library(papaja)
library(rmarkdown)
library(tidyverse)
library(here)
library(glue)
library(metafor)
library(knitr)
library(gridExtra)
library(here)
library(heatmaply)
library(MuMIn)
library(glmulti)
library(PRISMAstatement)
DATA_PATH <- here("data/processed/syntactic_bootstrapping_tidy_data.csv")
RAW_DATA_PATH <- here("data/raw/syntactic_bootstrapping_raw_data.csv")
ma_data <- read_csv(DATA_PATH) %>% filter(paradigm_type == "action_matching")
get_MA_params <- function(moderator, df) {
this_data <- df
n = nrow(this_data)
if (moderator == "mean_age"){
model <- rma.mv(d_calc ~ log(mean_age), V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
} else if (moderator == "NULL"){
model <- rma.mv(d_calc, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- NA
this_moderator_estimate.cil <- NA
this_moderator_estimate.cih <- NA
this_moderator_z <- NA
this_moderator_p <- NA
}else if (moderator == "sentence_structure"){
model <- rma.mv(d_calc~sentence_structure, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "productive_vocab_median"){
model <- rma.mv(d_calc~productive_vocab_median, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "agent_argument_type_clean"){
model <- rma.mv(d_calc~agent_argument_type_clean, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "patient_argument_type_clean"){
model <- rma.mv(d_calc~patient_argument_type_clean, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "agent_argument_number"){
model <- rma.mv(d_calc~agent_argument_number, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "n_repetitions_sentence"){
model <- rma.mv(d_calc~n_repetitions_sentence, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "stimuli_modality"){
model <- rma.mv(d_calc~stimuli_modality, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "n_repetitions_video"){
model <- rma.mv(d_calc~n_repetitions_video, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "stimuli_actor"){
model <- rma.mv(d_calc~stimuli_actor, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "transitive_event_type"){
model <- rma.mv(d_calc~transitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "intransitive_event_type"){
model <- rma.mv(d_calc~intransitive_event_type, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "visual_stimuli_pair"){
model <- rma.mv(d_calc~visual_stimuli_pair, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "test_method"){
model <- rma.mv(d_calc~test_method, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "presentation_type"){
model <- rma.mv(d_calc~presentation_type, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "character_identification"){
model <- rma.mv(d_calc~character_identification, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "practice_phase"){
model <- rma.mv(d_calc~practice_phase, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "test_mass_or_distributed"){
model <- rma.mv(d_calc~test_mass_or_distributed, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "n_train_test_pair"){
model <- rma.mv(d_calc~n_train_test_pair, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}else if (moderator == "n_test_trial_per_pair"){
model <- rma.mv(d_calc~n_test_trial_per_pair, V = d_var_calc,
random = ~ 1 | short_cite/same_infant/x_1,
method = "REML",
data = this_data)
