Getting predictions on a every level of a grouped dataset

Sam_dplyr_predictions.R

# load in packages
library(nlsLoop)
library(dplyr)
library(tidyr)

# load up Chlorella data
data('Chlorella_TRC')

head(Chlorella_TRC)

p <- expand.grid(curve_id = unique(Chlorella_TRC$curve_id),
                     growth.temp = unique(Chlorella_TRC$growth.temp)) %>%
  mutate(., ln.c = rnorm(n(), mean = 2, sd = 1),
         Ea = rnorm(n(), mean = 0.65, sd = 0.5),
         Eh = rnorm(n(), mean = 2, sd = 1),
         Th = rnorm(n(), mean = 310, sd = 4))

# create new dataset of temperature values
new_d <- group_by(Chlorella_TRC, curve_id, growth.temp) %>%
  summarise(., min = min(K, na.rm = FALSE),
            max = max(K, na.rm = FALSE)) %>%
  data.frame() %>%
  group_by(., curve_id, growth.temp) %>%
  do(data.frame(K = seq(.$min, .$max, length.out = 100))) %>%
  data.frame() %>%
  merge(., p, by = c('curve_id', 'growth.temp')) %>%
  mutate(., pred = schoolfield.high(ln.c, Ea, Eh, Th, temp = K, Tc = 15)) %>%
  select(., -c(ln.c, Ea, Eh, Th))