From 836fdb5bd4864770d533fd38d16d3723df229b94 Mon Sep 17 00:00:00 2001 From: Matthew Date: Thu, 26 Sep 2024 11:37:53 +0100 Subject: [PATCH 1/6] Update conditional_transform.R added in a stop and example --- R/conditional_transform.R | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/R/conditional_transform.R b/R/conditional_transform.R index 88cce90..5a50429 100644 --- a/R/conditional_transform.R +++ b/R/conditional_transform.R @@ -15,10 +15,14 @@ #' @export #' #' @examples -#' 3 +#' conditional_transform(example_counterbalance, flip = "x", cond_column = "cue_order", cond_values = 2) conditional_transform <- function(data, flip = c("x", "y"), cond_column, cond_values, resolution_x = 1920, resolution_y = 1080) { + if(missing(cond_column)) { + stop("missing column names. Please check your data and the documentation") + } + if(sum(colnames(data) %in% c("x", "y", cond_column)) != 3) { stop("missing column names. Please check your data and the documentation") } From 35fa98faa9b223b4280321974446f6d06a051c68 Mon Sep 17 00:00:00 2001 From: Matthew Date: Thu, 26 Sep 2024 11:38:26 +0100 Subject: [PATCH 2/6] added in figures from readme --- man/explore_dispersion_tolerance.Rd | 28 ++++++++++++++++++++++++++++ man/figures/unnamed-chunk-10-1.png | Bin 0 -> 26084 bytes man/figures/unnamed-chunk-15-1.png | Bin 0 -> 39662 bytes man/figures/unnamed-chunk-16-1.png | Bin 40467 -> 52483 bytes man/figures/unnamed-chunk-17-1.png | Bin 0 -> 41547 bytes man/figures/unnamed-chunk-19-1.png | Bin 0 -> 40467 bytes 6 files changed, 28 insertions(+) create mode 100644 man/explore_dispersion_tolerance.Rd create mode 100644 man/figures/unnamed-chunk-10-1.png create mode 100644 man/figures/unnamed-chunk-15-1.png create mode 100644 man/figures/unnamed-chunk-17-1.png create mode 100644 man/figures/unnamed-chunk-19-1.png diff --git a/man/explore_dispersion_tolerance.Rd b/man/explore_dispersion_tolerance.Rd new file mode 100644 index 0000000..348cf1c --- /dev/null +++ b/man/explore_dispersion_tolerance.Rd @@ -0,0 +1,28 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/explore_dispersion_tolerance.R +\name{explore_dispersion_tolerance} +\alias{explore_dispersion_tolerance} +\title{Fixation detection using inverse saccade identification} +\usage{ +explore_dispersion_tolerance( + data, + algorithm = c("dispersion", "VTI", "both"), + tolerance_min = 75, + tolerance_max = 120, + step = 5 +) +} +\arguments{ +\item{data}{A dataframe with raw data (time, x, y, trial) for one participant} + +\item{algorithm}{A string of either "dispersion", "VTI", or "both" to specify which algorithm to test} + +\item{tolerance_min}{the minimum dispersion tolerance to test} + +\item{tolerance_max}{the maximum dispersion tolerance to test} + +\item{step}{the step to take between the minimum and maximum tolerance values to explore} +} +\description{ +Description +} diff --git 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z)2~csie)wXwQ)(aY$j6bL@9z6*lk&-uBWaduMUXxbOWW48GyZuizZy7loZVI@A4_+)=`#pvn17~u zdx1x_ZV;U6WB4HG0+dqeKsi3twtNOC=l@Gm6lD!p*V<6x&_UuEgSzN;QFE>3`!=!7 z0GY+)-e#QfV&Ps;R1%8JhFtUkSq7^8G4c0xL1t6P^z_S%GNpjp`|*ekvvP(WB_a5Y zbv06hglm0vmLZfdOwRD78L)Umt@x!^Ts2HZQRt{;S$l)U>z(Kvmh8|}nQini8}v1% zC^89_LXj}qBzk;Cl@E%#GiqvwRZI1xkX1ZYL6uV!3#_N;*sAiM;tFy{czpk%)b35T z-|0ZvMDSpZ*F_}1bHgSP;3sR6Nbax#DPnBng_3Gj_@hMg|d1b^SYn;870KP2_x?