add sourcedata code for sequence and repetition trials

code/highspeed-analysis-repetition.Rmd

@@ -1,4 +1,4 @@
-## Repetition trials
+## Decoding: Repetition trials
 
 ### Preparation
 
@@ -164,6 +164,15 @@ ggsave(filename = "highspeed_plot_decoding_repetition_timecourses_supplement.pdf
        dpi = "retina", width = 6, height = 5)
 ```
 
+#### Source Data File Fig. 4a
+
+```{r, echo=TRUE}
+subset(dt_pred_rep_timecourses, classification == "ovr") %>%
+  select(-classification, -num_sub) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_4a.csv"),
+            row.names = FALSE)
+```
+
 ### Mean probabilities
 
 #### Figure 4b
@@ -277,6 +286,15 @@ ggsave(filename = "highspeed_plot_decoding_repetition_probabilities_mean_all_trs
        dpi = "retina", width = 4, height = 3)
 ```
 
+#### Source Data File Fig. 4b
+
+```{r, echo=TRUE}
+subset(dt_pred_rep_mean_prob_plot, classification == "ovr") %>%
+  select(-classification, -num_trials) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_4b.csv"),
+            row.names = FALSE)
+```
+
 #### Figure 4c
 
 ```{r}
@@ -380,6 +398,15 @@ ggsave(filename = "highspeed_plot_decoding_repetition_average_probabilities.pdf"
        dpi = "retina", width = 5, height = 3)
 ```
 
+#### Source Data File Fig. 4c
+
+```{r, echo=TRUE}
+subset(dt_pred_rep_a2_sub, classification == "ovr") %>%
+  select(-classification) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_4c.csv"),
+            row.names = FALSE)
+```
+
 #### Figure S8
 
 ```{r}
@@ -495,6 +522,15 @@ ggsave(filename = "highspeed_plot_decoding_repetition_duration_supplement.pdf",
        dpi = "retina", width = 3.5, height = 2)
 ```
 
+#### Source Data File Fig. S8
+
+```{r, echo=TRUE}
+subset(dt_pred_rep_all_reps_mean, classification == "ovr") %>%
+  select(-classification, -num_subs) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_s8.csv"),
+            row.names = FALSE)
+```
+
 ### Step-sizes
 
 Are there more within sequence items in the classifier predictions?
@@ -708,21 +744,21 @@ out-of-sequence elements:
 transition_type <- function(head, tail){
   if (head == 1 & tail == 2){
     transition_type = "forward"
-  } else if(head == 2 & tail == 1) {
+  } else if (head == 2 & tail == 1) {
     transition_type = "backward"
-  } else if(head == 1 & tail == 1) {
+  } else if (head == 1 & tail == 1) {
     transition_type = "repetition 1"
-  } else if(head == 2 & tail == 2) {
+  } else if (head == 2 & tail == 2) {
     transition_type = "repetition 2"
   #} else if(head == tail) {
   #  transition_type = "repetition"
-  } else if(head %in% c(1,2) & tail > 2) {
+  } else if (head %in% c(1,2) & tail > 2) {
     transition_type = "outwards"
-  } else if(head > 2 & tail %in% c(1,2)) {
+  } else if (head > 2 & tail %in% c(1,2)) {
     transition_type = "inwards"
-  } else if(head > 2 & tail > 2 & head != tail) {
+  } else if (head > 2 & tail > 2 & head != tail) {
     transition_type = "outside"
-  } else if(head > 2 & tail > 2 & head == tail) {
+  } else if (head > 2 & tail > 2 & head == tail) {
     transition_type = "repetition out"
   } else {
       transition_type = "ERROR!"
@@ -874,6 +910,15 @@ ggsave(filename = "highspeed_plot_decoding_repetition_transition_matrix.pdf",
        dpi = "retina", width = 8, height = 3)
 ```
 
+#### Source Data File Fig. 4e
+
+```{r, echo=TRUE}
+subset(dt_pred_rep_step_trans, classification == "ovr" & change %in% c(2,9)) %>%
+  select(-classification, -num_data) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_4e.csv"),
+            row.names = FALSE)
+```
+
 #### Figure 4d
 
