misc
2023-03-31
arousal.extended.wbaseline <- readRDS("arousal.extended.wbaseline.RDS")
m3 <- lmer(rating ~ cueing*baseline + session + (1 + cueing | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)))## boundary (singular) fit: see help('isSingular')tab_model(m3)| rating | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.05 | 1.69 – 2.40 | <0.001 |
| cueing [Uncued] | -0.32 | -0.58 – -0.07 | 0.012 |
| baseline | 0.33 | 0.27 – 0.39 | <0.001 |
| session [4] | -0.27 | -0.35 – -0.18 | <0.001 |
|
cueing [Uncued] × baseline |
0.11 | 0.04 – 0.18 | 0.003 |
| Random Effects | |||
| σ2 | 0.67 | ||
| τ00 item | 0.14 | ||
| τ00 participant | 0.34 | ||
| τ11 participant.cueingUncued | 0.03 | ||
| ρ01 participant | -1.00 | ||
| ICC | 0.37 | ||
| N participant | 17 | ||
| N item | 48 | ||
| Observations | 1517 | ||
| Marginal R2 / Conditional R2 | 0.183 / 0.484 | ||
tab_model(m3, show.reflvl = TRUE)| rating | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.05 | 1.69 – 2.40 | <0.001 |
| baseline | 0.33 | 0.27 – 0.39 | <0.001 |
| Cued | Reference | ||
| Uncued | -0.32 | -0.58 – -0.07 | 0.012 |
| cueingUncued:baseline | 0.11 | 0.04 – 0.18 | 0.003 |
| session4 | -0.27 | -0.35 – -0.18 | <0.001 |
| Random Effects | |||
| σ2 | 0.67 | ||
| τ00 item | 0.14 | ||
| τ00 participant | 0.34 | ||
| τ11 participant.cueingUncued | 0.03 | ||
| ρ01 participant | -1.00 | ||
| ICC | 0.37 | ||
| N participant | 17 | ||
| N item | 48 | ||
| Observations | 1517 | ||
| Marginal R2 / Conditional R2 | 0.183 / 0.484 | ||
emmeans(m3,"cueing")## NOTE: Results may be misleading due to involvement in interactions## cueing emmean SE df lower.CL upper.CL
## Cued 2.94 0.154 20.5 2.62 3.26
## Uncued 2.96 0.117 24.9 2.72 3.20
##
## Results are averaged over the levels of: session
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95# set "female" as the reference level for "gender"
m3 <- lmer(rating ~ cueing*baseline + session + (1 + cueing | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% set_ref_level(cueing , "Uncued") )## boundary (singular) fit: see help('isSingular')tab_model(m3)| rating | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 1.72 | 1.43 – 2.01 | <0.001 |
| cueing [Cued] | 0.32 | 0.07 – 0.58 | 0.012 |
| baseline | 0.44 | 0.38 – 0.49 | <0.001 |
| session [4] | -0.27 | -0.35 – -0.18 | <0.001 |
| cueing [Cued] × baseline | -0.11 | -0.18 – -0.04 | 0.003 |
| Random Effects | |||
| σ2 | 0.67 | ||
| τ00 item | 0.14 | ||
| τ00 participant | 0.17 | ||
| τ11 participant.cueingCued | 0.03 | ||
| ρ01 participant | 1.00 | ||
| N participant | 17 | ||
| N item | 48 | ||
| Observations | 1517 | ||
| Marginal R2 / Conditional R2 | 0.262 / NA | ||
tab_model(m3, show.reflvl = TRUE)| rating | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 1.72 | 1.43 – 2.01 | <0.001 |
| baseline | 0.44 | 0.38 – 0.49 | <0.001 |
| Uncued | Reference | ||
| cueingCued:baseline | -0.11 | -0.18 – -0.04 | 0.003 |
| Cued | 0.32 | 0.07 – 0.58 | 0.012 |
| session4 | -0.27 | -0.35 – -0.18 | <0.001 |
| Random Effects | |||
| σ2 | 0.67 | ||
| τ00 item | 0.14 | ||
| τ00 participant | 0.17 | ||
| τ11 participant.cueingCued | 0.03 | ||
| ρ01 participant | 1.00 | ||
| N participant | 17 | ||
| N item | 48 | ||
| Observations | 1517 | ||
| Marginal R2 / Conditional R2 | 0.262 / NA | ||
emmeans(m3,"cueing")## NOTE: Results may be misleading due to involvement in interactions## cueing emmean SE df lower.CL upper.CL
## Uncued 2.96 0.117 24.9 2.72 3.20
## Cued 2.94 0.154 20.5 2.62 3.26
##
## Results are averaged over the levels of: session
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95plot_model(m3,type = "pred",terms = c("baseline","cueing"))
Does the data show people rating stuff higher as a function of their baseline rating?
