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Set up models for misproduction-hesitation ordering at the word level…
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…. Other tweaks.
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@l-acs
l-acs committed Feb 1, 2024
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Showing 1 changed file with 112 additions and 76 deletions.
188 changes: 112 additions & 76 deletions code/analysisWordLevelReadAloudBeta.R
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Expand Up @@ -269,38 +269,38 @@ errorDat$timePerWord_gmc <- errorDat$timePerWord - mean(errorDat$timePerWord)
### SECTION 3.5: preparing for misprod-hes sequential analyses

# ignore the misprod-hes columns for now
# errorDatMisprodHes <- select(errorDat, !contains("_syllables"))
#
# # First: look at a given misproduction and check for nearby hesitations
# justMisprodWithHesBefore <- cbind(errorDatMisprodHes,
# hes_position = 0, # "before",
# misprod_tally = errorDat$misprod_with_hes_in_previous_syllables)
#
# justMisprodWithHesAfter <- cbind(errorDatMisprodHes,
# hes_position = 1, # "after",
# misprod_tally = errorDat$misprod_with_hes_in_next_syllables)
errorDatMisprodHes <- select(errorDat, !contains("any_"))

# First: look at a given misproduction and check for nearby hesitations
justMisprodWithHesBefore <- cbind(errorDatMisprodHes,
hes_position = 0, # "before",
misprod_in_adjacent_window = errorDat$misprod_with_any_prior_hesitation)

justMisprodWithHesAfter <- cbind(errorDatMisprodHes,
hes_position = 1, # "after",
misprod_in_adjacent_window = errorDat$misprod_with_any_upcoming_hesitation)


# stack the ones before and the ones after as rows of a single df (my attempt at long form)
# errorDatLongMisprodWithRelHes <- rbind(justMisprodWithHesBefore, justMisprodWithHesAfter)
#
# # track the binary relative position as a factor
# errorDatLongMisprodWithRelHes$hes_position <- as.factor(errorDatLongMisprodWithRelHes$hes_position)
#
# # Then: look at a given hesitation and check for nearby misproductions
# justHesWithMisprodBefore <- cbind(errorDatMisprodHes,
# misprod_position = 0, # "before",
# hes_tally = errorDat$hes_with_misprod_in_previous_syllables)
#
# justHesWithMisprodAfter <- cbind(errorDatMisprodHes,
# misprod_position = 1, # "after",
# hes_tally = errorDat$hes_with_misprod_in_next_syllables)
#
# # stack the ones before and the ones after as rows of a single df (my attempt at long form)
# errorDatLongHesWithRelMisprod <- rbind(justHesWithMisprodBefore, justHesWithMisprodAfter)
#
# # track the binary relative position as a factor
# errorDatLongHesWithRelMisprod$misprod_position <- as.factor(errorDatLongHesWithRelMisprod$misprod_position)
errorDatLongMisprodWithRelHes <- rbind(justMisprodWithHesBefore, justMisprodWithHesAfter)

# track the binary relative position as a factor
errorDatLongMisprodWithRelHes$hes_position <- as.factor(errorDatLongMisprodWithRelHes$hes_position)

# Then: look at a given hesitation and check for nearby misproductions
justHesWithMisprodBefore <- cbind(errorDatMisprodHes,
misprod_position = 0, # "before",
hes_in_adjacent_window = errorDat$hesitation_with_any_prior_misprod)

justHesWithMisprodAfter <- cbind(errorDatMisprodHes,
misprod_position = 1, # "after",
hes_in_adjacent_window = errorDat$hesitation_with_any_upcoming_misprod)

# stack the ones before and the ones after as rows of a single df (my attempt at long form)
errorDatLongHesWithRelMisprod <- rbind(justHesWithMisprodBefore, justHesWithMisprodAfter)

# track the binary relative position as a factor
errorDatLongHesWithRelMisprod$misprod_position <- as.factor(errorDatLongHesWithRelMisprod$misprod_position)



Expand Down Expand Up @@ -437,71 +437,71 @@ summary(f_model7)


# Accuracy/comprehension as explained by disfluencies *and* SA: hesitations per syllable with scaared
f_model8 <- glmer(challengeACC ~ hesitation * scaaredSoc_gmc + (1|id) + (1|passage),
data=errorDat, family = "binomial")
summary(f_model8)
# f_model8 <- glmer(challengeACC ~ hesitation * scaaredSoc_gmc + (1|id) + (1|passage),
# data=errorDat, family = "binomial")
# summary(f_model8)

# Accuracy/comprehension as explained by disfluencies: hesitations per word with scaared
f_model9 <- glmer(challengeACC ~ words_with_hes * scaaredSoc_gmc + (1|id) + (1|passage),
f_model9 <- glmer(challengeACC ~ hesitation * scaaredSoc_gmc + (1|id) + (1|passage),
data=errorDat, family = "binomial")
summary(f_model9)


# Accuracy/comprehension as explained by errors: misproductions per syllable with scaared
f_model10 <- glmer(challengeACC ~ misprod * scaaredSoc_gmc + (1|id) + (1|passage),
data=errorDat, family = "binomial")
summary(f_model10)
# f_model10 <- glmer(challengeACC ~ misprod * scaaredSoc_gmc + (1|id) + (1|passage),
# data=errorDat, family = "binomial")
# summary(f_model10)

# Accuracy/comprehension as explained by errors: misproductions per word with scaared
f_model11 <- glmer(challengeACC ~ words_with_misprod * scaaredSoc_gmc + (1|id) + (1|passage),
f_model11 <- glmer(challengeACC ~ misprod * scaaredSoc_gmc + (1|id) + (1|passage),
data=errorDat, family = "binomial")
summary(f_model11)



