| Title: | Optimal Plot Size Estimation for Field Experiments |
| Version: | 0.1.0 |
| Description: | Provides methods for determining optimum plot size and shape in field experiments using Fairfield-Smith's variance law approach. It will evaluate field variability, determine optimum plot size and shape and study fertility trends across the field. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| Imports: | ggplot2 |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2025-09-30 12:33:47 UTC; lenovo |
| Author: | Y.A. Garde  [aut,
cre, cph],
S.G. Patil [aut],
M.S. Shitap [aut] |
| Maintainer: | Y.A. Garde <y.garde@yahoo.co.in> |
| Repository: | CRAN |
| Date/Publication: | 2025-10-06 08:30:02 UTC |
Compute 3x3 moving averages
Description
Compute 3x3 moving averages
Usage
compute_moving_avg(mat)
Arguments
mat |
A numeric matrix (at least 3 rows and 3 columns) |
Value
A numeric matrix of 3x3 moving averages
Fertility Classes heatmap with 3 * 3 moving average values —
Description
Fertility Classes heatmap with 3 * 3 moving average values —
Usage
ferti_analysis(mat)
Arguments
mat |
A matrix to be converted into a horizontal vector |
Value
Heatmap
Fit Fairfield-Smith's variance law to matrix data with ggplot2 plots
Description
This function fits the Fairfield-Smith variance law, computes weighted R^2 on the log-log scale, identifies the optimum plot size and recommended shape, and produces two ggplot2 visualisations (original and log scale).
Usage
fit_variance_law(df_mat, plot_curve = TRUE)
Arguments
df_mat |
numeric matrix of data |
plot_curve |
logical, if TRUE returns ggplot objects |
Value
list with results:
df_shapes: data.frame of plot shapes
V1: variance at 1x1
b_hat: estimated variance law coefficient
R2_log: weighted R^2 on log-log scale
x_opt: optimum plot size (units)
Vx_opt: predicted variance at optimum
best_shape: recommended shape for optimum plot size
plots: list of ggplot objects if plot_curve = TRUE
Generate valid plot sizes and shapes
Description
Generate valid plot sizes and shapes
Usage
generate_plot_shapes(df_mat)
Arguments
df_mat |
numeric matrix of data |
Value
data.frame of possible plot sizes and shapes
Compute T values (sum of block totals) for a given h x w plot
Description
Compute T values (sum of block totals) for a given h x w plot
Usage
get_Tvals(df_mat, h, w)
Arguments
df_mat |
numeric matrix of data |
h |
rows in plot |
w |
cols in plot |
Value
numeric vector of block totals
Make a horizontal vector from a matrix
Description
Creates a row-wise vector from a matrix. For every second row, the elements are reversed.
Usage
make_horizontal(mat)
Arguments
mat |
A matrix to be converted into a horizontal vector |
Value
A numeric vector
Make a vertical vector from a matrix
Description
Creates a column-wise vector from a matrix. For every second column, the elements are reversed.
Usage
make_vertical(mat)
Arguments
mat |
A matrix to be converted into a vertical vector |
Value
A numeric vector
Compute population variance for given h x w plot
Description
Compute population variance for given h x w plot
Usage
population_variance(df_mat, h, w)
Arguments
df_mat |
numeric matrix of data |
h |
rows in plot |
w |
cols in plot |
Value
numeric variance
Compute first-order serial correlation of a vector
Description
Computes the correlation between consecutive elements of a numeric vector.
Usage
serial_corr(vec)
Arguments
vec |
A numeric vector |
Value
Numeric value of the serial correlation
computes the first-order serial correlation for both directions.
Description
computes the first-order serial correlation for both directions.
Usage
serial_corrl(df_mat)
Arguments
df_mat |
A numeric matrix |
Value
A named list with two elements:
vertical: first-order serial correlation along vertical snake
horizontal: first-order serial correlation along horizontal snake