 ```{r, echo=TRUE}
@@ -929,10 +974,21 @@ ggsave(filename = "highspeed_plot_decoding_repetition_transition_types.pdf",
        dpi = "retina", width = 5, height = 3)
 ```
 
+#### Source Data File Fig. 4d
+
+```{r, echo=TRUE}
+subset(
+  dt_pred_rep_step_version1, classification == "ovr" & change %in% c(2,9) &
+  step_type != "repetition 1" & step_type != "repetition 2" & step_type != "repetition out"
+) %>%
+  select(-classification) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_4d.csv"),
+            row.names = FALSE)
+```
 
-compare the average proportion of forward transitions with the average proportion
-of outwards steps in the change = 2 condition. also compare the average
-proportion of forward transitions with the average number of within out-of-sequence transitions:
+We compare the average proportion of forward transitions with the average proportion
+of outwards steps in the change = 2 condition.
+We also compare the average proportion of forward transitions with the average number of within out-of-sequence transitions:
 
 ```{r, results = "hold"}
 dt_pred_rep_trans_test = dt_pred_rep_step_version1 %>%
@@ -968,10 +1024,13 @@ plot_grid(plot_grid(fig_a, fig_b, labels = c("a", "b")),
           ncol = 1, label_fontface = "bold")
 ```
 
-```{r, echo=FALSE, include=FALSE, eval=FALSE}
+```{r, echo=FALSE, eval=FALSE, include=FALSE}
 ggsave(filename = "highspeed_plot_decoding_repetition.pdf",
        plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
        dpi = "retina", width = 10, height = 10)
+```
+
+```{r}
 ggsave(filename = "wittkuhn_schuck_figure_4.pdf",
        plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
        dpi = "retina", width = 10, height = 10)

code/highspeed-analysis-sequence.Rmd

@@ -1,4 +1,4 @@
-## Detecting sequentiality
+## Decoding: Sequence trials
 
 ### Initialization
 
@@ -163,6 +163,8 @@ dt_pred_seq_prob = dt_pred_seq %>%
   setDT(.)
 ```
 
+#### Figure 3a
+
 We plot the decoding probability time-courses:
 
 ```{r}
@@ -208,6 +210,15 @@ fig_seq_probas = plot_raw_probas(dt1 = subset(dt_pred_seq_prob, classification =
 fig_seq_probas
 ```
 
+#### Source Data File Fig. 3a
+
+```{r, echo=TRUE}
+subset(dt_pred_seq_prob, classification == "ovr") %>%
+  select(-classification, -num_subs) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_3a.csv"),
+            row.names = FALSE)
+```
+
 We plot the decoding probabilities as a heat-map:
 
 ```{r, echo=FALSE}
@@ -298,7 +309,7 @@ fig_seq_probas_cue = plot_raw_probas_cue(dt1 = subset(dt_pred_seq_prob_cue, clas
 fig_seq_probas_cue
 ```
 
-### Regression slopes
+### Regression slope timecourses
 
 ```{r, echo=FALSE, eval=FALSE}
 # select positions within every TR that should be selected:
@@ -483,6 +494,7 @@ plot_seq_cor_time = function(dt, variable){
 }
 ```
 
+#### Figure 3b
 
 ```{r}
 fig_seq_cor_time = plot_seq_cor_time(dt = subset(
@@ -506,6 +518,45 @@ ggsave(filename = "highsspeed_plot_decoding_sequence_timecourses_slopes.pdf",
        dpi = "retina", width = 5.5, height = 3)
 ```
 
+#### Source Data File Fig. 3b
+
+```{r, echo=TRUE}
+subset(
+  seq_test_time(data = dt_pred_seq_cor, variable = "mean_slope"),
+  classification == "ovr") %>%
+  select(-classification, -num_subs, -num_comp, -pvalue_adjust,
+         -pvalue_round, -pvalue_adjust_round, -pvalue, -df, -cohens_d,
+         -tvalue, -significance) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_3b.csv"),
+            row.names = FALSE)
+```
+
+#### Source Data File Fig. S5a
+
+```{r, echo=TRUE}
+subset(
+  seq_test_time(data = dt_pred_seq_cor, variable = "mean_cor"),
+  classification == "ovr") %>%
+  select(-classification, -num_subs, -num_comp, -pvalue_adjust,
+         -pvalue_round, -pvalue_adjust_round, -pvalue, -df, -cohens_d,
+         -tvalue, -significance) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_s5a.csv"),
+            row.names = FALSE)
+```
+
+#### Source Data File Fig. S5c
+
+```{r, echo=TRUE}
+subset(
+  seq_test_time(data = dt_pred_seq_cor, variable = "mean_step"),
+  classification == "ovr") %>%
+  select(-classification, -num_subs, -num_comp, -pvalue_adjust,
+         -pvalue_round, -pvalue_adjust_round, -pvalue, -df, -cohens_d,
+         -tvalue, -significance) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_s5c.csv"),
+            row.names = FALSE)
+```
+
 We test depending on the target cue position:
 
 ```{r}
@@ -693,6 +744,8 @@ dt_between_results = dt_between %>%
 rmarkdown::paged_table(dt_between_results)
 ```
 
