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Fix more word-level models
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@l-acs
l-acs committed Sep 12, 2024
1 parent aba51a4 commit 4cfab07
Showing 1 changed file with 106 additions and 68 deletions.
174 changes: 106 additions & 68 deletions code/analysisWordLevelReadAloudBeta.R
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Original file line number Diff line number Diff line change
Expand Up @@ -471,6 +471,13 @@ if (DEBUG) {

### SECTION 3.2: mean-center continuous predictors

# now prevent ourselves from using data that isn't explicitly set up to be
# predictor or outcome: use the version that omitted the ambiguous columns
errorDatBackup <- errorDat
errorDat <- errorDatPredictorsOutcomes



#center continuous predictors
errorDat$age_gmc <- errorDat$age - mean(errorDat$age)
errorDat$bfne_gmc <- errorDat$bfne - mean(errorDat$bfne)
Expand All @@ -492,23 +499,29 @@ add_gmc <- function(df, col) { # makes a new column: centered version of variabl
mutate("{{col}}_gmc" := gmc(vec)) # ex. challengeAvgSub->challengeAvgSub_gmc
}

sandboxDat %>% add_gmc(log10frequency) %>% slice_sample(n = 50) %>% as.data.frame()

if (DEBUG) {
sandboxDat %>%
add_gmc(log10frequency) %>%
slice_sample(n = 50) %>%
as.data.frame()
}

errorDat <-
errorDat %>%
add_gmc(log10frequency)


# sanity check
for (col_index in which(stringr::str_detect(colnames(errorDat), ".*_gmc"))) {
colname <- names(errorDat[col_index])
col <- errorDat[[col_index]]
print(colname)

avg <- mean(col)
print(paste(' mean:', avg,
' rounded mean:', round(avg, digits = 10)))
if (DEBUG) {
for (col_index in which(stringr::str_detect(colnames(errorDat), ".*_gmc"))) {
colname <- names(errorDat[col_index])
col <- errorDat[[col_index]]
print(colname)

avg <- mean(col)
print(paste(' mean:', avg,
' rounded mean:', round(avg, digits = 10)))
}
}

### SECTION 3.3: preparing for misprod-hes sequential analyses
Expand Down Expand Up @@ -550,6 +563,10 @@ errorDatLongHesWithRelMisprod$misprod_position <- as.factor(errorDatLongHesWithR


### SECTION 4: MODEL RESULTS
# again, now prevent ourselves from using data that isn't explicitly set up to
# be predictor or outcome: use the version that omitted the ambiguous columns


#misprod x bfne
# model1 <- lmerTest::lmer(misprod ~ bfne_gmc + (1|id) + (1|passage),
# data=errorDat, REML=TRUE)
Expand All @@ -561,9 +578,25 @@ model2 <- lmerTest::lmer(misprod ~ scaaredSoc_gmc + (1|id) + (1|passage),
summary(model2)

#misprod x scaaredSoc control for word
model2.5 <- lmerTest::lmer(misprod ~ scaaredSoc_gmc + (1|id) + (1|passage) + (1|word),
data=errorDat, REML=TRUE)
# this version fails - as intended: object 'misprod' not found
if (DEBUG) {
old_model2.5 <- lmerTest::lmer(misprod ~ scaaredSoc_gmc + (1|id) + (1|passage) + (1|word),
data=errorDat, REML=TRUE)
}

model2.5 <- lmerTest::lmer(misprod_outcome ~ scaaredSoc_gmc + (1|id) + (1|passage) + (1|word),
data=errorDat, REML=TRUE)
summary(model2.5)

if(DEBUG) { # compare
wrong_model2.5 <- lmerTest::lmer(misprod_predictor ~ scaaredSoc_gmc + (1|id) + (1|passage) + (1|word),
data=errorDat, REML=TRUE)
# Error in mkRespMod(fr, REML = REMLpass) : response must be numeric
}

summary(wrong_model2.5)


# tldr 2/28/24 SA indv. do not misproduce more/less


Expand All @@ -578,12 +611,12 @@ summary(model2.5)
# summary(model4)

#hesitation x scaaredSoc
model5 <- lmerTest::lmer(hesitation ~ scaaredSoc_gmc + (1|id) + (1|passage),
model5 <- lmerTest::lmer(hesitation_outcome ~ scaaredSoc_gmc + (1|id) + (1|passage),
data=errorDat, REML=TRUE)
summary(model5)

