Package 'fitlandr' reference manual

Title:	Fit Vector Fields and Potential Landscapes from Intensive Longitudinal Data
Description:	A toolbox for estimating vector fields from intensive longitudinal data, and construct potential landscapes thereafter. The vector fields can be estimated with two nonparametric methods: the Multivariate Vector Field Kernel Estimator (MVKE) by Bandi & Moloche (2018) <doi:10.1017/S0266466617000305> and the Sparse Vector Field Consensus (SparseVFC) algorithm by Ma et al. (2013) <doi:10.1016/j.patcog.2013.05.017>. The potential landscapes can be constructed with a simulation-based approach with the 'simlandr' package (Cui et al., 2021) <doi:10.31234/osf.io/pzva3>, or the Bhattacharya et al. (2011) method for path integration <doi:10.1186/1752-0509-5-85>.
Authors:	Jingmeng Cui [aut, cre]
Maintainer:	Jingmeng Cui <[email protected]>
License:	GPL (>= 3)
Version:	0.1.0.9000
Built:	2025-02-20 05:32:09 UTC
Source:	https://github.com/Sciurus365/fitlandr

Add a grid to a `vectorfield` object to enable linear interpolation

Description

Add a grid to a vectorfield object to enable linear interpolation

Usage

add_interp_grid(vf, lims = vf$lims, n = vf$n)
add_interp_grid(vf, lims = vf$lims, n = vf$n)

Arguments

`vf`	A `vectorfield` object estimated by `fit_2d_vf()`.
`lims`	The limits of the range for the vector field estimation as `⁠c(<xl>, <xu>, <yl>, <yu>)⁠`. If missing, the range of the data extended by 10% for both sides will be used.
`n`	The number of equally spaced points in each axis, at which the vectors are to be estimated.

Value

A vectorfield project with an interp_grid field.

Find equilibrium points for a vector field

Description

Find equilibrium points for a vector field

Usage

find_eqs(vf, starts, jacobian_params = list(), ...)
find_eqs(vf, starts, jacobian_params = list(), ...)

Arguments

`vf`	A `vectorfield` object estimated by `fit_2d_vf()`.
`starts`	A vector indicating the starting value for solving the equilibrium point, or a list of vectors providing multiple starting values together.
`jacobian_params`	Parameters passed to `numDeriv::jacobian()`.
`...`	Parameters passed to `rootSolve::multiroot()`.

Value

A list of equilibrium points and their details. Use print.vectorfield_eqs() to inspect it.

Estimate a 2D potential landscape from data with the MVKE method

Description

This function is a wrapper of the MVKE method (see MVKE()) that produces a 2D potential landscape from 1D data. The landscape is constructed by estimating the gradient of the data and then integrating it. The MVKE method is a non-parametric method that estimates the gradient of the data by using a kernel density estimator. The potential landscape is then constructed by integrating the gradient.

Usage

fit_2d_ld(
  data,
  x,
  lims,
  n = 200L,
  vector_position = "start",
  na_action = "omit_data_points",
  method = c("MVKE"),
  subdivisions = 100L,
  rel.tol = .Machine$double.eps^0.25,
  abs.tol = rel.tol,
  stop.on.error = TRUE,
  keep.xy = FALSE,
  aux = NULL,
  ...
)

## S3 method for class ''2d_MVKE_landscape''
summary(object, ...)
fit_2d_ld(
  data,
  x,
  lims,
  n = 200L,
  vector_position = "start",
  na_action = "omit_data_points",
  method = c("MVKE"),
  subdivisions = 100L,
  rel.tol = .Machine$double.eps^0.25,
  abs.tol = rel.tol,
  stop.on.error = TRUE,
  keep.xy = FALSE,
  aux = NULL,
  ...
)