+#### Figure 3d
+
 We plot the correlations between the regression slope time courses predicted by the model vs. the observed data *between* participants:
 
 ```{r, echo=TRUE, warning=FALSE, message=FALSE}
@@ -728,6 +781,14 @@ fig_seq_cor_between = ggplot(
 fig_seq_cor_between
 ```
 
+#### Source Data File Fig. 3d
+
+```{r, echo=TRUE}
+dt_between %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_3d.csv"),
+            row.names = FALSE)
+```
+
 #### Correlation within participants
 
 We calculate the correlations between the predicted and the observed time courses *within* participants for each of the five speed conditions (inter-stimulus intervals):
@@ -794,6 +855,9 @@ dt_within_cor_results = setDT(dt_within_cor) %>%
 # show the table with the t-test results:
 rmarkdown::paged_table(dt_within_cor_results)
 ```
+
+#### Figure 3e
+
 We plot the correlations between the predicted and the observed time courses *within* participants for each of the five speed conditions (inter-stimulus intervals):
 
 ```{r, echo=TRUE}
@@ -829,8 +893,16 @@ fig_seq_cor_within = ggplot(
 fig_seq_cor_within
 ```
 
+#### Source Data File Fig. 3e
 
+```{r, echo=TRUE}
+dt_within_cor %>%
+  select(-num_trs, -pvalue) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_3e.csv"),
+            row.names = FALSE)
+```
 
+### Regression slope means
 
 Calculate the average correlation or average regression slope
 for each time period (forward versus backward) for all five speed conditions:
@@ -897,7 +969,7 @@ rmarkdown::paged_table(
   filter(pvalue_adjust < 0.05, classification == "ovr"))
 ```
 
-
+#### Figure 3c
 
 ```{r, echo=TRUE}
 plot_seq_cor_period = function(data, variable){
@@ -964,6 +1036,41 @@ fig_seq_step_period = plot_seq_cor_period(data = dt_pred_seq_cor, variable = "me
 fig_seq_cor_period; fig_seq_step_period; fig_seq_slope_period;
 ```
 
+#### Source Data File Fig. 3e / S5b / S5d
+
+```{r}
+dt_pred_seq_cor %>%
+  select(-classification, -num_trials, -color) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_3c.csv"),
+            row.names = FALSE)
+dt_pred_seq_cor %>%
+  select(-classification, -num_trials, -color) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_s5b.csv"),
+            row.names = FALSE)
+dt_pred_seq_cor %>%
+  select(-classification, -num_trials, -color) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_s5d.csv"),
+            row.names = FALSE)
+```
+
+#### Source Data File Fig. S5b
+
+```{r}
+dt_pred_seq_cor %>%
+  select(-classification, -num_trials) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_3c.csv"),
+            row.names = FALSE)
+```
+
+#### Source Data File Fig. S5c
+
+```{r}
+dt_pred_seq_cor %>%
+  select(-classification, -num_trials) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_3c.csv"),
+            row.names = FALSE)
+```
+
 ```{r, echo = FALSE}
 plot_seq_cor_facet = function(dt, variable){
 
@@ -1286,10 +1393,11 @@ dt_pred_seq_pos_tr = dt_pred_seq_pos %>%
   setorder(., classification, period, tITI, seq_tr)
 
 dt_pred_seq_pos_tr %>%
-  filter(classification == "ovr", pvalue_adjust < 0.05)
+  filter(classification == "ovr", pvalue_adjust < 0.05) %>%
+  rmarkdown::paged_table(.)
 ```
 