# hesitation x scaaredSoc, control for word
model5.5 <- lmerTest::lmer(hesitation ~ scaaredSoc_gmc + (1|id) + (1|passage) + (1|word),
model5.5 <- lmerTest::lmer(hesitation_outcome ~ scaaredSoc_gmc + (1|id) + (1|passage) + (1|word),
data=errorDat, REML=TRUE)
summary(model5.5)

Expand All @@ -604,7 +637,7 @@ summary(model5.5)
# Now, misproduction-hesitation relationships

# Errors as explained by disfluency: rate of misproduced syllables from rate of hesitated syllables
f_model20 <- lmerTest::lmer(misprod ~ hesitation + (1|id) + (1|passage),
f_model20 <- lmerTest::lmer(misprod_outcome ~ hesitation_predictor + (1|id) + (1|passage),
data=errorDat, REML=TRUE)
summary(f_model20) # ***
# should we (1|word) here?
Expand All @@ -630,7 +663,7 @@ summary(f_model20) # ***
# Now, misproduction-hesitation interactions with social anxiety

# Errors as explained by disfluency and SA: rate of misproduced syllables from rate of hesitated syllables and scaared
f_model23 <- lmerTest::lmer(misprod ~ hesitation * scaaredSoc_gmc + (1|id) + (1|passage),
f_model23 <- lmerTest::lmer(misprod_outcome ~ hesitation_predictor * scaaredSoc_gmc + (1|id) + (1|passage),
data=errorDat, REML=TRUE)
summary(f_model23)

Expand All @@ -654,7 +687,7 @@ summary(f_model23)

# What happens when we control for age?
#hesitation x scaaredSoc
age_model1 <- lmerTest::lmer(hesitation ~ scaaredSoc_gmc + age_gmc + (1|id) + (1|passage),
age_model1 <- lmerTest::lmer(hesitation_outcome ~ scaaredSoc_gmc + age_gmc + (1|id) + (1|passage),
data=errorDat, REML=TRUE)
summary(age_model1)

Expand All @@ -675,78 +708,80 @@ summary(age_model1)
# does misproduction location relative to a hesitation predict how many
# instances we get in a particular reading?

hes_with_rel_misprod_model_1 <- lmerTest::lmer(hes_in_adjacent_window ~ misprod_position + (1|id) + (1|passage),
data=errorDatLongHesWithRelMisprod, REML=TRUE)
summary(hes_with_rel_misprod_model_1) # n.s., 0.271

misprod_with_rel_hes_model_1 <- lmerTest::lmer(misprod_in_adjacent_window ~ hes_position + (1|id) + (1|passage),
data=errorDatLongMisprodWithRelHes, REML=TRUE)
summary(misprod_with_rel_hes_model_1) # n.s., 0.108

## does it interact with SA?
hes_with_rel_misprod_model_3 <- lmerTest::lmer(hes_in_adjacent_window ~ misprod_position * scaaredSoc_gmc + (1|id) + (1|passage),
data=errorDatLongHesWithRelMisprod, REML=TRUE)
summary(hes_with_rel_misprod_model_3) # n.s.

misprod_with_rel_hes_model_4 <- lmerTest::lmer(misprod_in_adjacent_window ~ hes_position * scaaredSoc_gmc + (1|id) + (1|passage),
data=errorDatLongMisprodWithRelHes, REML=TRUE)
summary(misprod_with_rel_hes_model_4) # n.s.

# what if we control for word?
hes_with_rel_misprod_model_1.5 <- lmerTest::lmer(hes_in_adjacent_window ~ misprod_position + (1|id) + (1|passage) + (1|word),
data=errorDatLongHesWithRelMisprod, REML=TRUE)
summary(hes_with_rel_misprod_model_1.5) # n.s., sameish
# TODO unfixed per earlier predictor/outcome differentiation
if (FALSE) {
hes_with_rel_misprod_model_1 <- lmerTest::lmer(hes_in_adjacent_window ~ misprod_position + (1|id) + (1|passage),
data=errorDatLongHesWithRelMisprod, REML=TRUE)
summary(hes_with_rel_misprod_model_1) # n.s., 0.271

misprod_with_rel_hes_model_1.5 <- lmerTest::lmer(misprod_in_adjacent_window ~ hes_position + (1|id) + (1|passage) + (1|word),
data=errorDatLongMisprodWithRelHes, REML=TRUE)
summary(misprod_with_rel_hes_model_1.5) # ., 0.0974
misprod_with_rel_hes_model_1 <- lmerTest::lmer(misprod_in_adjacent_window ~ hes_position + (1|id) + (1|passage),
data=errorDatLongMisprodWithRelHes, REML=TRUE)
summary(misprod_with_rel_hes_model_1) # n.s., 0.108

