Childes data
This is for all kids in Childes in the NA and UK corpora for children with more than one timepoint between 12 and 36 months of age. There are two things that are different from the previous analyses: (1) I’m using a different age range of kids, and (2) I’m using the MTLD measure in the childesr package. This measure is new to the package and not well documented.
IND_VARS_NA <- here("exploratory_analyses/17_taxonomic_childes/data/child_stats_eng_na.csv")
PREDICT_VARS_NA<- here("exploratory_analyses/17_taxonomic_childes/data/mean_hypernym_eng_na.csv")
IND_VARS_UK <- here("exploratory_analyses/17_taxonomic_childes/data/child_stats_eng_uk.csv")
PREDICT_VARS_UK<- here("exploratory_analyses/17_taxonomic_childes/data/mean_hypernym_eng_uk.csv")
independent_vars_na <- read_csv(IND_VARS_NA,
col_names = c("corpus", "target_child_name", "target_child_age",
"num_utterances", "mlu_w", "num_types", "num_tokens", "hdd", "mtld", "mlu_m",
"num_morphemes")) %>%
mutate(collection = "NA")
predictor_vars_na <- read_csv(PREDICT_VARS_NA)
independent_vars_uk <- read_csv(IND_VARS_UK,
col_names = c("corpus", "target_child_name", "target_child_age",
"num_utterances", "mlu_w", "num_types", "num_tokens", "hdd", "mtld", "mlu_m",
"num_morphemes")) %>%
mutate(collection = "UK")
predictor_vars_uk <- read_csv(PREDICT_VARS_UK)
tidy_pred_vars <- bind_rows(predictor_vars_na, predictor_vars_uk)
tidy_ind_vars <- bind_rows(independent_vars_uk, independent_vars_na) %>%
group_by(collection, target_child_name, target_child_age, corpus) %>% # collapse across multiple transcripts at same age point
summarize_if(is.numeric, mean)
full_df <- inner_join(tidy_ind_vars, tidy_pred_vars)
There are 232 that satisfy this criteria.
multiple_transcript_children <- full_df %>% # (after getting rid of children with multiple trasncripts at same timepoint)
ungroup() %>%
count(corpus, target_child_name) %>%
filter(n > 1) %>%
select(-n)
# add in session info and child_id
full_df_by_session <- full_df %>%
right_join(multiple_transcript_children) %>%
arrange(corpus, target_child_name, target_child_age) %>%
group_by(corpus, target_child_name) %>%
mutate(session_num = 1:n(),
child_id = paste0(corpus, "_", target_child_name)) %>%
select(child_id, target_child_age, session_num, everything())
full_df_with_delta <- full_df_by_session %>%
mutate(subsequent_age = lead(target_child_age), by = session_num,
subsequent_mtld = lead(mtld), by = session_num,
subsequent_tokens= lead(num_tokens), by = session_num,
delta_age = subsequent_age - target_child_age,
delta_mtld = subsequent_mtld - mtld)
Models predicting vocab size with previous mean vocab hypernym
These models are predicting change in mtld from current timepoint to next timepoint as a function of: (1) child/parents’s mean hypernym level of vocab, (2) change in age from timepoint 1 to 2, (3) mtld at timepoint 1, (4) log number of child tokens at timepoint 1, and (5) log number of child tokens at timepoint2.
MTLD
hypernyms all
CHILD
lmer(delta_mtld ~ child_hypernyms_all+ delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control = lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
3.3226528 |
0.3906014 |
8.5065054 |
fixed |
| child_hypernyms_all |
-0.5179360 |
0.1093310 |
-4.7373205 |
fixed |
| delta_age |
0.1943315 |
0.0250463 |
7.7588755 |
fixed |
| mtld |
-0.7829746 |
0.0203930 |
-38.3942621 |
fixed |
| log(num_tokens) |
0.2125548 |
0.0492574 |
4.3151863 |
fixed |
| log(subsequent_tokens) |
-0.0104818 |
0.0497629 |
-0.2106357 |
fixed |
| sd_(Intercept).child_id |
1.1175458 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0685614 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.2562873 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0436554 |
NA |
NA |
Residual |
lmer(delta_mtld ~ child_hyponyms_all + delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control = lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
2.8501344 |
0.3769596 |
7.5608478 |
fixed |
| child_hyponyms_all |
0.3081902 |
0.1342489 |
2.2956618 |
fixed |
| delta_age |
0.1794414 |
0.0254646 |
7.0466910 |
fixed |
| mtld |
-0.7753797 |
0.0202649 |
-38.2621426 |
fixed |
| log(num_tokens) |
0.2425965 |
0.0488150 |
4.9697150 |
fixed |
| log(subsequent_tokens) |
0.0005853 |
0.0498374 |
0.0117442 |
fixed |
| sd_(Intercept).child_id |
1.1659156 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0715246 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.2668122 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0446271 |
