# fit_coneshaped_model: Fit cone-shaped model In rcaiman: CAnopy IMage ANalysis

 fit_coneshaped_model R Documentation

## Fit cone-shaped model

### Description

Statistical modeling to predict the digital numbers from spherical coordinates.

### Usage

fit_coneshaped_model(sky_points, use_azimuth_angle = TRUE)

### Arguments

 sky_points The data.frame returned by extract_rl or a data.frame with same structure and names. use_azimuth_angle Logical vector of length one. If TRUE, the Equation 4 from \insertCiteDiaz2018;textualrcaiman) is used: sDN = a + b \cdot θ + c \cdot θ^2 + d \cdot sin(φ) + e \cdot cos(φ), where sDN is sky digital number, a,b,c,d and e are coefficients, θ is zenith angle, and φ is azimuth angle. If FALSE, the next simplified version based on \insertCiteWagner2001;textualrcaiman is used: sDN = a + b \cdot θ + c \cdot θ^2.

### Details

An explanation of this model can be found on \insertCiteDiaz2018;textualrcaiman, under the heading Estimation of the sky DN as a previous step for our method.

### Value

A list of two objects, one of class function and the other of class lm (see lm). If the fitting fails, it returns NULL. The function requires two arguments–zenith and azimuth in degrees–to return relative luminance.

### References

\insertAllCited

thr_image

Other Sky Reconstruction Functions: cie_sky_model_raster(), fit_cie_sky_model(), fit_trend_surface(), fix_reconstructed_sky(), interpolate_sky_points(), ootb_sky_reconstruction()

### Examples

## Not run:
path <- system.file("external/DSCN4500.JPG", package = "rcaiman")
caim <- read_caim(path, c(1280, 960) - 745, 745 * 2, 745 * 2)
z <- zenith_image(ncol(caim), lens("Nikon_FCE9"))
a <- azimuth_image(z)
r <- gbc(caim$Blue) g <- sky_grid_segmentation(z, a, 10) bin <- find_sky_pixels(r, z, a) sky_points <- extract_sky_points(r, bin, g) sky_points <- extract_rl(r, z, a, sky_points, NULL) model <- fit_coneshaped_model(sky_points$sky_points)
sky_cs <- model\$fun(z, a)
persp(sky_cs, theta = 90, phi = 0) #a flipped rounded cone!

## End(Not run)

rcaiman documentation built on Sept. 20, 2022, 1:05 a.m.