Syntactic Boostrapping MA
Data V2
Anjie Cao and Molly Lewis
2020-06-26
DATA_PATH <- here("data/processed/syntactic_bootstrapping_tidy_data.csv") # make all variables (i.e. things that might change) as capital letters at the top of the scripts
ma_data <- read_csv(DATA_PATH) %>%
filter(language == "English",
population_type == "typically_developing",
stimuli_modality == "video"|stimuli_modality == "animation",
!is.na(mean_age))
ma_data## # A tibble: 89 x 48
## coder unique_id link grammatical_cla… short_cite expt_num expt_condition
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 alan… arunacha… http… verb Arunachal… 1 transitive
## 2 alan… arunacha… http… verb Arunachal… 1 intransitive
## 3 alan… arunacha… http… verb Arunachal… 1 transitive
## 4 alan… arunacha… http… verb Arunachal… 1 intransitive
## 5 alan… arunacha… http… verb Arunachal… 1 PP
## 6 anjie arunacha… http… verb Arunachal… 1 DO
## 7 anjie arunacha… http… verb Arunachal… 2 PP_DO_no_delay
## 8 alan… bunger20… http… verb Bunger, A… 1 transitive
## 9 alan… bunger20… http… verb Bunger, A… 1 unaccusitive_…
## 10 alan… bunger20… http… verb Bunger, A… 1 transitive
## # … with 79 more rows, and 41 more variables: dependent_measure <chr>,
## # test_type <chr>, same_infant <chr>, language <chr>, mean_age <dbl>,
## # productive_vocab_mean <dbl>, productive_vocab_median <dbl>,
## # population_type <chr>, sentence_structure <chr>, agent_argument_type <chr>,
## # patient_argument_type <chr>, verb_type <chr>, stimuli_type <chr>,
## # stimuli_modality <chr>, stimuli_actor <chr>, presentation_type <chr>,
## # character_identification <chr>, practice_phase <chr>,
## # test_mass_or_distributed <chr>, n_train_test_pair <dbl>,
## # n_test_trial_per_pair <dbl>, n_repetitions_sentence <dbl>,
## # n_repetitions_video <dbl>, example_target_sentence <chr>,
## # test_question <chr>, inclusion_certainty <dbl>, n_1 <dbl>, x_1 <dbl>,
## # x_2 <dbl>, x_2_raw <dbl>, sd_1 <dbl>, sd_2 <dbl>, sd_2_raw <dbl>, t <dbl>,
## # d <dbl>, d_calc <dbl>, d_var_calc <dbl>, es_method <chr>,
## # test_method <chr>, agent_argument_type_clean <chr>,
## # patient_argument_type_clean <chr>With Raw Calculation
ma_data <- ma_data %>%
mutate(
d_calc_raw = case_when(
!is.na(x_2_raw) & !is.na(sd_2_raw) ~ (x_1 - x_2_raw) / sqrt((sd_1^2 + sd_2_raw^2)/2),
TRUE ~ NA_real_
),
d_var_calc_raw = case_when(
!is.na(x_2_raw) & !is.na(sd_2_raw) ~ (1 / n_1) + (d_calc_raw ^ 2 / (2 * n_1)),
TRUE ~ NA_real_
))
ma_data_with_raw <- ma_data %>% filter(!is.na(d_calc_raw))
cor.test(ma_data_with_raw$d_calc,
ma_data_with_raw$d_calc_raw)##
## Pearson's product-moment correlation
##
## data: ma_data_with_raw$d_calc and ma_data_with_raw$d_calc_raw
## t = 2.2614, df = 5, p-value = 0.07323
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09037938 0.95353401
## sample estimates:
## cor
## 0.7110752ma_data_with_raw## # A tibble: 7 x 50
## coder unique_id link grammatical_cla… short_cite expt_num expt_condition
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 alan… bunger20… http… verb Bunger, A… 1 transitive
## 2 anjie bunger20… http… verb Bunger, A… 1 unaccusitive_…
## 3 izzy… lidz2009 http… verb Lidz, J.,… 1 low_lexical
## 4 izzy… lidz2009 http… verb Lidz, J.,… 1 high_lexical
## 5 izzy… lidz2009 http… verb Lidz, J.,… 2 familiarizati…
## 6 izzy… lidz2009 http… verb Lidz, J.,… 2 familiarizati…
## 7 maya… naigles2… http… verb Naigles, … 1 transitive_in…
## # … with 43 more variables: dependent_measure <chr>, test_type <chr>,
## # same_infant <chr>, language <chr>, mean_age <dbl>,
## # productive_vocab_mean <dbl>, productive_vocab_median <dbl>,
## # population_type <chr>, sentence_structure <chr>, agent_argument_type <chr>,
## # patient_argument_type <chr>, verb_type <chr>, stimuli_type <chr>,
## # stimuli_modality <chr>, stimuli_actor <chr>, presentation_type <chr>,
## # character_identification <chr>, practice_phase <chr>,
## # test_mass_or_distributed <chr>, n_train_test_pair <dbl>,
## # n_test_trial_per_pair <dbl>, n_repetitions_sentence <dbl>,
## # n_repetitions_video <dbl>, example_target_sentence <chr>,
## # test_question <chr>, inclusion_certainty <dbl>, n_1 <dbl>, x_1 <dbl>,
## # x_2 <dbl>, x_2_raw <dbl>, sd_1 <dbl>, sd_2 <dbl>, sd_2_raw <dbl>, t <dbl>,
## # d <dbl>, d_calc <dbl>, d_var_calc <dbl>, es_method <chr>,
## # test_method <chr>, agent_argument_type_clean <chr>,
## # patient_argument_type_clean <chr>, d_calc_raw <dbl>, d_var_calc_raw <dbl>Data Overview
n_effect_sizes <- ma_data %>%
filter(!is.na(d_calc)) %>%
nrow()
n_papers <- ma_data %>%
distinct(unique_id) %>%
nrow()There are 89 effect sizes collected from 21 different papers.
Here are the papers in this analysis:
ma_data %>%
count(short_cite) %>%
arrange(-n) %>%
DT::datatable()ma_model <- rma(ma_data$d_calc, ma_data$d_var_calc)
ma_model##
## Random-Effects Model (k = 89; tau^2 estimator: REML)
##
## tau^2 (estimated amount of total heterogeneity): 1.4856 (SE = 0.2511)
## tau (square root of estimated tau^2 value): 1.2188
## I^2 (total heterogeneity / total variability): 93.50%
## H^2 (total variability / sampling variability): 15.37
##
## Test for Heterogeneity:
## Q(df = 88) = 651.3374, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.6436 0.1375 4.6817 <.0001 0.3742 0.9130 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1forest(ma_model,
header = T,
slab = ma_data$unique_id,
col = "red",
cex = .7)
#funnel plot {.tabset} ## vanilla
ma_data %>%
mutate(color = ifelse(sentence_structure == "transitive", "red", "blue"),
color_sample_size = ifelse(n_1 < 10, "red", ifelse(n_1 < 20, "orange", "yellow")),
color_confidence = ifelse(inclusion_certainty == 1, "red", "black"))-> ma_data_funnel
ma_data_funnel %>% filter (abs(d_calc) < 5) -> ma_data_funnel_no_outlier
ss_colors <- ma_data_funnel$color
ss_colors_no_outlier <- ma_data_funnel_no_outlier$color
ma_model_funnel <- rma(ma_data_funnel$d_calc, ma_data_funnel$d_var_calc)
ma_model_funnel_no_outlier <- rma(ma_data_funnel_no_outlier$d_calc, ma_data_funnel_no_outlier$d_var_calc)
f1<- funnel(ma_model_funnel, xlab = "Effect Size", col = ss_colors)
legend("topright",bg = "white",legend = c("transitive","intransitive"),pch=16,col=c("red", "blue"))
title(main = "All effect sizes break down by sentence structure")
f2<- funnel(ma_model_funnel_no_outlier, xlab = "Effect Size", col = ss_colors_no_outlier)
legend("topright",bg = "white",legend = c("transitive","intransitive"),pch=16,col=c("red", "blue"))
title(main = "effect sizes excluded outliers (abs <5) break down by sentence structure")
take int occount sentence structure?
ma_model_sentence_structure <- rma(ma_data_funnel$d_calc~ma_data_funnel$sentence_structure, ma_data_funnel$d_var_calc)
ma_model_no_outlier_ss <- rma(ma_data_funnel_no_outlier$d_calc~ma_data_funnel_no_outlier$sentence_structure, ma_data_funnel_no_outlier$d_var_calc)
f3 <- funnel(ma_model_sentence_structure, xlab = "effect size", col = ss_colors)
f3_b <- funnel(ma_model_no_outlier_ss, xlab = "effect size", col = ss_colors_no_outlier)
take int account sentence structure and age?
ma_model_ss_age <- rma(ma_data_funnel$d_calc~ma_data_funnel$sentence_structure + ma_data_funnel $mean_age, ma_data_funnel$d_var_calc)
ma_model_funnel_no_outlier_ss_age <- rma(ma_data_funnel_no_outlier$d_calc~ma_data_funnel_no_outlier$sentence_structure+ma_data_funnel_no_outlier$mean_age, ma_data_funnel_no_outlier$d_var_calc)
f4 <- funnel(ma_model_ss_age, xlab = "effect size", col = ss_colors)
f4_b <- funnel(ma_model_funnel_no_outlier_ss_age, xlab = "effect size", col = ss_colors_no_outlier)
Variable Summary
Continuous Variables
CONTINUOUS_VARS <- c("n_1", "x_1", "sd_1", "d_calc", "d_var_calc", "mean_age")
long_continuous <- ma_data %>%
pivot_longer(cols = CONTINUOUS_VARS)
long_continuous %>%
ggplot(aes(x = value)) +
geom_histogram() +
facet_wrap(~ name, scale = "free_x") +
labs(title = "Distribution of continuous measures")
long_continuous %>%
group_by(name) %>%
summarize(mean = mean(value),
sd = sd(value)) %>%
kable()| name | mean | sd |
|---|---|---|
| d_calc | 0.5475230 | 1.9494978 |
| d_var_calc | 0.2712837 | 0.5926745 |
| mean_age | 908.9631685 | 352.2592520 |
| n_1 | 14.3370787 | 5.2265577 |
| sd_1 | 0.1291530 | 0.0848206 |
| x_1 | 0.5587837 | 0.1171591 |
Categorical Variables
CATEGORICAL_VARS <- c("sentence_structure", "agent_argument_type", "patient_argument_type", "stimuli_actor",
"presentation_type", "character_identification",
"test_mass_or_distributed", "practice_phase", "test_method")
long_categorical <- ma_data %>%
pivot_longer(cols = CATEGORICAL_VARS) %>%
count(name, value) # this is a short cut for group_by() %>% summarize(count = n())
long_categorical %>%
ggplot(aes(x = value, y = n)) +
facet_wrap(~ name, scale = "free_x") +
geom_col(position = 'dodge',width=0.4) +
theme(text = element_text(size=8),
axis.text.x = element_text(angle = 90, hjust = 1)) # rotate x-axis text
Explore Moderators
no moderators
all
m1 <- rma.mv(d_calc, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m1)##
## Multivariate Meta-Analysis Model (k = 89; method: REML)
##
## logLik Deviance AIC BIC AICc
## -244.8124 489.6248 493.6248 498.5794 493.7659
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.4701 0.6856 21 no short_cite
##
## Test for Heterogeneity:
## Q(df = 88) = 651.3374, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.5313 0.1558 3.4099 0.0006 0.2259 0.8367 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36
ma_data_young <- ma_data %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months < 36)
m_young <- rma.mv(d_calc, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_young)##
## Multivariate Meta-Analysis Model (k = 64; method: REML)
##
## logLik Deviance AIC BIC AICc
## -176.0199 352.0397 356.0397 360.3260 356.2397
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5423 0.7364 17 no short_cite
##
## Test for Heterogeneity:
## Q(df = 63) = 485.9227, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.4763 0.1857 2.5647 0.0103 0.1123 0.8403 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36
ma_data_old <- ma_data %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months > 36 | age_months == 36)
m_old <- rma.mv(d_calc, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_old)##
## Multivariate Meta-Analysis Model (k = 25; method: REML)
##
## logLik Deviance AIC BIC AICc
## -64.2439 128.4879 132.4879 134.8440 133.0593
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2730 0.5225 5 no short_cite
##
## Test for Heterogeneity:
## Q(df = 24) = 162.8705, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## 0.7844 0.2541 3.0868 0.0020 0.2863 1.2825 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1age only
colored by unique_id
ma_data %>%
ggplot(aes(x = mean_age/30.44, y = d_calc,color = unique_id)) +
geom_point() +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") 
all
ma_data %>%
ggplot(aes(x = mean_age/30.44, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
geom_smooth(color = "red") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") +
theme(legend.position = "none") 
m_age <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age)##
## Multivariate Meta-Analysis Model (k = 89; method: REML)
##
## logLik Deviance AIC BIC AICc
## -243.1132 486.2264 492.2264 499.6241 492.5156
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.4743 0.6887 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 87) = 650.4593, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.9156, p-val = 0.1663
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2207 0.2736 0.8064 0.4200 -0.3156 0.7570
## mean_age 0.0004 0.0003 1.3841 0.1663 -0.0001 0.0008
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1forest(m_age,
header = T,
slab = ma_data$unique_id,
col = "red",
cex = .7
)
funnel(m_age)
young, < 36
ma_data_young %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
geom_smooth(color = "red") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days)") +
theme(legend.position = "none") 
m_age_young <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_age_young)##
## Multivariate Meta-Analysis Model (k = 64; method: REML)
##
## logLik Deviance AIC BIC AICc
## -175.0470 350.0940 356.0940 362.4754 356.5078
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5566 0.7461 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 62) = 481.9203, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0052, p-val = 0.9424
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4229 0.7651 0.5527 0.5804 -1.0767 1.9225
## mean_age 0.0001 0.0010 0.0722 0.9424 -0.0019 0.0020
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1forest(m_age_young,
header = T,
slab = ma_data_young$unique_id,
col = "red",
cex = .7
)
old, >= 36
ma_data_old %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
geom_smooth(color = "red") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days)") +
theme(legend.position = "none") 
m_age_old <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_old)##
## Multivariate Meta-Analysis Model (k = 25; method: REML)
##
## logLik Deviance AIC BIC AICc
## -64.1885 128.3771 134.3771 137.7836 135.6402
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.2837 0.5326 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 23) = 162.8205, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.6500, p-val = 0.4201
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4561 0.4839 0.9426 0.3459 -0.4923 1.4045
## mean_age 0.0002 0.0003 0.8062 0.4201 -0.0003 0.0008
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1age and test type
all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = test_method)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown my method") 
m_age_method <- rma.mv(d_calc ~ mean_age + test_method, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_method)##
## Multivariate Meta-Analysis Model (k = 89; method: REML)
##
## logLik Deviance AIC BIC AICc
## -242.1728 484.3457 492.3457 502.1631 492.8395
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.4929 0.7021 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 86) = 645.7299, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.0861, p-val = 0.3524
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2118 0.2767 0.7655 0.4440 -0.3304 0.7540
## mean_age 0.0003 0.0003 1.1305 0.2583 -0.0002 0.0008
## test_methodpoint 0.1572 0.3779 0.4159 0.6775 -0.5836 0.8979
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1age and sentence structure
all
ma_data %>%
filter(sentence_structure != "bare_verb") %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, , color = sentence_structure)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days)") 
m_age_sentence <- rma.mv(d_calc ~ mean_age + sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_sentence)##
## Multivariate Meta-Analysis Model (k = 89; method: REML)
##
## logLik Deviance AIC BIC AICc
## -226.2980 452.5961 460.5961 470.4135 461.0899
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5523 0.7431 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 86) = 640.0520, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 35.7496, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt -0.0442 0.2865 -0.1542 0.8775 -0.6058
## mean_age 0.0003 0.0003 1.1359 0.2560 -0.0002
## sentence_structuretransitive 0.5146 0.0885 5.8175 <.0001 0.3412
## ci.ub
## intrcpt 0.5174
## mean_age 0.0008
## sentence_structuretransitive 0.6880 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Interaction:
m_age_sentence <- rma.mv(d_calc ~ mean_age * sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_sentence)##
## Multivariate Meta-Analysis Model (k = 89; method: REML)
##
## logLik Deviance AIC BIC AICc
## -226.0945 452.1890 462.1890 474.4022 462.9485
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5529 0.7436 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 85) = 639.5505, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 36.5311, p-val < .0001
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.1063 0.3336 0.3188 0.7499
## mean_age 0.0001 0.0003 0.3483 0.7276
## sentence_structuretransitive 0.3223 0.2352 1.3704 0.1706
## mean_age:sentence_structuretransitive 0.0002 0.0002 0.8826 0.3775
## ci.lb ci.ub
## intrcpt -0.5474 0.7601
## mean_age -0.0005 0.0008
## sentence_structuretransitive -0.1387 0.7833
## mean_age:sentence_structuretransitive -0.0003 0.0007
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1< 36, young
ma_data_young_only <- ma_data %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months < 36)
ma_data_young_only %>%
filter(sentence_structure != "bare_verb") %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, , color = sentence_structure)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days)") 
m_age_sentence_young <- rma.mv(d_calc ~ mean_age + sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
summary(m_age_sentence_young)##
## Multivariate Meta-Analysis Model (k = 64; method: REML)
##
## logLik Deviance AIC BIC AICc
## -164.9549 329.9097 337.9097 346.3532 338.6240
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.6117 0.7821 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 61) = 474.0765, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 20.2255, p-val < .0001
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.5789 0.7784 0.7437 0.4570 -0.9467
## mean_age -0.0005 0.0010 -0.4934 0.6217 -0.0025
## sentence_structuretransitive 0.4600 0.1023 4.4963 <.0001 0.2595
## ci.ub
## intrcpt 2.1046
## mean_age 0.0015
## sentence_structuretransitive 0.6606 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1interaction:
m_age_sentence_young <- rma.mv(d_calc ~ mean_age * sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
summary(m_age_sentence_young)##
## Multivariate Meta-Analysis Model (k = 64; method: REML)
##
## logLik Deviance AIC BIC AICc
## -164.6983 329.3966 339.3966 349.8683 340.5077
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.6009 0.7752 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 60) = 466.1636, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 20.9234, p-val = 0.0001