data.aggr <- arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% group_by(participant,cueing,baseline) %>% summarise(meanRating=mean(rating, na.rm=TRUE)) %>% na.omit()## `summarise()` has grouped output by 'participant', 'cueing'. You can override
## using the `.groups` argument.library(afex)## ************
## Welcome to afex. For support visit: http://afex.singmann.science/
## - Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()
## - Methods for calculating p-values with mixed(): 'S', 'KR', 'LRT', and 'PB'
## - 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests
## - NEWS: emmeans() for ANOVA models now uses model = 'multivariate' as default.
## - Get and set global package options with: afex_options()
## - Set orthogonal sum-to-zero contrasts globally: set_sum_contrasts()
## - For example analyses see: browseVignettes("afex")
## ************
##
## Attaching package: 'afex'
##
## The following object is masked from 'package:lme4':
##
## lmeraw <- aov_ez("participant", "meanRating", data.aggr %>% na.omit(), within = c("cueing", "baseline"))## Warning: Missing values for following ID(s):
## PT03, PT05, PT07, PT08, PT12, PT17, PT18, PT19
## Removing those cases from the analysis.library(ggplot2)
ggplot(data.aggr, aes(x = baseline, y = meanRating, group = cueing, color = cueing)) +
stat_summary(fun = "mean", geom = "point", size = 1.5) +
stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.1) +
labs(x = "Baseline", y = "Mean Rating", color = "Cueing") +
theme_minimal()
data.aggr <- arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% group_by(participant,cueing,baseline,session) %>% summarise(meanRating=mean(rating, na.rm=TRUE)) %>% na.omit()## `summarise()` has grouped output by 'participant', 'cueing', 'baseline'. You
## can override using the `.groups` argument.ggplot(data.aggr, aes(x = baseline, y = meanRating, group = cueing, color = cueing)) +
stat_summary(fun = "mean", geom = "point", size = 1.5) +
stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.1) +
labs(x = "Baseline", y = "Mean Rating", color = "Cueing") +
theme_minimal() + facet_wrap(~session)
ggplot(arousal.extended.wbaseline, aes(x=factor(rating), group=cueing,fill=cueing))+
geom_bar(stat="count", position=position_dodge())+
theme_minimal()
ggplot(arousal.extended.wbaseline, aes(x=factor(rating), group=cueing,fill=cueing))+
geom_bar(stat="count", position=position_dodge())+
theme_minimal() + facet_wrap(~session) + coord_flip()
ggplot(arousal.extended.wbaseline %>% filter(baseline >= 4), aes(x=factor(rating), group=cueing,fill=cueing))+
geom_bar(stat="count", position=position_dodge())+
theme_minimal() + facet_wrap(~session) + coord_flip()
m3.0 <- lmer(rating ~ cueing + baseline + session + (1 | participant) + (1 | item), data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)))
m3.1 <- lmer(rating ~ cueing + baseline + session + (1 + cueing | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)))## boundary (singular) fit: see help('isSingular')m3.x <- lmer(rating ~ cueing* baseline + session + (1 + cueing | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)))## boundary (singular) fit: see help('isSingular')m3.x2 <- lmer(rating ~ cueing* baseline + cueing*session + (1 + cueing | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)))## boundary (singular) fit: see help('isSingular')anova(m3.0,m3.1 ,m3.x,m3.x2)## refitting model(s) with ML (instead of REML)## Data: arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), ...
