dput(AM_cost_resorb[,1]) c(176.01685819061, 21.6704613594849, 19.007554742708, 50.1865574864131, 17.6174002411188, 40.2758022496774, 11.0963214407251, 1249.94375253114, 694.894678288085, 339.786950220117, 42.1452961176151, 220.352895161601, 19.6303352674776, 9.10350287678884, 10.6222946396451, 44.1984352318898, 21.8069112975004, 42.1237630342764, 22.7551891190248, 12.9587850506626, 12.0207189111152, 20.2704921282476, 13.3441156357956, 9.13092569988769, 1781.08346869568, 71.2690023512206, 80.2376892286713, 344.114362037227, 208.830841645638, 91.1778810401913, 2220.0120768657, 41.4820962277111, 16.5730025748281, 32.30173229022, 108.703930214512, 51.6770035143256, 709.071405759588, 87.9618878732223, 10.4198968123037, 34.4951840238729, 57.8603720445067, 72.3289197551429, 30.2366643066749, 23.8696161364716, 270.014690419247, 13.8170113452005, 39.5159584479013, 27.764841260433, 18.0311836472615, 40.5709477295999, 33.1888820958952, 9.03112843931787, 4.63738971549635, 12.7591169313099, 4.7998894219979, 8.93458248803248, 7.33904760386628, 12.0940344070925, 7.17364602165948, 6.514191844409, 9.69911157978057, 6.57874454980745, 7.90556524435596)
dput(AM_leafP*1000) c(0.02840637, 0.230728821, 0.2630533, 0.099628272, 0.28381032, 0.12414402, 0.45059978, 0.00400018, 0.00719533500000001, 0.014715103086687, 0.118637201789886, 0.022690875, 0.254707825, 0.54923913, 0.470708088, 0.113126176837872, 0.22928510745, 0.118697847481752, 0.219730100850697, 0.38583864, 0.4159485, 0.24666396693114, 0.374696992776912, 0.547589605297248, 0.00280728, 0.070156727820596, 0.062314855376136, 0.01453005323695, 0.02394282358199, 0.0548378613646, 0.00225224, 0.120533928, 0.301695482, 0.15479046, 0.045996497, 0.096754836, 0.00705147600000001, 0.0568428, 0.47985120103071, 0.14494777, 0.08641493, 0.069128642, 0.165362156, 0.20947132, 0.018517511, 0.36187275779699, 0.126531158458224, 0.180083867690804, 0.277297380904852, 0.1232408972382, 0.15065285976048, 0.55364067, 1.07819275643191, 0.39187665, 1.04169066418176, 0.55962324, 0.68128731, 0.41342697, 0.69699564, 0.76755492, 0.515511133042674, 0.760023430328564, 0.632465844687028)
I want to replace the points in my graph with a line like in the first picture, the second picture is what I have.
This is my code
plot(AM_leafP*1000,AM_cost_resorb[,1], type="p", pch= 15, col="red",ylab="Cost of reabsorbtion (kg C m^-2 yr^-1)", xlab="leaf P before senescence (g P/m2)",ylim=c(0,500))
I was able to create a continous line through my points like this
lines((AM_leafP*1000)[order(AM_leafP*1000)], (AM_cost_resorb[,1])[order(AM_leafP*1000)], pch=16)
but its not quite what im looking for, I want a smooth line without the points
I think I have to use predict for the 1/x curve but I am not sure how,
CodePudding user response：
Assuming f(1/x) fits the data well. One can use the lm() function to fix the desired function y= a/x b and then use the predict() function to estimate the desired points.