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.3173 0.8359 0.3796 0.7043
## mean_age -0.0001 0.0011 -0.1101 0.9123
## sentence_structuretransitive 0.8695 0.4937 1.7612 0.0782
## mean_age:sentence_structuretransitive -0.0006 0.0007 -0.8484 0.3962
## ci.lb ci.ub
## intrcpt -1.3211 1.9557
## mean_age -0.0023 0.0020
## sentence_structuretransitive -0.0981 1.8371 .
## mean_age:sentence_structuretransitive -0.0019 0.0008
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_old <- ma_data %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months > 36 | age_months == 36)
ma_data_old %>%
filter(sentence_structure != "bare_verb") %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = sentence_structure)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days)") 
m_age_sentence_old <- rma.mv(d_calc ~ mean_age + sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_sentence_old)##
## Multivariate Meta-Analysis Model (k = 25; method: REML)
##
## logLik Deviance AIC BIC AICc
## -57.1229 114.2458 122.2458 126.6100 124.5988
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3969 0.6300 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 158.1930, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 15.1840, p-val = 0.0005
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt -0.0625 0.5259 -0.1187 0.9055 -1.0933
## mean_age 0.0003 0.0003 0.8496 0.3955 -0.0003
## sentence_structuretransitive 0.6866 0.1803 3.8087 0.0001 0.3333
## ci.ub
## intrcpt 0.9684
## mean_age 0.0008
## sentence_structuretransitive 1.0399 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1interaction
m_age_sentence_old <- rma.mv(d_calc ~ mean_age * sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_sentence_old)##
## Multivariate Meta-Analysis Model (k = 25; method: REML)
##
## logLik Deviance AIC BIC AICc
## -57.2911 114.5823 124.5823 129.8049 128.5823
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3888 0.6235 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 21) = 151.6021, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 15.2409, p-val = 0.0016
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.2310 1.1815 0.1955 0.8450
## mean_age 0.0000 0.0008 0.0491 0.9608
## sentence_structuretransitive 0.3367 1.2773 0.2636 0.7921
## mean_age:sentence_structuretransitive 0.0002 0.0009 0.2761 0.7825
## ci.lb ci.ub
## intrcpt -2.0847 2.5468
## mean_age -0.0016 0.0016
## sentence_structuretransitive -2.1668 2.8401
## mean_age:sentence_structuretransitive -0.0015 0.0020
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1age with stimuli_actor
all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_actor)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by stimuli_actor") 
m_age_stimuli_actor <- rma.mv(d_calc ~ mean_age + stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
m_age_stimuli_actor_interaction <- rma.mv(d_calc ~ mean_age * stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_stimuli_actor)##
## Multivariate Meta-Analysis Model (k = 89; method: REML)
##
## logLik Deviance AIC BIC AICc
## -241.9956 483.9911 491.9911 501.8085 492.4850
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.4949 0.7035 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 86) = 646.2284, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 1.9157, p-val = 0.3837
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.2085 0.3503 0.5951 0.5518 -0.4781 0.8950
## mean_age 0.0004 0.0003 1.3817 0.1671 -0.0001 0.0009
## stimuli_actorperson 0.0152 0.2866 0.0530 0.9577 -0.5466 0.5770
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_stimuli_actor_interaction)##
## Multivariate Meta-Analysis Model (k = 89; method: REML)
##
## logLik Deviance AIC BIC AICc
## -239.0537 478.1073 488.1073 500.3206 488.8668
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5955 0.7717 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 85) = 645.8629, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.7587, p-val = 0.0800
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.5761 0.4079 1.4123 0.1579 -0.2234
## mean_age -0.0001 0.0003 -0.3247 0.7454 -0.0008
## stimuli_actorperson -0.9011 0.5267 -1.7107 0.0871 -1.9334
## mean_age:stimuli_actorperson 0.0011 0.0005 2.1986 0.0279 0.0001
## ci.ub
## intrcpt 1.3756
## mean_age 0.0006
## stimuli_actorperson 0.1313 .
## mean_age:stimuli_actorperson 0.0022 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36, young
ma_data_young_only %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_actor)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by stimuli_actor, young only") 
m_age_stimuli_actor_young <- rma.mv(d_calc ~ mean_age + stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
m_age_stimuli_actor_interaction_young <- rma.mv(d_calc ~ mean_age * stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
summary(m_age_stimuli_actor_young)##
## Multivariate Meta-Analysis Model (k = 64; method: REML)
##
## logLik Deviance AIC BIC AICc
## -173.9907 347.9813 355.9813 364.4248 356.6956
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5932 0.7702 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 61) = 480.1647, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.1148, p-val = 0.9442
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3133 0.8293 0.3778 0.7056 -1.3122 1.9388
## mean_age 0.0001 0.0010 0.1137 0.9095 -0.0018 0.0021
## stimuli_actorperson 0.1087 0.3322 0.3273 0.7435 -0.5424 0.7598
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_stimuli_actor_interaction_young)##
## Multivariate Meta-Analysis Model (k = 64; method: REML)
##
## logLik Deviance AIC BIC AICc
## -172.7341 345.4682 355.4682 365.9399 356.5793
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.6599 0.8123 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 60) = 470.5154, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 0.7711, p-val = 0.8564
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.8975 1.1604 0.7734 0.4393 -1.3769
## mean_age -0.0007 0.0015 -0.4890 0.6248 -0.0037
## stimuli_actorperson -1.0777 1.5666 -0.6879 0.4915 -4.1481
## mean_age:stimuli_actorperson 0.0017 0.0021 0.7879 0.4308 -0.0025
## ci.ub
## intrcpt 3.1719
## mean_age 0.0022
## stimuli_actorperson 1.9928
## mean_age:stimuli_actorperson 0.0058
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = stimuli_actor)) +
geom_point() +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by stimuli_actor, old only") 
m_age_stimuli_actor_old <- rma.mv(d_calc ~ mean_age + stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
m_age_stimuli_actor_interaction_old <- rma.mv(d_calc ~ mean_age * stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_age_stimuli_actor_old)##
## Multivariate Meta-Analysis Model (k = 25; method: REML)
##
## logLik Deviance AIC BIC AICc
## -63.0110 126.0219 134.0219 138.3861 136.3749
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3748 0.6122 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 22) = 162.3480, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.7838, p-val = 0.6758
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5849 0.6274 0.9323 0.3512 -0.6447 1.8146
## mean_age 0.0002 0.0003 0.7870 0.4313 -0.0003 0.0008
## stimuli_actorperson -0.1972 0.5928 -0.3327 0.7393 -1.3591 0.9647
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_stimuli_actor_interaction_old)##
## Multivariate Meta-Analysis Model (k = 25; method: REML)
##
## logLik Deviance AIC BIC AICc
## -61.1591 122.3181 132.3181 137.5407 136.3181
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5153 0.7178 5 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 21) = 162.3479, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 5.1887, p-val = 0.1585
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 1.3170 0.7666 1.7180 0.0858 -0.1855