## Models:
## m3.0: rating ~ cueing + baseline + session + (1 | participant) + (1 | item)
## m3.1: rating ~ cueing + baseline + session + (1 + cueing | participant) + (1 | item)
## m3.x: rating ~ cueing * baseline + session + (1 + cueing | participant) + (1 | item)
## m3.x2: rating ~ cueing * baseline + cueing * session + (1 + cueing | participant) + (1 | item)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m3.0 7 3889.1 3926.3 -1937.5 3875.1
## m3.1 9 3883.6 3931.5 -1932.8 3865.6 9.4372 2 0.008928 **
## m3.x 10 3877.0 3930.2 -1928.5 3857.0 8.6259 1 0.003314 **
## m3.x2 11 3878.9 3937.5 -1928.5 3856.9 0.0769 1 0.781603
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(m3.0 ,m3.x)## refitting model(s) with ML (instead of REML)## Data: arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), ...
## Models:
## m3.0: rating ~ cueing + baseline + session + (1 | participant) + (1 | item)
## m3.x: rating ~ cueing * baseline + session + (1 + cueing | participant) + (1 | item)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m3.0 7 3889.1 3926.3 -1937.5 3875.1
## m3.x 10 3877.0 3930.2 -1928.5 3857.0 18.063 3 0.0004269 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(m3.0,m3.1 ,m3.x,m3.x2)| rating | rating | rating | rating | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 1.88 | 1.58 – 2.18 | <0.001 | 1.87 | 1.55 – 2.20 | <0.001 | 2.05 | 1.69 – 2.40 | <0.001 | 2.04 | 1.69 – 2.39 | <0.001 |
| cueing [Uncued] | 0.01 | -0.07 – 0.10 | 0.742 | 0.02 | -0.09 – 0.13 | 0.755 | -0.32 | -0.58 – -0.07 | 0.012 | -0.31 | -0.58 – -0.05 | 0.020 |
| baseline | 0.38 | 0.34 – 0.43 | <0.001 | 0.39 | 0.34 – 0.43 | <0.001 | 0.33 | 0.27 – 0.39 | <0.001 | 0.33 | 0.27 – 0.39 | <0.001 |
| session [4] | -0.27 | -0.36 – -0.18 | <0.001 | -0.27 | -0.36 – -0.18 | <0.001 | -0.27 | -0.35 – -0.18 | <0.001 | -0.26 | -0.38 – -0.14 | <0.001 |
|
cueing [Uncued] × baseline |
0.11 | 0.04 – 0.18 | 0.003 | 0.11 | 0.04 – 0.18 | 0.003 | ||||||
|
cueing [Uncued] × session [4] |
-0.02 | -0.19 – 0.14 | 0.783 | |||||||||
| Random Effects | ||||||||||||
| σ2 | 0.68 | 0.68 | 0.67 | 0.67 | ||||||||
| τ00 | 0.14 item | 0.14 item | 0.14 item | 0.14 item | ||||||||
| 0.25 participant | 0.32 participant | 0.34 participant | 0.34 participant | |||||||||
| τ11 | 0.02 participant.cueingUncued | 0.03 participant.cueingUncued | 0.03 participant.cueingUncued | |||||||||
| ρ01 | -1.00 participant | -1.00 participant | -1.00 participant | |||||||||
| ICC | 0.36 | 0.37 | ||||||||||
| N | 17 participant | 17 participant | 17 participant | 17 participant | ||||||||
| 48 item | 48 item | 48 item | 48 item | |||||||||
| Observations | 1517 | 1517 | 1517 | 1517 | ||||||||
| Marginal R2 / Conditional R2 | 0.180 / 0.477 | 0.260 / NA | 0.183 / 0.484 | 0.262 / NA | ||||||||
m3.0 <- lmer(rating ~ cueing + baseline + session + (1 | participant) + (1 | item), data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 3))
m3.1 <- lmer(rating ~ cueing + baseline + session + (1 + cueing | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 3))
m3.x <- lmer(rating ~ cueing* baseline + session + (1 + cueing | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 3))
anova(m3.0,m3.1 ,m3.x)## refitting model(s) with ML (instead of REML)## Data: arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), ...