# Accuracy/comprehension as explained by disfluencies *and* SA: hesitations per syllable with bfne
f_model12 <- glmer(challengeACC ~ hesitation * bfne_gmc + (1|id) + (1|passage),
data=errorDat, family = "binomial")
summary(f_model12)
# f_model12 <- glmer(challengeACC ~ hesitation * bfne_gmc + (1|id) + (1|passage),
# data=errorDat, family = "binomial")
# summary(f_model12)

# Accuracy/comprehension as explained by disfluencies *and* SA: hesitations per word with bfne
f_model13 <- glmer(challengeACC ~ words_with_hes * bfne_gmc + (1|id) + (1|passage),
data=errorDat, family = "binomial")
summary(f_model13)
# f_model13 <- glmer(challengeACC ~ words_with_hes * bfne_gmc + (1|id) + (1|passage),
# data=errorDat, family = "binomial")
# summary(f_model13)


# Accuracy/comprehension as explained by errors *and* SA: misproductions per syllable with bfne
f_model14 <- glmer(challengeACC ~ misprod * bfne_gmc + (1|id) + (1|passage),
data=errorDat, family = "binomial")
summary(f_model14)
# f_model14 <- glmer(challengeACC ~ misprod * bfne_gmc + (1|id) + (1|passage),
# data=errorDat, family = "binomial")
# summary(f_model14)

# Accuracy/comprehension as explained by errors *and* SA: misproductions per word with bfne
f_model15 <- glmer(challengeACC ~ words_with_misprod * bfne_gmc + (1|id) + (1|passage),
data=errorDat, family = "binomial")
summary(f_model15)
# f_model15 <- glmer(challengeACC ~ words_with_misprod * bfne_gmc + (1|id) + (1|passage),
# data=errorDat, family = "binomial")
# summary(f_model15)



# Accuracy/comprehension as explained by disfluencies *and* SA: hesitations per syllable with sps
f_model16 <- glmer(challengeACC ~ hesitation * sps_gmc + (1|id) + (1|passage),
data=errorDat, family = "binomial")
summary(f_model16)
# f_model16 <- glmer(challengeACC ~ hesitation * sps_gmc + (1|id) + (1|passage),
# data=errorDat, family = "binomial")
# summary(f_model16)

# Accuracy/comprehension as explained by disfluencies *and* SA: hesitations per word with sps
f_model17 <- glmer(challengeACC ~ words_with_hes * sps_gmc + (1|id) + (1|passage),
data=errorDat, family = "binomial")
summary(f_model17)
# f_model17 <- glmer(challengeACC ~ words_with_hes * sps_gmc + (1|id) + (1|passage),
# data=errorDat, family = "binomial")
# summary(f_model17)


# Accuracy/comprehension as explained by errors *and* SA: misproductions per syllable with sps
f_model18 <- glmer(challengeACC ~ misprod * sps_gmc + (1|id) + (1|passage),
data=errorDat, family = "binomial")
summary(f_model18)
# f_model18 <- glmer(challengeACC ~ misprod * sps_gmc + (1|id) + (1|passage),
# data=errorDat, family = "binomial")
# summary(f_model18)

# Accuracy/comprehension as explained by errors *and* SA: misproductions per word with sps
f_model19 <- glmer(challengeACC ~ words_with_misprod * sps_gmc + (1|id) + (1|passage),
data=errorDat, family = "binomial")
summary(f_model19)
# f_model19 <- glmer(challengeACC ~ words_with_misprod * sps_gmc + (1|id) + (1|passage),
# data=errorDat, family = "binomial")
# summary(f_model19)



Expand All @@ -522,7 +522,7 @@ summary(f_model21) # ***
f_model22 <- lmerTest::lmer(words_with_misprod ~ hesitation + (1|id) + (1|passage),
data=errorDat, REML=TRUE)
summary(f_model22) # ***

# NB my * comments here (this section of models at least) are out of date


# Now, misproduction-hesitation interactions with social anxiety
Expand Down Expand Up @@ -593,35 +593,65 @@ summary(age_model2)

# misprod-hes ordering

# Is the number of hesitations adjacent to misproductions in a particular
# reading predicted by the

# Does the position of misproductions relative to hesitations


# we have a number of occurrences of a misproduction in a particular position
# relative to a passage's hesitations. does knowing the position (before/after)
# predict the number of these sequences we have?

# 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_tally ~ misprod_position + (1|id) + (1|passage),
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)
summary(hes_with_rel_misprod_model_1.5) # n.s., sameish

misprod_with_rel_hes_model_1 <- lmerTest::lmer(misprod_tally ~ hes_position + (1|id) + (1|passage),
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)
summary(misprod_with_rel_hes_model_1.5) # ., 0.0974

## does it interact with SA?
hes_with_rel_misprod_model_3 <- lmerTest::lmer(hes_tally ~ misprod_position * scaaredSoc_gmc + (1|id) + (1|passage),
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)
summary(hes_with_rel_misprod_model_3.5) # n.s.

misprod_with_rel_hes_model_4 <- lmerTest::lmer(misprod_tally ~ hes_position * scaaredSoc_gmc + (1|id) + (1|passage),
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)
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) # ""



Expand Down Expand Up @@ -663,3 +693,9 @@ summary(wordfreq_model_3.5) # still yes, very slightly higher p
wordfreq_model_4.5 <- lmerTest::lmer(misprod ~ log10frequency * scaaredSoc_gmc + (1|id) + (1|passage) + (1|word),
data=errorDatAttestedFreqs, REML=TRUE)
summary(wordfreq_model_4.5) # still no, slightly lower p = 0.114


# hesitation ~ wf x SA
interact_plot(model = wordfreq_model_3,
pred = log10frequency, modx = scaaredSoc_gmc, interval = TRUE)

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