## S3 method for class ''2d_MVKE_landscape''
summary(object, ...)

Arguments

`data`	A data frame or matrix containing the data. The data frame should contain at least a column, with the column name indicated by `x`, that represents the dimension for landscape construction.
`x`	The column name of the data frame that represents the dimension for landscape construction.
`lims`	The limits of the range for the landscape calculation as `c(xl, xu)`.
`n`	The number of equally spaced points in the axis, at which the landscape is to be estimated.
`vector_position`	One of "start", "middle", or "end", representing the position of the vectors. If "start", for example, the starting point of a vector is regarded as the position of the vector.
`na_action`	One of "omit_data_points" or "omit_vectors". If using "omit_data_points", then only the `NA` points are omitted, and the points before and after an `NA` will form a vector. If using "omit_vectors", then the vectors will be omitted if either of its points is `NA`.
`method`	The method used to estimate the gradient. Currently only "MVKE" is supported.
`subdivisions`	the maximum number of subintervals.
`rel.tol`	relative accuracy requested.
`abs.tol`	absolute accuracy requested.
`stop.on.error`	logical. If true (the default) an error stops the function. If false some errors will give a result with a warning in the `message` component.
`keep.xy`	unused. For compatibility with S.
`aux`	unused. For compatibility with S.
`...`	Not used.
`object`	An object of class `⁠2d_MVKE_landscape⁠` returned by `fit_2d_ld()`.

Value

A ⁠2d_MVKE_landscape⁠ object, which contains the following components:

dist: A data frame containing the estimated potential landscape. The data frame has two columns: x and U, where x is the position and U is the potential.
p: A ggplot object containing the plot of the potential landscape.

Functions

summary(`2d_MVKE_landscape`): Find the local minima of the 2D potential landscape

Examples

# generate data
single_output_grad <- simlandr::sim_fun_grad(length = 200, seed = 1614)
# fit the landscape
l <- fit_2d_ld(single_output_grad, "x")

summary(l)
plot(l)

# different behaviors for different `na_action` choices

l1 <- fit_2d_ld(data.frame(x = c(1,2,1,2,NA,NA,NA,10,11,10,11)), "x")
plot(l1)

l2 <- fit_2d_ld(data.frame(x = c(1,2,1,2,NA,NA,NA,10,11,10,11)), "x", na_action = "omit_vectors")
plot(l2)


# generate data
single_output_grad <- simlandr::sim_fun_grad(length = 200, seed = 1614)
# fit the landscape
l <- fit_2d_ld(single_output_grad, "x")

summary(l)
plot(l)

# different behaviors for different `na_action` choices

l1 <- fit_2d_ld(data.frame(x = c(1,2,1,2,NA,NA,NA,10,11,10,11)), "x")
plot(l1)

l2 <- fit_2d_ld(data.frame(x = c(1,2,1,2,NA,NA,NA,10,11,10,11)), "x", na_action = "omit_vectors")
plot(l2)

Estimate a 2D vector field

Description

Estimate a 2D vector field from intensive longitudinal data. Two methods can be used: Multivariate Vector Field Kernel Estimator (MVKE, using MVKE()), or Sparse Vector Field Consensus (SparseVFC, using SparseVFC::SparseVFC()). Note that the input data are automatically normalized before being sent to the estimation engines to make sure the default parameter settings are close to the optimal. Therefore, you do not need to scale up or down the parameters of MVKE() or SparseVFC::SparseVFC(). We suggest the MVKE method to be used for psychological data because it has more realistic assumptions and produces more reasonable output.

Usage

fit_2d_vf(
  data,
  x,
  y,
  lims,
  n = 20,
  vector_position = "start",
  na_action = "omit_data_points",
  method = c("MVKE", "VFC"),
  ...
)
fit_2d_vf(
  data,
  x,
  y,
  lims,
  n = 20,
  vector_position = "start",
  na_action = "omit_data_points",
  method = c("MVKE", "VFC"),
  ...
)

Arguments

`data`	The data set used for estimating the vector field. Should be a data frame or a matrix.
`x`, `y`	Characters to indicate the name of the two variables.
`lims`	The limits of the range for the vector field estimation as `⁠c(<xl>, <xu>, <yl>, <yu>)⁠`. If missing, the range of the data extended by 10% for both sides will be used.
`n`	The number of equally spaced points in each axis, at which the vectors are to be estimated.
`vector_position`	One of "start", "middle", or "end", representing the position of the vectors. If "start", for example, the starting point of a vector is regarded as the position of the vector.
`na_action`	One of "omit_data_points" or "omit_vectors". If using "omit_data_points", then only the `NA` points are omitted, and the points before and after an `NA` will form a vector. If using "omit_vectors", then the vectors will be omitted if either of its points is `NA`.
`method`	One of "MVKE" or "VFC".
`...`	Other parameters to be passed to `MVKE()` or `SparseVFC::SparseVFC()`.

