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Knit directory: CPLASS/
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All functions that used in this projects
Description
This function runs the Continuous Piecewise Linear Approximation with Stochastic Search (CPLASS) algorithm on a 2D data in the form of \((x_i,y_i)_{i=1}^n\) observed at time \((t_i)_{i=1}^n\) believed to follow a continuous piecewise linear regression model (Gaussian noise). It is used for detecting changes in velocity problem. CPLASS returns the time changes, the estimated parameters.
Usage
CPLASS(t,x, y, lambda_r=1/30,
iter_max = 5000, burn_in=500, s_cap=1,
gamma=1.01, speed_pen=TRUE, Diagnostic = FALSE,
sd = NA, pen = "ssic") Arguments
| Argument | Description |
|---|---|
| x | A vector containing the data sequence (Cargo locations in x-axis) |
| y | A vector containing the data sequence (Cargo locations in y-axis) |
| t | A vector containing time |
| lambda_r | the rate used in the proposal of a new vector of changepoints |
| iter_max | The maximum number of iterations for running Metropolis-Hastings searching algorithm |
| burn_in | The number of burn-in steps in MH search algorithm |
| s_cap | The threshold for the output speed. If the inferred speed exceed
s_cap and the speed_pen is activated, then the
extra speed penalty will be introduced |
| gamma | The power in the strengthened Schwarz Information Criterion (sSIC) |
| speed_pen | If TRUE, adding the speed penalty to the penalty
function; if FALSE, we only use the linear penalty term
sSIC |
| Diagnostic | If TRUE, a dataframe with all of update in the
stochastic search will be printed |
| sd | The standard deviation value of the noise of the data. If it is unknown, then CPLASS will estimate it. |
| pen | Either aicc or ssic. The options to choose
the information criterion. aicc for the Corrected Akaike
Information Criterion, ssic for the strengthened Schwarz
Information Criterion. |
Output
A list of segment_inferred and
path_inferred will be returned after running the
algorithm.
segment_inferred is a tibble containing 8
columns| Columns | Description |
|---|---|
| cp_times | The inferred change times |
| durations | The inferred segment durations |
| states | A binary vector labeling the state of the associated segments,
0 for stationary, 1 for motile. The labels are
created using the cut-off method with a threshold of 100nm/s |
| speeds | The inferred segment speeeds |
| vx | The inferred velocity with respect to x-axis |
| vy | The inferred velocity with respect to y-axis |
path_inferred is a tibble containing 6 columns| Columns | Description |
|---|---|
| t | The input time |
| j | The label of time points corresponding to the labels of segments after using the cut-off method |
| x | The observed data with respect to x-axis |
| y | The observed data with respect to y-axis |
| a | The inferred piecewise linear lines (anchor locations w.r.t x-axis) |
| b | The inferred piecewise linear lines (anchor locations w.r.t y-axis) |
Example
For running CPLASS on a collection of paths, we introduced the
function CPLASS_paths.
Usage
CPLASS_paths(data, PARALLEL = FALSE,
lambda_r=1/30, iter_max = 5000, burn_in=500,
s_cap=1, gamma=1.01, speed_pen=TRUE, sd = NA,
pen = "ssic")Arguments
| Argument | Description |
|---|---|
| data | A list of paths where for each path we can specify \(t,x,y\) |
| PARALLEL | If TRUE, running parallel computing; if
FALSE, running sequentially |
| lambda_r | the rate used in the proposal of a new vector of changepoints |
| iter_max | The maximum number of iterations for running Metropolis-Hastings searching algorithm |
| burn_in | The number of burn-in steps in MH search algorithm |
| s_cap | The threshold for the output speed. If the inferred speed exceed
s_cap and the speed_pen is activated, then the
extra speed penalty will be introduced |
| gamma | The power in the strengthened Schwarz Information Criterion (sSIC) |
| speed_pen | If TRUE, adding the speed penalty to the penalty
function; if FALSE, we only use the linear penalty term
sSIC |
| sd | The standard deviation value of the noise of the data. If it is unknown, then CPLASS will estimate it. |
| pen | Either aicc or ssic. The options to choose
the information criterion. aicc for the Corrected Akaike
Information Criterion, ssic for the strengthened Schwarz
Information Criterion. |
Output
A list of paths, in each path, there is a similar output with two
sublists segments_inferred and path_inferred
as described in the CPLASS function.