## mean_age -0.0003 0.0004 -0.6696 0.5031 -0.0010
## stimuli_actorperson -1.9526 1.0845 -1.8005 0.0718 -4.0782
## mean_age:stimuli_actorperson 0.0013 0.0006 2.1021 0.0355 0.0001
## ci.ub
## intrcpt 2.8195 .
## mean_age 0.0005
## stimuli_actorperson 0.1730 .
## mean_age:stimuli_actorperson 0.0025 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1age with presentation_type
Decided to only compare asynchronous and immediate after:
ma_data %>%
count(presentation_type)## # A tibble: 3 x 2
## presentation_type n
## <chr> <int>
## 1 asynchronous 35
## 2 immediate_after 44
## 3 simultaneous 10ma_data_pt <- ma_data %>%
filter(presentation_type != "simultaneous")
ma_data_pt_young <- ma_data_pt %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months < 36)
ma_data_pt_old <- ma_data_pt %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months > 36 | age_months == 36) all
ma_data_pt %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = presentation_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by presentation type") 
m_age_pt <- rma.mv(d_calc ~ mean_age + presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_pt)
m_age_pt_interaction <- rma.mv(d_calc ~ mean_age * presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_pt)
summary(m_age_pt)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -211.2166 422.4332 430.4332 439.7561 430.9966
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5694 0.7546 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 569.9273, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.0550, p-val = 0.9729
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.5005 0.3325 1.5052 0.1323 -0.1512
## mean_age 0.0001 0.0003 0.2145 0.8302 -0.0006
## presentation_typeimmediate_after 0.0110 0.1974 0.0558 0.9555 -0.3758
## ci.ub
## intrcpt 1.1523
## mean_age 0.0007
## presentation_typeimmediate_after 0.3978
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_pt_interaction)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -210.0053 420.0106 430.0106 441.5981 430.8802
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5657 0.7522 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 564.1302, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 0.6228, p-val = 0.8912
##
## Model Results:
##
## estimate se zval pval
## intrcpt 1.1388 0.9112 1.2498 0.2114
## mean_age -0.0009 0.0013 -0.6747 0.4999
## presentation_typeimmediate_after -0.5998 0.8358 -0.7176 0.4730
## mean_age:presentation_typeimmediate_after 0.0010 0.0013 0.7528 0.4516
## ci.lb ci.ub
## intrcpt -0.6471 2.9247
## mean_age -0.0034 0.0017
## presentation_typeimmediate_after -2.2379 1.0383
## mean_age:presentation_typeimmediate_after -0.0016 0.0035
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36, young
ma_data_pt_young %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = presentation_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by presentation type, young only") 
m_data_pt_young <- rma.mv(d_calc ~ mean_age + presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_pt_young)
m_age_pt_interaction_young <- rma.mv(d_calc ~ mean_age * presentation_type, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_pt_young)
summary(m_age_pt)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -211.2166 422.4332 430.4332 439.7561 430.9966
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5694 0.7546 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 569.9273, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.0550, p-val = 0.9729
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.5005 0.3325 1.5052 0.1323 -0.1512
## mean_age 0.0001 0.0003 0.2145 0.8302 -0.0006
## presentation_typeimmediate_after 0.0110 0.1974 0.0558 0.9555 -0.3758
## ci.ub
## intrcpt 1.1523
## mean_age 0.0007
## presentation_typeimmediate_after 0.3978
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_pt_interaction)##
## Multivariate Meta-Analysis Model (k = 79; method: REML)
##
## logLik Deviance AIC BIC AICc
## -210.0053 420.0106 430.0106 441.5981 430.8802
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5657 0.7522 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 75) = 564.1302, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 0.6228, p-val = 0.8912
##
## Model Results:
##
## estimate se zval pval
## intrcpt 1.1388 0.9112 1.2498 0.2114
## mean_age -0.0009 0.0013 -0.6747 0.4999
## presentation_typeimmediate_after -0.5998 0.8358 -0.7176 0.4730
## mean_age:presentation_typeimmediate_after 0.0010 0.0013 0.7528 0.4516
## ci.lb ci.ub
## intrcpt -0.6471 2.9247
## mean_age -0.0034 0.0017
## presentation_typeimmediate_after -2.2379 1.0383
## mean_age:presentation_typeimmediate_after -0.0016 0.0035
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_pt_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = presentation_type)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by presentation type, young only") 
age with character_identification
all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = character_identification)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by character_identification") 
m_age_ci <- rma.mv(d_calc ~ mean_age + character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
m_age_stimuli_ci_interaction <- rma.mv(d_calc ~ mean_age * character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_ci)##
## Multivariate Meta-Analysis Model (k = 89; method: REML)
##
## logLik Deviance AIC BIC AICc
## -239.4300 478.8601 486.8601 496.6775 487.3539
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.4594 0.6778 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 86) = 640.0335, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 7.4764, p-val = 0.0238
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0673 0.2795 0.2410 0.8096 -0.4804 0.6151
## mean_age 0.0003 0.0003 1.1878 0.2349 -0.0002 0.0008
## character_identificationyes 0.5438 0.2307 2.3577 0.0184 0.0917 0.9959
##
## intrcpt
## mean_age
## character_identificationyes *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_stimuli_ci_interaction)##
## Multivariate Meta-Analysis Model (k = 89; method: REML)
##
## logLik Deviance AIC BIC AICc
## -236.1904 472.3807 482.3807 494.5940 483.1402
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.4908 0.7006 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 85) = 639.1328, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 12.6489, p-val = 0.0055
##
## Model Results:
##
## estimate se zval pval
## intrcpt -0.4579 0.3637 -1.2592 0.2080
## mean_age 0.0010 0.0004 2.5129 0.0120
## character_identificationyes 1.4686 0.4651 3.1577 0.0016
## mean_age:character_identificationyes -0.0012 0.0005 -2.2889 0.0221
## ci.lb ci.ub
## intrcpt -1.1707 0.2548
## mean_age 0.0002 0.0017 *
## character_identificationyes 0.5570 2.3801 **
## mean_age:character_identificationyes -0.0021 -0.0002 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36, young
ma_data_young_only %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = character_identification)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by character_identification, young only") 
m_age_ci_young <- rma.mv(d_calc ~ mean_age + character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
m_age_ci_interaction_young <- rma.mv(d_calc ~ mean_age * character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
summary(m_age_ci_young)##
## Multivariate Meta-Analysis Model (k = 64; method: REML)
##
## logLik Deviance AIC BIC AICc
## -171.4660 342.9320 350.9320 359.3755 351.6463
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5341 0.7308 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 61) = 463.4199, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 5.4861, p-val = 0.0644
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.3752 0.7600 0.4937 0.6215 -1.1143 1.8647
## mean_age -0.0001 0.0010 -0.1261 0.8997 -0.0020 0.0018
## character_identificationyes 0.5919 0.2528 2.3414 0.0192 0.0964 1.0873
##
## intrcpt
## mean_age
## character_identificationyes *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_ci_interaction_young)##
## Multivariate Meta-Analysis Model (k = 64; method: REML)
##
## logLik Deviance AIC BIC AICc
## -170.6867 341.3733 351.3733 361.8451 352.4845
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5415 0.7359 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 60) = 450.7936, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 5.4804, p-val = 0.1398
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.3265 0.9517 0.3430 0.7316
## mean_age -0.0001 0.0013 -0.0433 0.9655
## character_identificationyes 0.7033 1.4326 0.4909 0.6235
## mean_age:character_identificationyes -0.0002 0.0019 -0.0785 0.9374
## ci.lb ci.ub
## intrcpt -1.5389 2.1918
## mean_age -0.0026 0.0024
## character_identificationyes -2.1045 3.5111
## mean_age:character_identificationyes -0.0040 0.0037
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = character_identification)) +
geom_point() +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by character_identification, old only") 
age with practice_phase
all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = practice_phase)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by practice_phase") 
m_age_pf <- rma.mv(d_calc ~ mean_age + character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
m_age_pf_interaction <- rma.mv(d_calc ~ mean_age * character_identification, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_ci)##
## Multivariate Meta-Analysis Model (k = 89; method: REML)
##
## logLik Deviance AIC BIC AICc
## -239.4300 478.8601 486.8601 496.6775 487.3539
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.4594 0.6778 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 86) = 640.0335, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 7.4764, p-val = 0.0238
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0673 0.2795 0.2410 0.8096 -0.4804 0.6151
## mean_age 0.0003 0.0003 1.1878 0.2349 -0.0002 0.0008
## character_identificationyes 0.5438 0.2307 2.3577 0.0184 0.0917 0.9959
##
## intrcpt
## mean_age
## character_identificationyes *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_stimuli_ci_interaction)##
## Multivariate Meta-Analysis Model (k = 89; method: REML)
##
## logLik Deviance AIC BIC AICc
## -236.1904 472.3807 482.3807 494.5940 483.1402
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.4908 0.7006 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 85) = 639.1328, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 12.6489, p-val = 0.0055
##
## Model Results:
##
## estimate se zval pval
## intrcpt -0.4579 0.3637 -1.2592 0.2080
## mean_age 0.0010 0.0004 2.5129 0.0120
## character_identificationyes 1.4686 0.4651 3.1577 0.0016
## mean_age:character_identificationyes -0.0012 0.0005 -2.2889 0.0221
## ci.lb ci.ub
## intrcpt -1.1707 0.2548
## mean_age 0.0002 0.0017 *
## character_identificationyes 0.5570 2.3801 **
## mean_age:character_identificationyes -0.0021 -0.0002 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36, young
ma_data_young_only %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = practice_phase)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by practice_phase, young only") 
m_age_pf_young <- rma.mv(d_calc ~ mean_age + practice_phase, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
m_age_pf_interaction_young <- rma.mv(d_calc ~ mean_age * practice_phase, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
summary(m_age_pf_young)##
## Multivariate Meta-Analysis Model (k = 64; method: REML)
##
## logLik Deviance AIC BIC AICc
## -173.8766 347.7531 355.7531 364.1966 356.4674
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.4920 0.7015 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 61) = 449.3877, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.9782, p-val = 0.6132
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5614 0.7575 0.7411 0.4586 -0.9233 2.0461
## mean_age -0.0002 0.0010 -0.2340 0.8150 -0.0022 0.0017
## practice_phaseyes 0.2084 0.2109 0.9882 0.3231 -0.2049 0.6216
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_pf_interaction_young)##
## Multivariate Meta-Analysis Model (k = 64; method: REML)
##
## logLik Deviance AIC BIC AICc
## -171.2738 342.5475 352.5475 363.0192 353.6586
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.4943 0.7031 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 60) = 449.3782, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 4.6864, p-val = 0.1963
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.3653 0.8655 1.5774 0.1147 -0.3312 3.0617
## mean_age -0.0015 0.0012 -1.2555 0.2093 -0.0039 0.0008
## practice_phaseyes -1.9458 1.1366 -1.7119 0.0869 -4.1735 0.2819
## mean_age:practice_phaseyes 0.0033 0.0017 1.9280 0.0539 -0.0001 0.0066
##
## intrcpt
## mean_age
## practice_phaseyes .
## mean_age:practice_phaseyes .
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = practice_phase)) +
geom_point() +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by practice_phase, old only") 
age with test_mass_or_distributed
all
ma_data %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = test_mass_or_distributed)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by test type") 
m_age_tt <- rma.mv(d_calc ~ mean_age + test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
m_age_tt_interaction <- rma.mv(d_calc ~ mean_age * test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_age_tt)##
## Multivariate Meta-Analysis Model (k = 89; method: REML)
##
## logLik Deviance AIC BIC AICc
## -241.4988 482.9975 490.9975 500.8149 491.4913
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.4795 0.6925 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 86) = 639.7404, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.7214, p-val = 0.2565
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.0916 0.3096 0.2958 0.7674 -0.5152 0.6983
## mean_age 0.0004 0.0003 1.5270 0.1268 -0.0001 0.0009
## test_mass_or_distributedmass 0.3187 0.3549 0.8981 0.3691 -0.3768 1.0142
##
## intrcpt
## mean_age
## test_mass_or_distributedmass
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_tt_interaction)##
## Multivariate Meta-Analysis Model (k = 89; method: REML)
##
## logLik Deviance AIC BIC AICc
## -240.2445 480.4889 490.4889 502.7022 491.2484
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.4856 0.6969 21 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 85) = 639.0930, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 3.5148, p-val = 0.3189
##
## Model Results:
##
## estimate se zval pval
## intrcpt 0.0461 0.3146 0.1466 0.8835
## mean_age 0.0004 0.0003 1.6742 0.0941
## test_mass_or_distributedmass 1.1573 1.0015 1.1556 0.2479
## mean_age:test_mass_or_distributedmass -0.0012 0.0013 -0.8962 0.3702
## ci.lb ci.ub
## intrcpt -0.5705 0.6627
## mean_age -0.0001 0.0010 .
## test_mass_or_distributedmass -0.8056 3.1203
## mean_age:test_mass_or_distributedmass -0.0038 0.0014
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1<36, young
ma_data_young_only %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = test_mass_or_distributed)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by test_mass_or_distributed, young only") 
m_age_tt_young <- rma.mv(d_calc ~ mean_age + test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
m_age_tt_interaction_young <- rma.mv(d_calc ~ mean_age * test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young_only)
summary(m_age_tt_young)##
## Multivariate Meta-Analysis Model (k = 64; method: REML)
##
## logLik Deviance AIC BIC AICc
## -173.4018 346.8037 354.8037 363.2471 355.5179
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5695 0.7547 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 61) = 475.4914, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.7424, p-val = 0.6899
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1707 0.8207 0.2080 0.8352 -1.4378 1.7792
## mean_age 0.0002 0.0010 0.2413 0.8093 -0.0017 0.0022
## test_mass_or_distributedmass 0.3474 0.4049 0.8581 0.3908 -0.4461 1.1410
##
## intrcpt
## mean_age
## test_mass_or_distributedmass
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_age_tt_interaction_young)##
## Multivariate Meta-Analysis Model (k = 64; method: REML)
##
## logLik Deviance AIC BIC AICc
## -171.8415 343.6830 353.6830 364.1548 354.7941
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5971 0.7727 17 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 60) = 475.2325, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.1125, p-val = 0.5494
##
## Model Results:
##
## estimate se zval pval
## intrcpt -0.9473 1.2535 -0.7558 0.4498
## mean_age 0.0017 0.0016 1.0634 0.2876
## test_mass_or_distributedmass 2.1684 1.5950 1.3595 0.1740
## mean_age:test_mass_or_distributedmass -0.0024 0.0021 -1.1816 0.2373
## ci.lb ci.ub
## intrcpt -3.4041 1.5094
## mean_age -0.0014 0.0048
## test_mass_or_distributedmass -0.9578 5.2946
## mean_age:test_mass_or_distributedmass -0.0065 0.0016
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1>= 36, old
ma_data_old %>%
mutate(age_months = mean_age/30.44) %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1, color = test_mass_or_distributed)) +
geom_point() +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (days), breakdown by test_mass_or_distributed, old only") 
repetition only
repetition only
ma_data %>%
ggplot(mapping = aes(x = n_repetitions_sentence, y = d_calc)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("number of repetition per novel verb") +
ggtitle("Syntactical Bootstrapping effect size vs. number of repetitions for verb") +
theme_classic() +
theme(legend.position = "none")
m_rep <- rma.mv(d_calc ~ n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_rep)##
## Multivariate Meta-Analysis Model (k = 80; method: REML)
##
## logLik Deviance AIC BIC AICc
## -206.8073 413.6145 419.6145 426.6846 419.9388
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5566 0.7460 18 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 78) = 571.7092, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0900, p-val = 0.7642
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.5147 0.2261 2.2763 0.0228 0.0715 0.9579 *
## n_repetitions_sentence 0.0049 0.0164 0.2999 0.7642 -0.0273 0.0372
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1repetition only, young
ma_data_young %>%
ggplot(mapping = aes(x = n_repetitions_sentence, y = d_calc)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("number of repetition per novel verb") +
ggtitle("Syntactical Bootstrapping effect size vs. number of repetitions for verb") +
theme_classic() +
theme(legend.position = "none")
m_rep_young <- rma.mv(d_calc ~ n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_rep_young)##
## Multivariate Meta-Analysis Model (k = 58; method: REML)
##
## logLik Deviance AIC BIC AICc
## -148.3685 296.7370 302.7370 308.8131 303.1986
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.6296 0.7935 15 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 56) = 429.3185, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.1446, p-val = 0.7038
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.4566 0.2624 1.7405 0.0818 -0.0576 0.9709 .
