rm_by_trajnum {pathviewr} | R Documentation |
Remove subjects by trajectory number
Description
Specify a minimum number of trajectories that each subject must complete during a treatment, trial, or session.
Usage
rm_by_trajnum(
obj_name,
trajnum = 5,
mirrored = FALSE,
treatment1,
treatment2,
...
)
Arguments
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
trajnum |
Minimum number of trajectories; must be numeric. |
mirrored |
Does the data have mirrored treatments? If so, arguments
|
treatment1 |
The first treatment or session during which the threshold must be met. |
treatment2 |
A second treatment or session during which the threshold must be met. |
... |
Additional arguments passed to/from other pathviewr functions. |
Details
Depending on analysis needs, users may want to remove subjects that
have not completed a certain number of trajectories during a treatment,
trial, or session. If mirrored = FALSE
, no treatment information is
necessary and subjects will be removed based on total number of trajectories
as specified in trajnum
. If mirrored = TRUE
, the
treatment1
and treatment2
parameters will allow users to
define during which treatments or sessions subjects must reach threshold as
specified in the trajnum
argument. For example, if mirrored =
TRUE
, setting treatment1 = "latA"
, treatment2 = "latB"
and
trajnum = 5
will remove subjects that have fewer than 5 trajectories
during the "latA"
treatment AND the "latB"
treatment.
treatment1
and treatment2
should be levels within a column
named "treatment"
.
Value
A viewr object; a tibble or data.frame with attribute
pathviewr_steps
that includes "viewr"
that now has fewer
observations (rows) as a result of removal of subjects with too few
trajectories according to the trajnum
parameter.
Author(s)
Melissa S. Armstrong
Examples
library(pathviewr)
## Import the example Motive data included in the package
motive_data <-
read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv",
package = 'pathviewr'))
## Clean, isolate, and label trajectories
motive_full <-
motive_data %>%
clean_viewr(desired_percent = 50,
max_frame_gap = "autodetect",
span = 0.95)
##Remove subjects that have not completed at least 150 trajectories:
motive_rm_unmirrored <-
motive_full %>%
rm_by_trajnum(trajnum = 150)
## Add treatment information
motive_full$treatment <- c(rep("latA", 100),
rep("latB", 100),
rep("latA", 100),
rep("latB", 149))
## Remove subjects by that have not completed at least 10 trajectories in
## both treatments
motive_rm_mirrored <-
motive_full %>%
rm_by_trajnum(
trajnum = 10,
mirrored = TRUE,
treatment1 = "latA",
treatment2 = "latB"
)