Participant-class: A Reference Class for representing consistency test...
In synr: Explore and Process Synesthesia Consistency Test Data
Participant-class R Documentation
A Reference Class for representing consistency test participants
Description
A Reference Class for representing consistency test participants
Fields
idA one-element character vector containing the participant's ID.
Set at class new() call.
test_dateA one-element Date vector which specifies the date
on which the participant did the consistency test.
graphemesA list of Grapheme class instances.
Methods
add_grapheme(grapheme)Add a passed grapheme to the participant's list
of graphemes. The grapheme's entry in
the list is named based on the grapheme's
symbol. Note that if you try to add
a grapheme with a symbol that's identical
to one of the graphemes already in the
participant's list of graphemes, the
already existing same-symbol grapheme
is overwritten.
add_graphemes(grapheme_list)Go through a passed list of Grapheme instances
and add each one using the add_grapheme() method.
check_valid_get_twcv(
min_complete_graphemes = 5,
dbscan_eps = 20,
dbscan_min_pts = 4,
max_var_tight_cluster = 150,
max_prop_single_tight_cluster = 0.6,
safe_num_clusters = 3,
safe_twcv = 250,
complete_graphemes_only = TRUE,
symbol_filter = NULL
) Checks if this participant's data are valid based on passed arguments.
This method aims to identify participants who had too few responses or
varied their response colors too little, by marking them as invalid.
Note that there are no absolutely correct values, as what is 'too little
variation' is highly subjective. You might need to tweak parameters to be
in line with your project's criteria, especially if you use another color
space than CIELUV, since the default values are based on what seems
to make sense in a CIELUV context. If you use the results in a
research article, make sure to reference synr and specify what parameter
values you passed to the function.
This method relies heavily on the DBSCAN algorithm and the package
'dbscan', and involves calculating a synr-specific 'Total Within-Cluster
Variance' (TWCV) score. You can find more information, and
what the parameters here mean, in
the documentation for the function validate_get_twcv.
Parameters
min_complete_graphemes The minimum number of graphemes
with complete (all non-NA color) responses that the participant data
must have for them to not be categorized as invalid based on this
criterion. Defaults to 5.
dbscan_eps Radius of 'epsilon neighborhood' when applying
DBSCAN clustering. Defaults to 20.
dbscan_min_pts Minimum number of points required in the
epsilon neighborhood for core points (including the core point
itself). Defaults to 4.
max_var_tight_cluster Maximum variance for an identified
DBSCAN cluster to be considered 'tight-knit'. Defaults to 150.
max_prop_single_tight_cluster Maximum proportion of
points allowed to be within a single 'tight-knit' cluster (exceeding
this leads to classification as invalid). Defaults to 0.6.
safe_num_clusters Minimum number of identified DBSCAN
clusters (including 'noise' cluster only if it consists of at least
'dbscan_min_pts' points) that guarantees validity if
points are 'non-tight-knit'. Defaults to 3.
safe_twcv Minimum total within-cluster variance (TWCV)
score that guarantees validity if points are 'non-tight-knit'.
Defaults to 250.
complete_graphemes_only A logical vector. If TRUE,
only data from graphemes that have all non-NA color responses
are used; if FALSE, even data from graphemes with some NA color
responses are used. Defaults to TRUE.
symbol_filter A character vector (or NULL) that specifies
which graphemes' data to use. Defaults to NULL, meaning data from
all of the participant's graphemes will be used.
Returns
A list with components
valid TRUE if categorized as valid, otherwise FALSE.
reason_invalid One-element character vector describing
why participant's data were deemed invalid, or empty string if
valid is TRUE.
twcv One-element numeric (or NA if there are no/too few
graphemes with complete responses) vector indicating participant's
calculated TWCV.
num_clusters One-element numeric (or NA if there are no/too few
graphemes with complete responses) vector indicating
the number of identified clusters counting toward the
tally compared with 'safe_num_clusters'.
get_all_colored_symbols(symbol_filter = NULL)Returns a character vector of symbols corresponding to graphemes for
which all responses have an associated non-NA color. If a
character vector is passed to symbol_filter, only
symbols in the passed vector are returned.
get_consistency_scores(
method = "euclidean",
symbol_filter = NULL,
na.rm = FALSE
)Returns a list of grapheme symbols with associated consistency scores.
If na.rm = TRUE, for each grapheme a consistency score calculation is
forced (except if ALL response colors associated with the grapheme
are NA). That probably isn't what you want, because it leads to things
like a perfect consistency score if all except one response color are
NA. Defaults to na.rm = FALSE.
If a character vector is passed to
symbol_filter, only consistency scores for graphemes with symbols
in the passed vector are returned.