## n_repetitions_sentence 0.0064 0.0169 0.3802 0.7038 -0.0267 0.0395
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1repetition only, old
ma_data_old %>%
ggplot(mapping = aes(x = n_repetitions_sentence, y = d_calc)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("number of repetition per novel verb") +
ggtitle("Syntactical Bootstrapping effect size vs. number of repetitions for verb") +
theme_classic() +
theme(legend.position = "none")
m_rep_old <- rma.mv(d_calc ~ n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_rep_old)##
## Multivariate Meta-Analysis Model (k = 22; method: REML)
##
## logLik Deviance AIC BIC AICc
## -52.5915 105.1829 111.1829 114.1701 112.6829
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3422 0.5850 4 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 20) = 129.2999, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 1.1460, p-val = 0.2844
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1286 0.6932 0.1856 0.8528 -1.2300 1.4873
## n_repetitions_sentence 0.1528 0.1427 1.0705 0.2844 -0.1269 0.4325
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1repetition with age
correlation
all
m_rep_age <- rma.mv(d_calc ~ mean_age + n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
m_rep_age_interaction <- rma.mv(d_calc ~ mean_age*n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data)
summary(m_rep_age)##
## Multivariate Meta-Analysis Model (k = 80; method: REML)
##
## logLik Deviance AIC BIC AICc
## -205.3150 410.6300 418.6300 428.0052 419.1856
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5704 0.7552 18 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 77) = 571.5893, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.0517, p-val = 0.3585
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1488 0.3464 0.4296 0.6675 -0.5302 0.8278
## mean_age 0.0004 0.0003 1.3990 0.1618 -0.0001 0.0009
## n_repetitions_sentence 0.0097 0.0168 0.5774 0.5636 -0.0232 0.0426
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_rep_age_interaction)##
## Multivariate Meta-Analysis Model (k = 80; method: REML)
##
## logLik Deviance AIC BIC AICc
## -204.3808 408.7616 418.7616 430.4153 419.6188
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.5823 0.7631 18 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 76) = 568.0341, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.0603, p-val = 0.5600
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 0.1440 0.3665 0.3927 0.6945 -0.5745
## mean_age 0.0004 0.0004 0.8632 0.3880 -0.0005
## n_repetitions_sentence 0.0115 0.0612 0.1886 0.8504 -0.1084
## mean_age:n_repetitions_sentence -0.0000 0.0001 -0.0297 0.9763 -0.0002
## ci.ub
## intrcpt 0.8624
## mean_age 0.0013
## n_repetitions_sentence 0.1315
## mean_age:n_repetitions_sentence 0.0002
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1young, <36
m_rep_age_young <- rma.mv(d_calc ~ mean_age + n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
m_rep_age_interaction_young <- rma.mv(d_calc ~ mean_age*n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_young)
summary(m_rep_age_young)##
## Multivariate Meta-Analysis Model (k = 58; method: REML)
##
## logLik Deviance AIC BIC AICc
## -147.3221 294.6442 302.6442 310.6735 303.4442
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.6629 0.8142 15 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 55) = 419.8915, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.2562, p-val = 0.8797
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.1502 0.9905 0.1517 0.8795 -1.7912 2.0916
## mean_age 0.0004 0.0012 0.3193 0.7495 -0.0019 0.0026
## n_repetitions_sentence 0.0096 0.0193 0.4976 0.6188 -0.0282 0.0473
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_rep_age_interaction_young)##
## Multivariate Meta-Analysis Model (k = 58; method: REML)
##
## logLik Deviance AIC BIC AICc
## -145.5265 291.0530 301.0530 310.9980 302.3030
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.6105 0.7814 15 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 54) = 416.5168, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.7136, p-val = 0.4379
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt -1.4756 1.4376 -1.0264 0.3047 -4.2934
## mean_age 0.0029 0.0020 1.4613 0.1439 -0.0010
## n_repetitions_sentence 0.1653 0.1009 1.6384 0.1013 -0.0324
## mean_age:n_repetitions_sentence -0.0003 0.0002 -1.5794 0.1142 -0.0006
## ci.ub
## intrcpt 1.3421
## mean_age 0.0069
## n_repetitions_sentence 0.3630
## mean_age:n_repetitions_sentence 0.0001
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1old > 36
m_rep_age_old <- rma.mv(d_calc ~ mean_age + n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
m_rep_age_interaction_old <- rma.mv(d_calc ~ mean_age*n_repetitions_sentence , V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_old)
summary(m_rep_age_old)##
## Multivariate Meta-Analysis Model (k = 22; method: REML)
##
## logLik Deviance AIC BIC AICc
## -52.5577 105.1154 113.1154 116.8932 115.9726
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.3229 0.5682 4 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 19) = 127.4277, p-val < .0001
##
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 2.0934, p-val = 0.3511
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt -0.3413 0.8382 -0.4071 0.6839 -1.9842 1.3017
## mean_age 0.0003 0.0003 0.9438 0.3453 -0.0003 0.0009
## n_repetitions_sentence 0.1676 0.1407 1.1910 0.2336 -0.1082 0.4434
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m_rep_age_interaction_old)##
## Multivariate Meta-Analysis Model (k = 22; method: REML)
##
## logLik Deviance AIC BIC AICc
## -49.1428 98.2857 108.2857 112.7375 113.2857
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.0279 0.1669 4 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 18) = 109.1988, p-val < .0001
##
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 19.6820, p-val = 0.0002
##
## Model Results:
##
## estimate se zval pval ci.lb
## intrcpt 9.9164 3.1299 3.1683 0.0015 3.7819
## mean_age -0.0086 0.0026 -3.2974 0.0010 -0.0137
## n_repetitions_sentence -3.3177 1.0299 -3.2215 0.0013 -5.3362
## mean_age:n_repetitions_sentence 0.0029 0.0009 3.4553 0.0005 0.0013
## ci.ub
## intrcpt 16.0509 **
## mean_age -0.0035 ***
## n_repetitions_sentence -1.2992 **
## mean_age:n_repetitions_sentence 0.0046 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1vocab
vocab (median)
ma_data_with_vocab <- ma_data %>%
mutate(vocab = case_when(!is.na(productive_vocab_median) ~ productive_vocab_median,
!is.na(productive_vocab_mean) ~ productive_vocab_mean,
TRUE ~ NA_real_),
vocab_source = case_when(!is.na(productive_vocab_median) ~ "median",
!is.na(productive_vocab_mean) ~ "mean",
TRUE ~ NA_character_))
ma_data_with_vocab %>%
ggplot(aes(x = productive_vocab_median, y = d_calc)) +
geom_point() +
geom_smooth(method = "lm") +
theme_classic()
m_vocab <- rma.mv(d_calc ~ productive_vocab_median, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_with_vocab)
summary(m_vocab)##
## Multivariate Meta-Analysis Model (k = 38; method: REML)
##
## logLik Deviance AIC BIC AICc
## -90.1699 180.3397 186.3397 191.0903 187.0897
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.8328 0.9126 7 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 36) = 251.2143, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0251, p-val = 0.8742
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8002 0.4672 1.7127 0.0868 -0.1155 1.7160 .
## productive_vocab_median -0.0011 0.0070 -0.1583 0.8742 -0.0147 0.0125
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1age comparison
ma_data_with_vocab_age <- ma_data_with_vocab %>%
filter(vocab_source == "median" &
!is.na(vocab))
ma_data_with_vocab_age %>%
ggplot(aes(x = mean_age, y = d_calc)) +
geom_point() +
geom_smooth(method = "lm") +
theme_classic()
m_age_with_vocab <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_with_vocab_age)
summary(m_age_with_vocab)##
## Multivariate Meta-Analysis Model (k = 38; method: REML)
##
## logLik Deviance AIC BIC AICc
## -89.8261 179.6521 185.6521 190.4027 186.4021
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.8407 0.9169 7 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 36) = 251.2678, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3468, p-val = 0.5559
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.3122 1.0155 1.2922 0.1963 -0.6781 3.3025
## mean_age -0.0008 0.0014 -0.5889 0.5559 -0.0035 0.0019
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1< 36, young
ma_data_with_vocab_age_young <- ma_data_with_vocab_age %>%
mutate(age_months = mean_age/30.44) %>%
filter(age_months < 36)
ma_data_with_vocab_age_young %>%
ggplot(aes(x = productive_vocab_median, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. vocab (months)") 
m_age_with_vocab_young <- rma.mv(d_calc ~ productive_vocab_median, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_with_vocab_age_young)
summary(m_age_with_vocab_young)##
## Multivariate Meta-Analysis Model (k = 38; method: REML)
##
## logLik Deviance AIC BIC AICc
## -90.1699 180.3397 186.3397 191.0903 187.0897
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.8328 0.9126 7 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 36) = 251.2143, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.0251, p-val = 0.8742
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 0.8002 0.4672 1.7127 0.0868 -0.1155 1.7160 .
## productive_vocab_median -0.0011 0.0070 -0.1583 0.8742 -0.0147 0.0125
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1< 36, young, age comparison
ma_data_with_vocab_age_young %>%
ggplot(aes(x = age_months, y = d_calc, size = n_1)) +
geom_point() +
geom_smooth(method = "lm") +
ylab("Effect Size") +
xlab("Age (days)") +
ggtitle("Syntactical Bootstrapping effect size vs. Age (months)") 
m_age_with_vocab_young <- rma.mv(d_calc ~ mean_age, V = d_var_calc,
random = ~ 1 | short_cite, data = ma_data_with_vocab_age_young)
summary(m_age_with_vocab)##
## Multivariate Meta-Analysis Model (k = 38; method: REML)
##
## logLik Deviance AIC BIC AICc
## -89.8261 179.6521 185.6521 190.4027 186.4021
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2 0.8407 0.9169 7 no short_cite
##
## Test for Residual Heterogeneity:
## QE(df = 36) = 251.2678, p-val < .0001
##
## Test of Moderators (coefficient 2):
## QM(df = 1) = 0.3468, p-val = 0.5559
##
## Model Results:
##
## estimate se zval pval ci.lb ci.ub
## intrcpt 1.3122 1.0155 1.2922 0.1963 -0.6781 3.3025
## mean_age -0.0008 0.0014 -0.5889 0.5559 -0.0035 0.0019
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1correlationt est
cor.test(ma_data_with_vocab$mean_age,
ma_data_with_vocab$productive_vocab_median)##
## Pearson's product-moment correlation
##
## data: ma_data_with_vocab$mean_age and ma_data_with_vocab$productive_vocab_median
## t = 12.859, df = 36, p-value = 5.051e-15
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8257365 0.9505231
## sample estimates:
## cor
## 0.9062051contingency table
ALL_CATEGORICAL_VARS <- c("test_type","presentation_type",
"agent_argument_type_clean", "patient_argument_type_clean",
"stimuli_modality", "stimuli_actor", "character_identification", "practice_phase", "test_mass_or_distributed", "test_method")
get_cross_counts <- function(args, df){
var1 = args[[1]]
var2 = args[[2]]
if (var1 != var2){
df %>%
select_(var1, var2) %>%
rename(v1 = var1,
v2 = var2) %>%
count(v1, v2) %>%
mutate(v1_long = glue("{var1}/{v1}"),
v2_long = glue("{var2}/{v2}")) %>%
select(v1_long, v2_long, n)
}
}
all_pair_counts <- list(ALL_CATEGORICAL_VARS,
ALL_CATEGORICAL_VARS) %>%
cross() %>%
map_df(get_cross_counts, ma_data) %>%
complete(v1_long, v2_long, fill = list(n = 0)) %>%
filter(v1_long != v2_long)
all_counts_wide <- all_pair_counts %>%
pivot_wider(names_from = v2_long, values_from = n)
all_counts_wide_matrix <- all_counts_wide %>%
select(-v1_long) %>%
as.matrix()
row.names(all_counts_wide_matrix) <- all_counts_wide$v1_long
heatmaply(all_counts_wide_matrix,
fontsize_row = 8,
fontsize_col = 8)Model selections??
http://www.metafor-project.org/doku.php/tips:model_selection_with_glmulti_and_mumin
eval(metafor:::.MuMIn)
ma_data## # A tibble: 89 x 50
## coder unique_id link grammatical_cla… short_cite expt_num expt_condition
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 alan… arunacha… http… verb Arunachal… 1 transitive
## 2 alan… arunacha… http… verb Arunachal… 1 intransitive
## 3 alan… arunacha… http… verb Arunachal… 1 transitive
## 4 alan… arunacha… http… verb Arunachal… 1 intransitive
## 5 alan… arunacha… http… verb Arunachal… 1 PP
## 6 anjie arunacha… http… verb Arunachal… 1 DO
## 7 anjie arunacha… http… verb Arunachal… 2 PP_DO_no_delay
## 8 alan… bunger20… http… verb Bunger, A… 1 transitive
## 9 alan… bunger20… http… verb Bunger, A… 1 unaccusitive_…
## 10 alan… bunger20… http… verb Bunger, A… 1 transitive
## # … with 79 more rows, and 43 more variables: dependent_measure <chr>,
## # test_type <chr>, same_infant <chr>, language <chr>, mean_age <dbl>,
## # productive_vocab_mean <dbl>, productive_vocab_median <dbl>,
## # population_type <chr>, sentence_structure <chr>, agent_argument_type <chr>,
## # patient_argument_type <chr>, verb_type <chr>, stimuli_type <chr>,
## # stimuli_modality <chr>, stimuli_actor <chr>, presentation_type <chr>,
## # character_identification <chr>, practice_phase <chr>,
## # test_mass_or_distributed <chr>, n_train_test_pair <dbl>,
## # n_test_trial_per_pair <dbl>, n_repetitions_sentence <dbl>,
## # n_repetitions_video <dbl>, example_target_sentence <chr>,
## # test_question <chr>, inclusion_certainty <dbl>, n_1 <dbl>, x_1 <dbl>,
## # x_2 <dbl>, x_2_raw <dbl>, sd_1 <dbl>, sd_2 <dbl>, sd_2_raw <dbl>, t <dbl>,
## # d <dbl>, d_calc <dbl>, d_var_calc <dbl>, es_method <chr>,
## # test_method <chr>, agent_argument_type_clean <chr>,
## # patient_argument_type_clean <chr>, d_calc_raw <dbl>, d_var_calc_raw <dbl>full <- rma.mv(d_calc, d_var_calc, mods = ~ mean_age + agent_argument_type_clean + patient_argument_type_clean + stimuli_modality + stimuli_actor + presentation_type + test_mass_or_distributed + practice_phase + character_identification + n_repetitions_sentence + n_repetitions_video + test_method,
data=ma_data, method="ML")