Value

A vectorfield object.

Examples

# generate data
single_output_grad <- simlandr::sim_fun_grad(length = 200, seed = 1614)
# fit the vector field
v2 <- fit_2d_vf(single_output_grad, x = "x", y = "y", method = "MVKE")
plot(v2)
# generate data
single_output_grad <- simlandr::sim_fun_grad(length = 200, seed = 1614)
# fit the vector field
v2 <- fit_2d_vf(single_output_grad, x = "x", y = "y", method = "MVKE")
plot(v2)

Estimate a 3D potential landscape from a vector field

Description

Two methods are available: method = "pathB" and method = "simlandr". See Details section.

Usage

fit_3d_vfld(
  vf,
  method = c("simlandr", "pathB"),
  .pathB_options = pathB_options(vf),
  .sim_vf_options = sim_vf_options(vf),
  .simlandr_options = simlandr_options(vf),
  linear_interp = FALSE
)
fit_3d_vfld(
  vf,
  method = c("simlandr", "pathB"),
  .pathB_options = pathB_options(vf),
  .sim_vf_options = sim_vf_options(vf),
  .simlandr_options = simlandr_options(vf),
  linear_interp = FALSE
)

Arguments

`vf`	A `vectorfield` object estimated by `fit_2d_vf()`.
`method`	The method used for landscape construction. Can be `pathB` or `simlandr`.
`.pathB_options`	Only for `method = "pathB"`. Options controlling the path-integral algorithm. Should be generated by `sim_vf_options()`.
`.sim_vf_options`	Only for `method = "simlandr"`. Options controlling the vector field simulation. Should be generated by `sim_vf_options()`.
`.simlandr_options`	Only for `method = "simlandr"`. Options controlling the landscape construction. Should be generated by `simlandr_options()`.
`linear_interp`	Use linear interpolation method to estimate the drift vector (and the diffusion matrix). This can speed up the calculation. If `TRUE`, be sure that a linear grid was calculated for the vector field using `⁠<vf> <- add_interp_grid(<vf>)⁠`.

Details

For method = "simlandr", the landscape is constructed based on the generalized potential landscape by Wang et al. (2008), implemented by the simlandr package. This function is a wrapper of sim_vf() and simlandr::make_3d_static(). Use those two functions separately for more customization.

For method = "pathB", the landscape is constructed based on the deterministic path-integral quasi-potential defined by Bhattacharya et al. (2011).

We recommend the simlandr method for psychological data because it is more stable.

Parallel computing based on future is supported for both methods. Use future::plan("multisession") to enable this and speed up computation.

Value

A landscape object as described in simlandr::make_3d_static(), or a ⁠3d_static_landscape_B⁠ object, which inherits from the landscape class and contains the following elements: dist, the distribution estimation for landscapes; plot, a 3D plot using plotly; plot_2, a 2D plot using ggplot2; x, y, from vf.