Use the method argument to specify what kind of color space
distances should be used when calculating consistency score
(usually 'manhattan' or 'euclidean' - see documentation for
the base R dist function for all options)
get_grapheme_mean_colors(symbol_filter = NULL, na.rm = FALSE)Returns a list of grapheme symbols with associated mean colors,
using the color space set at participant creation. Colors are represented
by 3-element vectors.
Example: if color space is RGB, vector element 1 represents
grapheme mean R value, element 2 mean G value, element 3
B value.
If na.rm = TRUE, for each grapheme a mean color is calculated even
if one its associated response colors is missing. Defaults to
na.rm = FALSE.
If a character vector is passed to symbol_filter, only
mean colors for graphemes with symbols
in the passed vector are returned.
get_mean_consistency_score(
symbol_filter = NULL,
method = "euclidean",
na.rm = FALSE
)Returns the mean consistency score with respect to
Grapheme instances associated with the participant.
If na.rm = FALSE, calculates the mean consistency score if
all of the participants' graphemes only have response
colors that are non-NA, otherwise returns NA.
If na.rm = TRUE, returns the mean consistency score for
all of the participant's graphemes that only have
non-NA response colors, while ignoring graphemes
that have at least one NA response color value. Note that
NA is returned in either case, if ALL of the participants'
graphemes have at least one NA response color value.
If a character vector is passed to
symbol_filter, only data from graphemes with symbols
in the passed vector are used when calculating the
mean score.
Use the method argument to specify what kind of color space
distances should be used when calculating consistency score
(usually 'manhattan' or 'euclidean' - see documentation for
the base R dist function for all options)
get_mean_response_time(symbol_filter = NULL, na.rm = FALSE)Returns the mean response time, with respect to all
Grapheme instances associated with the participant.
Weights response times based on number of valid responses
that each grapheme has. If na.rm = TRUE, returns mean response
time even if there are missing response times. If na.rm = FALSE,
returns mean response time if there is at least one response time
value for at least one of the participants' graphemes. If a
character vector is passed to symbol_filter, only data from
graphemes with symbols in the passed vector are used when
calculating the mean response time.
get_nonna_color_resp_mat(symbol_filter = NULL)Returns an n-by-3 matrix of all non-NA color responses' data,
where each column represents a color axis and each row a response
color. If a character vector is passed to symbol_filter,
only data from responses associated with graphemes with corresponding
symbols are included.
get_number_all_colored_graphemes(symbol_filter = NULL)Returns the number of graphemes for which all
responses have an associated non-NA color. If a
character vector is passed to symbol_filter, only
graphemes with symbols in the passed vector are counted.
get_participant_mean_color(symbol_filter = NULL, na.rm = FALSE)Returns average of all of participants' registered
response colors (based on the color space
set at participant initialization) as a 3-element vector.
Example: if color space is RGB, element 1 represents
mean R value, element 2 mean G value, element 3
B value.
If a character vector is passed to
symbol_filter, only data from graphemes with symbols
in the passed vector are used when calculating the
mean color.
If na.rm = FALSE, calculates the mean response color if
all of the participants' graphemes only have response
colors that are non-NA, otherwise returns NA.
If na.rm = TRUE, returns the mean response color based on
all non-NA response colors.
get_plot(
cutoff_line = FALSE,
mean_line = FALSE,
grapheme_size = 2,
grapheme_angle = 0,
grapheme_spacing = 0.25,
foreground_color = "black",
background_color = "white",
symbol_filter = NULL
)Returns a ggplot2 plot that describes this participant's
grapheme color responses and per-grapheme consistency scores.
If cutoff_line = TRUE, the plot will include a blue line that
indicates the value 135.30, which is the synesthesia
cut-off score recommended by Rothen, Seth, Witzel & Ward (2013)
for the L*u*v color space. If mean_line = TRUE, the plot will
include a green line that indicates the participant's mean
consistency score for graphemes with all-valid
response colors (if the participant has any such graphemes). If a vector
is passed to symbol_filter, this green line represents the mean score
for ONLY the symbols included in the filter.
Pass a value to grapheme_size to adjust the size of graphemes
shown at the bottom of the plot, e. g. increasing the size if
there's a lot of empty space otherwise, or decreasing the size if the
graphemes don't fit. The grapheme_angle
argument allows rotating graphemes. grapheme_spacing is for adjusting
how far grapheme symbols are spaced from each other.
If a character vector is passed to symbol_filter, only data for graphemes
with symbols in the passed vector are used.