NA |
NA |
Residual |
lmer(delta_mtld ~ child_hypernyms_all + child_hyponyms_all + delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control = lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
3.2878750 |
0.3910386 |
8.4080579 |
fixed |
| child_hypernyms_all |
-0.4857506 |
0.1121216 |
-4.3323534 |
fixed |
| child_hyponyms_all |
0.1780231 |
0.1369181 |
1.3002158 |
fixed |
| delta_age |
0.1910278 |
0.0252079 |
7.5780864 |
fixed |
| mtld |
-0.7826421 |
0.0203900 |
-38.3835440 |
fixed |
| log(num_tokens) |
0.2083489 |
0.0493616 |
4.2208717 |
fixed |
| log(subsequent_tokens) |
-0.0127375 |
0.0497916 |
-0.2558166 |
fixed |
| sd_(Intercept).child_id |
1.1156205 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0682625 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.2459639 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0435362 |
NA |
NA |
Residual |
PARENT
lmer(delta_mtld ~ parent_hypernyms_all + delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control=lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
2.4551272 |
0.4026144 |
6.0979615 |
fixed |
| parent_hypernyms_all |
-0.0700735 |
0.1247350 |
-0.5617787 |
fixed |
| delta_age |
0.1890162 |
0.0244858 |
7.7194343 |
fixed |
| mtld |
-0.7455593 |
0.0208114 |
-35.8245004 |
fixed |
| log(num_tokens) |
0.2826684 |
0.0527083 |
5.3628797 |
fixed |
| log(subsequent_tokens) |
0.0237697 |
0.0527309 |
0.4507745 |
fixed |
| sd_(Intercept).child_id |
1.0746134 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0558001 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.5392470 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0435735 |
NA |
NA |
Residual |
lmer(delta_mtld ~ parent_hyponyms_all + delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control = lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
2.5123528 |
0.4128488 |
6.0854064 |
fixed |
| parent_hyponyms_all |
-0.1919922 |
0.1822757 |
-1.0533071 |
fixed |
| delta_age |
0.1898546 |
0.0246572 |
7.6997691 |
fixed |
| mtld |
-0.7525245 |
0.0208214 |
-36.1419621 |
fixed |
| log(num_tokens) |
0.3035777 |
0.0528207 |
5.7473232 |
fixed |
| log(subsequent_tokens) |
0.0166772 |
0.0529411 |
0.3150137 |
fixed |
| sd_(Intercept).child_id |
1.0847746 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0547328 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.5445583 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0507541 |
NA |
NA |
Residual |
lmer(delta_mtld ~ parent_hypernyms_all + parent_hyponyms_all + delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control = lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
2.7010642 |
0.4128138 |
6.5430575 |
fixed |
| parent_hypernyms_all |
-0.1184262 |
0.1261851 |
-0.9385115 |
fixed |
| parent_hyponyms_all |
-0.4786621 |
0.1975930 |
-2.4224644 |
fixed |
| delta_age |
0.1888548 |
0.0245421 |
7.6951475 |
fixed |
| mtld |
-0.7508370 |
0.0208349 |
-36.0374596 |
fixed |
| log(num_tokens) |
0.2845241 |
0.0526732 |
5.4016843 |
fixed |
| log(subsequent_tokens) |
0.0205256 |
0.0526775 |
0.3896460 |
fixed |
| sd_(Intercept).child_id |
1.0887384 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0570999 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.5427898 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0412568 |
NA |
NA |
Residual |
BOTH
lmer(delta_mtld ~ child_hypernyms_all + parent_hypernyms_all + child_hyponyms_all + parent_hyponyms_all +delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control=lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
3.2576311 |
0.4220577 |
7.7184501 |
fixed |
| child_hypernyms_all |
-0.6109608 |
0.1197307 |
-5.1027909 |
fixed |
| parent_hypernyms_all |
0.0332598 |
0.1298652 |
0.2561103 |
fixed |
| child_hyponyms_all |
0.2460171 |
0.1407177 |
1.7483024 |
fixed |
| parent_hyponyms_all |
-0.4778946 |
0.2001013 |
-2.3882636 |
fixed |
| delta_age |
0.1971178 |
0.0243072 |
8.1094516 |
fixed |
| mtld |
-0.7649144 |
0.0210032 |
-36.4189948 |
fixed |
| log(num_tokens) |
0.2227676 |
0.0535333 |
4.1612941 |
fixed |
| log(subsequent_tokens) |
0.0018281 |
0.0524447 |
0.0348581 |
fixed |
| sd_(Intercept).child_id |
1.0126951 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0548253 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.5031373 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0377438 |
