Runs the CV-based methods on one set of data and tabulates what each
recommends: the optimal plot size \(X_o\), the CV at that size, and
comparable fit statistics. Given the raw grid instead of a CV table, it also
builds the table with calc_cv_shapes() and adds calc_paranaiba(), which
works on the basic units rather than on CV values.
Usage
compare_methods(
.data,
x = NULL,
cv = NULL,
trial = NULL,
methods = NULL,
step = 0.001,
weights = FALSE,
bootstrap = FALSE,
n_boot = 1000,
conf_level = 0.95
)Arguments
- .data
one of: a data frame holding a CV table (with
xandcvcolumns); a matrix, being the raw grid of basic experimental units of one trial; or a named list of such matrices. Grids are expanded into CV tables withcalc_cv_shapes(), and only then can the Paranaiba method take part.- x, cv
column names in
.datawhen it is a data frame (default"x"and"cv", the namescalc_cv_shapes()produces).- trial
optional column name identifying the trial.
- methods
which methods to run;
NULL(default) runs every applicable one. Any subset of"mcm","lrp","qrp"and"paranaiba", the last requiring a grid.- step
grid step for the breakpoint search of the LRP and QRP.
- weights
weighting for the fits, as in
fit_lrp().TRUEuses thencolumn, which a grid always provides. It reaches the MCM through that method's Federerdfargument, the same weighted least squares by another name. The Paranaiba row is unaffected: it is a closed form over the basic units and never sees the CV table.- bootstrap
logical; add a confidence interval for each \(X_o\), and the breakpoint-existence p-value where the method has one.
- n_boot, conf_level
bootstrap size and confidence level.
Value
An object of class "method_comparison": a list with
summary (one row per method and trial: trial,
method, Xo, CVxo, R2, RMSE, and
Xo_lwr, Xo_upr, p_breakpoint when
bootstrap = TRUE) and meta.
What the table is for
The methods disagree systematically: the MCM optimum is the smallest, the
LRP intermediate and the QRP the largest, an ordering reported across many
crops. Seeing the three side by side, with intervals when
bootstrap = TRUE, shows whether that ordering is a real difference or
three readings of the same imprecise quantity.
Which statistics are comparable
R2 and RMSE are recomputed here from the residuals on the
original CV scale, so they mean the same thing for every method even when
the fit itself was weighted. AIC and BIC are deliberately absent: the MCM has
two parameters against the plateau models' three plus a breakpoint, and the
breakpoint is not an ordinary parameter, so the information criteria are not
on a common footing. The Paranaiba estimate is a closed form with no fitted
residuals, so its R2 and RMSE are NA by nature, not by
omission.
References
Cargnelutti Filho, A. et al. (2025). Revista Vivencias, 21(43), 499-513, which compares the same three methods and reports 4.81, 7.19 and 10.25 m2 for the MCM, LRP and QRP respectively.
Examples
## The bundled simulated uniformity trial (see ?uniformity_trial): one
## 8 x 12 grid of 1 m2 basic units per trial.
grid1 <- as.matrix(uniformity_trial[uniformity_trial$trial == "T1",
grep("^col", names(uniformity_trial))])
## From a CV table (built here from the grid)
cv_tab <- calc_cv_shapes(list(T1 = grid1))
#> CV by shape for 1 trial(s).
cmp <- compare_methods(data.frame(x = cv_tab$x, cv = cv_tab$cv), step = 0.05)
#> Comparing 3 method(s) on 1 trial(s).
cmp
#> Plot-size methods compared
#> Trials: 1 | source: CV table | weighted: FALSE
#>
#> trial method Xo CVxo R2 RMSE
#> Trial 1 MCM 4.495 12.742 0.975 0.725
#> Trial 1 LRP 9.150 6.960 0.893 1.513
#> Trial 1 QRP 11.950 7.028 0.918 1.324
#>
#> Xo ranges from 4.495 to 11.950 (a factor of 2.7).
# \donttest{
## From the raw grid: the CV table is built on the way, and the Paranaiba
## method joins because it needs the basic units.
compare_methods(grid1, step = 0.05)
#> Comparing 4 method(s) on 1 trial(s).
#> Plot-size methods compared
#> Trials: 1 | source: raw grid | weighted: FALSE
#>
#> trial method Xo CVxo R2 RMSE
#> Trial 1 MCM 4.495 12.742 0.975 0.725
#> Trial 1 LRP 9.150 6.960 0.893 1.513
#> Trial 1 QRP 11.950 7.028 0.918 1.324
#> Trial 1 Paranaiba 4.794 10.720 NA NA
#>
#> Xo ranges from 4.495 to 11.950 (a factor of 2.7).
## Weighted by the number of plots per shape
compare_methods(grid1, step = 0.05, weights = TRUE)$summary
#> Comparing 4 method(s) on 1 trial(s).
#> trial method Xo CVxo R2 RMSE
#> 1 Trial 1 MCM 4.506414 12.726291 0.9753314 0.725023
#> 2 Trial 1 LRP 5.050000 9.156181 0.7937801 2.096258
#> 3 Trial 1 QRP 7.850000 8.682611 0.8502828 1.786139
#> 4 Trial 1 Paranaiba 4.793933 10.719560 NA NA
## With intervals, which is the point: the spread between methods is often
## smaller than the uncertainty within each one.
set.seed(1)
compare_methods(grid1, step = 0.05, bootstrap = TRUE, n_boot = 200)
#> Comparing 4 method(s) on 1 trial(s).
#> Plot-size methods compared
#> Trials: 1 | source: raw grid | weighted: FALSE
#>
#> trial method Xo CVxo R2 RMSE Xo_lwr Xo_upr p_breakpoint
#> Trial 1 MCM 4.495 12.742 0.975 0.725 4.171 4.787 NA
#> Trial 1 LRP 9.150 6.960 0.893 1.513 5.248 16.405 0.005
#> Trial 1 QRP 11.950 7.028 0.918 1.324 9.786 22.413 0.005
#> Trial 1 Paranaiba 4.794 10.720 NA NA NA NA NA
#>
#> Xo ranges from 4.495 to 11.950 (a factor of 2.7).
#> The intervals do not all overlap: the methods differ beyond their
#> own uncertainty.
## Several trials at once
grids <- lapply(split(uniformity_trial, uniformity_trial$trial),
function(d) as.matrix(d[, grep("^col", names(d))]))
compare_methods(grids, step = 0.05)$summary
#> Comparing 4 method(s) on 3 trial(s).
#> trial method Xo CVxo R2 RMSE
#> 1 T1 MCM 4.494865 12.741565 0.9753420 0.7248675
#> 2 T1 LRP 9.150000 6.960245 0.8925619 1.5130685
#> 3 T1 QRP 11.950000 7.028148 0.9177681 1.3237304
#> 4 T1 Paranaiba 4.793933 10.719560 NA NA
#> 5 T2 MCM 4.956314 11.533737 0.8820733 1.9801430
#> 6 T2 LRP 14.400000 3.916190 0.8341009 2.3486180
#> 7 T2 QRP 22.500000 3.375754 0.8665844 2.1061713
#> 8 T2 Paranaiba 4.638860 10.372807 NA NA
#> 9 T3 MCM 4.904998 12.210404 0.9067767 1.6560095
#> 10 T3 LRP 10.100000 6.101082 0.8019446 2.4137591
#> 11 T3 QRP 14.350000 5.966308 0.8298434 2.2373037
#> 12 T3 Paranaiba 4.936716 11.038833 NA NA
# }