res <- dredge(full, trace=2)##
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|======================================================================| 100%subset(res, delta <= 2, recalc.weights=FALSE)## Global model call: rma.mv(yi = d_calc, V = d_var_calc, mods = ~mean_age + agent_argument_type_clean +
## patient_argument_type_clean + stimuli_modality + stimuli_actor +
## presentation_type + test_mass_or_distributed + practice_phase +
## character_identification + n_repetitions_sentence + n_repetitions_video +
## test_method, data = ma_data, method = "ML")
## ---
## Model selection table
## (Int) agn_arg_typ_cln n_rpt_snt ptn_arg_typ_cln prs_typ stm_mdl
## 3753 + 0.02035 + + +
## 3754 + + 0.03729 + + +
## tst_mss_or_dst tst_mth df logLik AICc delta weight
## 3753 + + 10 -117.255 259.9 0.0 0.229
## 3754 + + 12 -114.837 261.7 1.8 0.093
## Models ranked by AICc(x)res## Global model call: rma.mv(yi = d_calc, V = d_var_calc, mods = ~mean_age + agent_argument_type_clean +
## patient_argument_type_clean + stimuli_modality + stimuli_actor +
## presentation_type + test_mass_or_distributed + practice_phase +
## character_identification + n_repetitions_sentence + n_repetitions_video +
## test_method, data = ma_data, method = "ML")
## ---
## Model selection table
## (Int) agn_arg_typ_cln chr_idn men_age n_rpt_snt n_rpt_vid
## 3753 + 2.035e-02
## 3754 + + 3.729e-02
## 3769 + 2.513e-02 -2.694e-02
## 3817 + 2.726e-02
## 3755 + + 2.183e-02
## 3757 + -6.208e-05 1.887e-02
## 4009 + 2.041e-02
## 3322 + + 5.841e-02 2.147e-01
## 4010 + + 3.990e-02
## 3770 + + 3.434e-02 3.362e-02
## 3258 + + 2.688e-02 1.000e-01
## 3758 + + -7.735e-05 3.549e-02
## 3818 + + 3.879e-02
## 3756 + + + 3.737e-02
## 4025 + 3.352e-02 -6.873e-02
## 4073 + 3.264e-02
## 3314 + + 2.086e-01
## 3773 + -6.060e-05 2.367e-02 -2.685e-02
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## 518 + 6
## logLik AICc delta weight
## 3753 -117.255 259.9 0.00 0.229
## 3754 -114.837 261.7 1.80 0.093
## 3769 -117.073 262.7 2.87 0.054
## 3817 -117.099 262.8 2.92 0.053
## 3755 -117.202 263.0 3.13 0.048
## 3757 -117.229 263.1 3.18 0.047
## 4009 -117.252 263.1 3.23 0.046
## 3322 -113.799 263.2 3.30 0.044
## 4010 -114.532 264.6 4.77 0.021
## 3770 -114.632 264.8 4.97 0.019
## 3258 -116.538 265.1 5.20 0.017
## 3758 -114.798 265.2 5.30 0.016
## 3818 -114.828 265.2 5.36 0.016
## 3756 -114.837 265.3 5.38 0.016
## 4025 -116.743 265.5 5.61 0.014
## 4073 -116.963 265.9 6.05 0.011
## 3314 -117.130 266.1 6.19 0.010
## 3773 -117.049 266.1 6.22 0.010
## 3771 -117.050 266.1 6.22 0.010
## 3821 -117.067 266.1 6.26 0.010
## 3833 -117.067 266.1 6.26 0.010
## 3819 -117.083 266.2 6.29 0.010
## 3759 -117.141 266.3 6.41 0.009
## 4011 -117.200 266.4 6.52 0.009
## 4013 -117.223 266.4 6.57 0.009
## 3514 -115.513 266.6 6.73 0.008
## 3834 -113.634 266.6 6.74 0.008
## 3326 -113.669 266.7 6.81 0.008
## 3578 -113.706 266.8 6.89 0.007
## 3324 -113.738 266.8 6.95 0.007
## 4074 -114.241 267.8 7.96 0.004
## 3242 -119.667 267.9 8.06 0.004
## 4014 -114.450 268.3 8.38 0.003
## 3318 -116.484 268.3 8.43 0.003
## 3746 -119.964 268.4 8.49 0.003
## 4012 -114.529 268.4 8.53 0.003
## 4026 -114.531 268.4 8.54 0.003
## 3260 -116.456 268.5 8.62 0.003
## 3745 -123.154 268.5 8.62 0.003
## 3774 -114.588 268.5 8.65 0.003
## 3772 -114.626 268.6 8.73 0.003
## 3262 -116.537 268.7 8.78 0.003
## 3513 -120.074 268.7 8.87 0.003
## 3570 -116.765 268.9 8.99 0.003
## 4029 -116.661 268.9 9.03 0.003
## 3822 -114.787 268.9 9.05 0.002
## 3760 -114.791 268.9 9.06 0.002
## 3820 -114.828 269.0 9.13 0.002
## 3809 -121.895 269.0 9.15 0.002
## 4089 -116.741 269.1 9.19 0.002
## 4027 -116.741 269.1 9.19 0.002
## 3316 -116.918 269.2 9.29 0.002
## 3497 -121.966 269.3 9.42 0.002
## 4077 -116.891 269.4 9.49 0.002
## 3826 -117.082 269.5 9.62 0.002
## 4075 -116.963 269.5 9.63 0.002
## 3775 -117.002 269.6 9.71 0.002
## 3823 -117.029 269.6 9.76 0.002
## 3837 -117.041 269.7 9.79 0.002
## 3835 -117.048 269.7 9.80 0.002
## 4015 -117.136 269.9 9.98 0.002
## 3516 -115.389 270.1 10.25 0.001
## 3498 -119.105 270.2 10.34 0.001
## 3241 -124.003 270.3 10.42 0.001
## 3838 -113.503 270.3 10.46 0.001
## 3518 -115.512 270.4 10.50 0.001
## 3250 -120.978 270.4 10.52 0.001
## 3836 -113.587 270.5 10.63 0.001
## 3582 -113.595 270.5 10.65 0.001
## 4090 -113.631 270.6 10.72 0.001
## 3328 -113.648 270.6 10.75 0.001
## 3580 -113.674 270.7 10.81 0.001
## 3762 -119.496 270.8 10.92 0.001
## 3761 -122.894 271.0 11.15 0.001
## 3306 -119.563 271.1 11.25 0.001
## 3246 -119.612 271.2 11.35 0.001
## 3244 -119.629 271.3 11.38 0.001
## 3750 -119.736 271.3 11.40 0.001
## 3577 -119.645 271.3 11.41 0.001
## 3825 -121.429 271.3 11.42 0.001
## 3515 -119.649 271.3 11.42 0.001
## 3257 -122.967 271.3 11.42 0.001
## 3506 -119.759 271.3 11.44 0.001
## 3810 -119.767 271.3 11.46 0.001
## 3321 -121.418 271.4 11.56 0.001
## 3749 -123.101 271.4 11.56 0.001
## 4002 -119.827 271.5 11.58 0.001
## 3574 -116.216 271.5 11.61 0.001
## 4001 -123.135 271.5 11.63 0.001
## 4078 -114.091 271.5 11.64 0.001
## 3747 -123.141 271.5 11.64 0.001
## 3748 -119.904 271.6 11.73 0.001
## 4076 -114.147 271.6 11.75 0.001
## 3561 -121.566 271.7 11.86 0.001
## 3813 -121.683 271.8 11.93 0.001
## 4065 -121.696 271.8 11.96 0.001
## 3830 -116.422 271.9 12.02 0.001
## 3320 -116.437 271.9 12.05 0.001
## 3499 -121.703 272.0 12.13 0.001
## 3517 -120.026 272.1 12.18 0.001
## 4082 -116.520 272.1 12.22 0.001
## 3811 -121.834 272.1 12.23 0.001
## 3264 -116.438 272.2 12.35 0.000
## 4016 -114.449 272.2 12.36 0.000
## 4030 -114.450 272.2 12.36 0.000
## 3501 -121.821 272.2 12.37 0.000
## 3572 -116.630 272.3 12.44 0.000
## 4028 -114.529 272.4 12.52 0.000
## 3776 -114.563 272.5 12.58 0.000
## 4093 -116.657 272.7 12.79 0.000
## 4031 -116.659 272.7 12.79 0.000
## 3828 -116.862 272.8 12.90 0.000
## 3243 -123.730 272.8 12.95 0.000
## 4091 -116.739 272.8 12.95 0.000
## 3562 -118.658 272.9 13.02 0.000
## 3824 -114.783 272.9 13.03 0.000
## 3245 -123.863 273.1 13.22 0.000
## 4079 -116.883 273.1 13.24 0.000
## 3313 -124.014 273.3 13.39 0.000
## 3839 -117.000 273.4 13.48 0.000
## 3305 -124.003 273.4 13.50 0.000
## 3569 -122.514 273.5 13.59 0.000
## 3254 -120.921 273.6 13.77 0.000
## 3502 -119.044 273.7 13.79 0.000
## 3500 -119.049 273.7 13.80 0.000
## 3252 -120.972 273.7 13.87 0.000
## 3259 -122.577 273.8 13.88 0.000
## 3766 -119.287 273.9 14.03 0.000
## 4017 -122.804 274.0 14.17 0.000
## 3520 -115.363 274.1 14.18 0.000
## 3765 -122.813 274.1 14.19 0.000
## 3829 -121.151 274.1 14.23 0.000
## 3563 -121.052 274.1 14.23 0.000
## 4066 -119.386 274.1 14.23 0.000
## 3763 -122.894 274.2 14.35 0.000
## 4018 -119.459 274.3 14.38 0.000
## 3814 -119.460 274.3 14.38 0.000
## 3764 -119.478 274.3 14.41 0.000
## 3234 -124.550 274.3 14.46 0.000
## 4006 -119.519 274.4 14.50 0.000
## 3136 -121.189 274.4 14.50 0.000
## 3261 -122.900 274.4 14.53 0.000
## 3579 -119.425 274.4 14.55 0.000
## 4069 -121.324 274.4 14.57 0.000
## 4094 -113.488 274.5 14.64 0.000
## 3840 -113.490 274.5 14.65 0.000
## 3827 -121.365 274.5 14.65 0.000
## 3308 -119.484 274.5 14.67 0.000
## 3751 -123.056 274.6 14.68 0.000
## 3812 -119.629 274.6 14.72 0.000
## 3310 -119.506 274.6 14.72 0.000
## 4005 -123.090 274.6 14.74 0.000
## 4081 -121.427 274.7 14.78 0.000
## 3198 -121.327 274.7 14.78 0.000
## 4003 -123.120 274.7 14.80 0.000
## 3323 -121.340 274.7 14.81 0.000
## 4092 -113.570 274.7 14.81 0.000
## 3584 -113.588 274.7 14.84 0.000
## 3510 -119.718 274.8 14.89 0.000
## 3248 -119.597 274.8 14.90 0.000
## 3134 -123.090 274.8 14.91 0.000
## 3752 -119.729 274.8 14.92 0.000
## 3508 -119.731 274.8 14.92 0.000
## 4004 -119.737 274.8 14.93 0.000
## 3565 -121.410 274.8 14.95 0.000
## 3325 -121.416 274.8 14.96 0.000
## 3581 -119.633 274.8 14.97 0.000
## 3886 -121.428 274.9 14.98 0.000
## 3519 -119.648 274.9 15.00 0.000
## 4067 -121.552 274.9 15.03 0.000
## 3130 -124.824 275.0 15.14 0.000
## 3815 -121.672 275.1 15.27 0.000
## 4086 -116.105 275.2 15.31 0.000
## 3503 -121.654 275.3 15.43 0.000
## 3576 -116.194 275.4 15.49 0.000
## 3132 -123.404 275.4 15.53 0.000
## 4080 -114.046 275.6 15.76 0.000
## 3832 -116.372 275.7 15.84 0.000
## 4084 -116.470 275.9 16.04 0.000
## 3247 -123.686 276.0 16.10 0.000
## 3200 -120.218 276.0 16.14 0.000
## 3307 -123.711 276.0 16.15 0.000
## 3490 -123.824 276.1 16.21 0.000
## 3194 -123.778 276.2 16.28 0.000
## 3317 -123.916 276.3 16.40 0.000
## 3564 -118.460 276.3 16.40 0.000
## 3882 -123.838 276.3 16.40 0.000
## 3309 -123.863 276.3 16.45 0.000
## 3888 -120.404 276.4 16.51 0.000
## 4032 -114.449 276.4 16.57 0.000
## 3315 -124.009 276.5 16.58 0.000
## 3566 -118.591 276.5 16.66 0.000
## 3648 -120.529 276.6 16.76 0.000
## 4095 -116.656 276.6 16.77 0.000
## 4070 -118.807 276.7 16.79 0.000
## 3573 -122.463 276.7 16.85 0.000
## 3571 -122.509 276.8 16.94 0.000
## 4021 -122.646 277.1 17.22 0.000
## 3950 -120.764 277.1 17.23 0.000
## 3256 -120.890 277.1 17.24 0.000
## 3263 -122.575 277.2 17.28 0.000
## 4068 -119.057 277.2 17.29 0.000
## 3646 -122.628 277.3 17.38 0.000
## 4019 -122.789 277.4 17.50 0.000
## 3504 -119.017 277.4 17.51 0.000
## 3767 -122.804 277.4 17.53 0.000
## 3298 -124.528 277.5 17.62 0.000
## 3236 -124.547 277.5 17.66 0.000
## 3238 -124.549 277.5 17.66 0.000
## 4085 -121.110 277.6 17.68 0.000
## 3489 -127.700 277.6 17.71 0.000
## 3505 -126.181 277.6 17.72 0.000
## 4022 -119.284 277.6 17.75 0.000
## 3831 -121.144 277.6 17.75 0.000
## 3768 -119.286 277.6 17.75 0.000
## 3567 -121.027 277.6 17.76 0.000
## 3190 -124.620 277.7 17.80 0.000
## 3392 -121.063 277.7 17.83 0.000
## 3196 -122.912 277.8 17.95 0.000
## 4071 -121.275 277.9 18.01 0.000
## 4007 -123.044 277.9 18.01 0.000
## 3816 -119.419 277.9 18.01 0.000
## 3454 -121.167 277.9 18.04 0.000
## 4020 -119.455 278.0 18.09 0.000
## 3642 -124.710 278.0 18.14 0.000
## 4083 -121.346 278.0 18.15 0.000
## 4008 -119.503 278.1 18.18 0.000
## 3710 -121.246 278.1 18.20 0.000
## 3878 -124.822 278.1 18.21 0.000
## 3902 -121.261 278.1 18.23 0.000
## 3386 -124.753 278.1 18.23 0.000
## 3390 -123.090 278.2 18.30 0.000
## 3583 -119.423 278.2 18.32 0.000
## 3327 -121.338 278.3 18.38 0.000
## 3312 -119.461 278.3 18.40 0.000
## 3512 -119.650 278.4 18.48 0.000
## 3644 -123.239 278.5 18.60 0.000
## 3884 -123.292 278.6 18.71 0.000
## 3388 -123.404 278.8 18.93 0.000
## 3450 -123.408 278.8 18.94 0.000
## 4096 -113.464 278.9 19.05 0.000
## 3966 -119.803 279.0 19.08 0.000
## 3126 -126.862 279.0 19.09 0.000
## 3898 -123.485 279.0 19.10 0.000
## 3946 -123.567 279.1 19.26 0.000
## 3712 -119.968 279.3 19.41 0.000
## 4088 -116.101 279.3 19.44 0.000
## 3311 -123.671 279.3 19.47 0.000
## 3904 -120.034 279.4 19.54 0.000
## 3554 -123.815 279.4 19.56 0.000
## 3492 -123.824 279.4 19.57 0.000
## 3494 -123.824 279.4 19.57 0.000
## 3706 -123.778 279.6 19.68 0.000
## 3319 -123.914 279.6 19.75 0.000
## 3952 -120.199 279.7 19.87 0.000
## 3456 -120.218 279.8 19.91 0.000
## 3638 -125.684 279.8 19.93 0.000
## 3122 -128.812 279.8 19.94 0.000
## 3702 -124.052 279.9 20.03 0.000
## 3507 -125.753 279.9 20.07 0.000
## 3233 -130.414 280.1 20.23 0.000
## 3942 -124.181 280.2 20.29 0.000
## 3553 -127.473 280.2 20.31 0.000
## 954 -124.096 280.2 20.32 0.000
## 3491 -127.487 280.2 20.34 0.000
## 3575 -122.440 280.2 20.34 0.000
## 3568 -118.447 280.2 20.35 0.000
## 4072 -118.656 280.3 20.41 0.000
## 3493 -127.547 280.3 20.46 0.000
## 3509 -126.051 280.5 20.67 0.000
## 3192 -124.383 280.6 20.69 0.000
## 3962 -122.508 280.6 20.72 0.000
## 4023 -122.646 280.6 20.75 0.000
## 3880 -124.421 280.6 20.77 0.000
## 3128 -126.158 280.8 20.88 0.000
## 3297 -129.294 280.8 20.90 0.000
## 3300 -124.520 280.8 20.96 0.000
## 3302 -124.525 280.9 20.98 0.000
## 3240 -124.547 280.9 21.02 0.000
## 3446 -124.551 280.9 21.03 0.000
## 3186 -127.859 281.0 21.08 0.000
## 3900 -122.715 281.0 21.13 0.000
## 3640 -124.669 281.1 21.26 0.000
## 3452 -122.820 281.2 21.34 0.000
## 4087 -121.090 281.2 21.36 0.000
## 3894 -124.760 281.3 21.45 0.000
## 3708 -122.888 281.4 21.48 0.000
## 3874 -128.079 281.4 21.52 0.000
## 4024 -119.282 281.5 21.66 0.000
## 1706 -126.484 281.6 21.69 0.000
## 3002 -123.060 281.7 21.82 0.000
## 3249 -129.764 281.7 21.84 0.000
## 1705 -129.719 281.7 21.85 0.000
## 3634 -128.267 281.8 21.90 0.000
## 3382 -126.677 281.8 21.92 0.000
## 3968 -119.273 281.9 22.01 0.000
## 3948 -123.237 282.1 22.18 0.000
## 3124 -128.483 282.2 22.33 0.000
## 3958 -123.494 282.3 22.45 0.000
## 3235 -130.171 282.5 22.65 0.000
## 3704 -123.610 282.6 22.68 0.000
## 1978 -123.535 282.6 22.77 0.000
## 3237 -130.239 282.7 22.79 0.000
## 3050 -123.568 282.7 22.84 0.000
## 3378 -128.747 282.7 22.86 0.000
## 3556 -123.814 283.0 23.09 0.000
## 3558 -123.815 283.0 23.09 0.000
## 3496 -123.824 283.0 23.11 0.000
## 3384 -125.608 283.0 23.14 0.000
## 3555 -127.366 283.2 23.30 0.000
## 3557 -127.380 283.2 23.32 0.000
## 1002 -125.607 283.2 23.34 0.000
## 3511 -125.741 283.3 23.41 0.000
## 3495 -127.433 283.3 23.43 0.000
## 3696 -123.869 283.3 23.44 0.000
## 3944 -124.010 283.4 23.48 0.000
## 1769 -129.050 283.5 23.59 0.000
## 956 -123.982 283.5 23.67 0.000
## 3632 -125.789 283.6 23.70 0.000
## 1709 -129.123 283.6 23.74 0.000
## 3887 -127.511 283.6 23.75 0.000
## 1722 -125.814 283.6 23.75 0.000
## 3624 -127.606 283.7 23.78 0.000
## 3448 -124.159 283.7 23.78 0.000
## 3698 -127.633 283.7 23.83 0.000
## 3301 -129.243 283.7 23.85 0.000
## 958 -124.082 283.7 23.87 0.000
## 3964 -122.202 283.8 23.88 0.000
## 1018 -124.091 283.8 23.89 0.000
## 3299 -129.262 283.8 23.89 0.000
## 1770 -125.914 283.8 23.95 0.000
## 3896 -124.287 283.9 24.03 0.000
## 3890 -127.742 283.9 24.05 0.000
## 3188 -127.765 284.0 24.09 0.000
## 3251 -129.378 284.0 24.12 0.000
## 746 -127.702 284.0 24.13 0.000
## 3636 -127.845 284.1 24.25 0.000
## 3442 -127.846 284.1 24.26 0.000
## 1721 -129.443 284.3 24.38 0.000
## 1710 -126.143 284.3 24.41 0.000
## 3304 -124.519 284.4 24.50 0.000
## 3253 -129.602 284.4 24.57 0.000
## 3876 -128.010 284.5 24.58 0.000
## 3938 -128.035 284.5 24.63 0.000
## 698 -128.008 284.6 24.74 0.000
## 1707 -129.628 284.6 24.75 0.000
## 1962 -126.335 284.7 24.79 0.000
## 1708 -126.378 284.8 24.88 0.000
## 1961 -129.708 284.8 24.91 0.000
## 3066 -122.789 284.9 25.05 0.000
## 3006 -122.814 285.0 25.10 0.000
## 3380 -128.287 285.0 25.14 0.000
## 2794 -126.524 285.0 25.17 0.000
## 2026 -124.808 285.2 25.32 0.000
## 3004 -123.004 285.4 25.48 0.000
## 3239 -130.109 285.5 25.58 0.000
## 2025 -128.501 285.6 25.73 0.000
## 3647 -128.514 285.6 25.75 0.000
## 3960 -123.314 285.7 25.80 0.000
## 1773 -128.547 285.7 25.82 0.000
## 3054 -123.239 285.8 25.95 0.000
## 3622 -130.358 286.0 26.08 0.000
## 682 -130.313 286.0 26.12 0.000
## 3692 -127.067 286.1 26.26 0.000
## 1980 -123.395 286.1 26.27 0.000
## 1725 -128.846 286.3 26.42 0.000
## 3052 -123.480 286.3 26.44 0.000
## 1977 -128.859 286.3 26.44 0.000
## 3688 -127.291 286.4 26.51 0.000
## 1982 -123.517 286.4 26.51 0.000
## 2042 -123.518 286.4 26.51 0.000
## 3954 -127.306 286.4 26.54 0.000
## 3559 -127.325 286.4 26.57 0.000
## 1785 -128.979 286.6 26.68 0.000
## 1726 -125.523 286.6 26.75 0.000
## 1004 -125.528 286.6 26.76 0.000
## 1697 -133.684 286.6 26.77 0.000
## 3560 -123.814 286.7 26.81 0.000
## 1771 -129.044 286.7 26.81 0.000
## 3700 -127.465 286.7 26.85 0.000
## 1006 -125.607 286.8 26.92 0.000
## 1711 -129.119 286.8 26.96 0.000
## 1965 -129.123 286.8 26.97 0.000
## 1774 -125.645 286.9 26.99 0.000
## 3303 -129.232 286.9 27.03 0.000
## 2746 -127.452 286.9 27.03 0.000
## 3951 -127.474 286.9 27.07 0.000
## 3892 -127.591 287.0 27.11 0.000
## 3903 -127.492 287.0 27.11 0.000
## 3184 -127.555 287.1 27.24 0.000
## 1724 -125.775 287.1 27.25 0.000
## 1698 -130.947 287.1 27.26 0.000
## 3255 -129.348 287.1 27.26 0.000
## 1786 -125.805 287.2 27.31 0.000
## 3444 -127.708 287.2 27.34 0.000
## 748 -127.616 287.2 27.36 0.000
## 1020 -123.958 287.3 27.39 0.000
## 960 -123.980 287.3 27.44 0.000
## 762 -127.668 287.3 27.46 0.000
## 750 -127.671 287.3 27.47 0.000
## 1772 -125.898 287.4 27.50 0.000
## 3049 -129.398 287.4 27.52 0.000
## 1723 -129.400 287.4 27.53 0.000
## 1022 -124.078 287.5 27.63 0.000
## 700 -127.762 287.5 27.65 0.000
## 3180 -129.513 287.6 27.75 0.000
## 1966 -126.047 287.7 27.80 0.000
## 684 -129.571 287.7 27.87 0.000
## 3940 -127.994 287.8 27.91 0.000
## 1712 -126.123 287.8 27.95 0.000
## 1963 -129.620 287.8 27.97 0.000
## 1761 -132.842 287.9 28.00 0.000
## 702 -127.976 288.0 28.08 0.000
## 1762 -129.763 288.0 28.09 0.000
## 3631 -131.314 288.0 28.12 0.000
## 3695 -129.735 288.1 28.20 0.000
## 1964 -126.255 288.1 28.21 0.000
## 3135 -131.378 288.1 28.25 0.000
## 3694 -128.092 288.2 28.31 0.000
## 1273 -131.458 288.3 28.41 0.000
## 3070 -122.481 288.3 28.42 0.000
## 2796 -126.385 288.3 28.47 0.000
## 3630 -129.891 288.4 28.51 0.000
## 3686 -129.973 288.4 28.51 0.000
## 2029 -128.204 288.4 28.53 0.000
## 3943 -130.035 288.5 28.63 0.000
## 2730 -129.960 288.5 28.64 0.000
## 2810 -126.488 288.6 28.68 0.000
## 2798 -126.491 288.6 28.69 0.000
## 1193 -134.614 288.6 28.70 0.000
## 3879 -131.676 288.6 28.72 0.000
## 3068 -122.662 288.7 28.78 0.000
## 3885 -131.666 288.7 28.82 0.000
## 1449 -133.235 288.8 28.88 0.000
## 2028 -124.733 288.8 28.94 0.000
## 2030 -124.733 288.8 28.94 0.000
## 3941 -131.838 288.9 29.04 0.000
## 2297 -131.776 288.9 29.04 0.000
## 2041 -128.462 288.9 29.05 0.000
## 1197 -133.322 288.9 29.05 0.000
## 938 -130.166 288.9 29.06 0.000
## 3008 -122.804 288.9 29.07 0.000
## 2027 -128.476 289.0 29.08 0.000
## 1981 -128.477 289.0 29.08 0.000
## 3711 -128.504 289.0 29.13 0.000
## 1789 -128.514 289.0 29.15 0.000
## 1775 -128.526 289.1 29.18 0.000
## 2298 -128.544 289.1 29.21 0.000
## 1453 -131.882 289.1 29.26 0.000
## 1701 -133.481 289.1 29.27 0.000
## 3628 -130.298 289.2 29.32 0.000
## 686 -130.299 289.2 29.32 0.000
## 1194 -131.933 289.2 29.36 0.000
## 738 -132.028 289.3 29.42 0.000
## 1713 -133.571 289.3 29.45 0.000
## 1699 -133.679 289.5 29.67 0.000
## 1953 -133.680 289.5 29.67 0.000
## 3053 -128.795 289.6 29.71 0.000
## 1714 -130.579 289.6 29.72 0.000
## 3620 -132.187 289.6 29.74 0.000
## 1727 -128.844 289.7 29.81 0.000
## 3883 -132.169 289.7 29.83 0.000
## 1979 -128.857 289.7 29.84 0.000
## 2748 -127.079 289.7 29.86 0.000
## 1465 -132.193 289.8 29.88 0.000
## 3056 -123.216 289.8 29.89 0.000
## 3690 -130.584 289.8 29.89 0.000
## 3391 -130.596 289.8 29.92 0.000
## 3956 -127.259 289.9 29.98 0.000
## 1265 -133.865 289.9 30.04 0.000
## 1787 -128.976 290.0 30.08 0.000
## 1777 -132.358 290.0 30.08 0.000
## 1984 -123.338 290.0 30.14 0.000
## 2732 -129.014 290.0 30.15 0.000
## 1274 -129.027 290.1 30.18 0.000
## 2018 -129.133 290.1 30.19 0.000
## 1529 -130.744 290.1 30.21 0.000
## 1702 -130.825 290.1 30.21 0.000
## 3120 -130.752 290.1 30.23 0.000
## 2044 -123.393 290.1 30.24 0.000
## 3618 -133.971 290.1 30.25 0.000
## 250 -130.778 290.2 30.28 0.000
## 3949 -130.794 290.2 30.31 0.000
## 1954 -130.880 290.2 30.32 0.000
## 3684 -130.882 290.2 30.33 0.000
## 1967 -129.119 290.2 30.36 0.000
## 1700 -130.936 290.3 30.44 0.000
## 1277 -130.857 290.3 30.44 0.000
## 1198 -130.860 290.3 30.44 0.000
## 2046 -123.499 290.3 30.46 0.000
## 3967 -127.402 290.4 30.51 0.000
## 1790 -125.521 290.4 30.52 0.000
## 1728 -125.522 290.4 30.52 0.000
## 1008 -125.525 290.4 30.53 0.000
## 994 -130.987 290.4 30.54 0.000
## 1209 -134.066 290.4 30.54 0.000
## 2750 -127.449 290.5 30.60 0.000
## 3065 -129.260 290.5 30.65 0.000
## 2017 -132.664 290.6 30.69 0.000
## 1450 -131.001 290.6 30.73 0.000
## 3199 -131.016 290.6 30.76 0.000
## 3682 -132.698 290.6 30.76 0.000
## 1776 -125.643 290.6 30.76 0.000
## 1469 -131.035 290.7 30.80 0.000
## 3440 -127.553 290.7 30.81 0.000
## 764 -127.560 290.7 30.82 0.000
## 2809 -131.060 290.7 30.84 0.000
## 3051 -129.364 290.7 30.85 0.000
## 1765 -132.758 290.8 30.88 0.000
## 752 -127.606 290.8 30.92 0.000
## 1763 -132.792 290.8 30.95 0.000
## 766 -127.636 290.9 30.98 0.000
## 3112 -132.811 290.9 30.98 0.000
## 1788 -125.773 290.9 31.02 0.000
## 1195 -134.315 290.9 31.04 0.000
## 3436 -129.471 290.9 31.07 0.000
## 940 -129.487 291.0 31.10 0.000
## 3623 -134.398 291.0 31.11 0.000
## 1778 -129.611 291.0 31.15 0.000
## 704 -127.759 291.1 31.22 0.000
## 1257 -134.414 291.1 31.24 0.000
## 688 -129.562 291.1 31.25 0.000
## 1521 -132.952 291.1 31.27 0.000
## 1213 -132.915 291.2 31.32 0.000
## 1970 -129.708 291.2 31.34 0.000
## 1210 -131.312 291.2 31.35 0.000
## 1024 -123.957 291.2 31.37 0.000
## 1764 -129.724 291.2 31.37 0.000
## 1451 -132.943 291.3 31.38 0.000
## 1766 -129.745 291.3 31.41 0.000
## 3703 -131.437 291.3 31.44 0.000
## 2786 -131.441 291.3 31.45 0.000
## 3895 -131.483 291.4 31.53 0.000
## 1968 -126.034 291.4 31.54 0.000
## 1275 -131.429 291.5 31.58 0.000
## 946 -131.512 291.5 31.59 0.000
## 1266 -131.561 291.6 31.69 0.000
## 2986 -129.799 291.6 31.72 0.000
## 3959 -129.900 291.6 31.72 0.000
## 1001 -133.118 291.6 31.73 0.000
## 3643 -133.120 291.6 31.73 0.000
## 3131 -134.689 291.7 31.79 0.000
## 3639 -133.224 291.7 31.81 0.000
## 1261 -133.170 291.7 31.83 0.000
## 3901 -131.578 291.8 31.88 0.000
## 2031 -128.112 291.8 31.93 0.000
## 1513 -133.235 291.8 31.96 0.000
## 1454 -129.921 291.8 31.97 0.000
## 3376 -129.936 291.9 32.00 0.000
## 2301 -131.644 291.9 32.01 0.000
## 3957 -131.729 291.9 32.02 0.000
## 2299 -131.652 291.9 32.03 0.000
## 2734 -129.954 291.9 32.03 0.000
## 2045 -128.171 291.9 32.05 0.000
## 1196 -131.660 291.9 32.05 0.000
## 2800 -126.285 291.9 32.05 0.000
## 1199 -133.288 291.9 32.07 0.000
## 1717 -133.393 292.0 32.15 0.000
## 3178 -133.348 292.1 32.19 0.000
## 1533 -130.031 292.1 32.19 0.000
## 2812 -126.367 292.1 32.21 0.000
## 2553 -131.774 292.1 32.27 0.000
## 1467 -131.778 292.2 32.28 0.000
## 1957 -133.464 292.2 32.29 0.000
## 1703 -133.472 292.2 32.31 0.000
## 1466 -130.103 292.2 32.33 0.000
## 2814 -126.444 292.2 32.36 0.000
## 1258 -131.828 292.3 32.38 0.000
## 690 -133.510 292.3 32.38 0.000
## 1969 -133.514 292.3 32.39 0.000
## 742 -131.915 292.3 32.39 0.000
## 3042 -130.247 292.3 32.42 0.000
## 1455 -131.854 292.3 32.43 0.000
## 942 -130.161 292.3 32.45 0.000
## 1517 -131.875 292.3 32.47 0.000
## 1715 -133.570 292.4 32.50 0.000
## 3176 -131.971 292.4 32.51 0.000
## 754 -131.973 292.4 32.51 0.000
## 3072 -122.434 292.4 32.54 0.000
## 3626 -133.525 292.4 32.54 0.000
## 2043 -128.428 292.4 32.56 0.000
## 1983 -128.428 292.4 32.56 0.000
## 2300 -128.452 292.5 32.61 0.000
## 740 -132.028 292.5 32.62 0.000
## 2032 -124.605 292.5 32.67 0.000
## 1791 -128.490 292.6 32.68 0.000
## 3182 -131.991 292.6 32.71 0.000
## 1955 -133.675 292.6 32.71 0.000
## 2302 -128.510 292.6 32.72 0.000
## 1278 -128.513 292.6 32.73 0.000
## 2554 -128.514 292.6 32.73 0.000
## 674 -135.246 292.7 32.80 0.000
## 1214 -130.357 292.7 32.84 0.000
## 1269 -133.742 292.7 32.85 0.000
## 1211 -133.679 292.7 32.85 0.000
## 3899 -132.067 292.7 32.86 0.000
## 3372 -132.091 292.8 32.91 0.000
## 1718 -130.501 292.8 32.93 0.000
## 249 -135.275 292.8 32.96 0.000
## 3116 -133.765 292.9 33.02 0.000
## 3947 -132.149 292.9 33.02 0.000
## 2793 -133.768 292.9 33.03 0.000
## 1267 -133.857 293.0 33.08 0.000
## 1716 -130.579 293.0 33.08 0.000
## 3069 -128.698 293.0 33.10 0.000
## 3455 -130.498 293.0 33.12 0.000
## 1441 -136.876 293.0 33.15 0.000
## 1779 -132.304 293.0 33.17 0.000
## 1781 -132.306 293.1 33.18 0.000
## 3881 -135.392 293.1 33.19 0.000
## 998 -130.651 293.1 33.23 0.000
## 2033 -132.358 293.2 33.28 0.000
## 3055 -128.794 293.2 33.29 0.000
## 1530 -128.800 293.2 33.30 0.000
## 2020 -128.931 293.2 33.32 0.000
## 2736 -128.847 293.3 33.40 0.000
## 254 -130.644 293.3 33.41 0.000
## 3191 -134.034 293.3 33.43 0.000
## 1010 -130.754 293.3 33.43 0.000
## 1531 -130.664 293.3 33.45 0.000
## 1452 -130.674 293.3 33.47 0.000
## 2752 -127.000 293.4 33.48 0.000
## 1958 -130.785 293.4 33.49 0.000
## 3172 -134.069 293.4 33.50 0.000
## 252 -130.707 293.4 33.54 0.000
## 2988 -128.925 293.4 33.55 0.000
## 3110 -135.627 293.4 33.56 0.000
## 1704 -130.824 293.4 33.57 0.000
## 3965 -130.734 293.5 33.59 0.000
## 506 -130.757 293.5 33.64 0.000
## 2019 -132.542 293.5 33.65 0.000
## 3875 -135.678 293.5 33.67 0.000
## 1956 -130.875 293.6 33.68 0.000
## 2034 -129.111 293.6 33.68 0.000
## 1262 -130.786 293.6 33.70 0.000
## 2022 -129.123 293.6 33.70 0.000
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## 1276 -129.012 293.6 33.73 0.000
## 1457 -135.711 293.6 33.73 0.000
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## 2813 -130.825 293.7 33.78 0.000
## 1200 -130.828 293.7 33.78 0.000
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## 2021 -132.630 293.7 33.82 0.000
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## 1279 -130.852 293.7 33.83 0.000
## 1767 -132.646 293.7 33.86 0.000
## 3046 -129.228 293.8 33.91 0.000
## 2790 -130.996 293.8 33.92 0.000
## 1259 -134.214 293.8 33.92 0.000
## 1185 -138.659 293.8 33.93 0.000
## 3877 -135.819 293.8 33.95 0.000
## 3108 -135.843 293.9 34.00 0.000
## 1471 -130.947 293.9 34.02 0.000
## 1470 -129.167 293.9 34.04 0.000
## 2811 -130.965 293.9 34.06 0.000
## 1212 -130.973 293.9 34.07 0.000
## 1525 -132.761 294.0 34.09 0.000
## 170 -135.838 294.0 34.09 0.000
## 234 -134.309 294.0 34.11 0.000
## 1514 -130.997 294.0 34.12 0.000
## 3691 -134.316 294.0 34.12 0.000
## 3687 -134.392 294.0 34.15 0.000
## 1522 -131.118 294.0 34.16 0.000
## 3366 -134.401 294.0 34.17 0.000
## 3067 -129.242 294.1 34.19 0.000
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## 3387 -134.415 294.2 34.32 0.000
## 950 -131.199 294.2 34.32 0.000
## 2048 -123.337 294.2 34.34 0.000
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## 1445 -136.060 294.3 34.43 0.000
## 1523 -132.952 294.3 34.47 0.000
## 761 -134.496 294.4 34.48 0.000
## 3170 -136.088 294.4 34.49 0.000
## 1792 -125.520 294.4 34.50 0.000
## 1515 -132.915 294.4 34.56 0.000
## 1972 -129.552 294.4 34.56 0.000
## 2802 -131.321 294.4 34.57 0.000
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## 1780 -129.566 294.5 34.59 0.000
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## 3174 -134.671 294.6 34.70 0.000
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## 2788 -131.432 294.7 34.79 0.000
## 1017 -133.033 294.7 34.79 0.000
## 1768 -129.679 294.7 34.82 0.000
## 1003 -133.063 294.7 34.85 0.000
## 1974 -129.705 294.7 34.87 0.000
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## 3707 -133.091 294.8 34.91 0.000
## 2303 -131.401 294.8 34.93 0.000
## 172 -134.726 294.8 34.94 0.000
## 1005 -133.111 294.8 34.95 0.000
## 3438 -131.420 294.8 34.96 0.000
## 1268 -131.546 294.9 35.02 0.000
## 676 -134.835 294.9 35.03 0.000
## 1263 -133.162 294.9 35.05 0.000
## 1468 -129.677 294.9 35.06 0.000
## 2738 -133.247 294.9 35.06 0.000
## 3106 -137.838 294.9 35.07 0.000
## 1189 -137.846 295.0 35.09 0.000
## 3893 -134.873 295.0 35.11 0.000
## 1249 -137.874 295.0 35.15 0.000
## 1461 -134.927 295.1 35.22 0.000
## 426 -134.884 295.1 35.26 0.000
## 1898 -133.269 295.1 35.26 0.000
## 2990 -129.779 295.1 35.26 0.000
## 2282 -133.271 295.1 35.27 0.000
## 1719 -133.365 295.2 35.29 0.000
## 1973 -133.379 295.2 35.32 0.000
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## 3937 -136.524 295.2 35.36 0.000