@}^9CtzFW_hn4~x&0al6~9ti zal-%o9di|65V-Vizd!tadk%0ca5+9MX#bOP)dLZ)&7%V6Key)xKS{#oC;3www}~YR z1nMHSe{SCoLUaTFcZmM)5dAwx$^Y*x`o9b9{{wr`E&Bdx3_)T~03r literal 0 HcmV?d00001 From d669076e318b634abb8c05eb2907bb49e2f934bd Mon Sep 17 00:00:00 2001 From: Matthew Date: Thu, 26 Sep 2024 11:44:17 +0100 Subject: [PATCH 3/6] Update README.Rmd --- README.Rmd | 30 ++++++++++++++++++++---------- 1 file changed, 20 insertions(+), 10 deletions(-) diff --git a/README.Rmd b/README.Rmd index db21ea4..acada8a 100644 --- a/README.Rmd +++ b/README.Rmd @@ -41,7 +41,7 @@ Available functions: | `AOI_time()` | Time on AOIs; works with rectangular and circular AOIs; works with raw and fixation data | | `combine_eyes()` | Combines binocular data (i.e., average or "best eye") | | `compare_algorithms()` | Provides a comparison between the dispersion and VTI fixation algorithms with correlations and plot | -| `conditional_transform` | Implements a single-axis flip for specific trials to normalise data with counterbalanced designs | +| `conditional_transform()` | Implements a single-axis flip for specific trials to normalise data with counterbalanced designs | | `fixation_dispersion()` | Dispersion algorithm for fixation detection | | `fixation_VTI()` | An inverse saccade algorithm for fixation detection | | `hdf5_to_csv()` | converts eyetracking data retrieved from TOBII eyetrackers to csv | @@ -146,7 +146,7 @@ comparison <- compare_algorithms(raw_data_f, min_dur = 120, disp_tol = 100, min_ ``` -The code used to produce the plots within `compare_algorithms()` is shown below +The code used to produce the plots within `compare_algorithms()` is shown below: ```{r eval=FALSE} @@ -159,6 +159,16 @@ ggplot(comparison$plot, ``` +#### Controlling for counterbalanced data + +Where the experiment includes counterbalancing of predictive and non-predictive cues, currently eyetools can normalise this data for a single axis (either a horizontal or vertical flip) using the function `conditional_transform()`. As of version 0.6.0, it cannot rotate data. `conditional_transform()` takes the data that requires data to have a column that specifies the ordering of the cues, in the example data below this is handled with the variable `cue_order` that is either a 1 or 2 depending on which side of the x midline the predictive cue is presented. The flip is specified as being across the x midline and the values to flip are also specified (note: this can take multiple values). + +```{r} + +conditional_transform(example_counterbalance, flip = "x", cond_column = "cue_order", cond_values = 2) + +``` + ### Plotting data @@ -174,7 +184,7 @@ t_raw <- dplyr::filter(example_raw_sac, trial == 9) t_fix <- fixation_dispersion(t_raw, disp_tol = 100, min_dur = 150) raw_plot <- plot_spatial(raw_data = t_raw, plot_header = TRUE) -fixation_plot <- plot_spatial(raw_data = t_raw, fixation_data = t_fix) +fixation_plot <- plot_spatial(raw_data = t_raw, fix_data = t_fix) raw_plot/fixation_plot # combined plot with patchwork @@ -217,7 +227,7 @@ t_raw <- filter(example_raw_sac, trial == 9) # single trial for plotting purpose # process fixations t_fix <- fixation_dispersion(t_raw, disp_tol = 100, min_dur = 150) -plot_spatial(raw_data = t_raw, fixation_data = t_fix, AOIs = AOI_regions) +plot_spatial(raw_data = t_raw, fix_data = t_fix, AOIs = AOI_regions) ``` @@ -237,7 +247,7 @@ t_raw <- filter(example_raw_sac, between(trial,1,10)) # process fixations t_fix <- fixation_dispersion(t_raw, disp_tol = 100, min_dur = 150) -plot_spatial(raw_data = t_raw, fixation_data = t_fix, AOIs = AOI_regions) +plot_spatial(raw_data = t_raw, fix_data = t_fix, AOIs = AOI_regions) ``` @@ -256,7 +266,7 @@ t_raw <- filter(example_raw_sac, trial == 13) # process fixations t_fix <- fixation_dispersion(t_raw, disp_tol = 100, min_dur = 150) -plot_spatial(raw_data = t_raw, fixation_data = t_fix, AOIs = AOI_regions) +plot_spatial(raw_data = t_raw, fix_data = t_fix, AOIs = AOI_regions) AOI_time(t_fix, AOIs = AOI_regions) @@ -265,7 +275,7 @@ AOI_time(t_fix, AOIs = AOI_regions) ### Processing saccades -The function `VTI_saccade()` provides a means of processing the