Examples


# generate data
single_output_grad <- simlandr::sim_fun_grad(length = 200, seed = 1614)
# fit the vector field
v2 <- fit_2d_vf(single_output_grad, x = "x", y = "y", method = "MVKE")
plot(v2)
# fit the landscape
future::plan("multisession")
set.seed(1614)
l2 <- fit_3d_vfld(v2,
.sim_vf_options = sim_vf_options(chains = 16, stepsize = 1, forbid_overflow = TRUE),
.simlandr_options = simlandr_options(adjust = 5, Umax = 4))
plot(l2, 2)
future::plan("sequential")

# generate data
single_output_grad <- simlandr::sim_fun_grad(length = 200, seed = 1614)
# fit the vector field
v2 <- fit_2d_vf(single_output_grad, x = "x", y = "y", method = "MVKE")
plot(v2)
# fit the landscape
future::plan("multisession")
set.seed(1614)
l2 <- fit_3d_vfld(v2,
.sim_vf_options = sim_vf_options(chains = 16, stepsize = 1, forbid_overflow = TRUE),
.simlandr_options = simlandr_options(adjust = 5, Umax = 4))
plot(l2, 2)
future::plan("sequential")

Multivariate vector field kernel estimator

Description

See references for details.

Usage

MVKE(d, v, h = 0.2, kernel = c("exp", "Gaussian"))
MVKE(d, v, h = 0.2, kernel = c("exp", "Gaussian"))

Arguments

`d`	The dataset. Should be a matrix or a data frame, with each row representing a random vector.
`v`	The vectors corresponding to the dataset. Should be a matrix or a data frame with the same shape as `d`. If missing, then the vectors will be calculated from the dataset.
`h`	The bandwidth for the kernel estimator.
`kernel`	The type of kernel estimator used. "exp" by default (`exp()`), and if "Gaussian" then `stats::dnorm()` will be used.

Value

A function(x), which then returns the $\mu$ and $a$ estimators at the position $x$ .

References

Bandi, F. M., & Moloche, G. (2018). On the functional estimation of multivariate diffusion processes. Econometric Theory, 34(4), 896-946. https://doi.org/10.1017/S0266466617000305

Return a normalized prediction function

Description

Return a normalized prediction function

Usage

normalize_predict_f(vf)
normalize_predict_f(vf)

Arguments

`vf`	A `vectorfield` object estimated by `fit_2d_vf()`.

Value

A function that takes a vector x and returns a list of v, the drift part, and a, the diffusion part.

Options controlling the path-integral algorithm

Description

See path_integral_B(), align_pot_B() for details.

Usage

pathB_options(
  vf,
  lims = rlang::expr(vf$lims),
  n_path_int = 20,
  stepsize = 0.01,
  tol = 0.01,
  numTimeSteps = 1400,
  n = 200,
  digits = 2,
  linear = TRUE,
  ...
)
pathB_options(
  vf,
  lims = rlang::expr(vf$lims),
  n_path_int = 20,
  stepsize = 0.01,
  tol = 0.01,
  numTimeSteps = 1400,
  n = 200,
  digits = 2,
  linear = TRUE,
  ...
)

Arguments

`vf`	A `vectorfield` object estimated by `fit_2d_vf()`.
`lims`	The limits of the range for the estimation as `⁠c(<xl>, <xu>, <yl>, <yu>)⁠`.
`n_path_int`	The number of equally spaced points in each axis, at which the path integrals is to be calculated.
`stepsize`	The stepsize for Euler–Maruyama simulation of the system.
`tol`	The tolerance to test convergence.
`numTimeSteps`	Number of time steps for integrating along each path (to ensure uniform arrays). Choose high-enough number for convergence with given stepsize.
`n`	The number of equally spaced points in each axis, at which the landscape is to be estimated.
`digits`	Currently, the raw sample points in some regions are too dense that may crashes interpolation. To avoid this problem, only one point of all with the same first several digits. is kept. Use this parameter to indicate how many digits are considered. Note that this is a temporary solution and might be changed in the near future.
`linear`	logical – indicating whether linear or spline interpolation should be used.
`...`	Not in use.

Value

A list containing the parameters of the corresponding function. Only intended to be used within fit_3d_vfld()

Plot a 2D vector field

Description

Plot a 2D vector field estimated by fit_2d_vf(). Powered by ggplot2::ggplot().

