fix_detection_jumps: Fix Detection Jumps

In matiasandina/stepfinder: Step Detection in One Dimensional Data.

Description

This function calculates derivatives, finds jumps above threshold and calls functions to recognize/fix the step. It can manage 3 ways to detect a step: derivatives + convolution, bare derivatives, and manual.

Arguments

df	data.frame containing 'x' and 'y' coordinates of subject 'id'.
v_thresh	numeric, threshold velocity above which a detection is considered wrong and marked for step detection.
tol	numeric, to be used to pass to 'cluster_candidate_list()'.
use_convolution	logical, whether to use convolution to find step. If TRUE will call 'find_step()', else it will use bare derivatives and thresholding for the candidates.
manual_removal	logical, whether to go to manual inspection and removal of the steps. See 'manual_step_removal()'.

Value

data.frame with the same input columns, positions may or may not be fixed depending on user input.

Examples

library(stepfinder)
set.seed(123)
df <- data.frame(id = "first_target", frameID=1:1000, x=rnorm(1000, 0, 5), y = rnorm(1000, 0, 5))
df$x[50:80] <- 200
df$y[c(100:150, 400:420)] <- 200
diagnose_detection(df)
fix_detection_jumps(df)
explore parameter values

matiasandina/stepfinder documentation built on Oct. 25, 2019, 9:24 a.m.