data for saccades, based on a "velocity threshold identification" algorithm, as described in Salvucci and Goldberg (2000). As described above, it is wise to use the `smoother()` function on the data first. THe sample rate can be set if known, or can be approximated using the timestamps in the data. The threshold determines the degrees of visual angle per second needed to indicate the presence of a saccadic eye-movement. +The function `saccade_VTI()` provides a means of processing the data for saccades, based on a "velocity threshold identification" algorithm, as described in Salvucci and Goldberg (2000). As described above, it is wise to use the `smoother()` function on the data first. THe sample rate can be set if known, or can be approximated using the timestamps in the data. The threshold determines the degrees of visual angle per second needed to indicate the presence of a saccadic eye-movement. ```{r} @@ -273,7 +283,7 @@ t_raw <- filter(example_raw_sac, between(trial,1,10)) t_smooth <- smoother(t_raw) -VTI_saccade(t_smooth, sample_rate = 300) +saccade_VTI(t_smooth, sample_rate = 300) @@ -287,9 +297,9 @@ t_smooth <- filter(t_smooth, trial == 8) t_fix <- fixation_dispersion(t_smooth, disp_tol = 100, min_dur = 150) -t_sac <- VTI_saccade(t_smooth, sample_rate = 300, threshold = 100) +t_sac <- saccade_VTI(t_smooth, sample_rate = 300, threshold = 100) -plot_spatial(raw_data = t_smooth, fixation_data = t_fix, sac_data = t_sac) +plot_spatial(raw_data = t_smooth, fix_data = t_fix, sac_data = t_sac) ``` From c8afcf563ce991549d485aad871838e827007411 Mon Sep 17 00:00:00 2001 From: Matthew Date: Thu, 26 Sep 2024 12:46:48 +0100 Subject: [PATCH 4/6] Update conditional_transform.Rd --- man/conditional_transform.Rd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/man/conditional_transform.Rd b/man/conditional_transform.Rd index 4c6086f..304cc73 100644 --- a/man/conditional_transform.Rd +++ b/man/conditional_transform.Rd @@ -35,5 +35,5 @@ The function takes the dataframe and performs a single axis flip based on the va The primary use of this function is to correct or normalise the data when counterbalancing stimulus placement within experiments (e.g., having a target stimulus appear on the left and right equally often) } \examples{ -3 +conditional_transform(example_counterbalance, flip = "x", cond_column = "cue_order", cond_values = 2) } From ea49b92e59bae0eeef5fd1c5aac2e193059ba6f0 Mon Sep 17 00:00:00 2001 From: Matthew Date: Thu, 26 Sep 2024 12:47:04 +0100 Subject: [PATCH 5/6] fixed error with including AOI_names --- R/AOI_seq.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/AOI_seq.R b/R/AOI_seq.R index 7c6b15e..3060a04 100644 --- a/R/AOI_seq.R +++ b/R/AOI_seq.R @@ -41,7 +41,7 @@ AOI_seq <- function(data, data <- stack(split_list_names) data <- data.frame(trial = as.numeric(data$ind), - AOI = as.numeric(data$value)) + AOI = data$value) # add in entry_n by way of indexing each trial get_row_n <- function(i) { From 79aea6dcdc51edf5513655f9b3c2953c990f12e3 Mon Sep 17 00:00:00 2001 From: tombeesley Date: Thu, 26 Sep 2024 12:50:59 +0100 Subject: [PATCH 6/6] Increment version number to 0.6.1 --- DESCRIPTION | 2 +- NEWS.md | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index b2bbe63..9f8b611 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: eyetools Title: Tools for eye data analysis -Version: 0.6.0 +Version: 0.6.1 Authors@R: c( person("Beesley", "Tom", email = "t.beesley@lancaster.ac.uk", role = c("aut", "cre")), person("Ivory", "Matthew", email = "matthew.ivory@lancaster.ac.uk", role = "aut")) diff --git a/NEWS.md b/NEWS.md index 2e291b7..2faeebb 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,5 @@ +# eyetools 0.6.1 + # eyetools 0.6.0 # eyetools 0.5.1