Graphemes are sorted left-to-right by 1. length and
2. unicode value (this means among other things that digits
come before letters).
get_plot_data(symbol_filter = NULL)Returns a data frame with the following columns:
1. grapheme (grapheme names - of type character)
2. consistency_score (of type numeric)
3... color_resp<x>, where x is a digit: hold response hex color codes
(number of columns depends on number of response colors
associated with each grapheme).
The data frame is intended to be used for plotting participant data,
using .get_plot(). The call will end with an error
if not all of the participant's graphemes have the same number
of color responses. This is intended.
If a character vector is passed to symbol_filter, only data for graphemes
with symbols in the passed vector are used.
get_symbols()Returns a character vector with all symbols for
graphemes associated with the participant.
has_graphemes()Returns TRUE if there is at least one
grapheme in the participant's graphemes list,
otherwise returns FALSE
save_plot(
save_dir = NULL,
file_format = "png",
dpi = 300,
cutoff_line = FALSE,
mean_line = FALSE,
grapheme_size = 2,
grapheme_angle = 0,
foreground_color = "black",
background_color = "white",
symbol_filter = NULL,
...
)Saves a ggplot2 plot that describes this participant's
grapheme color responses and per-grapheme consistency scores,
using the ggsave function.
If a character vector is passed to symbol_filter, only data for graphemes
with symbols in the passed vector are used.
If save_dir is not specified, the plot is saved to the current
working directory. Otherwise, the plot is saved to the specified
directory. The file is saved using the specified file_format,
e. g. JPG (see ggplot2::ggsave documentation for list of
supported formats), and the resolution specified with
the dpi argument.
If cutoff_line = TRUE, the plot will include a blue line that
indicates the value 135.30, which is the synesthesia
cut-off score recommended by Rothen, Seth, Witzel & Ward (2013)
for the L*u*v color space. If mean_line = TRUE, the plot will
include a green line that indicates the participant's mean
consistency score for graphemes with all-valid response colors
(if the participant has any such graphemes). If a vector
is passed to symbol_filter, this green line represents the mean score
for ONLY the symbols included in the filter.
Pass a value to grapheme_size to adjust the size of graphemes
shown at the bottom of the plot, e. g. increasing the size if
there's empty space otherwise, or decreasing the size if the
graphemes don't fit. Similarly, you can use the grapheme_angle
argument to rotate the graphemes, which might help them fit better.
Apart from these, all other arguments
that ggsave accepts (e. g. 'scale') also work with this function, since
all arguments are passed on to ggsave.
set_date(in_date)Takes in a one-element character vector with a date
in the format 'YYYY-MM-DD' and sets the participant's
test_date to the specified date.
synr documentation built on Aug. 23, 2022, 5:06 p.m.
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| Participant-class | R Documentation |
A Reference Class for representing consistency test participants
Description
A Reference Class for representing consistency test participants
Fields
idA one-element character vector containing the participant's ID. Set at class new() call.
test_dateA one-element Date vector which specifies the date on which the participant did the consistency test.
graphemesA list of
Graphemeclass instances.
Methods
add_grapheme(grapheme)Add a passed grapheme to the participant's list of graphemes. The grapheme's entry in the list is named based on the grapheme's symbol. Note that if you try to add a grapheme with a symbol that's identical to one of the graphemes already in the participant's list of graphemes, the already existing same-symbol grapheme is overwritten.
add_graphemes(grapheme_list)Go through a passed list of Grapheme instances and add each one using the add_grapheme() method.
check_valid_get_twcv( min_complete_graphemes = 5, dbscan_eps = 20, dbscan_min_pts = 4, max_var_tight_cluster = 150, max_prop_single_tight_cluster = 0.6, safe_num_clusters = 3, safe_twcv = 250, complete_graphemes_only = TRUE, symbol_filter = NULL )Checks if this participant's data are valid based on passed arguments. This method aims to identify participants who had too few responses or varied their response colors too little, by marking them as invalid. Note that there are no absolutely correct values, as what is 'too little variation' is highly subjective. You might need to tweak parameters to be in line with your project's criteria, especially if you use another color space than CIELUV, since the default values are based on what seems to make sense in a CIELUV context. If you use the results in a research article, make sure to reference synr and specify what parameter values you passed to the function.