Usage

## S3 method for class 'vectorfield'
plot(
  x,
  arrow = grid::arrow(length = grid::unit(0.1, "cm")),
  show_estimated_vector = TRUE,
  estimated_vector_enlarge = 1,
  estimated_vector_options = list(),
  show_point = TRUE,
  point_options = list(size = 0.5),
  show_original_vector = FALSE,
  original_vector_enlarge = 1,
  original_vector_options = list(),
  show_used_vector = FALSE,
  used_vector_options = list(color = "red"),
  show_v_norm = FALSE,
  v_norm_options = list(),
  ...
)
## S3 method for class 'vectorfield'
plot(
  x,
  arrow = grid::arrow(length = grid::unit(0.1, "cm")),
  show_estimated_vector = TRUE,
  estimated_vector_enlarge = 1,
  estimated_vector_options = list(),
  show_point = TRUE,
  point_options = list(size = 0.5),
  show_original_vector = FALSE,
  original_vector_enlarge = 1,
  original_vector_options = list(),
  show_used_vector = FALSE,
  used_vector_options = list(color = "red"),
  show_v_norm = FALSE,
  v_norm_options = list(),
  ...
)

Arguments

`x`	A `vectorfield` object estimated by `fit_2d_vf()`.
`arrow`	The description of the arrow heads of the vectors on the plot (representing the vector field). Generated by `grid::arrow()`. Also see the `arrow` parameter of `ggplot2::geom_segment()`.
`show_estimated_vector`	Show the vectors from the estimated model? `TRUE` by default.
`estimated_vector_enlarge`	A number. How many times should the vectors (representing the estimated vector field) be enlarged on the plot? This can be useful when the estimated vector field is too strong or too weak.
`estimated_vector_options`	A list passing other customized parameters to `ggplot2::geom_segment()` to control the vectors representing the estimated vector field.
`show_point`	Show the original data points? `TRUE` by default.
`point_options`	A list passing other customized parameters to `ggplot2::geom_point()` to control the points representing the original data point.
`show_original_vector`	Show the original vectors (i.e., the vectors between data points)? `FALSE` by default.
`original_vector_enlarge`	A number. How many times should the original vectors be enlarged on the plot?
`original_vector_options`	A list passing other customized parameters to `ggplot2::geom_segment()` to control the vectors representing the original data.
`show_used_vector`	Only for vector fields estimated by the "VFC" method. Should the vectors from the original data that are considered inliers be specially marked? `FALSE` by default.
`used_vector_options`	Only for vector fields estimated by the "VFC" method. A list passing other customized parameters to `ggplot2::geom_segment()` to control the vectors representing the inliers. Red by default.
`show_v_norm`	Show the norm of the estimated vectors (the strength of the vector field)? `FALSE` by default.
`v_norm_options`	A list passing other customized parameters to `ggplot2::geom_raster()` to control the layer representing the norm of the estimated vectors.
`...`	Not in use.

Value

A ggplot2 plot.

Calculate the vector value at a given position

Description

Calculate the vector value at a given position

Usage

## S3 method for class 'vectorfield'
predict(object, pos, linear_interp = FALSE, calculate_a = TRUE, ...)
## S3 method for class 'vectorfield'
predict(object, pos, linear_interp = FALSE, calculate_a = TRUE, ...)

Arguments

`object`	A `vectorfield` project generated by `fit_2d_vf()`.
`pos`	A vector, the position of the vector.
`linear_interp`	Use linear interpolation method to estimate the drift vector (and the diffusion matrix). This can speed up the calculation. If `TRUE`, be sure that a linear grid was calculated for the vector field using `⁠<vf> <- add_interp_grid(<vf>)⁠`.
`calculate_a`	Effective when `linear_interp == TRUE`. Do you want to calculate the diffusion matrix? Use `FALSE` can save some time.
`...`	Not in use.

Value

A list of v, the drift part that is used for vector fields, and a (when calculate_a == TRUE), the diffusion part at a given position.

Reorder a simulation output in time order

Description

Then simlandr::check_conv() can be used meaningfully.

Usage

reorder_output(s, chains)
reorder_output(s, chains)

Arguments

`s`	A simulation output, possibly generated by `sim_vf()`
`chains`	How many chains simulations should be performed?

