I have a series of points over time and I want to fit a curve to these points and then find the area under the curve. I can do it like this:
fit<-loess(mass ~ hour, df)
f <- function(x) predict(fit,newdata=x)
answer<-integrate(f, 0, 60)$value
So far, so good, but my problem is that I have replications of the points. I want a fit and a value for each replication.
trt rep hour mass
y a 1 3
y a 2 5
y a 3 8
y a 4 6
y b 1 2
y b 2 3
y b 3 5
y b 4 4
n c 1 4
n c 2 6
n c 3 8
n c 4 7
n d 1 5
n d 2 7
n d 3 8
n d 4 7
I want to dplyr::group_by(rep), but the grouping doesn't come through to fit. I'm could also do grouping in nlme and use nlsList, but the package doesn't work well with loess or polynomials in general.
Seems like case for do(). With your sample data
dd <- read.table(text="trt rep hour mass
y a 1 3
y a 2 5
y a 3 8
y a 4 6
y b 1 2
y b 2 3
y b 3 5
y b 4 4
n c 1 4
n c 2 6
n c 3 8
n c 4 7
n d 1 5
n d 2 7
n d 3 8
n d 4 7 ", header=T)
and your area calculation
get_area <- function(df) {
fit <- loess(mass ~ hour, df)
f <- function(x) predict(fit,newdata=x)
integrate(f, 0, 60)$value
}
you can just run
dd %>% group_by(rep) %>% do(area=get_area(.))