames
##
## version arg ncol nrow
## -------- ------------------------------------- ----- -----
## x data.frame(ma_data_Molly$d_calc) 1 53
## y data.frame(ma_data_Anjie$d_var_calc) 1 53
##
##
##
## Table: Summary of overall comparison
##
## statistic value
## ------------------------------------------------------------ ------
## Number of by-variables 0
## Number of non-by variables in common 0
## Number of variables compared 0
## Number of variables in x but not y 1
## Number of variables in y but not x 1
## Number of variables compared with some values unequal 0
## Number of variables compared with all values equal 0
## Number of observations in common 53
## Number of observations in x but not y 0
## Number of observations in y but not x 0
## Number of observations with some compared variables unequal 0
## Number of observations with all compared variables equal 53
## Number of values unequal 0
##
##
##
## Table: Variables not shared
##
## version variable position class
## -------- ------------------------- --------- --------
## x ma_data_Molly.d_calc 1 numeric
## y ma_data_Anjie.d_var_calc 1 numeric
##
##
##
## Table: Other variables not compared
##
## | |
## |:-------------------------------|
## |No other variables not compared |
##
##
##
## Table: Observations not shared
##
## | |
## |:--------------------------|
## |No observations not shared |
##
##
##
## Table: Differences detected by variable
##
## | |
## |:-----------------------------------|
## |No differences detected by variable |
##
##
##
## Table: Differences detected
##
## | |
## |:-----------------------|
## |No differences detected |
##
##
##
## Table: Non-identical attributes
##
## | |
## |:---------------------------|
## |No non-identical attributes |
summary(comparedf(data.frame(ma_data_Molly$d_var_calc), data.frame(ma_data_Anjie$d_var_calc)))
##
##
## Table: Summary of data.frames
##
## version arg ncol nrow
## -------- ------------------------------------- ----- -----
## x data.frame(ma_data_Molly$d_var_calc) 1 53
## y data.frame(ma_data_Anjie$d_var_calc) 1 53
##
##
##
## Table: Summary of overall comparison
##
## statistic value
## ------------------------------------------------------------ ------
## Number of by-variables 0
## Number of non-by variables in common 0
## Number of variables compared 0
## Number of variables in x but not y 1
## Number of variables in y but not x 1
## Number of variables compared with some values unequal 0
## Number of variables compared with all values equal 0
## Number of observations in common 53
## Number of observations in x but not y 0
## Number of observations in y but not x 0
## Number of observations with some compared variables unequal 0
## Number of observations with all compared variables equal 53
## Number of values unequal 0
##
##
##
## Table: Variables not shared
##
## version variable position class
## -------- ------------------------- --------- --------
## x ma_data_Molly.d_var_calc 1 numeric
## y ma_data_Anjie.d_var_calc 1 numeric
##
##
##
## Table: Other variables not compared
##
## | |
## |:-------------------------------|
## |No other variables not compared |
##
##
##
## Table: Observations not shared
##
## | |
## |:--------------------------|
## |No observations not shared |
##
##
##
## Table: Differences detected by variable
##
## | |
## |:-----------------------------------|
## |No differences detected by variable |
##
##
##
## Table: Differences detected
##
## | |
## |:-----------------------|
## |No differences detected |
##
##
##
## Table: Non-identical attributes
##
## | |
## |:---------------------------|
## |No non-identical attributes |
x_1_molly <- round(ma_data_Molly$x_1, digits = 2)
x_1_anjie <- round(ma_data_Anjie$x_1, digits = 2)
setdiff(x_1_anjie, x_1_molly)
## numeric(0)
sd_1_molly <- round(ma_data_Molly$sd_1, digits = 2)
sd_1_anjie <- round(ma_data_Anjie$sd_1, digits = 2)
setdiff(sd_1_anjie, sd_1_molly)
## numeric(0)
setdiff(sd_1_molly, sd_1_anjie)
## numeric(0)
negligible?
d_calc_molly <- round(ma_data_Molly$d_calc, digits = 2)
d_calc_anjie <- round(ma_data_Anjie$d_calc, digits = 2)
setdiff(d_calc_anjie, d_calc_molly)
## [1] -0.70 0.79 1.45 -1.01
setdiff(d_calc_molly,d_calc_anjie)
## [1] -0.71 0.80 1.46 -0.95
d_var_calc_molly <- round(ma_data_Molly$d_var_calc, digits = 2)
d_var_calc_anjie <- round(ma_data_Anjie$d_var_calc, digits = 2)
setdiff(d_var_calc_molly,d_var_calc_anjie)
## numeric(0)
Small discrepancies but acceptable?