this_moderator_estimate <- model$b[2]
this_moderator_estimate.cil <- model$ci.lb[2]
this_moderator_estimate.cih <- model$ci.ub[2]
this_moderator_z <- model$zval[2]
this_moderator_p <- model$pval[2]
}
params <- data.frame(this_moderator = moderator,
n = n,
estimate = model$b[1],
estimate.cil = model$ci.lb[1],
estimate.cih = model$ci.ub[1],
z = model$zval[1],
p = model$pval[1],
mod_estimate = this_moderator_estimate,
mod_estimate.cil = this_moderator_estimate.cil,
mod_estimate.cih = this_moderator_estimate.cih,
moderator_z = this_moderator_z,
moderator_p = this_moderator_p,
Q = model$QE,
Qp = model$QEp)
}
v <- c( "NULL", "mean_age","productive_vocab_median", "sentence_structure", "agent_argument_type_clean", "patient_argument_type_clean","agent_argument_number", "n_repetitions_sentence", "n_repetitions_video", "stimuli_modality", "stimuli_actor", "transitive_event_type","intransitive_event_type", "visual_stimuli_pair", "test_method","presentation_type","character_identification", "practice_phase", "test_mass_or_distributed", "n_train_test_pair", "n_test_trial_per_pair" )
all_models <- map_df(v,ma_data, .f = get_MA_params)
all_models
## this_moderator n estimate estimate.cil estimate.cih
## 1 NULL 80 0.54372609 0.32634347 0.7611087
## 2 mean_age 80 1.26517839 -2.85575976 5.3861165
## 3 productive_vocab_median 80 0.83304058 0.11402605 1.5520551
## 4 sentence_structure 80 0.29078025 -0.03440018 0.6159607
## 5 agent_argument_type_clean 80 0.40372034 0.02200663 0.7854341
## 6 patient_argument_type_clean 80 0.29235297 -0.03207797 0.6167839
## 7 agent_argument_number 80 0.49058464 0.21354939 0.7676199
## 8 n_repetitions_sentence 80 0.56760816 0.18135958 0.9538567
## 9 n_repetitions_video 80 0.62166656 0.20930891 1.0340242
## 10 stimuli_modality 80 0.52318717 0.12255427 0.9238201
## 11 stimuli_actor 80 0.48368019 0.15854106 0.8088193
## 12 transitive_event_type 80 0.46527843 0.17455830 0.7559986
## 13 intransitive_event_type 80 0.08587173 -0.74653205 0.9182755
## 14 visual_stimuli_pair 80 0.08513332 -0.77261510 0.9428817
## 15 test_method 80 0.48877244 0.23779180 0.7397531
## 16 presentation_type 80 0.50668527 0.17021917 0.8431514
## 17 character_identification 80 0.54202907 0.24391194 0.8401462
## 18 practice_phase 80 0.46758962 0.12582941 0.8093498
## 19 test_mass_or_distributed 80 0.52912579 0.24438243 0.8138691
## 20 n_train_test_pair 80 0.63638321 0.18569215 1.0870743
## 21 n_test_trial_per_pair 80 0.48414313 -0.16151979 1.1298061
## z p mod_estimate mod_estimate.cil mod_estimate.cih
## 1 4.9023403 9.470159e-07 NA NA NA
## 2 0.6017329 5.473519e-01 -0.106774003 -0.716310641 0.502762635
## 3 2.2707879 2.315982e-02 -0.006941832 -0.022283612 0.008399947
## 4 1.7526234 7.966668e-02 0.428187474 0.009430533 0.846944415
## 5 2.0729602 3.817598e-02 0.133117003 -0.408373698 0.674607705
## 6 1.7661734 7.736676e-02 0.392552459 -0.084939834 0.870044752
## 7 3.4707794 5.189501e-04 0.092770599 -0.433632886 0.619174083
## 8 2.8802476 3.973629e-03 -0.002614142 -0.038504252 0.033275967
## 9 2.9548235 3.128480e-03 -0.028323165 -0.156232509 0.099586178
## 10 2.5595202 1.048168e-02 0.030482315 -0.448757340 0.509721970
## 11 2.9156618 3.549350e-03 0.111653449 -0.329217327 0.552524224
## 12 3.1367934 1.708065e-03 