## does it interact with SA?
hes_with_rel_misprod_model_3.5 <- lmerTest::lmer(hes_in_adjacent_window ~ misprod_position * scaaredSoc_gmc + (1|id) + (1|passage) + (1|word),
data=errorDatLongHesWithRelMisprod, REML=TRUE)
summary(hes_with_rel_misprod_model_3.5) # n.s.
## does it interact with SA?
hes_with_rel_misprod_model_3 <- lmerTest::lmer(hes_in_adjacent_window ~ misprod_position * scaaredSoc_gmc + (1|id) + (1|passage),
data=errorDatLongHesWithRelMisprod, REML=TRUE)
summary(hes_with_rel_misprod_model_3) # n.s.

misprod_with_rel_hes_model_4.5 <- lmerTest::lmer(misprod_in_adjacent_window ~ hes_position * scaaredSoc_gmc + (1|id) + (1|passage) + (1|word),
data=errorDatLongMisprodWithRelHes, REML=TRUE)
summary(misprod_with_rel_hes_model_4.5) # n.s.
misprod_with_rel_hes_model_4 <- lmerTest::lmer(misprod_in_adjacent_window ~ hes_position * scaaredSoc_gmc + (1|id) + (1|passage),
data=errorDatLongMisprodWithRelHes, REML=TRUE)
summary(misprod_with_rel_hes_model_4) # n.s.

# and if we ignore passage?
hes_with_rel_misprod_model_1.6 <- lmerTest::lmer(hes_in_adjacent_window ~ misprod_position + (1|id) + (1|word),
# what if we control for word?
hes_with_rel_misprod_model_1.5 <- lmerTest::lmer(hes_in_adjacent_window ~ misprod_position + (1|id) + (1|passage) + (1|word),
data=errorDatLongHesWithRelMisprod, REML=TRUE)
summary(hes_with_rel_misprod_model_1.6) # n.s., sameish
summary(hes_with_rel_misprod_model_1.5) # n.s., sameish

misprod_with_rel_hes_model_1.6 <- lmerTest::lmer(misprod_in_adjacent_window ~ hes_position + (1|id) + (1|word),
misprod_with_rel_hes_model_1.5 <- lmerTest::lmer(misprod_in_adjacent_window ~ hes_position + (1|id) + (1|passage) + (1|word),
data=errorDatLongMisprodWithRelHes, REML=TRUE)
summary(misprod_with_rel_hes_model_1.6) # made no difference, as you might expect
summary(misprod_with_rel_hes_model_1.5) # ., 0.0974

## does it interact with SA?
hes_with_rel_misprod_model_3.6 <- lmerTest::lmer(hes_in_adjacent_window ~ misprod_position * scaaredSoc_gmc + (1|id) + (1|word),
## does it interact with SA?
hes_with_rel_misprod_model_3.5 <- lmerTest::lmer(hes_in_adjacent_window ~ misprod_position * scaaredSoc_gmc + (1|id) + (1|passage) + (1|word),
data=errorDatLongHesWithRelMisprod, REML=TRUE)
summary(hes_with_rel_misprod_model_3.6) # ""
summary(hes_with_rel_misprod_model_3.5) # n.s.

misprod_with_rel_hes_model_4.6 <- lmerTest::lmer(misprod_in_adjacent_window ~ hes_position * scaaredSoc_gmc + (1|id) + (1|word),
misprod_with_rel_hes_model_4.5 <- lmerTest::lmer(misprod_in_adjacent_window ~ hes_position * scaaredSoc_gmc + (1|id) + (1|passage) + (1|word),
data=errorDatLongMisprodWithRelHes, REML=TRUE)
summary(misprod_with_rel_hes_model_4.6) # ""
summary(misprod_with_rel_hes_model_4.5) # n.s.