## 1260 -131.621 295.2 35.37 0.000
## 692 -133.419 295.3 35.40 0.000
## 2557 -131.642 295.3 35.41 0.000
## 1834 -134.968 295.3 35.43 0.000
## 2555 -131.652 295.3 35.43 0.000
## 1456 -129.868 295.3 35.44 0.000
## 1959 -133.456 295.4 35.48 0.000
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## 2722 -135.070 295.4 35.50 0.000
## 1518 -129.907 295.4 35.52 0.000
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## 1971 -133.503 295.4 35.57 0.000
## 2290 -133.505 295.4 35.57 0.000
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## 1442 -135.137 295.5 35.64 0.000
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## 3891 -135.660 296.6 36.68 0.000
## 1000 -130.618 296.6 36.69 0.000
## 3430 -134.079 296.6 36.72 0.000
## 1902 -132.335 296.7 36.79 0.000
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## 1253 -137.297 296.8 36.90 0.000
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## 1205 -137.323 296.8 36.96 0.000
## 1532 -128.743 296.8 36.96 0.000
## 3627 -137.297 296.9 37.00 0.000
## 508 -130.659 296.9 37.02 0.000
## 3114 -137.306 296.9 37.02 0.000
## 1960 -130.783 296.9 37.02 0.000
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## 2024 -128.931 296.9 37.04 0.000
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## 2804 -131.313 298.0 38.08 0.000
## 1890 -136.372 298.0 38.11 0.000
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## 2740 -133.110 298.0 38.14 0.000
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## 1976 -129.550 298.2 38.28 0.000
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## 1975 -133.336 298.5 38.60 0.000
## 749 -136.554 298.5 38.60 0.000
## 2978 -135.021 298.5 38.61 0.000
## 1830 -136.630 298.5 38.62 0.000
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## 1997 -259.405 539.4 279.51 0.000
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## 1999 -259.330 541.8 281.97 0.000
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## 922 -259.335 541.9 281.98 0.000
## 2013 -259.347 541.9 282.01 0.000
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## 2015 -259.288 544.5 284.58 0.000
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## 264 -312.395 640.2 380.30 0.000
## 2049 -318.382 640.9 381.03 0.000
## 2053 -317.447 641.2 381.30 0.000
## 261 -317.696 641.7 381.80 0.000
## 2308 -313.285 642.0 382.08 0.000
## 66 -315.622 642.0 382.09 0.000
## 2561 -317.870 642.0 382.15 0.000
## 1537 -317.955 642.2 382.32 0.000
## 2565 -316.876 642.2 382.35 0.000
## 2118 -313.474 642.3 382.46 0.000
## 1 -320.251 642.5 382.67 0.000
## 70 -315.135 643.3 383.42 0.000
## 258 -316.306 643.3 383.46 0.000
## 578 -315.211 643.4 383.57 0.000
## 5 -319.812 643.8 383.89 0.000
## 2114 -315.385 643.8 383.92 0.000
## 2630 -313.024 643.8 383.97 0.000
## 2310 -314.568 644.5 384.64 0.000
## 513 -320.244 644.6 384.75 0.000
## 2626 -314.817 645.0 385.14 0.000
## 262 -316.009 645.0 385.17 0.000
## 2306 -316.031 645.1 385.21 0.000
## 582 -314.859 645.1 385.23 0.000
## 2 -318.603 645.7 385.81 0.000
## 517 -319.708 645.7 385.82 0.000
## 2050 -318.138 647.0 387.12 0.000
## 514 -318.224 647.2 387.30 0.000
## 2054 -317.141 647.3 387.43 0.000
## 6 -318.527 647.8 387.90 0.000
## 2562 -317.549 648.1 388.25 0.000
## 2566 -316.636 648.7 388.78 0.000
## 518 -318.195 649.4 389.54 0.000
## Models ranked by AICc(x)rma.glmulti <- function(formula,data)
{rma.mv(formula, d_var_calc, data=ma_data, method="ML")
}
res <- glmulti(d_calc ~mean_age + agent_argument_type_clean + patient_argument_type_clean + stimuli_modality + stimuli_actor + presentation_type + test_mass_or_distributed + practice_phase + character_identification + n_repetitions_sentence + n_repetitions_video + test_method, data=ma_data,
level=1, fitfunction=rma.glmulti, crit="aicc", confsetsize=32)## Initialization...
## TASK: Exhaustive screening of candidate set.
## Fitting...
##
## After 50 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type
## Crit= 292.678728228696
## Mean crit= 404.4182826873
##
## After 100 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type
## Crit= 292.678728228696
## Mean crit= 311.548251352577
##
## After 150 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed
## Crit= 286.641145775671
## Mean crit= 296.940605673671
##
## After 200 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed
## Crit= 286.641145775671
## Mean crit= 294.471397922448
##
## After 250 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed
## Crit= 286.641145775671
## Mean crit= 292.749831951005
##
## After 300 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed
## Crit= 286.641145775671
## Mean crit= 292.070665644294
##
## After 350 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed
## Crit= 286.641145775671
## Mean crit= 292.026674018333
##
## After 400 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed
## Crit= 286.641145775671
## Mean crit= 291.377790833312
##
## After 450 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed
## Crit= 286.641145775671
## Mean crit= 291.302369836876
##
## After 500 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed
## Crit= 286.641145775671
## Mean crit= 291.271673029516
##
## After 550 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed
## Crit= 286.641145775671
## Mean crit= 291.064759272244
##
## After 600 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+n_repetitions_sentence
## Crit= 285.991583665706
## Mean crit= 290.512913316387
##
## After 650 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 288.699193607978
##
## After 700 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 287.925460547565
##
## After 750 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 287.283315025428
##
## After 800 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 286.333209862378
##
## After 850 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 286.287708689164##
## After 900 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 286.287708689164
##
## After 950 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 285.858279272056
##
## After 1000 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 285.760848462709
##
## After 1050 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 285.698615181401##
## After 1100 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 285.698615181401
##
## After 1150 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 285.698615181401##
## After 1200 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 285.698615181401##
## After 1250 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 285.698615181401##
## After 1300 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 285.698615181401##
## After 1350 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 285.698615181401##
## After 1400 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 285.698615181401##
## After 1450 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 285.698615181401##
## After 1500 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 285.698615181401##
## After 1550 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 285.698615181401##
## After 1600 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 285.698615181401##
## After 1650 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence
## Crit= 281.568954471054
## Mean crit= 285.596918930928
##
## After 1700 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+stimuli_actor+presentation_type+n_repetitions_sentence+n_repetitions_video
## Crit= 280.192192853889
## Mean crit= 285.230359133445
##
## After 1750 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+stimuli_actor+presentation_type+n_repetitions_sentence+n_repetitions_video
## Crit= 280.192192853889
## Mean crit= 284.75484863065
##
## After 1800 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+stimuli_actor+presentation_type+n_repetitions_sentence+n_repetitions_video
## Crit= 280.192192853889
## Mean crit= 284.658446957853
##
## After 1850 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+stimuli_actor+presentation_type+n_repetitions_sentence+n_repetitions_video
## Crit= 280.192192853889
## Mean crit= 284.649686648831
##
## After 1900 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+stimuli_actor+presentation_type+n_repetitions_sentence+n_repetitions_video
## Crit= 280.192192853889
## Mean crit= 284.637064337356
##
## After 1950 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+stimuli_actor+presentation_type+n_repetitions_sentence+n_repetitions_video
## Crit= 280.192192853889
## Mean crit= 284.538806741689
##
## After 2000 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+stimuli_actor+presentation_type+n_repetitions_sentence+n_repetitions_video
## Crit= 280.192192853889
## Mean crit= 284.523169839666##
## After 2050 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+stimuli_actor+presentation_type+n_repetitions_sentence+n_repetitions_video
## Crit= 280.192192853889
## Mean crit= 284.523169839666
##
## After 2100 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+stimuli_actor+presentation_type+n_repetitions_sentence+n_repetitions_video
## Crit= 280.192192853889
## Mean crit= 284.523169839666##
## After 2150 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+stimuli_actor+presentation_type+n_repetitions_sentence+n_repetitions_video
## Crit= 280.192192853889
## Mean crit= 284.523169839666##
## After 2200 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+stimuli_actor+presentation_type+n_repetitions_sentence+n_repetitions_video
## Crit= 280.192192853889
## Mean crit= 284.523169839666##
## After 2250 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+test_method
## Crit= 268.366067144949
## Mean crit= 279.131153037746
##
## After 2300 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+test_method
## Crit= 268.366067144949
## Mean crit= 278.821303529587
##
## After 2350 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+test_method
## Crit= 268.366067144949
## Mean crit= 278.693467373625
##
## After 2400 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+test_method
## Crit= 268.366067144949
## Mean crit= 275.593458552071##
## After 2450 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+test_method
## Crit= 268.366067144949
## Mean crit= 275.593458552071
##
## After 2500 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+test_method
## Crit= 268.366067144949
## Mean crit= 274.545289153901
##
## After 2550 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+test_method
## Crit= 268.366067144949
## Mean crit= 274.093336676771##
## After 2600 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+test_method
## Crit= 268.366067144949
## Mean crit= 274.093336676771
##
## After 2650 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+test_method
## Crit= 268.366067144949
## Mean crit= 273.557904112048##
## After 2700 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+test_method
## Crit= 268.366067144949
## Mean crit= 273.557904112048
##
## After 2750 models:
## Best model: d_calc~1+agent_argument_type_clean+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+test_method
## Crit= 268.366067144949
## Mean crit= 273.434844454392
##
## After 2800 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 269.520218966964##
## After 2850 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 269.520218966964
##
## After 2900 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 268.419004931085
##
## After 2950 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 268.001902817386##
## After 3000 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 268.001902817386
##
## After 3050 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.993712237386##
## After 3100 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.993712237386
##
## After 3150 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.993712237386##
## After 3200 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.69423188624##
## After 3250 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.69423188624
##
## After 3300 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.69423188624##
## After 3350 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.69423188624##
## After 3400 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.69423188624##
## After 3450 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.491670566454##
## After 3500 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.491670566454
##
## After 3550 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.491670566454##
## After 3600 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.491670566454##
## After 3650 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.491670566454##
## After 3700 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.481384590304##
## After 3750 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.481384590304
##
## After 3800 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 266.481384590304##
## After 3850 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 265.706691812403##
## After 3900 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 265.706691812403
##
## After 3950 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 265.48311590383
##
## After 4000 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 265.305866518961##
## After 4050 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 265.305866518961
##
## After 4100 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 265.237063780875##
## After 4150 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 265.237063780875
##
## After 4200 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 265.237063780875##
## After 4250 models:
## Best model: d_calc~1+patient_argument_type_clean+stimuli_modality+presentation_type+test_mass_or_distributed+n_repetitions_sentence+test_method