0.200635947 -0.259573181 0.660845076
## 13 0.2021921 8.397665e-01 0.503762340 -0.397150461 1.404675141
## 14 0.1945305 8.457605e-01 0.538330662 -0.481873119 1.558534443
## 15 3.8169334 1.351206e-04 0.224944373 -0.281881434 0.731770180
## 16 2.9515154 3.162187e-03 0.088033355 -0.393961428 0.570028138
## 17 3.5635573 3.658628e-04 0.005299301 -0.435793561 0.446392162
## 18 2.6815843 7.327445e-03 0.129845937 -0.315124725 0.574816599
## 19 3.6421130 2.704093e-04 0.037976907 -0.409932472 0.485886287
## 20 2.7675014 5.648779e-03 -0.036307901 -0.191628833 0.119013031
## 21 1.4696571 1.416546e-01 0.035534992 -0.322344033 0.393414017
## moderator_z moderator_p Q Qp
## 1 NA NA 443.6688 3.550434e-52
## 2 -0.34333162 0.73134900 442.3177 2.591585e-52
## 3 -0.88684244 0.37516373 160.5343 2.439720e-18
## 4 2.00410297 0.04505903 436.2674 3.171850e-51
## 5 0.48182643 0.62992925 436.9261 4.178829e-52
## 6 1.61131120 0.10711191 405.1890 8.685177e-47
## 7 0.34541381 0.72978330 441.0000 1.858539e-52
## 8 -0.14275869 0.88648076 443.5178 1.575623e-52
## 9 -0.43399788 0.66428998 443.6623 1.483907e-52
## 10 0.12466464 0.90078905 440.7145 5.036090e-52
## 11 0.49637388 0.61963065 439.0096 1.020198e-51
## 12 0.85447942 0.39283941 438.7228 4.784070e-52
## 13 1.09595073 0.27310034 442.0426 1.205069e-52
## 14 1.03421368 0.30103628 436.5166 3.395079e-53
## 15 0.86989033 0.38436034 439.3029 9.035840e-52
## 16 0.35797525 0.72036183 436.1230 1.406290e-51
## 17 0.02354706 0.98121390 441.5061 3.627968e-52
## 18 0.57193290 0.56736744 443.2801 1.738860e-52
## 19 0.16617953 0.86801567 443.2344 1.772110e-52
## 20 -0.45816219 0.64683592 443.6627 1.483661e-52
## 21 0.19461131 0.84569725 443.2991 1.725184e-52
mod_print <- all_models %>%
mutate(esimate_print = round(estimate, 2),
CI_print = paste0(" [",
round(estimate.cil, 2),
", ",
round(estimate.cih, 2),
"]"),
estimate_print_full = paste(esimate_print, CI_print),
z_print = round(z, 2),
p_print = round(p, 2),
p_print = ifelse(p_print <.001, "<.001", paste0("= ", p_print)),
mod_estimate_print = round(mod_estimate, 2),
mod_CI_print = paste0(" [",
round(mod_estimate.cil, 2),
", ",
round(mod_estimate.cih, 2),
"]"),
mod_estimate_print_full = paste(mod_estimate_print, mod_CI_print),
mod_z_print = round(moderator_z, 2),
mod_p_print = round(moderator_p, 2),
mod_p_print = ifelse(mod_p_print < .001, "<.001",
paste0("= ", mod_p_print)),
Q_print = round(Q, 2),
Qp_print = round(Qp, 2),
Qp_print = ifelse(Qp_print < .001, "<.001", paste0("= ", Qp_print)))
mod_print
## this_moderator n estimate estimate.cil estimate.cih
## 1 NULL 80 0.54372609 0.32634347 0.7611087
## 2 mean_age 80 1.26517839 -2.85575976 5.3861165
## 3 productive_vocab_median 80 0.83304058 0.11402605 1.5520551
## 4 sentence_structure 80 0.29078025 -0.03440018 0.6159607
## 5 agent_argument_type_clean 80 0.40372034 0.02200663 0.7854341
## 6 patient_argument_type_clean 80 0.29235297 -0.03207797 0.6167839
## 7 agent_argument_number 80 0.49058464 0.21354939 0.7676199
## 8 n_repetitions_sentence 80 0.56760816 0.18135958 0.9538567
## 9 n_repetitions_video 80 0.62166656 0.20930891 1.0340242
## 10 stimuli_modality 80 0.52318717 0.12255427 0.9238201
## 11 stimuli_actor 80 0.48368019 0.15854106 0.8088193
## 12 transitive_event_type 80 0.46527843 