## Models:
## m3.0: rating ~ cueing + baseline + session + (1 | participant) + (1 | item)
## m3.1: rating ~ cueing + baseline + session + (1 + cueing | participant) + (1 | item)
## m3.x: rating ~ cueing * baseline + session + (1 + cueing | participant) + (1 | item)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m3.0 7 2601.7 2636.2 -1293.8 2587.7
## m3.1 9 2589.6 2634.0 -1285.8 2571.6 16.0376 2 0.0003292 ***
## m3.x 10 2591.2 2640.5 -1285.6 2571.2 0.4486 1 0.5029881
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(m3.0 ,m3.x)## refitting model(s) with ML (instead of REML)## Data: arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), ...
## Models:
## m3.0: rating ~ cueing + baseline + session + (1 | participant) + (1 | item)
## m3.x: rating ~ cueing * baseline + session + (1 + cueing | participant) + (1 | item)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m3.0 7 2601.7 2636.2 -1293.8 2587.7
## m3.x 10 2591.2 2640.5 -1285.6 2571.2 16.486 3 0.0009012 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(m3.0,m3.1 ,m3.x)| rating | rating | rating | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 1.67 | 1.25 – 2.08 | <0.001 | 1.62 | 1.18 – 2.07 | <0.001 | 1.71 | 1.19 – 2.24 | <0.001 |
| cueing [Uncued] | 0.06 | -0.04 – 0.17 | 0.232 | 0.09 | -0.06 – 0.25 | 0.249 | -0.08 | -0.62 – 0.46 | 0.772 |
| baseline | 0.44 | 0.36 – 0.52 | <0.001 | 0.44 | 0.37 – 0.52 | <0.001 | 0.42 | 0.31 – 0.53 | <0.001 |
| session [4] | -0.26 | -0.36 – -0.16 | <0.001 | -0.26 | -0.36 – -0.16 | <0.001 | -0.26 | -0.36 – -0.16 | <0.001 |
|
cueing [Uncued] × baseline |
0.04 | -0.09 – 0.18 | 0.516 | ||||||
| Random Effects | |||||||||
| σ2 | 0.65 | 0.64 | 0.64 | ||||||
| τ00 | 0.12 item | 0.12 item | 0.12 item | ||||||
| 0.28 participant | 0.41 participant | 0.41 participant | |||||||
| τ11 | 0.06 participant.cueingUncued | 0.06 participant.cueingUncued | |||||||
| ρ01 | -0.93 participant | -0.94 participant | |||||||
| ICC | 0.38 | 0.39 | 0.39 | ||||||
| N | 17 participant | 17 participant | 17 participant | ||||||
| 48 item | 48 item | 48 item | |||||||
| Observations | 1024 | 1024 | 1024 | ||||||
| Marginal R2 / Conditional R2 | 0.111 / 0.450 | 0.115 / 0.461 | 0.115 / 0.461 | ||||||
#without baseline
m3.0 <- lmer(rating ~ cueing + session + (1 | participant) + (1 | item), data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 3))
m3.1 <- lmer(rating ~ cueing + session + (1 + cueing | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 3))
m3.x <- lmer(rating ~ cueing* baseline + session + (1 + cueing | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 3))
anova(m3.0,m3.1 ,m3.x)## refitting model(s) with ML (instead of REML)## Data: arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), ...