This method relies heavily on the DBSCAN algorithm and the package 'dbscan', and involves calculating a synr-specific 'Total Within-Cluster Variance' (TWCV) score. You can find more information, and what the parameters here mean, in the documentation for the function
validate_get_twcv.Parameters
min_complete_graphemesThe minimum number of graphemes with complete (all non-NA color) responses that the participant data must have for them to not be categorized as invalid based on this criterion. Defaults to 5.dbscan_epsRadius of 'epsilon neighborhood' when applying DBSCAN clustering. Defaults to 20.dbscan_min_ptsMinimum number of points required in the epsilon neighborhood for core points (including the core point itself). Defaults to 4.max_var_tight_clusterMaximum variance for an identified DBSCAN cluster to be considered 'tight-knit'. Defaults to 150.max_prop_single_tight_clusterMaximum proportion of points allowed to be within a single 'tight-knit' cluster (exceeding this leads to classification as invalid). Defaults to 0.6.safe_num_clustersMinimum number of identified DBSCAN clusters (including 'noise' cluster only if it consists of at least 'dbscan_min_pts' points) that guarantees validity if points are 'non-tight-knit'. Defaults to 3.safe_twcvMinimum total within-cluster variance (TWCV) score that guarantees validity if points are 'non-tight-knit'. Defaults to 250.complete_graphemes_onlyA logical vector. If TRUE, only data from graphemes that have all non-NA color responses are used; if FALSE, even data from graphemes with some NA color responses are used. Defaults to TRUE.symbol_filterA character vector (or NULL) that specifies which graphemes' data to use. Defaults to NULL, meaning data from all of the participant's graphemes will be used.
Returns
A list with components
validTRUE if categorized as valid, otherwise FALSE.reason_invalidOne-element character vector describing why participant's data were deemed invalid, or empty string if valid is TRUE.twcvOne-element numeric (or NA if there are no/too few graphemes with complete responses) vector indicating participant's calculated TWCV.num_clustersOne-element numeric (or NA if there are no/too few graphemes with complete responses) vector indicating the number of identified clusters counting toward the tally compared with 'safe_num_clusters'.
get_all_colored_symbols(symbol_filter = NULL)Returns a character vector of symbols corresponding to graphemes for which all responses have an associated non-NA color. If a character vector is passed to symbol_filter, only symbols in the passed vector are returned.
get_consistency_scores( method = "euclidean", symbol_filter = NULL, na.rm = FALSE )Returns a list of grapheme symbols with associated consistency scores. If na.rm = TRUE, for each grapheme a consistency score calculation is forced (except if ALL response colors associated with the grapheme are NA). That probably isn't what you want, because it leads to things like a perfect consistency score if all except one response color are NA. Defaults to na.rm = FALSE.
If a character vector is passed to symbol_filter, only consistency scores for graphemes with symbols in the passed vector are returned.
Use the method argument to specify what kind of color space distances should be used when calculating consistency score (usually 'manhattan' or 'euclidean' - see documentation for the base R dist function for all options)
get_grapheme_mean_colors(symbol_filter = NULL, na.rm = FALSE)Returns a list of grapheme symbols with associated mean colors, using the color space set at participant creation. Colors are represented by 3-element vectors.
Example: if color space is RGB, vector element 1 represents grapheme mean R value, element 2 mean G value, element 3 B value.
If na.rm = TRUE, for each grapheme a mean color is calculated even if one its associated response colors is missing. Defaults to na.rm = FALSE.
If a character vector is passed to symbol_filter, only mean colors for graphemes with symbols in the passed vector are returned.
get_mean_consistency_score( symbol_filter = NULL, method = "euclidean", na.rm = FALSE )Returns the mean consistency score with respect to Grapheme instances associated with the participant.
If na.rm = FALSE, calculates the mean consistency score if all of the participants' graphemes only have response colors that are non-NA, otherwise returns NA. If na.rm = TRUE, returns the mean consistency score for all of the participant's graphemes that only have non-NA response colors, while ignoring graphemes that have at least one NA response color value. Note that NA is returned in either case, if ALL of the participants' graphemes have at least one NA response color value.
If a character vector is passed to symbol_filter, only data from graphemes with symbols in the passed vector are used when calculating the mean score.
Use the method argument to specify what kind of color space distances should be used when calculating consistency score (usually 'manhattan' or 'euclidean' - see documentation for the base R dist function for all options)
get_mean_response_time(symbol_filter = NULL, na.rm = FALSE)Returns the mean response time, with respect to all Grapheme instances associated with the participant. Weights response times based on number of valid responses that each grapheme has. If na.rm = TRUE, returns mean response time even if there are missing response times. If na.rm = FALSE, returns mean response time if there is at least one response time value for at least one of the participants' graphemes. If a character vector is passed to symbol_filter, only data from graphemes with symbols in the passed vector are used when calculating the mean response time.
get_nonna_color_resp_mat(symbol_filter = NULL)Returns an n-by-3 matrix of all non-NA color responses' data, where each column represents a color axis and each row a response color. If a character vector is passed to symbol_filter, only data from responses associated with graphemes with corresponding symbols are included.
get_number_all_colored_graphemes(symbol_filter = NULL)Returns the number of graphemes for which all responses have an associated non-NA color. If a character vector is passed to symbol_filter, only graphemes with symbols in the passed vector are counted.
get_participant_mean_color(symbol_filter = NULL, na.rm = FALSE)Returns average of all of participants' registered response colors (based on the color space set at participant initialization) as a 3-element vector. Example: if color space is RGB, element 1 represents mean R value, element 2 mean G value, element 3 B value.
