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-SAS 2024 Conference
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SAS 2024 Conference
SAS 2024 Conference
-Placeholder for information about poster presentation.
+Does Smile Synchrony Predict Working Alliance Quality in Psychotherapy?
+ + +Additional Figures Below
+Histograms of Variables:
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Figures including all sessions and patients (without clustering):
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+Show the code
+data %>%
+ ggplot(mapping = aes(x = fisher_z, y = wai)) +
+ geom_point() +
+ geom_smooth(method = "lm", se = FALSE, fullrange = TRUE) +
+ labs(
+ x = "Smile Synchrony",
+ y = "Working Alliance Scores",
+ title = "Working Alliance by Smile Synchrony"
+ )
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In this figure, each dot represents the relationship between working alliance scores and smile synchrony (including all sessions from patients)
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+Show the code
+data %>%
+ ggplot(mapping = aes(x = fisher_z, y = wai, colour = factor(patient_id))) +
+ geom_point() +
+ geom_smooth(mapping = aes(group = patient_id), method = "lm", se = FALSE, fullrange = TRUE) +
+ labs(
+ colour = "Patient ID",
+ x = "Smile Synchrony",
+ y = "Working Alliance Scores",
+ title = "Working Alliance by Smile Synchrony"
+ )
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In this figure, each line represents the relationship between working alliance scores and smile synchrony for each patient. This figure includes data from all sessions.
+Figures split by individual patients:
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+Show the code
+data %>%
+ group_by(patient_id) %>%
+ ggplot(mapping = aes(x = fisher_z, y = wai)) +
+ geom_point() +
+ geom_smooth(mapping = aes(group = patient_id), method = "lm", se = FALSE, fullrange = TRUE) +
+ labs(title = "Working Alliance by Smile Synchrony for each Patient",
+ x = "Smile Synchrony",
+ y = "Working Alliance Scores") +
+ scale_x_continuous(breaks=seq(from=0.2,to=0.4,by=0.1)) +
+ coord_cartesian(xlim = c(0.2, 0.4), ylim = c(0, 70)) +
+ theme_gray(base_size = 12) +
+ theme(plot.title = element_text(hjust = 0.5)) +
+ facet_wrap(~patient_id, ncol = 8) +
+ theme(panel.spacing = grid::unit(1, "lines"))
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Each subfigure represents the relationship between working alliance scores and smile synchrony for each patient. This figure includes data from all sessions, but we have some missing data (e.g., patient #1025, patient #1072).
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+Show the code
+data %>%
+ group_by(session) %>%
+ ggplot(mapping = aes(x = session, y = wai)) +
+ coord_cartesian(ylim = c(20, 65)) +
+ geom_point() +
+ geom_smooth(mapping = aes(group = patient_id), method = "lm", se = FALSE, fullrange = TRUE) +
+ labs(title = "Working Alliance Scores across Sessions",
+ x = "Session",
+ y = "Working Alliance Scores") +
+ theme_gray(base_size = 12) +
+ theme(plot.title = element_text(hjust = 0.5)) +
+ facet_wrap(~patient_id)
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+Show the code
+data %>%
+ group_by(session) %>%
+ ggplot(mapping = aes(x = session, y = fisher_z)) +
+ geom_point() +
+ geom_smooth(mapping = aes(group = patient_id), method = "lm", se = FALSE, fullrange = TRUE) +
+ labs(title = "Smile Synchrony across Sessions",
+ x = "Session",
+ y = "Smile Synchrony") +
+ scale_y_continuous(breaks=seq(from=0.2,to=0.4,by=0.1)) +
+ theme_gray(base_size = 12) +
+ theme(plot.title = element_text(hjust = 0.5)) +
+ facet_wrap(~patient_id)
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