## Models:
## m3.0: rating ~ cueing + session + (1 | participant) + (1 | item)
## m3.1: rating ~ cueing + session + (1 + cueing | participant) + (1 | item)
## m3.x: rating ~ cueing * baseline + session + (1 + cueing | participant) + (1 | item)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m3.0 6 2704.4 2734.0 -1346.2 2692.4
## m3.1 8 2696.9 2736.3 -1340.5 2680.9 11.528 2 0.003139 **
## m3.x 10 2591.2 2640.5 -1285.6 2571.2 109.736 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(m3.0 ,m3.x)## refitting model(s) with ML (instead of REML)## Data: arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), ...
## Models:
## m3.0: rating ~ cueing + session + (1 | participant) + (1 | item)
## m3.x: rating ~ cueing * baseline + session + (1 + cueing | participant) + (1 | item)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m3.0 6 2704.4 2734.0 -1346.2 2692.4
## m3.x 10 2591.2 2640.5 -1285.6 2571.2 121.26 4 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(m3.0,m3.1 ,m3.x)| rating | rating | rating | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 3.30 | 2.98 – 3.62 | <0.001 | 3.28 | 2.92 – 3.65 | <0.001 | 1.71 | 1.19 – 2.24 | <0.001 |
| cueing [Uncued] | 0.04 | -0.06 – 0.15 | 0.416 | 0.07 | -0.09 – 0.22 | 0.380 | -0.08 | -0.62 – 0.46 | 0.772 |
| session [4] | -0.26 | -0.37 – -0.16 | <0.001 | -0.26 | -0.37 – -0.16 | <0.001 | -0.26 | -0.36 – -0.16 | <0.001 |
| baseline | 0.42 | 0.31 – 0.53 | <0.001 | ||||||
|
cueing [Uncued] × baseline |
0.04 | -0.09 – 0.18 | 0.516 | ||||||
| Random Effects | |||||||||
| σ2 | 0.70 | 0.69 | 0.64 | ||||||
| τ00 | 0.23 item | 0.23 item | 0.12 item | ||||||
| 0.34 participant | 0.46 participant | 0.41 participant | |||||||
| τ11 | 0.06 participant.cueingUncued | 0.06 participant.cueingUncued | |||||||
| ρ01 | -0.85 participant | -0.94 participant | |||||||
| ICC | 0.45 | 0.46 | 0.39 | ||||||
| N | 17 participant | 17 participant | 17 participant | ||||||
| 48 item | 48 item | 48 item | |||||||
| Observations | 1024 | 1024 | 1024 | ||||||
| Marginal R2 / Conditional R2 | 0.014 / 0.457 | 0.014 / 0.465 | 0.115 / 0.461 | ||||||
m3.0 <- lmer(rating ~ cueing + baseline + session + (1 | participant) + (1 | item), data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 2))
m3.1 <- lmer(rating ~ cueing + baseline + session + (1 + cueing | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 2))## boundary (singular) fit: see help('isSingular')m3.x <- lmer(rating ~ cueing* baseline + session + (1 + cueing | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 2))## boundary (singular) fit: see help('isSingular')anova(m3.0,m3.1 ,m3.x)## refitting model(s) with ML (instead of REML)## Data: arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), ...
## Models:
## m3.0: rating ~ cueing + baseline + session + (1 | participant) + (1 | item)
## m3.1: rating ~ cueing + baseline + session + (1 + cueing | participant) + (1 | item)
## m3.x: rating ~ cueing * baseline + session + (1 + cueing | participant) + (1 | item)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m3.0 7 3508.6 3545.1 -1747.3 3494.6
## m3.1 9 3498.6 3545.5 -1740.3 3480.6 13.9750 2 0.0009233 ***
## m3.x 10 3498.3 3550.4 -1739.2 3478.3 2.2889 1 0.1302984
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(m3.0 ,m3.x)## refitting model(s) with ML (instead of REML)## Data: arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), ...
