gdd_calc: Compute Effective Accumulated Temperature and Developmental...

In insectecol: Insect Ecology Data Analysis Toolkit

Compute Effective Accumulated Temperature and Developmental Parameters (Linear or Nonlinear Models)

Description

Fits a temperature-dependent development model to constant- temperature data. By default the classic linear degree-day model

V = (T - C) / K

is fitted by ordinary least squares, yielding the developmental threshold temperature C (deg C) and the effective accumulated temperature K (degree-days). Nonlinear models describing the full response curve (optimum and upper threshold included) are available via model.

Usage

gdd_calc(
  data,
  temp_col = NULL,
  duration_col = NULL,
  by = NULL,
  model = c("linear", "logan", "lactin", "briere1", "briere2", "wang", "auto"),
  start = NULL,
  conf_level = 0.95,
  min_n = 3,
  maxiter = 1000
)

Arguments

data	A data.frame with at least a temperature column and a duration column.
temp_col, duration_col	Column names (auto-detected by default).
by	Grouping variable(s), e.g. "stage"; NULL fits the overall model.
model	Single model name or "auto"; default "linear".
start	Optional named list of starting values for a nonlinear model, e.g. list(a = 1e-4, T0 = 10, Tm = 35).
conf_level	Confidence level, default 0.95.
min_n	Minimum rows per group, default 3.
maxiter	Iteration limit passed to nls.control.

Details

Available models:

• "linear" (default): V = (T - C)/K. Analytic OLS; the only model providing K.

• "logan": Logan-6 (Logan et al. 1976). Does NOT define a lower threshold; gives T_m and T_{opt}.

• "lactin": Lactin et al. (1995). \lambda < 0 lets the curve cross zero, so the lower threshold is derived numerically.

• "briere1": V = aT(T - T_0)\sqrt{T_m - T} (Briere et al. 1999); T_0 is the lower threshold.

• "briere2": V = aT(T - T_0)(T_m - T)^{1/m}; m adds flexibility.

• "wang": Wang et al. (1982), 7 parameters — needs at least 9 temperature points.

• "auto": fits all candidates and selects the best per group by AICc; the full comparison table is stored in object$comparison.

Nonlinear fits use nls (port algorithm, bounded) with heuristic starting values and deterministic restarts (start entries not belonging to the fitted model are ignored). Standard errors are asymptotic; derived quantities (Topt, Vmax, numerically derived C) carry no SE. Each nonlinear model requires at least (number of parameters + 2) temperature points.

Value

A "gdd" object: results (summary table, one row per group; the selected model in "auto" mode), fits (per-group details), data (cleaned data), and comparison (model comparison table, "auto" only).

See Also

[gdd_read()], [gdd_check()], [gdd_compare()], [gdd_predict()], [gdd_plot()], [gdd_export()], [gdd_daily()]

Examples

# csv example shipped with the package (inst/extdata)
f  <- system.file("extdata", "gdd_example.csv", package = "insectecol")
df <- gdd_read(f)
fit1 <- gdd_calc(df, by = "stage")                       # linear (default)

fit2 <- gdd_calc(df, by = "stage", model = "briere1")    # nonlinear
fit3 <- gdd_calc(df, by = "stage", model = "auto")       # AICc selection

insectecol documentation built on Oct. 5, 2026, 5:08 p.m.