0.17455830 0.7559986
## 13 intransitive_event_type 80 0.08587173 -0.74653205 0.9182755
## 14 visual_stimuli_pair 80 0.08513332 -0.77261510 0.9428817
## 15 test_method 80 0.48877244 0.23779180 0.7397531
## 16 presentation_type 80 0.50668527 0.17021917 0.8431514
## 17 character_identification 80 0.54202907 0.24391194 0.8401462
## 18 practice_phase 80 0.46758962 0.12582941 0.8093498
## 19 test_mass_or_distributed 80 0.52912579 0.24438243 0.8138691
## 20 n_train_test_pair 80 0.63638321 0.18569215 1.0870743
## 21 n_test_trial_per_pair 80 0.48414313 -0.16151979 1.1298061
## z p mod_estimate mod_estimate.cil mod_estimate.cih
## 1 4.9023403 9.470159e-07 NA NA NA
## 2 0.6017329 5.473519e-01 -0.106774003 -0.716310641 0.502762635
## 3 2.2707879 2.315982e-02 -0.006941832 -0.022283612 0.008399947
## 4 1.7526234 7.966668e-02 0.428187474 0.009430533 0.846944415
## 5 2.0729602 3.817598e-02 0.133117003 -0.408373698 0.674607705
## 6 1.7661734 7.736676e-02 0.392552459 -0.084939834 0.870044752
## 7 3.4707794 5.189501e-04 0.092770599 -0.433632886 0.619174083
## 8 2.8802476 3.973629e-03 -0.002614142 -0.038504252 0.033275967
## 9 2.9548235 3.128480e-03 -0.028323165 -0.156232509 0.099586178
## 10 2.5595202 1.048168e-02 0.030482315 -0.448757340 0.509721970
## 11 2.9156618 3.549350e-03 0.111653449 -0.329217327 0.552524224
## 12 3.1367934 1.708065e-03 0.200635947 -0.259573181 0.660845076
## 13 0.2021921 8.397665e-01 0.503762340 -0.397150461 1.404675141
## 14 0.1945305 8.457605e-01 0.538330662 -0.481873119 1.558534443
## 15 3.8169334 1.351206e-04 0.224944373 -0.281881434 0.731770180
## 16 2.9515154 3.162187e-03 0.088033355 -0.393961428 0.570028138
## 17 3.5635573 3.658628e-04 0.005299301 -0.435793561 0.446392162
## 18 2.6815843 7.327445e-03 0.129845937 -0.315124725 0.574816599
## 19 3.6421130 2.704093e-04 0.037976907 -0.409932472 0.485886287
## 20 2.7675014 5.648779e-03 -0.036307901 -0.191628833 0.119013031
## 21 1.4696571 1.416546e-01 0.035534992 -0.322344033 0.393414017
## moderator_z moderator_p Q Qp esimate_print CI_print
## 1 NA NA 443.6688 3.550434e-52 0.54 [0.33, 0.76]
## 2 -0.34333162 0.73134900 442.3177 2.591585e-52 1.27 [-2.86, 5.39]
## 3 -0.88684244 0.37516373 160.5343 2.439720e-18 0.83 [0.11, 1.55]
## 4 2.00410297 0.04505903 436.2674 3.171850e-51 0.29 [-0.03, 0.62]
## 5 0.48182643 0.62992925 436.9261 4.178829e-52 0.40 [0.02, 0.79]
## 6 1.61131120 0.10711191 405.1890 8.685177e-47 0.29 [-0.03, 0.62]
## 7 0.34541381 0.72978330 441.0000 1.858539e-52 0.49 [0.21, 0.77]
## 8 -0.14275869 0.88648076 443.5178 1.575623e-52 0.57 [0.18, 0.95]
## 9 -0.43399788 0.66428998 443.6623 1.483907e-52 0.62 [0.21, 1.03]
## 10 0.12466464 0.90078905 440.7145 5.036090e-52 0.52 [0.12, 0.92]
## 11 0.49637388 0.61963065 439.0096 1.020198e-51 0.48 [0.16, 0.81]
## 12 0.85447942 0.39283941 438.7228 4.784070e-52 0.47 [0.17, 0.76]
## 13 1.09595073 0.27310034 442.0426 1.205069e-52 0.09 [-0.75, 0.92]
## 14 1.03421368 0.30103628 436.5166 3.395079e-53 0.09 [-0.77, 0.94]
## 15 0.86989033 0.38436034 439.3029 9.035840e-52 0.49 [0.24, 0.74]
## 16 0.35797525 0.72036183 436.1230 1.406290e-51 0.51 [0.17, 0.84]
## 17 0.02354706 0.98121390 441.5061 3.627968e-52 0.54 [0.24, 0.84]
## 18 0.57193290 0.56736744 443.2801 1.738860e-52 0.47 [0.13, 0.81]
## 19 