## Models:
## m3.0: rating ~ cueing + baseline + session + (1 | participant) + (1 | item)
## m3.x: rating ~ cueing * baseline + session + (1 + cueing | participant) + (1 | item)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m3.0 7 3508.6 3545.1 -1747.3 3494.6
## m3.x 10 3498.3 3550.4 -1739.2 3478.3 16.264 3 0.001001 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(m3.0,m3.1 ,m3.x)| rating | rating | rating | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 1.74 | 1.42 – 2.07 | <0.001 | 1.72 | 1.36 – 2.08 | <0.001 | 1.84 | 1.45 – 2.23 | <0.001 |
| cueing [Uncued] | 0.05 | -0.04 – 0.14 | 0.311 | 0.06 | -0.07 – 0.19 | 0.345 | -0.17 | -0.50 – 0.15 | 0.300 |
| baseline | 0.42 | 0.37 – 0.47 | <0.001 | 0.42 | 0.37 – 0.48 | <0.001 | 0.39 | 0.32 – 0.46 | <0.001 |
| session [4] | -0.28 | -0.37 – -0.19 | <0.001 | -0.28 | -0.37 – -0.19 | <0.001 | -0.28 | -0.37 – -0.19 | <0.001 |
|
cueing [Uncued] × baseline |
0.07 | -0.02 – 0.16 | 0.125 | ||||||
| Random Effects | |||||||||
| σ2 | 0.69 | 0.68 | 0.68 | ||||||
| τ00 | 0.12 item | 0.12 item | 0.12 item | ||||||
| 0.26 participant | 0.37 participant | 0.37 participant | |||||||
| τ11 | 0.03 participant.cueingUncued | 0.04 participant.cueingUncued | |||||||
| ρ01 | -1.00 participant | -1.00 participant | |||||||
| ICC | 0.36 | ||||||||
| N | 17 participant | 17 participant | 17 participant | ||||||
| 48 item | 48 item | 48 item | |||||||
| Observations | 1360 | 1360 | 1360 | ||||||
| Marginal R2 / Conditional R2 | 0.162 / 0.462 | 0.236 / NA | 0.236 / NA | ||||||
#without baseline
m3.0 <- lmer(rating ~ cueing + session + (1 | participant) + (1 | item), data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 2))
m3.1 <- lmer(rating ~ cueing + session + (1 + cueing | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 2))## boundary (singular) fit: see help('isSingular')m3.x <- lmer(rating ~ cueing* baseline + session + (1 + cueing | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 2))## boundary (singular) fit: see help('isSingular')anova(m3.0,m3.1 ,m3.x)## refitting model(s) with ML (instead of REML)## Data: arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), ...
## Models:
## m3.0: rating ~ cueing + session + (1 | participant) + (1 | item)
## m3.1: rating ~ cueing + session + (1 + cueing | participant) + (1 | item)
## m3.x: rating ~ cueing * baseline + session + (1 + cueing | participant) + (1 | item)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m3.0 6 3713.7 3745.0 -1850.9 3701.7
## m3.1 8 3707.7 3749.4 -1845.8 3691.7 10.027 2 0.006648 **
## m3.x 10 3498.3 3550.4 -1739.2 3478.3 213.403 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(m3.0 ,m3.x)## refitting model(s) with ML (instead of REML)## Data: arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), ...