Warnings
At the moment, we do not encourage using CPLASS_paths
unless you provide the correct format of the data so that
CPLASS_paths can read it. We are working on updating this
function so that it is more user-friendly in the near future. To run
CPLASS with a list of paths, please click here ← CPLASS
Analysis.
Description
This function return a plot for a segmented path after running CPLASS.
Usage
plot_path_inferred(path_info,xy_width = NA,t_lim = NA, motor, max_speed, state_shaded = TRUE, title_ind = NA, show_time_changes = FALSE)Arguments
| Argument | Description |
|---|---|
| path_info | A list of segmented trajectory. The trajectory must contain the two
sublists including path_inferred and
segment_inferred information as CPLASS
output. |
| xy_width | Adjust the frame size of the plot |
| t_lim | The limit of t in the plot |
| motor | Title of the plot, e.g., motor = "Kinesin" |
| lambda_r | The rate used in the proposal of a new vector of changepoints |
| max_speed | The maximum speed among all collection of inferred segment speeds after running CPLASS |
| state_shaded | TRUE for showing the color of the motile/stationary
segments |
| title_ind | TRUE for showing index of paths, e.g.,
path 1, path 2 |
| show_time_changes | TRUE for showing the change times in the plot with
dashed lines |
Output
A plot with four panels showing the xy plot, x-vs-t, y-vs-t, and a plot on segment durations/segment speeds.
Example
Description
This function return a plot for a simulated trajectory where we can compare the actual vs inferred trajectories.
Usage
plot_path_actual_and_inferred(path_info, xy_width = NA, t_lim = NA, state_shaded = TRUE)Arguments
| Argument | Description |
|---|---|
| path_info | A list of segmented trajectory. The trajectory must contain the two
sublists including path_inferred and
segment_inferred information as CPLASS
output. |
| xy_width | Adjust the frame size of the plot |
| t_lim | The limit of t in the plot |
| motor | Title of the plot, e.g., motor = "Kinesin" |
| state_shaded | TRUE for showing the color of the motile/stationary
segments |
Output
A plot with four panels showing the xy plot, x-vs-t, y-vs-t, and a plot on segment durations/segment speeds.
Example
Description
This function return a plot for a simulated trajectory where we can compare the actual vs inferred trajectories.
Usage
plot_csa(csa, legend = TRUE)Arguments
| Argument | Description |
|---|---|
| csa | A data frame that include calculated CSAs |
| legend | TRUE show the legend of the plot |
Output
CSA plot
Example
sessionInfo()R version 4.5.1 (2025-06-13)
Platform: aarch64-apple-darwin20
Running under: macOS Ventura 13.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.7.2
loaded via a namespace (and not attached):
[1] vctrs_0.6.5 httr_1.4.7 cli_3.6.5 knitr_1.50
[5] rlang_1.1.6 xfun_0.53 stringi_1.8.7 processx_3.8.6
[9] promises_1.3.3 jsonlite_2.0.0 glue_1.8.0 rprojroot_2.1.1
[13] git2r_0.36.2 htmltools_0.5.8.1 httpuv_1.6.16 ps_1.9.1
[17] sass_0.4.10 rmarkdown_2.29 jquerylib_0.1.4 tibble_3.3.0
[21] evaluate_1.0.4 fastmap_1.2.0 yaml_2.3.10 lifecycle_1.0.4
[25] whisker_0.4.1 stringr_1.5.1 compiler_4.5.1 fs_1.6.6
[29] pkgconfig_2.0.3 Rcpp_1.1.0 rstudioapi_0.17.1 later_1.4.3
[33] digest_0.6.37 R6_2.6.1 pillar_1.11.0 callr_3.7.6
[37] magrittr_2.0.3 bslib_0.9.0 tools_4.5.1 cachem_1.1.0
[41] getPass_0.2-4