NA |
NA |
Residual |
hypernyms first
CHILD
lmer(delta_mtld ~ child_hypernyms_first + delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control = lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
3.2235891 |
0.3864817 |
8.3408580 |
fixed |
| child_hypernyms_first |
-0.5229381 |
0.1156237 |
-4.5227579 |
fixed |
| delta_age |
0.1954141 |
0.0251062 |
7.7835113 |
fixed |
| mtld |
-0.7815793 |
0.0203871 |
-38.3369797 |
fixed |
| log(num_tokens) |
0.2257742 |
0.0488960 |
4.6174382 |
fixed |
| log(subsequent_tokens) |
-0.0092622 |
0.0497916 |
-0.1860192 |
fixed |
| sd_(Intercept).child_id |
1.1219413 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0678836 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.2673652 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0442476 |
NA |
NA |
Residual |
lmer(delta_mtld ~ child_hypernyms_first + child_hyponyms_all + delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control = lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
3.1954904 |
0.3862055 |
8.2740685 |
fixed |
| child_hypernyms_first |
-0.4954458 |
0.1167520 |
-4.2435753 |
fixed |
| child_hyponyms_all |
0.2285663 |
0.1349059 |
1.6942655 |
fixed |
| delta_age |
0.1914627 |
0.0252352 |
7.5871181 |
fixed |
| mtld |
-0.7813385 |
0.0203761 |
-38.3457751 |
fixed |
| log(num_tokens) |
0.2185597 |
0.0490708 |
4.4539703 |
fixed |
| log(subsequent_tokens) |
-0.0126711 |
0.0498201 |
-0.2543367 |
fixed |
| sd_(Intercept).child_id |
1.1166094 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0673961 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.2519085 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0440003 |
NA |
NA |
Residual |
PARENT
lmer(delta_mtld ~ parent_hypernyms_first + delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control=lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
2.4594054 |
0.4028852 |
6.104482 |
fixed |
| parent_hypernyms_first |
-0.0379819 |
0.1252290 |
-0.303299 |
fixed |
| delta_age |
0.1889583 |
0.0245026 |
7.711751 |
fixed |
| mtld |
-0.7458942 |
0.0208189 |
-35.827676 |
fixed |
| log(num_tokens) |
0.2823124 |
0.0527316 |
5.353765 |
fixed |
| log(subsequent_tokens) |
0.0243834 |
0.0527335 |
0.462389 |
fixed |
| sd_(Intercept).child_id |
1.0753888 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0558765 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.5394238 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0435696 |
NA |
NA |
Residual |
lmer(delta_mtld ~ parent_hypernyms_first + parent_hyponyms_all + delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control = lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
2.6976939 |
0.4129212 |
6.5331929 |
fixed |
| parent_hypernyms_first |
-0.0800480 |
0.1263789 |
-0.6333967 |
fixed |
| parent_hyponyms_all |
-0.4667530 |
0.1971028 |
-2.3680692 |
fixed |
| delta_age |
0.1889133 |
0.0245569 |
7.6928834 |
fixed |
| mtld |
-0.7509832 |
0.0208428 |
-36.0307851 |
fixed |
| log(num_tokens) |
0.2842725 |
0.0527005 |
5.3941180 |
fixed |
| log(subsequent_tokens) |
0.0213581 |
0.0526826 |
0.4054106 |
fixed |
| sd_(Intercept).child_id |
1.0889180 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0571127 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.5429819 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0413706 |
NA |
NA |
Residual |
BOTH
lmer(delta_mtld ~ child_hypernyms_first + parent_hypernyms_first + child_hyponyms_all + parent_hyponyms_all +delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control=lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
3.1439936 |
0.4184528 |
7.5133770 |
fixed |
| child_hypernyms_first |
-0.6173603 |
0.1262165 |
-4.8912821 |
fixed |
| parent_hypernyms_first |
0.0898299 |
0.1316641 |
0.6822653 |
fixed |
| child_hyponyms_all |
0.3136954 |
0.1382720 |
2.2686842 |
fixed |
| parent_hyponyms_all |
-0.4646096 |
0.1998322 |
-2.3249989 |
fixed |
| delta_age |
0.1970364 |
0.0243631 |
8.0874858 |
fixed |
| mtld |
-0.7640585 |
0.0210228 |
-36.3443010 |
fixed |
| log(num_tokens) |
0.2338906 |
0.0533420 |
4.3847400 |
fixed |
| log(subsequent_tokens) |
0.0035103 |
0.0524757 |
0.0668934 |
fixed |
| sd_(Intercept).child_id |
1.0164440 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0543061 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.5093642 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0384655 |
NA |
NA |
Residual |
hyponyms leaf
CHILD