## Models:
## m3.0: rating ~ cueing + session + (1 | participant) + (1 | item)
## m3.x: rating ~ cueing * baseline + session + (1 + cueing | participant) + (1 | item)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m3.0 6 3713.7 3745.0 -1850.9 3701.7
## m3.x 10 3498.3 3550.4 -1739.2 3478.3 223.43 4 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(m3.0,m3.1 ,m3.x)| rating | rating | rating | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 3.13 | 2.79 – 3.47 | <0.001 | 3.13 | 2.75 – 3.50 | <0.001 | 1.84 | 1.45 – 2.23 | <0.001 |
| cueing [Uncued] | 0.05 | -0.04 – 0.15 | 0.276 | 0.07 | -0.06 – 0.19 | 0.304 | -0.17 | -0.50 – 0.15 | 0.300 |
| session [4] | -0.28 | -0.38 – -0.18 | <0.001 | -0.28 | -0.38 – -0.18 | <0.001 | -0.28 | -0.37 – -0.19 | <0.001 |
| baseline | 0.39 | 0.32 – 0.46 | <0.001 | ||||||
|
cueing [Uncued] × baseline |
0.07 | -0.02 – 0.16 | 0.125 | ||||||
| Random Effects | |||||||||
| σ2 | 0.78 | 0.78 | 0.68 | ||||||
| τ00 | 0.30 item | 0.29 item | 0.12 item | ||||||
| 0.38 participant | 0.48 participant | 0.37 participant | |||||||
| τ11 | 0.03 participant.cueingUncued | 0.04 participant.cueingUncued | |||||||
| ρ01 | -1.00 participant | -1.00 participant | |||||||
| ICC | 0.46 | 0.46 | |||||||
| N | 17 participant | 17 participant | 17 participant | ||||||
| 48 item | 48 item | 48 item | |||||||
| Observations | 1360 | 1360 | 1360 | ||||||
| Marginal R2 / Conditional R2 | 0.014 / 0.469 | 0.014 / 0.472 | 0.236 / NA | ||||||
m3.0y <- lmer(rating ~ cueing + session + (1 | participant) + (1 | item), data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 4))
m3.0 <- lmer(rating ~ cueing + baseline + session + (1 | participant) + (1 | item), data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 4))
m3.1 <- lmer(rating ~ cueing + baseline + session + (1 + cueing | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 4))
m3.x <- lmer(rating ~ cueing* baseline + session + (1 + cueing | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% filter(baseline >= 4))
anova(m3.0y,m3.0,m3.1 ,m3.x)## refitting model(s) with ML (instead of REML)## Data: arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), ...
## Models:
## m3.0y: rating ~ cueing + session + (1 | participant) + (1 | item)
## m3.0: rating ~ cueing + baseline + session + (1 | participant) + (1 | item)
## m3.1: rating ~ cueing + baseline + session + (1 + cueing | participant) + (1 | item)
## m3.x: rating ~ cueing * baseline + session + (1 + cueing | participant) + (1 | item)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m3.0y 6 1591.0 1617.5 -789.52 1579.0
## m3.0 7 1555.5 1586.3 -770.73 1541.5 37.5843 1 8.754e-10 ***
## m3.1 9 1544.5 1584.2 -763.25 1526.5 14.9574 2 0.000565 ***
## m3.x 10 1545.4 1589.5 -762.69 1525.4 1.1192 1 0.290082
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(m3.0y,m3.0 ,m3.x)## refitting model(s) with ML (instead of REML)## Data: arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), ...
## Models:
## m3.0y: rating ~ cueing + session + (1 | participant) + (1 | item)
## m3.0: rating ~ cueing + baseline + session + (1 | participant) + (1 | item)
## m3.x: rating ~ cueing * baseline + session + (1 + cueing | participant) + (1 | item)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m3.0y 6 1591.0 1617.5 -789.52 1579.0
## m3.0 7 1555.5 1586.3 -770.73 1541.5 37.584 1 8.754e-10 ***
## m3.x 10 1545.4 1589.5 -762.69 1525.4 16.077 3 0.001094 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1tab_model(m3.0y,m3.0,m3.1 ,m3.x)| rating | rating | rating | rating | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 3.58 | 3.25 – 3.90 | <0.001 | 1.50 | 0.77 – 2.22 | <0.001 | 1.40 | 0.66 – 2.15 | <0.001 | 1.07 | 0.10 – 2.04 | 0.031 |
| cueing [Uncued] | 0.10 | -0.03 – 0.24 | 0.129 | 0.12 | -0.01 – 0.25 | 0.078 | 0.16 | -0.06 – 0.38 | 0.161 | 0.83 | -0.44 – 2.09 | 0.202 |
