I made a nls loop and get values calculated in console. Now I want to extract those values, specify which values are from which group and put everything in a dataframe to continue working.
my loop so far:
for (i in seq_along(trtlist2)) { loopmm.nls <-
nls(rate ~ (Vmax * conc /(Km + conc)),
data=subset(M3, M3$trtlist==trtlist2[i]),
start=list(Km=200, Vmax=2), trace=TRUE )
summary(loopmm.nls)
print(summary(loopmm.nls))
}
the output in console: (this is what I want to extract and put in a dataframe, I have this same "parameters" thing like 20 times)
Parameters:
Estimate Std. Error t value Pr(>|t|)
Km 23.29820 9.72304 2.396 0.0228 *
Vmax 0.10785 0.01165 9.258 1.95e-10 ***
---
different ways of extracting data from the console that work but not in the loop (so far!)
#####extract data in diff ways from nls#####
## extract coefficients as matrix
Kinall <- summary(mm.nls)$parameters
## extract coefficients save as dataframe
Kin <- as.data.frame(Kinall)
colnames(Kin) <- c("values", "SE", "T", "P")
###create Km Vmax df
Kms <- Kin[1, ]
Vmaxs <- Kin[2, ]
#####extract coefficients each manually
Km <- unname(coef(summary(mm.nls))["Km", "Estimate"])
Vmax <- unname(coef(summary(mm.nls))["Vmax", "Estimate"])
KmSE <- unname(coef(summary(mm.nls))["Km", "Std. Error"])
VmaxSE <- unname(coef(summary(mm.nls))["Vmax", "Std. Error"])
KmP <- unname(coef(summary(mm.nls))["Km", "Pr(>|t|)"])
VmaxP <- unname(coef(summary(mm.nls))["Vmax", "Pr(>|t|)"])
KmT <- unname(coef(summary(mm.nls))["Km", "t value"])
VmaxT <- unname(coef(summary(mm.nls))["Vmax", "t value"])
one thing that works if you extract data through append, but somehow that only works for "estimates" not the rest
Kms <- append(Kms, unname(coef(loopmm.nls)["Km"] ))
Vmaxs <- append(Vmaxs, unname(coef(loopmm.nls)["Vmax"] ))
}
Kindf <- data.frame(trt = trtlist2, Vmax = Vmaxs, Km = Kms)
I would just keep everything in the dataframe for ease. You can nest by the group and then run the regression then pull the coefficients out. Just make sure you have tidyverse and broom installed on your computer.
library(tidyverse)
#example
mtcars |>
nest(data = -cyl) |>
mutate(model = map(data, ~nls(mpg~hp^b,
data = .x,
start = list(b = 1))),
clean_mod = map(model, broom::tidy)) |>
unnest(clean_mod) |>
select(-c(data, model))
#> # A tibble: 3 x 6
#> cyl term estimate std.error statistic p.value
#> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 6 b 0.618 0.0115 53.6 2.83e- 9
#> 2 4 b 0.731 0.0217 33.7 1.27e-11
#> 3 8 b 0.504 0.0119 42.5 2.46e-15
#what I expect will work for your data
All_M3_models <- M3 |>
nest(data = -trtlist) |>
mutate(model = map(data, ~nls(rate ~ (Vmax * conc /(Km + conc)),
data=.x,
start=list(Km=200, Vmax=2))),
clean_mod = map(model, broom::tidy))|>
unnest(clean_mod) |>
select(-c(data, model))