I am trying to model plant decomposition curves using the litterfitter package. My dataset has three columns, time, mass remaining and site code. I want to apply the model over categories of site code and extract the model parameters but am having some difficulty. Below is an example with error codes.
library(litterfitter)
library(tidyverse)
decomp_test <- structure(list(site_code = c("CCPp1a", "CCPp1a", "CCPp1a", "CCPp1a",
"CCPp1a", "CCPp1b", "CCPp1b", "CCPp1b", "CCPp1b", "CCPp1b", "CCPp1c",
"CCPp1c", "CCPp1c", "CCPp1c", "CCPp1c", "CCPp1d", "CCPp1d", "CCPp1d",
"CCPp1d", "CCPp1d", "CCPp1e", "CCPp1e", "CCPp1e", "CCPp1e", "CCPp1e",
"CCPp1f", "CCPp1f", "CCPp1f", "CCPp1f", "CCPp1f"), days_between = c(0L,
118L, 229L, 380L, 572L, 0L, 118L, 229L, 380L, 572L, 0L, 118L,
229L, 380L, 572L, 0L, 118L, 229L, 380L, 572L, 0L, 118L, 229L,
380L, 572L, 0L, 118L, 229L, 380L, 572L), mass_remaining = c(1,
0.7587478816, 0.7366473295, 0.6038150404, 0.6339366063, 1, 0.7609346914,
0.7487194938, 0.7336179508, 0.6595702348, 1, 0.777213425, 0.734006734,
0.6963752241, 0.5827854154, 1, 0.7716566866, 0.7002094345, 0.6913555798,
0.7519095328, 1, 0.7403565314, 0.6751289171, 0.6572164948, 0.620339994,
1, 0.8126440236, 0.7272999401, 0.7223268259, 0.6805293006)), row.names = c(NA,
-30L), class = "data.frame")
#Test data-frame with a small number of sites
discrete_paralell <-
decomp_test %>%
nest(-site_code) %>%
mutate(fit = map(decomp_test, ~ fit_litter(time=decomp_test$days_between ,mass.remaining= decomp_test$mass_remaining,
model='discrete.parallel',iters=1000)),
results = map(fit, glance)) %>%
unnest(results)
Error: Problem with mutate() column fit.
i fit = map(...).
i fit must be size 6 or 1, not 3.
#or
discrete_paralell <-
decomp_test %>%
nest(-site_code) %>%
mutate(fit = map(decomp_test, ~ fit_litter(time=decomp_test$days_between ,mass.remaining= decomp_test$mass_remaining,
model='discrete.parallel',iters=1000)),
coef = map_dbl(fit, "coef"),
actual = map_dbl(fit, "mass"),
preds = map_dbl(fit, "predicted"),
AIC = map_dbl(fit, "fitAIC"),
model = map_dbl(fit, "model"))
Error: Problem with mutate() column fit.
i fit = map(...).
i fit must be size 6 or 1, not 3.
I understand that not all of the models will fit, I will examine all of the fits relative to other models later.
How about this:
library(litterfitter)
library(tidyverse)
decomp_test <- structure(list(site_code = c("CCPp1a", "CCPp1a", "CCPp1a", "CCPp1a",
"CCPp1a", "CCPp1b", "CCPp1b", "CCPp1b", "CCPp1b", "CCPp1b", "CCPp1c",
"CCPp1c", "CCPp1c", "CCPp1c", "CCPp1c", "CCPp1d", "CCPp1d", "CCPp1d",
"CCPp1d", "CCPp1d", "CCPp1e", "CCPp1e", "CCPp1e", "CCPp1e", "CCPp1e",
"CCPp1f", "CCPp1f", "CCPp1f", "CCPp1f", "CCPp1f"), days_between = c(0L,
118L, 229L, 380L, 572L, 0L, 118L, 229L, 380L, 572L, 0L, 118L,
229L, 380L, 572L, 0L, 118L, 229L, 380L, 572L, 0L, 118L, 229L,
380L, 572L, 0L, 118L, 229L, 380L, 572L), mass_remaining = c(1,
0.7587478816, 0.7366473295, 0.6038150404, 0.6339366063, 1, 0.7609346914,
0.7487194938, 0.7336179508, 0.6595702348, 1, 0.777213425, 0.734006734,
0.6963752241, 0.5827854154, 1, 0.7716566866, 0.7002094345, 0.6913555798,
0.7519095328, 1, 0.7403565314, 0.6751289171, 0.6572164948, 0.620339994,
1, 0.8126440236, 0.7272999401, 0.7223268259, 0.6805293006)), row.names = c(NA,
-30L), class = "data.frame")
#Test data-frame with a small number of sites
discrete_paralell <-
decomp_test %>%
group_by(site_code) %>%
summarise(fit = list(fit_litter(time=days_between ,mass.remaining= mass_remaining,
model='discrete.parallel',iters=1000)$optimFit$par)) %>%
unnest_wider(fit) %>%
setNames(c("site_code", "par_1", "par_2", "par_3"))
#> Number of successful fits: 898 out of 1000
#> Number of successful fits: 912 out of 1000
#> Warning in fit_litter(time = days_between, mass.remaining = mass_remaining, :
#> one or more parameters fit on the boundary, check fit closely
#> Number of successful fits: 866 out of 1000
#> Number of successful fits: 981 out of 1000
#> Warning in fit_litter(time = days_between, mass.remaining = mass_remaining, :
#> one or more parameters fit on the boundary, check fit closely
#> Number of successful fits: 895 out of 1000
#> Warning in fit_litter(time = days_between, mass.remaining = mass_remaining, :
#> one or more parameters fit on the boundary, check fit closely
#> Number of successful fits: 907 out of 1000
#> Warning in fit_litter(time = days_between, mass.remaining = mass_remaining, :
#> one or more parameters fit on the boundary, check fit closely
#> New names:
#> • `` -> `...1`
#> • `` -> `...2`
#> • `` -> `...3`
#> New names:
#> New names:
#> New names:
#> New names:
#> New names:
#> • `` -> `...1`
#> • `` -> `...2`
#> • `` -> `...3`
discrete_paralell
#> # A tibble: 6 × 4
#> site_code par_1 par_2 par_3
#> <chr> <dbl> <dbl> <dbl>
#> 1 CCPp1a 0.819 0.000512 0.596
#> 2 CCPp1b 0.738 0.0001 0.0161
#> 3 CCPp1c 0.120 0.257 0.000685
#> 4 CCPp1d 0.256 0.0187 0.0001
#> 5 CCPp1e 0.332 0.0119 0.0001
#> 6 CCPp1f 0.273 0.00954 0.0001
Created on 2023-01-17 by the reprex package (v2.0.1)