-```{r, eval = FALSE}
+```{r, eval = FALSE, echo=TRUE}
 cfg = list(variable = "mean_position", threshold = 2.021, baseline = 3,
   grouping = c("classification", "tITI"), n_perms = 10000, n_trs = 13)
 dt_pred_seq_pos_cluster = cluster_permutation(dt_pred_seq_pos_sub, cfg)
@@ -1306,6 +1414,8 @@ emmeans_results = emmeans(lme_seq_pos, list(pairwise ~ period | tITI))
 emmeans_pvalues = round_pvalues(summary(emmeans_results[[2]])$p.value)
 ```
 
+#### Figure 3g
+
 ```{r, echo=TRUE}
 variable = "position_diff"
 plot_data = dt_pred_seq_pos %>%
@@ -1357,6 +1467,16 @@ ggsave(filename = "highspeed_plot_decoding_sequence_position_period.pdf",
        dpi = "retina", width = 5, height = 3)
 ```
 
+#### Source Data File Fig. 3g
+
+```{r, echo=TRUE}
+subset(plot_data, classification == "ovr") %>%
+  select(-classification, -color) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_3g.csv"),
+            row.names = FALSE)
+```
+
+#### Figure 3f
 
 ```{r, echo = FALSE}
 plot_seq_pos <- function(dt){
@@ -1407,6 +1527,18 @@ fig_seq_pos_time = plot_seq_pos(dt = subset(dt_pred_seq_pos_tr, classification =
 fig_seq_pos_time
 ```
 
+#### Source Data File Fig. 3f
+
+```{r, echo=TRUE}
+subset(dt_pred_seq_pos_tr, classification == "ovr") %>%
+  select(-classification, -num_subs, -num_comp, -pvalue_adjust,
+         -pvalue_rounded, -pvalue_adjust_rounded, -pvalue, -df,
+         -tvalue, -significance, -sd_position) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_3f.csv"),
+            row.names = FALSE)
+```
+
+
 ```{r, echo = FALSE}
 plot_seq_pos_facet <- function(dt){
   # create separate datatable to plot rectangles indicating forward / backward period:
@@ -1532,7 +1664,8 @@ dt_pred_seq_step_stat = dt_pred_seq_step_mean %>%
   setorder(., classification, zone, tITI)
 
 dt_pred_seq_step_stat %>%
-  filter(classification == "ovr", pvalue_adjust < 0.05)
+  filter(classification == "ovr", pvalue_adjust < 0.05) %>%
+  rmarkdown::paged_table(.)
 ```
 
 We compare each period to the baseline:
@@ -1571,9 +1704,12 @@ dt_pred_seq_step_stat_baseline = dt_pred_seq_step_mean %>%
   setorder(., classification, period_short, zone, tITI)
 
 dt_pred_seq_step_stat_baseline %>%
-  filter(classification == "ovr", pvalue < 0.05)
+  filter(classification == "ovr", pvalue < 0.05) %>%
+  rmarkdown::paged_table(.)
 ```
 
+#### Figure 3h
+
 ```{r}
 # plot average correlation or betas for each speed condition and time period:
 fig_seq_step = ggplot(data = subset(dt_pred_seq_step_mean, classification == "ovr"), aes(
@@ -1608,6 +1744,15 @@ fig_seq_step = ggplot(data = subset(dt_pred_seq_step_mean, classification == "ov
 fig_seq_step
 ```
 
+#### Source Data File Fig. 3h
+
+```{r, echo=TRUE}
+subset(dt_pred_seq_step_mean, classification == "ovr") %>%
+  select(-classification) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_3h.csv"),
+            row.names = FALSE)
+```
+
 ```{r, echo=FALSE, eval=FALSE, include=FALSE}
 ggsave(filename = "highspeed_plot_decoding_sequence_step_size.pdf",
        plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
@@ -1626,6 +1771,114 @@ ggsave(filename = "highspeed_plot_decoding_sequence_between_tr.pdf",
        dpi = "retina", width = 10, height = 4)
 ```
 