base_mv_molly <- rma.uni(d_calc, d_var_calc,
data=ma_data_Molly)
base_mv_anjie <- rma.uni(d_calc, d_var_calc,
data=ma_data_Anjie)
summary(base_mv_molly)
##
## Random-Effects Model (k = 53; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -44.0984 88.1968 92.1968 96.0993 92.4417
##
## tau^2 (estimated amount of total heterogeneity): 0.1525 (SE = 0.0489)
## tau (square root of estimated tau^2 value): 0.3905
## I^2 (total heterogeneity / total variability): 63.86%
## H^2 (total variability / sampling variability): 2.77
##
## Test for Heterogeneity:
## Q(df = 52) = 139.1246, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3412 0.0693 4.9220 <.0001 0.2054 0.4771 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(base_mv_anjie)
##
## Random-Effects Model (k = 53; tau^2 estimator: REML)
##
## logLik deviance AIC BIC AICc
## -44.2354 88.4707 92.4707 96.3732 92.7156
##
## tau^2 (estimated amount of total heterogeneity): 0.1532 (SE = 0.0491)
## tau (square root of estimated tau^2 value): 0.3914
## I^2 (total heterogeneity / total variability): 63.96%
## H^2 (total variability / sampling variability): 2.78
##
## Test for Heterogeneity:
## Q(df = 52) = 139.4183, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3405 0.0694 4.9041 <.0001 0.2044 0.4766 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
multi_mv_molly <- rma.mv(d_calc, d_var_calc,
random = ~ 1 | short_cite/same_infant/row_id, data=ma_data_Molly)
multi__mv_anjie <- rma.mv(d_calc, d_var_calc,
random = ~ 1 | short_cite/same_infant/row_id, data=ma_data_Anjie)
summary(multi_mv_molly)
##
## Multivariate Meta-Analysis Model (k = 53; method: REML)
##
## logLik Deviance AIC BIC AICc
## -43.5676 87.1352 95.1352 102.9402 95.9863
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0251 0.1584 15 no short_cite
## sigma^2.2 0.1351 0.3675 52 no short_cite/same_infant
## sigma^2.3 0.0000 0.0000 53 no short_cite/same_infant/row_id
##
## Test for Heterogeneity:
## Q(df = 52) = 139.1246, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3250 0.0841 3.8653 0.0001 0.1602 0.4898 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(multi__mv_anjie)
##
## Multivariate Meta-Analysis Model (k = 53; method: REML)
##
## logLik Deviance AIC BIC AICc
## -43.7029 87.4059 95.4059 103.2108 96.2569
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0251 0.1584 15 no short_cite
## sigma^2.2 0.1357 0.3684 52 no short_cite/same_infant
## sigma^2.3 0.0000 0.0000 53 no short_cite/same_infant/row_id
##
## Test for Heterogeneity:
## Q(df = 52) = 139.4183, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.3242 0.0842 3.8518 0.0001 0.1592 0.4892 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
trimfill(base_mv_molly)
##
## Estimated number of missing studies on the left side: 11 (SE = 4.8066)
##
## Random-Effects Model (k = 64; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.2891 (SE = 0.0720)
## tau (square root of estimated tau^2 value): 0.5377
## I^2 (total heterogeneity / total variability): 75.20%
## H^2 (total variability / sampling variability): 4.03
##
## Test for Heterogeneity:
## Q(df = 63) = 210.6251, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1871 0.0800 2.3393 0.0193 0.0303 0.3438 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
trimfill(base_mv_anjie)
##
## Estimated number of missing studies on the left side: 11 (SE = 4.8069)
##
## Random-Effects Model (k = 64; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 0.2904 (SE = 0.0723)
## tau (square root of estimated tau^2 value): 0.5389
## I^2 (total heterogeneity / total variability): 75.28%
## H^2 (total variability / sampling variability): 4.05
##
## Test for Heterogeneity:
## Q(df = 63) = 210.9602, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.1861 0.0801 2.3229 0.0202 0.0291 0.3431 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1