Modified ALCC

In soiltestcorr: Soil Test Correlation and Calibration

knitr::opts_chunk$set(
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Description

This tutorial is intended to show how to deploy the mod_alcc() function for estimating critical soil test values using the Modified Arcsine-Log Calibration Curve, originally introduced by Dyson & Conyers (2013) and modified by Correndo et al. (2017). This function produces the estimation of critical soil test values (CSTV) for a target relative yield (ry) with confidence intervals at adjustable confidence levels. Behind the scenes, the mod_alcc() is based on the standardized major axis (SMA), a bivariate regression model that assumes that both axis are random variables (Warton et al., 2006).

General Instructions

1. Load your dataframe with soil test value (stv) and relative yield (ry) data.

2. Specify the following arguments into the function mod_alcc()

   1. data (optional)

   2. stv (soil test value) and ry (relative yield) columns or vectors,

   3. target of relative yield (e.g. 90%),

   4. desired confidence level (e.g. 0.95 for 1 - alpha(0.05)).

      • Used for the estimation of critical soil test value (CSTV) lower and upper limits.

   5. tidy TRUE (produces a data.frame with results) or FALSE-default- (store results as list),

   6. plot TRUE (produces a ggplot as main output) or FALSE (default; no plot, only results as data.frame or list)

   7. Run and check results.
   8. Check residuals plot, and warnings related to potential leverage points.
   9. Adjust curve plots as desired with additional ggplot2 functions.

Tutorial

library(soiltestcorr)

Suggested packages

# Install if needed 
library(ggplot2) # Plots
library(dplyr) # Data wrangling
library(tidyr) # Data wrangling
library(purrr) # Mapping

This is a basic example using three different datasets:

Load datasets

# Native fake dataset from soiltestcorr package
corr_df <- soiltestcorr::freitas1966

Fit mod_alcc()

1. Individual fits

RY target = 90%, confidence level = 0.95, replace with your desired values

1.1. tidy = FALSE

It returns a LIST (may be more efficient for multiple fits at once)

# Using dataframe argument, tidy = FALSE -> return a LIST
mod_alcc(data = corr_df, ry = RY, stv = STK, target=90, confidence = 0.95,
         tidy = TRUE)

1.2. tidy = TRUE

It returns a data.frame (more organized results)

# Using dataframe argument, tidy = FALSE -> return a LIST
mod_alcc(data = corr_df, ry = RY, stv = STK, target=90, confidence = 0.95, tidy = TRUE)

1.3. Alternative using the vectors

You can call stv and ry vectors using the $.

The tidy argument still applies for controlling the output type

fit_vectors_tidy <- mod_alcc(ry = corr_df$RY,
                             stv = corr_df$STK,
                             target = 90,
                             confidence = 0.95)

fit_vectors_list <- mod_alcc(ry = corr_df$RY,
                             stv = corr_df$STK,
                             target = 90,
                             confidence = 0.95,
                             tidy = FALSE)

2. Multiple fits at once

# Example 1. Fake dataset manually created
data_1 <- data.frame("RY"  = c(65,80,85,88,90,94,93,96,97,95,98,100,99,99,100),
                     "STV" = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15))

# Example 2. Native fake dataset from soiltestcorr package
data_2 <- soiltestcorr::data_test


# Example 3. Native dataset from soiltestcorr package, Freitas et al.  (1966), used by Cate & Nelson (1971)
data_3 <- soiltestcorr::freitas1966 %>% 
  rename(STV = STK)

data.all <- bind_rows(data_1, data_2, data_3, .id = "id")

Note: the stv column needs to have the same name for all datasets if binding rows.

2.1. Using map()

# Run multiple examples at once with purrr::map()
data.all %>%
  nest(data = c("STV", "RY")) %>% 
  mutate(model = map(data, ~ mod_alcc(stv = .$STV, ry = .$RY, target = 90))) %>%
  unnest(model)

Note: the output table is still generated despite warnings regarding confidence level and leverage points.

2.2. Using group_modify()

Alternatively, with group_modify, nested data is not required. However, it still requires a grouping variable (in this case, id) to identify each dataset. group_map() may also be used, though list_rbind() is required to return a tidy data frame of the model results instead of a list.

data.all %>% 
  group_by(id) %>% 
  group_modify(~ mod_alcc(data = ., STV, RY, target = 90, confidence = 0.95))

3. Bootstrapping

Bootstrapping is a suitable method for obtaining confidence intervals for parameters or derived quantities. Bootstrapping is a resampling technique (with replacement) that draws samples from the original data with the same size. If you have groups within your data, you can specify grouping variables as arguments in order to maintain, within each resample, the same proportion of observations than in the original dataset.

