Fit an exponential model

Fits the exponential model (y ~ exp(m * x + b)), estimating parameters m and b using stats::nls(). The log-linear version fits the model log(y) ~ x using stats::lm(), where the y-intercept is b and the slope of the line is m. The log-linear version overestimates the importance of small values but is widely used. NA and zero values are removed observation-wise prior to fitting.

pb210_fit_exponential(x, y, subset = NULL)

pb210_fit_loglinear(x, y, subset = NULL)

pb210_fit_exponential_manual(m, b)

pb210_fit_exponential_zero()

pb210_fit_exponential_constant(value)

# S3 method for exponential_manual
predict(object, newdata, ...)

# S3 method for exponential_manual
coef(object, ...)

# S3 method for lm_loglinear
predict(object, newdata, ...)

# S3 method for lm_loglinear
coef(object, ...)

Arguments

x	An independent variable like depth or cumulative dry mass.
y	A dependent variable that responds exponentially to x.
subset	NULL to do no subsetting, a logical or numeric vector to subset manually, or a function of two variables (x and y) that returns NULL, a logical vector, or a numeric vector that will be used to subset the data. This is particularly useful in conjunction with finite_head() or finite_tail().
m, b, value	Directly specify coefficients for a manual fit.
object	A model fit object.
newdata	A tibble with a column x.
...	Not used.

Value

A model object like that returned by stats::nls(), with a stats::predict() method.

Examples

fake_depth <- 0:10
fake_pb210 <- exp(5 - fake_depth) + rnorm(11, sd = 0.005)

fit_exp <- pb210_fit_exponential(fake_depth, fake_pb210)
fit_loglinear <- pb210_fit_loglinear(fake_depth, fake_pb210)

coefficients(fit_exp)
#>         b         m 
#>  5.000006 -1.000037 
coefficients(fit_loglinear)
#>          b          m 
#>  4.9468010 -0.9794711 

tibble::tibble(
  new_depth = 0:5,
  fitted_exp = predict(fit_exp, newdata = tibble::tibble(x = new_depth)),
  fitted_loglin = predict(fit_loglinear, newdata = tibble::tibble(x = new_depth))
)
#> # A tibble: 6 x 3
#>   new_depth fitted_exp fitted_loglin
#>       <int>      <dbl>         <dbl>
#> 1         0     148.          141.  
#> 2         1      54.6          52.8 
#> 3         2      20.1          19.8 
#> 4         3       7.39          7.45
#> 5         4       2.72          2.80
#> 6         5       1.00          1.05