Predict values from a fitted 2D-Gaussian

predict_gaussian_2D(fit_object, X_values, Y_values, ...)

fit_object | Either the output of |
---|---|

X_values | vector of numeric values for the x-axis |

Y_values | vector of numeric values for the y-axis |

... | Additional arguments |

A data.frame with the supplied `X_values`

and `Y_values`

along with the predicted values of the 2D-Gaussian
(`predicted_values`

)

This function assumes Gaussian parameters have been fitted beforehand. No
fitting of parameters is done within this function; these can be
supplied via the object created by `gaussplotR::fit_gaussian_2D()`

.

If `fit_object`

is not an object created by
`gaussplotR::fit_gaussian_2D()`

, `predict_gaussian_2D()`

attempts
to parse `fit_object`

as a list of two items. The coefficients of the
fit must be supplied as a one-row, named data.frame within
`fit_object$coefs`

, and details of the methods for fitting the Gaussian
must be contained as a character vector in `fit_object$fit_method`

. This
character vector in `fit_object$fit_method`

must be a named vector that
provides information about the method, amplitude constraint choice, and
orientation constraint choice, using the names `method`

,
`amplitude`

, and `orientation`

. `method`

must be one of:
`"elliptical"`

, `"elliptical_log"`

, or `"circular"`

.
`amplitude`

and `orientation`

must each be either
`"unconstrained"`

or `"constrained"`

. For example, ```
c(method =
"elliptical", amplitude = "unconstrained", orientation = "unconstrained")
```

.
One exception to this is when `method = "circular"`

, in which case
`orientation`

must be `NA`

, e.g.: ```
c(method = "circular",
amplitude = "unconstrained", orientation = NA)
```

.

Vikram B. Baliga

if (interactive()) { ## Load the sample data set data(gaussplot_sample_data) ## The raw data we'd like to use are in columns 1:3 samp_dat <- gaussplot_sample_data[,1:3] #### Example 1: Unconstrained elliptical #### ## This fits an unconstrained elliptical by default gauss_fit <- fit_gaussian_2D(samp_dat) ## Generate a grid of x- and y- values on which to predict grid <- expand.grid(X_values = seq(from = -5, to = 0, by = 0.1), Y_values = seq(from = -1, to = 4, by = 0.1)) ## Predict the values using predict_gaussian_2D gauss_data <- predict_gaussian_2D( fit_object = gauss_fit, X_values = grid$X_values, Y_values = grid$Y_values, ) ## Plot via ggplot2 and metR library(ggplot2); library(metR) ggplot_gaussian_2D(gauss_data) ## Produce a 3D plot via rgl rgl_gaussian_2D(gauss_data) #### Example 2: Constrained elliptical_log #### ## This fits a constrained elliptical, as in Priebe et al. 2003 gauss_fit <- fit_gaussian_2D( samp_dat, method = "elliptical_log", constrain_orientation = -1 ) ## Generate a grid of x- and y- values on which to predict grid <- expand.grid(X_values = seq(from = -5, to = 0, by = 0.1), Y_values = seq(from = -1, to = 4, by = 0.1)) ## Predict the values using predict_gaussian_2D gauss_data <- predict_gaussian_2D( fit_object = gauss_fit, X_values = grid$X_values, Y_values = grid$Y_values, ) ## Plot via ggplot2 and metR ggplot_gaussian_2D(gauss_data) ## Produce a 3D plot via rgl rgl_gaussian_2D(gauss_data) }