If a character vector is passed to symbol_filter, only data from graphemes with symbols in the passed vector are used when calculating the mean color.
If na.rm = FALSE, calculates the mean response color if all of the participants' graphemes only have response colors that are non-NA, otherwise returns NA. If na.rm = TRUE, returns the mean response color based on all non-NA response colors.
get_plot( cutoff_line = FALSE, mean_line = FALSE, grapheme_size = 2, grapheme_angle = 0, grapheme_spacing = 0.25, foreground_color = "black", background_color = "white", symbol_filter = NULL )Returns a ggplot2 plot that describes this participant's grapheme color responses and per-grapheme consistency scores.
If cutoff_line = TRUE, the plot will include a blue line that indicates the value 135.30, which is the synesthesia cut-off score recommended by Rothen, Seth, Witzel & Ward (2013) for the L*u*v color space. If mean_line = TRUE, the plot will include a green line that indicates the participant's mean consistency score for graphemes with all-valid response colors (if the participant has any such graphemes). If a vector is passed to symbol_filter, this green line represents the mean score for ONLY the symbols included in the filter.
Pass a value to grapheme_size to adjust the size of graphemes shown at the bottom of the plot, e. g. increasing the size if there's a lot of empty space otherwise, or decreasing the size if the graphemes don't fit. The grapheme_angle argument allows rotating graphemes. grapheme_spacing is for adjusting how far grapheme symbols are spaced from each other.
If a character vector is passed to symbol_filter, only data for graphemes with symbols in the passed vector are used.
Graphemes are sorted left-to-right by 1. length and 2. unicode value (this means among other things that digits come before letters).
get_plot_data(symbol_filter = NULL)Returns a data frame with the following columns:
1. grapheme (grapheme names - of type character)
2. consistency_score (of type numeric)
3... color_resp<x>, where x is a digit: hold response hex color codes (number of columns depends on number of response colors associated with each grapheme).
The data frame is intended to be used for plotting participant data, using .get_plot(). The call will end with an error if not all of the participant's graphemes have the same number of color responses. This is intended.
If a character vector is passed to symbol_filter, only data for graphemes with symbols in the passed vector are used.
get_symbols()Returns a character vector with all symbols for graphemes associated with the participant.
has_graphemes()Returns TRUE if there is at least one grapheme in the participant's graphemes list, otherwise returns FALSE
save_plot( save_dir = NULL, file_format = "png", dpi = 300, cutoff_line = FALSE, mean_line = FALSE, grapheme_size = 2, grapheme_angle = 0, foreground_color = "black", background_color = "white", symbol_filter = NULL, ... )Saves a ggplot2 plot that describes this participant's grapheme color responses and per-grapheme consistency scores, using the ggsave function.
If a character vector is passed to symbol_filter, only data for graphemes with symbols in the passed vector are used.
If save_dir is not specified, the plot is saved to the current working directory. Otherwise, the plot is saved to the specified directory. The file is saved using the specified file_format, e. g. JPG (see ggplot2::ggsave documentation for list of supported formats), and the resolution specified with the dpi argument.
If cutoff_line = TRUE, the plot will include a blue line that indicates the value 135.30, which is the synesthesia cut-off score recommended by Rothen, Seth, Witzel & Ward (2013) for the L*u*v color space. If mean_line = TRUE, the plot will include a green line that indicates the participant's mean consistency score for graphemes with all-valid response colors (if the participant has any such graphemes). If a vector is passed to symbol_filter, this green line represents the mean score for ONLY the symbols included in the filter.
Pass a value to grapheme_size to adjust the size of graphemes shown at the bottom of the plot, e. g. increasing the size if there's empty space otherwise, or decreasing the size if the graphemes don't fit. Similarly, you can use the grapheme_angle argument to rotate the graphemes, which might help them fit better.
Apart from these, all other arguments that ggsave accepts (e. g. 'scale') also work with this function, since all arguments are passed on to ggsave.
set_date(in_date)Takes in a one-element character vector with a date in the format 'YYYY-MM-DD' and sets the participant's test_date to the specified date.
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