lmer(delta_mtld ~ child_hyponyms_leaf + delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control = lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
2.8667826 |
0.3772981 |
7.5981902 |
fixed |
| child_hyponyms_leaf |
0.2748196 |
0.1217628 |
2.2570081 |
fixed |
| delta_age |
0.1768955 |
0.0255840 |
6.9143075 |
fixed |
| mtld |
-0.7735754 |
0.0202600 |
-38.1824003 |
fixed |
| log(num_tokens) |
0.2415001 |
0.0489294 |
4.9356882 |
fixed |
| log(subsequent_tokens) |
0.0027272 |
0.0498155 |
0.0547452 |
fixed |
| sd_(Intercept).child_id |
1.1705970 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0707084 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.2779667 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0448094 |
NA |
NA |
Residual |
lmer(delta_mtld ~ child_hypernyms_all + child_hyponyms_leaf + delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control = lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
3.3110874 |
0.3902806 |
8.4838646 |
fixed |
| child_hypernyms_all |
-0.4988977 |
0.1098115 |
-4.5432200 |
fixed |
| child_hyponyms_leaf |
0.2219834 |
0.1215583 |
1.8261476 |
fixed |
| delta_age |
0.1881750 |
0.0252821 |
7.4430112 |
fixed |
| mtld |
-0.7814888 |
0.0203852 |
-38.3360132 |
fixed |
| log(num_tokens) |
0.2036279 |
0.0495265 |
4.1114960 |
fixed |
| log(subsequent_tokens) |
-0.0127819 |
0.0497740 |
-0.2567987 |
fixed |
| sd_(Intercept).child_id |
1.1156562 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0673405 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.2492370 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0434365 |
NA |
NA |
Residual |
PARENT
lmer(delta_mtld ~ parent_hyponyms_leaf + delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control = lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
2.4181651 |
0.4071478 |
5.9392800 |
fixed |
| parent_hyponyms_leaf |
-0.0169953 |
0.1389437 |
-0.1223179 |
fixed |
| delta_age |
0.1900083 |
0.0246318 |
7.7139389 |
fixed |
| mtld |
-0.7495543 |
0.0208439 |
-35.9604044 |
fixed |
| log(num_tokens) |
0.3016714 |
0.0529134 |
5.7012280 |
fixed |
| log(subsequent_tokens) |
0.0182479 |
0.0529582 |
0.3445718 |
fixed |
| sd_(Intercept).child_id |
1.0782239 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0542837 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.5432701 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0515024 |
NA |
NA |
Residual |
lmer(delta_mtld ~ parent_hypernyms_all + parent_hyponyms_leaf + delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control = lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
2.6041775 |
0.4077458 |
6.3867675 |
fixed |
| parent_hypernyms_all |
-0.0931201 |
0.1251389 |
-0.7441342 |
fixed |
| parent_hyponyms_leaf |
-0.3290750 |
0.1620998 |
-2.0300772 |
fixed |
| delta_age |
0.1896830 |
0.0245327 |
7.7318395 |
fixed |
| mtld |
-0.7500581 |
0.0208482 |
-35.9771450 |
fixed |
| log(num_tokens) |
0.2851794 |
0.0527068 |
5.4106762 |
fixed |
| log(subsequent_tokens) |
0.0217371 |
0.0526941 |
0.4125150 |
fixed |
| sd_(Intercept).child_id |
1.0848525 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0568584 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.5418679 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0419633 |
NA |
NA |
Residual |
BOTH
lmer(delta_mtld ~ child_hypernyms_all+ parent_hypernyms_all + child_hyponyms_leaf + parent_hyponyms_leaf +delta_age + mtld + log(num_tokens) + log(subsequent_tokens) +
(session_num|child_id), full_df_with_delta,
control=lmerControl(optimizer="bobyqa")) %>%
tidy() %>%
kable()
| (Intercept) |
3.1929657 |
0.4175598 |
7.6467268 |
fixed |
| child_hypernyms_all |
-0.6356928 |
0.1167819 |
-5.4434172 |
fixed |
| parent_hypernyms_all |
0.0654549 |
0.1283361 |
0.5100273 |
fixed |
| child_hyponyms_leaf |
0.2670171 |
0.1242386 |
2.1492293 |
fixed |
| parent_hyponyms_leaf |
-0.3139801 |
0.1627750 |
-1.9289212 |
fixed |
| delta_age |
0.1951064 |
0.0243755 |
8.0041877 |
fixed |
| mtld |
-0.7629508 |
0.0210071 |
-36.3187897 |
fixed |
| log(num_tokens) |
0.2165729 |
0.0538395 |
4.0225656 |
fixed |
| log(subsequent_tokens) |
0.0046007 |
0.0524090 |
0.0877838 |
fixed |
| sd_(Intercept).child_id |
1.0082473 |
NA |
NA |
child_id |
| sd_session_num.child_id |
0.0539129 |
NA |
NA |
child_id |
| cor_(Intercept).session_num.child_id |
-0.5039109 |
NA |
NA |
child_id |
| sd_Observation.Residual |
1.0382978 |
NA |
NA |
Residual |