Value

A reordered matrix of the simulation output.

Simulation from vector fields

Description

Parallel computing based on future is supported. Use future::plan("multisession") to enable this.

Usage

sim_vf(
  vf,
  noise = 1,
  noise_warmup = noise,
  chains = 10,
  length = 10000,
  discard = 0.3,
  stepsize = 0.01,
  sparse = 1,
  forbid_overflow = FALSE,
  linear_interp = FALSE,
  inits = matrix(c(stats::runif(chains, min = vf$lims[1], max = vf$lims[2]),
    stats::runif(chains, min = vf$lims[3], max = vf$lims[4])), ncol = 2)
)
sim_vf(
  vf,
  noise = 1,
  noise_warmup = noise,
  chains = 10,
  length = 10000,
  discard = 0.3,
  stepsize = 0.01,
  sparse = 1,
  forbid_overflow = FALSE,
  linear_interp = FALSE,
  inits = matrix(c(stats::runif(chains, min = vf$lims[1], max = vf$lims[2]),
    stats::runif(chains, min = vf$lims[3], max = vf$lims[4])), ncol = 2)
)

Arguments

`vf`	A `vectorfield` object estimated by `fit_2d_vf()`.
`noise`	Relative noise of the simulation. Set this smaller when the simulation is unstable (e.g., when the elements in the diffusion matrix are not finite), and set this larger when the simulation converges too slowly.
`noise_warmup`	The noise used for the warming-up period.
`chains`	How many chains simulations should be performed?
`length`	The simulation length for each chain.
`discard`	How much of the starting part of each chain should be discarded? (Warming-up period.)
`stepsize`	The stepsize for Euler–Maruyama simulation of the system.
`sparse`	A number. How much do you want to sparse the output? When the noise is small, sparse the output may make the density estimation more efficient.
`forbid_overflow`	If `TRUE`, when the simulated system runs out of the margins specified in `vf`, the system will be moved back to the previous value. This can help to stabilize the simulation. `FALSE` by default.
`linear_interp`	Use linear interpolation method to estimate the drift vector (and the diffusion matrix). This can speed up the calculation. If `TRUE`, be sure that a linear grid was calculated for the vector field using `⁠<vf> <- add_interp_grid(<vf>)⁠`.
`inits`	The initial values of each chain.

Value

A matrix of the simulated data.

Options controlling the vector field simulation

Description

See sim_vf() for details.

Usage

sim_vf_options(
  vf,
  noise = 1,
  noise_warmup = noise,
  chains = 10,
  length = 10000,
  discard = 0.3,
  stepsize = 0.01,
  sparse = 1,
  forbid_overflow = FALSE,
  linear_interp = FALSE,
  inits = rlang::expr(matrix(c(stats::runif(chains, min = vf$lims[1], max = vf$lims[2]),
    stats::runif(chains, min = vf$lims[3], max = vf$lims[4])), ncol = 2))
)
sim_vf_options(
  vf,
  noise = 1,
  noise_warmup = noise,
  chains = 10,
  length = 10000,
  discard = 0.3,
  stepsize = 0.01,
  sparse = 1,
  forbid_overflow = FALSE,
  linear_interp = FALSE,
  inits = rlang::expr(matrix(c(stats::runif(chains, min = vf$lims[1], max = vf$lims[2]),
    stats::runif(chains, min = vf$lims[3], max = vf$lims[4])), ncol = 2))
)

Arguments

`vf`	A `vectorfield` object estimated by `fit_2d_vf()`.
`noise`	Relative noise of the simulation. Set this smaller when the simulation is unstable (e.g., when the elements in the diffusion matrix are not finite), and set this larger when the simulation converges too slowly.
`noise_warmup`	The noise used for the warming-up period.
`chains`	How many chains simulations should be performed?
`length`	The simulation length for each chain.
`discard`	How much of the starting part of each chain should be discarded? (Warming-up period.)
`stepsize`	The stepsize for Euler–Maruyama simulation of the system.
`sparse`	A number. How much do you want to sparse the output? When the noise is small, sparse the output may make the density estimation more efficient.
`forbid_overflow`	If `TRUE`, when the simulated system runs out of the margins specified in `vf`, the system will be moved back to the previous value. This can help to stabilize the simulation. `FALSE` by default.
`linear_interp`	Use linear interpolation method to estimate the drift vector (and the diffusion matrix). This can speed up the calculation. If `TRUE`, be sure that a linear grid was calculated for the vector field using `⁠<vf> <- add_interp_grid(<vf>)⁠`.
`inits`	The initial values of each chain.

