I have a dataframe df
dput(df)
structure(list(x = c(49, 50, 51, 52, 53, 54, 55, 56, 1, 2, 3,
4, 5, 14, 15, 16, 17, 2, 3, 4, 5, 6, 10, 11, 3, 30, 64, 66, 67,
68, 69, 34, 35, 37, 39, 2, 17, 18, 99, 100, 102, 103, 67, 70,
72), y = c(2268.14043972082, 2147.62290922552, 2269.1387550775,
2247.31983098201, 1903.39138268307, 2174.78291538358, 2359.51909126411,
2488.39004804939, 212.851575751527, 461.398994384333, 567.150629704352,
781.775113821961, 918.303706148872, 1107.37695799186, 1160.80594193377,
1412.61328924168, 1689.48879626486, 260.737164468854, 306.72700499362,
283.410379620422, 366.813913489692, 387.570173754128, 388.602676983443,
477.858510450125, 128.198042456082, 535.519377609133, 1028.8780498564,
1098.54431357711, 1265.26965941035, 1129.58344809909, 820.922447928053,
749.343583476846, 779.678206156474, 646.575242339517, 733.953282899613,
461.156280127354, 906.813018662913, 798.186995701282, 831.365377249207,
764.519073183124, 672.076289062505, 669.879217186302, 1341.47673353751,
1401.44881976186, 1640.27575962036)), .Names = c("x", "y"), row.names = c(NA,
-45L), class = "data.frame")
I have created two non-linear regression (nls1 and nls2) based on my dataset.
library(stats)
nls1 <- nls(y~A*(x^B)*(exp(k*x)),
data = df,
start = list(A = 1000, B = 0.170, k = -0.00295))
nls2<-nls(y~A*x^3+B*x^2+C*x+D, data=df,
start = list(A=0.02, B=-0.6, C= 50, D=200))
I then computed bootstrap objects for these two functions to get multiple sets of parameters (A,B and k for nls1 and A, B, C and D for nls2).
library(nlstools)
Boo1 <- nlsBoot(nls1, niter = 200)
Boo2 <- nlsBoot(nls2, niter = 200)
Based on this bootstrap objects, I would like to compute r-squared of each combination of parameters and save the min, max and median of my r-squared values for each bootstrap object into one new dataframe. The dataframe could look like new.df.
structure(list(Median = c(NA, NA), Max = c(NA, NA), Min = c(NA,
NA)), .Names = c("Median", "Max", "Min"), row.names = c("nls1",
"nls2"), class = "data.frame")
The idea is then to do some box plots with the median, min and max values for each non-linear model based on bootstrapping to compare them. Can someone help me out with that? Thanks in advance.
Answer from @bunk
stat <- function(dat, inds) { fit <- try(nls(y~A*(x^B)*(exp(k*x)), data = dat[inds,], start = list(A = 1000, B = 0.170, k = -0.00295)), silent=TRUE); f1 <- if (inherits(fit, "nls")) AIC(fit) else NA; fit2 <- try(nls(y~A*x^3+B*x^2+C*x+D, data = dat[inds,], start = list(A=0.02, B=-0.6, C= 50, D=200)), silent=TRUE); f2 <- if (inherits(fit2, "nls")) AIC(fit2) else NA; c(f1, f2) }; res <- boot(df, stat, R=200). Then, to get medians for example, apply(res$t, 2, median, na.rm=TRUE)