Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(gaussplotR)
## We'll also use lattice, ggplot2 and metR
library(lattice); library(ggplot2); library(metR)
## Load the sample data set
data(gaussplot_sample_data)
## The raw data we'd like to use are in columns 1:3
samp_dat <-
gaussplot_sample_data[,1:3]
## ----raw_data_contour---------------------------------------------------------
lattice::levelplot(
response ~ X_values * Y_values,
data = samp_dat,
col.regions = colorRampPalette(
c("white", "blue")
)(100),
xlim = c(-5, 0),
ylim = c(-1, 4),
asp = 1
)
## ----u_e----------------------------------------------------------------------
gauss_fit_ue <-
fit_gaussian_2D(samp_dat)
gauss_fit_ue
attributes(gauss_fit_ue)
## ----predict_and_plot_ue------------------------------------------------------
## Generate a grid of x- and y- values on which to predict
grid <-
expand.grid(X_values = seq(from = -5, to = 0, by = 0.1),
Y_values = seq(from = -1, to = 4, by = 0.1))
## Predict the values using predict_gaussian_2D
gauss_data_ue <-
predict_gaussian_2D(
fit_object = gauss_fit_ue,
X_values = grid$X_values,
Y_values = grid$Y_values,
)
## Plot via ggplot2 and metR
ggplot_gaussian_2D(gauss_data_ue)
## ----c_e----------------------------------------------------------------------
gauss_fit_ce <-
fit_gaussian_2D(samp_dat,
constrain_orientation = 0)
gauss_fit_ce
## ----predict_and_plot_ce------------------------------------------------------
## Predict the values using predict_gaussian_2D
gauss_data_ce <-
predict_gaussian_2D(
fit_object = gauss_fit_ce,
X_values = grid$X_values,
Y_values = grid$Y_values,
)
## Plot via ggplot2 and metR
ggplot_gaussian_2D(gauss_data_ce)
## ----uel----------------------------------------------------------------------
gauss_fit_uel <-
fit_gaussian_2D(samp_dat,
method = "elliptical_log")
gauss_fit_uel
## Predict the values using predict_gaussian_2D
gauss_data_uel <-
predict_gaussian_2D(
fit_object = gauss_fit_uel,
X_values = grid$X_values,
Y_values = grid$Y_values,
)
## Plot via ggplot2 and metR
ggplot_gaussian_2D(gauss_data_uel)
## ----cel----------------------------------------------------------------------
gauss_fit_cel <-
fit_gaussian_2D(
samp_dat,
method = "elliptical_log",
constrain_orientation = -0.66
)
gauss_fit_cel
## Predict the values using predict_gaussian_2D
gauss_data_cel <-
predict_gaussian_2D(
fit_object = gauss_fit_cel,
X_values = grid$X_values,
Y_values = grid$Y_values,
)
## Plot via ggplot2 and metR
ggplot_gaussian_2D(gauss_data_cel)
## ----cir----------------------------------------------------------------------
gauss_fit_cir <-
fit_gaussian_2D(samp_dat,
method = "circular")
gauss_fit_cir
## Predict the values using predict_gaussian_2D
gauss_data_cir <-
predict_gaussian_2D(
fit_object = gauss_fit_cir,
X_values = grid$X_values,
Y_values = grid$Y_values,
)
## Plot via ggplot2 and metR
ggplot_gaussian_2D(gauss_data_cir)
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