Value

A list containing the parameters of the corresponding function. Only intended to be used within fit_3d_vfld()

Options controlling the landscape construction

Description

To control the behavior of simlandr::make_3d_static(), but with default values accommodated for fitlandr. See simlandr::make_3d_static() for details.

Usage

simlandr_options(
  vf,
  x = rlang::expr(vf$x),
  y = rlang::expr(vf$y),
  lims = rlang::expr(vf$lims),
  kde_fun = c("ks", "MASS"),
  n = 200,
  adjust = 1,
  h,
  Umax = 5
)
simlandr_options(
  vf,
  x = rlang::expr(vf$x),
  y = rlang::expr(vf$y),
  lims = rlang::expr(vf$lims),
  kde_fun = c("ks", "MASS"),
  n = 200,
  adjust = 1,
  h,
  Umax = 5
)

Arguments

`vf`	A `vectorfield` object estimated by `fit_2d_vf()`.
`x`, `y`	The names of the target variables.
`lims`	The limits of the range for the density estimator as `c(xl, xu)` for 2D landscapes, `c(xl, xu, yl, yu)` for 3D landscapes, `c(xl, xu, yl, yu, zl, zu)` for 4D landscapes. If missing, the range of the data extended by 10% for both sides will be used. For landscapes based on multiple simulations, the largest range of all simulations (which means the lowest lower limit and the highest upper limit) will be used by default.
`kde_fun`	Which kernel estimator to use? Choices: "ks" `ks::kde()` (default; faster and using less memory); "base" `base::density()` (only for 2D landscapes); "MASS" `MASS::kde2d()` (only for 3D landscapes).
`n`	The number of equally spaced points in each axis, at which the density is to be estimated.
`adjust`	The multiplier to the bandwidth. The bandwidth used is actually `adjust * h`. This makes it easy to specify values like "half the default" bandwidth.
`h`	A number, or possibly a vector for 3D and 4D landscapes, specifying the smoothing bandwidth to be used. If missing, the default value of the kernel estimator will be used (but `bw = "SJ"` for `base::density()`). Note that the definition of bandwidth might be different for different kernel estimators. For landscapes based on multiple simulations, the largest `h` of all simulations will be used by default.
`Umax`	The maximum displayed value of potential.

Value

A list containing the parameters of the corresponding function. Only intended to be used within fit_3d_vfld()

Package 'fitlandr'

Help Index

Add a grid to a vectorfield object to enable linear interpolation

Description

Usage

Arguments

Value

Find equilibrium points for a vector field

Description

Usage

Arguments

Value

Estimate a 2D potential landscape from data with the MVKE method

Description

Usage

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Value

Functions

Examples

Estimate a 2D vector field

Description

Usage

Arguments

Value

See Also

Examples

Estimate a 3D potential landscape from a vector field

Description

Usage

Arguments

Details

Value

Examples

Multivariate vector field kernel estimator

Description

Usage

Arguments

Value

References

Return a normalized prediction function

Description

Usage

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Value

Options controlling the path-integral algorithm

Description

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Value

Plot a 2D vector field

Description

Usage

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Value

Calculate the vector value at a given position

Description

Usage

Arguments

Value

See Also

Reorder a simulation output in time order

Description

Usage

Arguments

Value

Simulation from vector fields

Description

Usage

Arguments

Value

Options controlling the vector field simulation

Description

Usage

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Value

Options controlling the landscape construction

Description

Usage

Arguments

Value

Add a grid to a `vectorfield` object to enable linear interpolation