+```{r}
+plot_grid(fig_seq_cor_time, fig_seq_cor_period, fig_seq_step_time, fig_seq_step_period,
+          labels = "auto", ncol = 2, nrow = 2, label_fontface = "bold")
+```
+
+```{r, echo=FALSE, eval=FALSE, include=FALSE}
+ggsave(filename = "highspeed_plot_decoding_sequence_correlation_step.pdf",
+       plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
+       dpi = "retina", width = 10, height = 7)
+ggsave(filename = "wittkuhn_schuck_figure_s5.pdf",
+       plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
+       dpi = "retina", width = 10, height = 7)
+```
+
+#### Source Data File Fig. 3f
+
+```{r}
+seq_test_time(data = dt_pred_seq_cor, variable = "mean_slope") %>%
+  filter(pvalue_adjust < 0.05 & classification == "ovr") %>%
+  rmarkdown::paged_table(.)
+
+seq_test_time(data = dt_pred_seq_cor, variable = "mean_cor") %>%
+  filter(pvalue_adjust < 0.05 & classification == "ovr") %>%
+  rmarkdown::paged_table(.)
+
+seq_test_time(data = dt_pred_seq_cor, variable = "mean_step") %>%
+  filter(pvalue_adjust < 0.05 & classification == "ovr") %>%
+  rmarkdown::paged_table(.)
+```
+
+#### Figure S6
+
+```{r}
+fig_seq_slope_time_facet = plot_seq_cor_facet(dt = subset(
+  seq_test_time(data = dt_pred_seq_cor, variable = "mean_slope"),
+  classification == "ovr"), variable = "mean_slope")
+
+fig_seq_cor_time_facet = plot_seq_cor_facet(dt = subset(
+  seq_test_time(data = dt_pred_seq_cor, variable = "mean_cor"),
+  classification == "ovr"), variable = "mean_cor")
+
+fig_seq_step_time_facet = plot_seq_cor_facet(dt = subset(
+  seq_test_time(data = dt_pred_seq_cor, variable = "mean_step"),
+  classification == "ovr"), variable = "mean_step")
+
+remove_xaxis = theme(axis.title.x = element_blank())
+remove_facets = theme(strip.background = element_blank(), strip.text.x = element_blank())
+
+plot_grid(fig_seq_slope_time_facet + remove_xaxis,
+          fig_seq_pos_time_facet  + remove_xaxis + theme(legend.position = "none") + remove_facets,
+          fig_seq_cor_time_facet + remove_xaxis + theme(legend.position = "none") + remove_facets,
+          fig_seq_step_time_facet + theme(legend.position = "none") + remove_facets,
+  labels = "auto", ncol = 1, label_fontface = "bold")
+```
+
+```{r, echo=FALSE, eval=FALSE, include=FALSE}
+ggsave(filename = "highspeed_plot_decoding_sequence_timecourse_slope_correlation_step.pdf",
+       plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
+       dpi = "retina", width = 7, height = 9)
+```
+
+```{r}
+ggsave(filename = "wittkuhn_schuck_figure_s6.pdf",
+       plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
+       dpi = "retina", width = 7, height = 9)
+```
+
+#### Source Data File Fig. S6a
+
+```{r}
+subset(seq_test_time(data = dt_pred_seq_cor, variable = "mean_slope"),
+  classification == "ovr") %>%
+  select(-classification, -num_subs, -num_comp) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_s6a.csv"),
+            row.names = FALSE)
+```
+
+#### Source Data File Fig. S6b
+
+```{r}
+subset(dt_pred_seq_pos_tr, classification == "ovr")  %>%
+  select(-classification, -num_subs, -num_comp) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_s6b.csv"),
+            row.names = FALSE)
+```
+
+#### Source Data File Fig. S6c
+
+```{r}
+subset(seq_test_time(data = dt_pred_seq_cor, variable = "mean_cor"),
+  classification == "ovr") %>%
+  select(-classification, -num_subs, -num_comp) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_s6c.csv"),
+            row.names = FALSE)
+```
+
+#### Source Data File Fig. S6d
+
+```{r}
+subset(seq_test_time(data = dt_pred_seq_cor, variable = "mean_step"),
+  classification == "ovr") %>%
+  select(-classification, -num_subs, -num_comp) %>%
+  write.csv(., file = file.path(path_sourcedata, "source_data_figure_s6d.csv"),
+            row.names = FALSE)
+```
+
+### Figure 3
+
 Plot Figure 3 in the main text:
 
 ```{r}
@@ -1645,12 +1898,14 @@ plot_grid(
 ggsave(filename = "highspeed_plot_decoding_sequence_data.pdf",
        plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
        dpi = "retina", width = 10, height = 12)
+```
+
+```{r}
 ggsave(filename = "wittkuhn_schuck_figure_3.pdf",
        plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
        dpi = "retina", width = 10, height = 12)
 ```
 
-
 ```{r, include=FALSE, echo=TRUE, eval=FALSE, warning=FALSE, message=FALSE}
 title_nomax = ggdraw() + draw_label("Sequence item with highest probability removed", fontface = "plain")
 title_nofirst = ggdraw() + draw_label("First sequence item removed", fontface = "plain")
@@ -1678,7 +1933,6 @@ plot_all = plot_grid(
 plot_all
 ```
 
-
 ```{r, echo=FALSE, eval=FALSE, include=FALSE}
 ggsave(filename = "highspeed_plot_decoding_sequence_slope_remove_items.pdf",
        plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
@@ -1688,65 +1942,4 @@ ggsave(filename = "wittkuhn_schuck_figure_s7.pdf",
        dpi = "retina", width = 10, height = 10)
 ```
 