This function returns a table with as many rows as the resampling size (n) containing the results for each resample.

set.seed(123)
boot_alcc <- boot_mod_alcc(data = corr_df,
                           stv = STK, ry = RY,
                           target = 90, n = 500)

boot_alcc %>% head(n = 5)

# CSTV Confidence Interval
quantile(boot_alcc$CSTV, probs = c(0.025, 0.5, 0.975))

# Plot
boot_alcc %>% 
  ggplot2::ggplot(aes(x = CSTV))+
  geom_histogram(color = "grey25", fill = "steelblue", bins = 10)

4. Plots

4.1. Calibration Curve

We can generate a ggplot with the same mod_alcc() function. We just need to specify the argument plot = TRUE.

plt_alcc <- mod_alcc(data = corr_df,
                     ry = RY, 
                     stv = STK, 
                     target = 95,
                     plot = TRUE)

plt_alcc

4.2. Fine-tune the plots

As ggplot object, plots can be adjusted in several ways, such as modifying titles and axis scales.

plt_alcc +
  # Main title
  ggtitle("My own plot title")+
  # Axis titles
  labs(x = "Soil Test K (ppm)",
       y = "Cotton RY(%)") +
  # Axis scales
  scale_x_continuous(limits = c(20,220),
                     breaks = seq(0,220, by = 25)) +
  # Axis limits
  scale_y_continuous(limits = c(30,100),
                     breaks = seq(20,100, by = 20))

3.2. SMA regression

Behind the scenes, the mod_alcc() runs a Standardized Major Axis regression (SMA).

We can extract the SMA fit and also check the residuals of this model out as follows:

fit_3 <- mod_alcc(data = corr_df, ry = RY, stv = STK, target = 90)
# Extract SMA regression fit and residuals from fit_3 (data_3, (Freitas et al., 1966))
SMA_freitas_1966 <- fit_3$SMA %>% as.data.frame()

SMA_freitas_1966 %>% 
  ggplot(aes(x = arc_RY, y = ln_STV))+
  ggtitle("SMA Regression. Dataset 3")+
  geom_point(shape=21, fill = "orange", size = 3, alpha = 0.75)+
  #SMA Line
  geom_path(aes(x=arc_RY, y = SMA_line, linetype = "SMA_fit"), linewidth = 1.5, col = "grey25")+
  scale_linetype_manual(name="", values = c("solid"))+
  #Critical value
  geom_vline(xintercept = 0, col = "grey10", size = 1.25, linetype = "dashed")+
  theme_bw()+
  # Axis titles
  labs(y = "ln_STV", y = "asin(sqrt(RY))-centered")

3.3. SMA residuals

# Residuals plot
SMA_freitas_1966 %>% 
  ggplot(aes(x = fitted_axis, y = residuals))+
  ggtitle("Residuals SMA. Dataset 3")+
  geom_point(shape=21, fill = "orange", size = 3, alpha = 0.75)+
  geom_hline(yintercept = 0, col = "grey10", linewidth = 1.25, linetype = "dashed")+
  theme_bw()+
  # Axis titles
  labs(x = "Fitted Axis -SMA- (see Warton et al. 2006)", y = "Residuals (STV units)")

References

Correndo, A.A., Salvagiotti, F., García, F.O. and Gutiérrez-Boem, F.H., 2017. A modification of the arcsine--log calibration curve for analysing soil test value--relative yield relationships. Crop and Pasture Science, 68(3), pp.297-304. 10.1071/CP16444

Dyson, C.B., Conyers, M.K., 2013. Methodology for online biometric analysis of soil test-crop response datasets. Crop & Pasture Science 64: 435--441. 10.1071/CP13009

Warton, D.I., Wright, I.J., Falster, D.S., Westoby, M., 2006. Bivariate line-fitting methods for allometry. Biol. Rev. Camb. Philos. Soc. 81, 259--291. 10.1017/S1464793106007007

soiltestcorr documentation built on July 3, 2024, 5:08 p.m.