## Crit= 259.875090991763
## Mean crit= 265.192558486631
## Completed.print(res)## glmulti.analysis
## Method: h / Fitting: rma.glmulti / IC used: aicc
## Level: 1 / Marginality: FALSE
## From 32 models:
## Best IC: 259.875090991763
## Best model:
## [1] "d_calc ~ 1 + patient_argument_type_clean + stimuli_modality + "
## [2] " presentation_type + test_mass_or_distributed + n_repetitions_sentence + "
## [3] " test_method"
## Evidence weight: 0.261145610413914
## Worst IC: 267.934353447782
## 2 models within 2 IC units.
## 25 models to reach 95% of evidence weight.top <- weightable(res)
top <- top[top$aicc <= min(top$aicc) + 2,]
top## model
## 1 d_calc ~ 1 + patient_argument_type_clean + stimuli_modality + presentation_type + test_mass_or_distributed + n_repetitions_sentence + test_method
## 2 d_calc ~ 1 + agent_argument_type_clean + patient_argument_type_clean + stimuli_modality + presentation_type + test_mass_or_distributed + n_repetitions_sentence + test_method
## aicc weights
## 1 259.8751 0.2611456
## 2 261.6742 0.1062193summary(res@objects[[1]])##
## Multivariate Meta-Analysis Model (k = 52; method: ML)
##
## logLik Deviance AIC BIC AICc
## -117.2546 247.8444 254.5092 274.0217 259.8751
##
## Variance Components: none
##
## Test for Residual Heterogeneity:
## QE(df = 42) = 247.8444, p-val < .0001
##
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 121.8377, p-val < .0001
##
## Model Results:
##
## estimate se zval pval
## intrcpt -1.2559 0.2981 -4.2138 <.0001
## patient_argument_type_cleannoun_phrase -0.9079 0.3476 -2.6117 0.0090
## patient_argument_type_cleanpronoun -0.9229 0.2788 -3.3097 0.0009
## patient_argument_type_cleanvarying_patient -0.5660 0.1695 -3.3391 0.0008
## stimuli_modalityvideo 0.5550 0.1511 3.6737 0.0002
## presentation_typeimmediate_after 1.1863 0.1975 6.0074 <.0001
## presentation_typesimultaneous 0.3147 0.2682 1.1737 0.2405
## test_mass_or_distributedmass 1.3666 0.1852 7.3804 <.0001
## n_repetitions_sentence 0.0203 0.0208 0.9761 0.3290
## test_methodpoint 1.4996 0.3004 4.9929 <.0001
## ci.lb ci.ub
## intrcpt -1.8401 -0.6718 ***
## patient_argument_type_cleannoun_phrase -1.5892 -0.2266 **
## patient_argument_type_cleanpronoun -1.4694 -0.3764 ***
## patient_argument_type_cleanvarying_patient -0.8983 -0.2338 ***
## stimuli_modalityvideo 0.2589 0.8511 ***
## presentation_typeimmediate_after 0.7993 1.5734 ***
## presentation_typesimultaneous -0.2109 0.8403
## test_mass_or_distributedmass 1.0037 1.7295 ***
## n_repetitions_sentence -0.0205 0.0612
## test_methodpoint 0.9109 2.0883 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(res, type="s")
summary(res@objects[[2]])##
## Multivariate Meta-Analysis Model (k = 52; method: ML)
##
## logLik Deviance AIC BIC AICc
## -114.8371 243.0094 253.6742 277.0892 261.6742
##
## Variance Components: none
##
## Test for Residual Heterogeneity:
## QE(df = 40) = 243.0094, p-val < .0001
##
## Test of Moderators (coefficients 2:12):
## QM(df = 11) = 126.6727, p-val < .0001
##
## Model Results:
##
## estimate se zval pval
## intrcpt -1.1837 0.2999 -3.9473 <.0001
## agent_argument_type_cleanpronoun -0.0303 0.5787 -0.0524 0.9582
## agent_argument_type_cleanvarying_agent -0.8660 0.4279 -2.0236 0.0430
## patient_argument_type_cleannoun_phrase -1.3336 0.5690 -2.3438 0.0191
## patient_argument_type_cleanpronoun -1.3443 0.5290 -2.5411 0.0111
## patient_argument_type_cleanvarying_patient -0.4940 0.1727 -2.8608 0.0042
## stimuli_modalityvideo 0.4810 0.1548 3.1081 0.0019
## presentation_typeimmediate_after 1.0153 0.2123 4.7833 <.0001
## presentation_typesimultaneous 0.2011 0.2731 0.7362 0.4616
## test_mass_or_distributedmass 1.8646 0.3390 5.4995 <.0001
## n_repetitions_sentence 0.0373 0.0222 1.6777 0.0934
## test_methodpoint 2.0107 0.4166 4.8265 <.0001
## ci.lb ci.ub
## intrcpt -1.7714 -0.5959 ***
## agent_argument_type_cleanpronoun -1.1646 1.1040
## agent_argument_type_cleanvarying_agent -1.7047 -0.0272 *
## patient_argument_type_cleannoun_phrase -2.4488 -0.2184 *
## patient_argument_type_cleanpronoun -2.3811 -0.3074 *
## patient_argument_type_cleanvarying_patient -0.8324 -0.1555 **
## stimuli_modalityvideo 0.1777 0.7844 **
## presentation_typeimmediate_after 0.5993 1.4313 ***
## presentation_typesimultaneous -0.3342 0.7363
## test_mass_or_distributedmass 1.2001 2.5291 ***
## n_repetitions_sentence -0.0063 0.0808 .
## test_methodpoint 1.1942 2.8272 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1plot(res, type="s")
eval(metafor:::.glmulti)
coef(res)## Estimate Uncond. variance
## mean_age -9.198097e-06 1.893316e-09
## character_identificationyes 5.467922e-03 9.263868e-04
## stimuli_actorperson -8.229037e-03 1.648763e-03
## practice_phaseyes 6.076742e-02 1.428510e-02
## n_repetitions_video 2.019532e-02 2.157982e-03
## agent_argument_type_cleanpronoun -2.830933e-02 4.870609e-02
## agent_argument_type_cleanvarying_agent -3.872563e-01 3.101160e-01
## stimuli_modalityvideo 4.829495e-01 4.973368e-02
## n_repetitions_sentence 2.847464e-02 6.135974e-04
## intrcpt -1.228645e+00 1.101651e-01
## patient_argument_type_cleannoun_phrase -1.084415e+00 2.343503e-01
## patient_argument_type_cleanpronoun -1.048603e+00 1.654739e-01
## patient_argument_type_cleanvarying_patient -5.552318e-01 3.614084e-02
## presentation_typeimmediate_after 1.049956e+00 1.008999e-01
## presentation_typesimultaneous 1.942349e-01 1.289344e-01
## test_mass_or_distributedmass 1.555343e+00 1.402493e-01
## test_methodpoint 1.719030e+00 2.085099e-01
## Nb models Importance
## mean_age 7 0.1236973
## character_identificationyes 7 0.1239561
## stimuli_actorperson 9 0.1465662
## practice_phaseyes 13 0.2261255
## n_repetitions_video 14 0.2588161
## agent_argument_type_cleanpronoun 16 0.3401289
## agent_argument_type_cleanvarying_agent 16 0.3401289
## stimuli_modalityvideo 24 0.8798984
## n_repetitions_sentence 31 0.9881474
## intrcpt 32 1.0000000
## patient_argument_type_cleannoun_phrase 32 1.0000000
## patient_argument_type_cleanpronoun 32 1.0000000
## patient_argument_type_cleanvarying_patient 32 1.0000000
## presentation_typeimmediate_after 32 1.0000000
## presentation_typesimultaneous 32 1.0000000
## test_mass_or_distributedmass 32 1.0000000
## test_methodpoint 32 1.0000000
## +/- (alpha=0.05)
## mean_age 8.528246e-05
## character_identificationyes 5.965464e-02
## stimuli_actorperson 7.958426e-02
## practice_phaseyes 2.342554e-01
## n_repetitions_video 9.104833e-02
## agent_argument_type_cleanpronoun 4.325534e-01
## agent_argument_type_cleanvarying_agent 1.091466e+00
## stimuli_modalityvideo 4.370925e-01
## n_repetitions_sentence 4.855007e-02
## intrcpt 6.505340e-01
## patient_argument_type_cleannoun_phrase 9.488135e-01
## patient_argument_type_cleanpronoun 7.972837e-01
## patient_argument_type_cleanvarying_patient 3.726037e-01
## presentation_typeimmediate_after 6.225774e-01
## presentation_typesimultaneous 7.037728e-01
## test_mass_or_distributedmass 7.340040e-01
## test_methodpoint 8.949761e-01mmi <- as.data.frame(coef(res))
mmi <- data.frame(Estimate=mmi$Est, SE=sqrt(mmi$Uncond), Importance=mmi$Importance, row.names=row.names(mmi))
mmi$z <- mmi$Estimate / mmi$SE
mmi$p <- 2*pnorm(abs(mmi$z), lower.tail=FALSE)
names(mmi) <- c("Estimate", "Std. Error", "Importance", "z value", "Pr(>|z|)")
mmi$ci.lb <- mmi[[1]] - qnorm(.975) * mmi[[2]]
mmi$ci.ub <- mmi[[1]] + qnorm(.975) * mmi[[2]]
mmi <- mmi[order(mmi$Importance, decreasing=TRUE), c(1,2,4:7,3)]
round(mmi, 4)## Estimate Std. Error z value Pr(>|z|)
## intrcpt -1.2286 0.3319 -3.7017 0.0002
## patient_argument_type_cleannoun_phrase -1.0844 0.4841 -2.2401 0.0251
## patient_argument_type_cleanpronoun -1.0486 0.4068 -2.5778 0.0099
## patient_argument_type_cleanvarying_patient -0.5552 0.1901 -2.9206 0.0035
## presentation_typeimmediate_after 1.0500 0.3176 3.3054 0.0009
## presentation_typesimultaneous 0.1942 0.3591 0.5409 0.5886
## test_mass_or_distributedmass 1.5553 0.3745 4.1531 0.0000
## test_methodpoint 1.7190 0.4566 3.7646 0.0002
## n_repetitions_sentence 0.0285 0.0248 1.1495 0.2503
## stimuli_modalityvideo 0.4829 0.2230 2.1656 0.0303
## agent_argument_type_cleanpronoun -0.0283 0.2207 -0.1283 0.8979
## agent_argument_type_cleanvarying_agent -0.3873 0.5569 -0.6954 0.4868
## n_repetitions_video 0.0202 0.0465 0.4347 0.6638
## practice_phaseyes 0.0608 0.1195 0.5084 0.6112
## stimuli_actorperson -0.0082 0.0406 -0.2027 0.8394
## character_identificationyes 0.0055 0.0304 0.1796 0.8574
## mean_age 0.0000 0.0000 -0.2114 0.8326
## ci.lb ci.ub Importance
## intrcpt -1.8792 -0.5781 1.0000
## patient_argument_type_cleannoun_phrase -2.0332 -0.1356 1.0000
## patient_argument_type_cleanpronoun -1.8459 -0.2513 1.0000
## patient_argument_type_cleanvarying_patient -0.9278 -0.1826 1.0000
## presentation_typeimmediate_after 0.4274 1.6725 1.0000
## presentation_typesimultaneous -0.5095 0.8980 1.0000
## test_mass_or_distributedmass 0.8213 2.2893 1.0000
## test_methodpoint 0.8241 2.6140 1.0000
## n_repetitions_sentence -0.0201 0.0770 0.9881
## stimuli_modalityvideo 0.0459 0.9200 0.8799
## agent_argument_type_cleanpronoun -0.4609 0.4042 0.3401
## agent_argument_type_cleanvarying_agent -1.4787 0.7042 0.3401
## n_repetitions_video -0.0709 0.1112 0.2588
## practice_phaseyes -0.1735 0.2950 0.2261
## stimuli_actorperson -0.0878 0.0714 0.1466
## character_identificationyes -0.0542 0.0651 0.1240
## mean_age -0.0001 0.0001 0.1237