-```{r}
-plot_grid(fig_seq_cor_time, fig_seq_cor_period, fig_seq_step_time, fig_seq_step_period,
-          labels = "auto", ncol = 2, nrow = 2, label_fontface = "bold")
-```
-
-```{r, echo=FALSE, eval=FALSE, include=FALSE}
-ggsave(filename = "highspeed_plot_decoding_sequence_correlation_step.pdf",
-       plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
-       dpi = "retina", width = 10, height = 7)
-ggsave(filename = "wittkuhn_schuck_figure_s5.pdf",
-       plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
-       dpi = "retina", width = 10, height = 7)
-```
-
-```{r}
-seq_test_time(data = dt_pred_seq_cor, variable = "mean_slope") %>%
-  filter(pvalue_adjust < 0.05 & classification == "ovr") %>%
-  rmarkdown::paged_table(.)
-
-seq_test_time(data = dt_pred_seq_cor, variable = "mean_cor") %>%
-  filter(pvalue_adjust < 0.05 & classification == "ovr") %>%
-  rmarkdown::paged_table(.)
-
-seq_test_time(data = dt_pred_seq_cor, variable = "mean_step") %>%
-  filter(pvalue_adjust < 0.05 & classification == "ovr") %>%
-  rmarkdown::paged_table(.)
-```
-
-
-
-```{r}
-fig_seq_slope_time_facet = plot_seq_cor_facet(dt = subset(
-  seq_test_time(data = dt_pred_seq_cor, variable = "mean_slope"),
-  classification == "ovr"), variable = "mean_slope")
-
-fig_seq_cor_time_facet = plot_seq_cor_facet(dt = subset(
-  seq_test_time(data = dt_pred_seq_cor, variable = "mean_cor"),
-  classification == "ovr"), variable = "mean_cor")
-
-fig_seq_step_time_facet = plot_seq_cor_facet(dt = subset(
-  seq_test_time(data = dt_pred_seq_cor, variable = "mean_step"),
-  classification == "ovr"), variable = "mean_step")
-
-remove_xaxis = theme(axis.title.x = element_blank())
-remove_facets = theme(strip.background = element_blank(), strip.text.x = element_blank())
-
-plot_grid(fig_seq_slope_time_facet + remove_xaxis,
-          fig_seq_pos_time_facet  + remove_xaxis + theme(legend.position = "none") + remove_facets,
-          fig_seq_cor_time_facet + remove_xaxis + theme(legend.position = "none") + remove_facets,
-          fig_seq_step_time_facet + theme(legend.position = "none") + remove_facets,
-  labels = "auto", ncol = 1, label_fontface = "bold")
 
-```
-
-```{r, echo=FALSE, eval=FALSE, include=FALSE}
-ggsave(filename = "highspeed_plot_decoding_sequence_timecourse_slope_correlation_step.pdf",
-       plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
-       dpi = "retina", width = 7, height = 9)
-ggsave(filename = "wittkuhn_schuck_figure_s6.pdf",
-       plot = last_plot(), device = cairo_pdf, path = path_figures, scale = 1,
-       dpi = "retina", width = 7, height = 9)
-```