| session [4] | -0.31 | -0.45 – -0.18 | <0.001 | -0.31 | -0.44 – -0.18 | <0.001 | -0.31 | -0.44 – -0.18 | <0.001 | -0.31 | -0.44 – -0.18 | <0.001 |
| baseline | 0.49 | 0.33 – 0.64 | <0.001 | 0.50 | 0.35 – 0.65 | <0.001 | 0.58 | 0.37 – 0.79 | <0.001 | |||
|
cueing [Uncued] × baseline |
-0.15 | -0.44 – 0.13 | 0.293 | |||||||||
| Random Effects | ||||||||||||
| σ2 | 0.65 | 0.62 | 0.59 | 0.59 | ||||||||
| τ00 | 0.19 item | 0.14 item | 0.14 item | 0.14 item | ||||||||
| 0.34 participant | 0.32 participant | 0.49 participant | 0.49 participant | |||||||||
| τ11 | 0.13 participant.cueingUncued | 0.12 participant.cueingUncued | ||||||||||
| ρ01 | -0.81 participant | -0.80 participant | ||||||||||
| ICC | 0.45 | 0.43 | 0.45 | 0.45 | ||||||||
| N | 17 participant | 17 participant | 17 participant | 17 participant | ||||||||
| 48 item | 48 item | 48 item | 48 item | |||||||||
| Observations | 610 | 610 | 610 | 610 | ||||||||
| Marginal R2 / Conditional R2 | 0.023 / 0.460 | 0.071 / 0.469 | 0.075 / 0.493 | 0.076 / 0.494 | ||||||||
Plotting observed and predicted data
m3.x <- lmer(rating ~ cueing* baseline + session*cueing + (1 | participant) + (1 | item) , data=arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)))
data.aggr <- arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% group_by(session,participant,cueing,baseline) %>% summarise(predicted=mean(rating, na.rm=TRUE)) %>% na.omit() %>% mutate(x = baseline, group_col=cueing)## `summarise()` has grouped output by 'session', 'participant', 'cueing'. You can
## override using the `.groups` argument.tmp <- tibble(get_model_data(m3.x, type = "pred", terms = c("baseline[all]","cueing")))
#aggregated across session
ggplot(tmp, aes(x = x, y = predicted, color = group_col,fill=group_col)) +
geom_line() +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.3) +
xlab("Baseline") +
ylab("Rating") + stat_summary(data=data.aggr, fun = "mean", geom = "point", size = 1.5) +
stat_summary(data=data.aggr,fun.data = mean_se, geom = "errorbar", width = 0.1) + theme_apa(base_size = 14) + labs(color=NULL, fill=NULL)
#model is still the same but we split the behavior by session
ggplot(tmp, aes(x = x, y = predicted, color = group_col,fill=group_col)) +
geom_line() +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.3) +
xlab("Baseline") +
ylab("Rating") + stat_summary(data=data.aggr, fun = "mean", geom = "point", size = 1.5) +
stat_summary(data=data.aggr,fun.data = mean_se, geom = "errorbar", width = 0.1) + theme_apa(base_size = 14) + facet_wrap(~session) + labs(color=NULL, fill=NULL)
# session specific model predictions + with session specific behavior
tmp <- tibble(get_model_data(m3.x, type = "pred", terms = c("baseline[all]","cueing","session")))
data.aggr <- arousal.extended.wbaseline %>% mutate(rating = as.numeric(rating), baseline=as.numeric(baseline)) %>% group_by(session,participant,cueing,baseline) %>% summarise(predicted=mean(rating, na.rm=TRUE)) %>% na.omit() %>% mutate(x = baseline, group_col=cueing, facet=session)## `summarise()` has grouped output by 'session', 'participant', 'cueing'. You can
## override using the `.groups` argument.ggplot(tmp, aes(x = x, y = predicted, color = group_col,fill=group_col)) +
geom_line() +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = 0.3) +
xlab("Baseline") +
ylab("Rating") + stat_summary(data=data.aggr, fun = "mean", geom = "point", size = 1.5) +
stat_summary(data=data.aggr,fun.data = mean_se, geom = "errorbar", width = 0.1) + theme_apa(base_size = 14) + labs(color=NULL, fill=NULL) + facet_wrap(~facet)