# and if we ignore passage?
hes_with_rel_misprod_model_1.6 <- lmerTest::lmer(hes_in_adjacent_window ~ misprod_position + (1|id) + (1|word),
data=errorDatLongHesWithRelMisprod, REML=TRUE)
summary(hes_with_rel_misprod_model_1.6) # n.s., sameish

misprod_with_rel_hes_model_1.6 <- lmerTest::lmer(misprod_in_adjacent_window ~ hes_position + (1|id) + (1|word),
data=errorDatLongMisprodWithRelHes, REML=TRUE)
summary(misprod_with_rel_hes_model_1.6) # made no difference, as you might expect

## does it interact with SA?
hes_with_rel_misprod_model_3.6 <- lmerTest::lmer(hes_in_adjacent_window ~ misprod_position * scaaredSoc_gmc + (1|id) + (1|word),
data=errorDatLongHesWithRelMisprod, REML=TRUE)
summary(hes_with_rel_misprod_model_3.6) # ""

misprod_with_rel_hes_model_4.6 <- lmerTest::lmer(misprod_in_adjacent_window ~ hes_position * scaaredSoc_gmc + (1|id) + (1|word),
data=errorDatLongMisprodWithRelHes, REML=TRUE)
summary(misprod_with_rel_hes_model_4.6) # ""
}


# Word frequency analysis with words absent from corpus dropped
# Does a word's frequency predict hesitation on that word?
errorDatAttestedFreqs <- filter(errorDat, log10frequency > 0)
wordfreq_model_1 <- lmerTest::lmer(hesitation ~ log10frequency + (1|id) + (1|passage) + (1|word),
wordfreq_model_1 <- lmerTest::lmer(hesitation_outcome ~ log10frequency_gmc + (1|id) + (1|passage) + (1|word),
data=errorDatAttestedFreqs, REML=TRUE)
summary(wordfreq_model_1)
wordfreq_model_2 <- lmerTest::lmer(misprod ~ log10frequency + (1|id) + (1|passage) + (1|word),
wordfreq_model_2 <- lmerTest::lmer(misprod_outcome ~ log10frequency_gmc + (1|id) + (1|passage) + (1|word),
data=errorDatAttestedFreqs, REML=TRUE)
summary(wordfreq_model_2)


# Do social anxiety and frequency interact to predict hesitation rate or misproduction rate?
wordfreq_model_3 <- lmerTest::lmer(hesitation ~ log10frequency * scaaredSoc_gmc + (1|id) + (1|passage) + (1|word),
wordfreq_model_3 <- lmerTest::lmer(hesitation_outcome ~ log10frequency_gmc * scaaredSoc_gmc + (1|id) + (1|passage) + (1|word),
data=errorDatAttestedFreqs, REML=TRUE)
summary(wordfreq_model_3) # looks good!

wordfreq_model_4 <- lmerTest::lmer(misprod ~ log10frequency * scaaredSoc_gmc + (1|id) + (1|passage) + (1|word),
wordfreq_model_4 <- lmerTest::lmer(misprod_outcome ~ log10frequency_gmc * scaaredSoc_gmc + (1|id) + (1|passage) + (1|word),
data=errorDatAttestedFreqs, REML=TRUE)
summary(wordfreq_model_4) # tldr no?

Expand All @@ -763,16 +798,19 @@ errorDat$log10frequency_with_absents_as_median <- case_match(
0 ~ subtlexus_median,
.default = errorDat$log10frequency)

compare_freq <- data.frame(cbind(old = errorDat$log10frequency,
new = errorDat$log10frequency_with_absents_as_median))
if (DEBUG) {
compare_freq <- data.frame(cbind(old = errorDat$log10frequency,
new = errorDat$log10frequency_with_absents_as_median))

filter(compare_freq, old != new) %>% # confirm it worked as expected
filter(old != 0 | new != subtlexus_median) %>%
nrow == 0 # TRUE
filter(compare_freq, old != new) %>% # confirm it worked as expected
filter(old != 0 | new != subtlexus_median) %>%
nrow == 0 # TRUE
}

# TODO ADD GMC

# Does a word's frequency predict hesitation on that word?
wordfreq_model_with_absents_as_median_1 <- lmerTest::lmer(hesitation ~ log10frequency_with_absents_as_median + (1|id) + (1|passage) + (1|word),
wordfreq_model_with_absents_as_median_1 <- lmerTest::lmer(hesitation_outcome ~ log10frequency_with_absents_as_median + (1|id) + (1|passage) + (1|word),
data=errorDat, REML=TRUE)
summary(wordfreq_model_with_absents_as_median_1)

Expand Down

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