0.16617953 0.86801567 443.2344 1.772110e-52 0.53 [0.24, 0.81]
## 20 -0.45816219 0.64683592 443.6627 1.483661e-52 0.64 [0.19, 1.09]
## 21 0.19461131 0.84569725 443.2991 1.725184e-52 0.48 [-0.16, 1.13]
## estimate_print_full z_print p_print mod_estimate_print mod_CI_print
## 1 0.54 [0.33, 0.76] 4.90 <.001 NA [NA, NA]
## 2 1.27 [-2.86, 5.39] 0.60 = 0.55 -0.11 [-0.72, 0.5]
## 3 0.83 [0.11, 1.55] 2.27 = 0.02 -0.01 [-0.02, 0.01]
## 4 0.29 [-0.03, 0.62] 1.75 = 0.08 0.43 [0.01, 0.85]
## 5 0.4 [0.02, 0.79] 2.07 = 0.04 0.13 [-0.41, 0.67]
## 6 0.29 [-0.03, 0.62] 1.77 = 0.08 0.39 [-0.08, 0.87]
## 7 0.49 [0.21, 0.77] 3.47 <.001 0.09 [-0.43, 0.62]
## 8 0.57 [0.18, 0.95] 2.88 <.001 0.00 [-0.04, 0.03]
## 9 0.62 [0.21, 1.03] 2.95 <.001 -0.03 [-0.16, 0.1]
## 10 0.52 [0.12, 0.92] 2.56 = 0.01 0.03 [-0.45, 0.51]
## 11 0.48 [0.16, 0.81] 2.92 <.001 0.11 [-0.33, 0.55]
## 12 0.47 [0.17, 0.76] 3.14 <.001 0.20 [-0.26, 0.66]
## 13 0.09 [-0.75, 0.92] 0.20 = 0.84 0.50 [-0.4, 1.4]
## 14 0.09 [-0.77, 0.94] 0.19 = 0.85 0.54 [-0.48, 1.56]
## 15 0.49 [0.24, 0.74] 3.82 <.001 0.22 [-0.28, 0.73]
## 16 0.51 [0.17, 0.84] 2.95 <.001 0.09 [-0.39, 0.57]
## 17 0.54 [0.24, 0.84] 3.56 <.001 0.01 [-0.44, 0.45]
## 18 0.47 [0.13, 0.81] 2.68 = 0.01 0.13 [-0.32, 0.57]
## 19 0.53 [0.24, 0.81] 3.64 <.001 0.04 [-0.41, 0.49]
## 20 0.64 [0.19, 1.09] 2.77 = 0.01 -0.04 [-0.19, 0.12]
## 21 0.48 [-0.16, 1.13] 1.47 = 0.14 0.04 [-0.32, 0.39]
## mod_estimate_print_full mod_z_print mod_p_print Q_print Qp_print
## 1 NA [NA, NA] NA <NA> 443.67 <.001
## 2 -0.11 [-0.72, 0.5] -0.34 = 0.73 442.32 <.001
## 3 -0.01 [-0.02, 0.01] -0.89 = 0.38 160.53 <.001
## 4 0.43 [0.01, 0.85] 2.00 = 0.05 436.27 <.001
## 5 0.13 [-0.41, 0.67] 0.48 = 0.63 436.93 <.001
## 6 0.39 [-0.08, 0.87] 1.61 = 0.11 405.19 <.001
## 7 0.09 [-0.43, 0.62] 0.35 = 0.73 441.00 <.001
## 8 0 [-0.04, 0.03] -0.14 = 0.89 443.52 <.001
## 9 -0.03 [-0.16, 0.1] -0.43 = 0.66 443.66 <.001
## 10 0.03 [-0.45, 0.51] 0.12 = 0.9 440.71 <.001
## 11 0.11 [-0.33, 0.55] 0.50 = 0.62 439.01 <.001
## 12 0.2 [-0.26, 0.66] 0.85 = 0.39 438.72 <.001
## 13 0.5 [-0.4, 1.4] 1.10 = 0.27 442.04 <.001
## 14 0.54 [-0.48, 1.56] 1.03 = 0.3 436.52 <.001
## 15 0.22 [-0.28, 0.73] 0.87 = 0.38 439.30 <.001
## 16 0.09 [-0.39, 0.57] 0.36 = 0.72 436.12 <.001
## 17 0.01 [-0.44, 0.45] 0.02 = 0.98 441.51 <.001
## 18 0.13 [-0.32, 0.57] 0.57 = 0.57 443.28 <.001
## 19 0.04 [-0.41, 0.49] 0.17 = 0.87 443.23 <.001
## 20 -0.04 [-0.19, 0.12] -0.46 = 0.65 443.66 <.001
## 21 0.04 [-0.32, 0.39] 0.19 = 0.85 443.30 <.001
mod_print_plot <- mod_print %>% mutate(
p_val = case_when(
moderator_p < 0.05 ~ "<0.05",
moderator_p > 0.05 & moderator_p < 0.1 ~ "0.05<p<0.1",
TRUE ~ "NS"
)
)
mod_print_plot %>%
ggplot(aes(x = this_moderator, y = mod_estimate, color = moderator_p)) +
geom_point(size = 2) +
geom_linerange(aes(ymin = mod_estimate.cil, ymax = mod_estimate.cih)) +
geom_hline(aes(yintercept = 0), linetype = 2) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
coord_flip()
mod_print_plot %>%
ggplot(aes(x = this_moderator, y = mod_estimate, color = p_val)) +
geom_point(size = 2) +
geom_linerange(aes(ymin = mod_estimate.cil, ymax = mod_estimate.cih)) +
geom_hline(aes(yintercept = 0), linetype = 2) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
coord_flip()