If a more complicated nonlinear function is required to fit the data then the nls() maybe required
x<- c(176.01685819061, 21.6704613594849, 19.007554742708, 50.1865574864131, 17.6174002411188, 40.2758022496774, 11.0963214407251, 1249.94375253114, 694.894678288085, 339.786950220117, 42.1452961176151, 220.352895161601, 19.6303352674776, 9.10350287678884, 10.6222946396451, 44.1984352318898, 21.8069112975004, 42.1237630342764, 22.7551891190248, 12.9587850506626, 12.0207189111152, 20.2704921282476, 13.3441156357956, 9.13092569988769, 1781.08346869568, 71.2690023512206, 80.2376892286713, 344.114362037227, 208.830841645638, 91.1778810401913, 2220.0120768657, 41.4820962277111, 16.5730025748281, 32.30173229022, 108.703930214512, 51.6770035143256, 709.071405759588, 87.9618878732223, 10.4198968123037, 34.4951840238729, 57.8603720445067, 72.3289197551429, 30.2366643066749, 23.8696161364716, 270.014690419247, 13.8170113452005, 39.5159584479013, 27.764841260433, 18.0311836472615, 40.5709477295999, 33.1888820958952, 9.03112843931787, 4.63738971549635, 12.7591169313099, 4.7998894219979, 8.93458248803248, 7.33904760386628, 12.0940344070925, 7.17364602165948, 6.514191844409, 9.69911157978057, 6.57874454980745, 7.90556524435596)
y<- c(0.02840637, 0.230728821, 0.2630533, 0.099628272, 0.28381032, 0.12414402, 0.45059978, 0.00400018, 0.00719533500000001, 0.014715103086687, 0.118637201789886, 0.022690875, 0.254707825, 0.54923913, 0.470708088, 0.113126176837872, 0.22928510745, 0.118697847481752, 0.219730100850697, 0.38583864, 0.4159485, 0.24666396693114, 0.374696992776912, 0.547589605297248, 0.00280728, 0.070156727820596, 0.062314855376136, 0.01453005323695, 0.02394282358199, 0.0548378613646, 0.00225224, 0.120533928, 0.301695482, 0.15479046, 0.045996497, 0.096754836, 0.00705147600000001, 0.0568428, 0.47985120103071, 0.14494777, 0.08641493, 0.069128642, 0.165362156, 0.20947132, 0.018517511, 0.36187275779699, 0.126531158458224, 0.180083867690804, 0.277297380904852, 0.1232408972382, 0.15065285976048, 0.55364067, 1.07819275643191, 0.39187665, 1.04169066418176, 0.55962324, 0.68128731, 0.41342697, 0.69699564, 0.76755492, 0.515511133042674, 0.760023430328564, 0.632465844687028)
#data frame for prediction
df <- data.frame(x=sort(x))
# fit model y= a/x b
model <-lm( y ~ I(1/x))
#summary(model)
#plot model
plot(df$x, predict(model, df), type="l", col="blue")
#optional
points(x, y)
CodePudding user response：
For smoothing, you can fit a linear model as foolws:
m <- lm(AM_cost_resorb~I(1/AM_leafP), data=data)
Then extract the predictied values on a new data set that covers the range of the exposure variable.
newx <- seq(min(data$AM_leafP), max(data$AM_leafP), by=0.01)
pr <- predict(m, newdata=data.frame(AM_leafP=newx))
And visualize:
plot(AM_cost_resorb~AM_leafP, data=data, type="p", pch= 15, col="red",ylab="Cost of reabsorbtion (kg C m^-2 yr^-1)", xlab="leaf P before senescence (g P/m2)", ylim=c(0,500), las=1)
lines(newx, y=pr, col="blue", lwd=2)
Data:
data <- structure(list(AM_cost_resorb = c(176.01685819061, 21.6704613594849,
19.007554742708, 50.1865574864131, 17.6174002411188, 40.2758022496774,
11.0963214407251, 1249.94375253114, 694.894678288085, 339.786950220117,
42.1452961176151, 220.352895161601, 19.6303352674776, 9.10350287678884,
10.6222946396451, 44.1984352318898, 21.8069112975004, 42.1237630342764,
22.7551891190248, 12.9587850506626, 12.0207189111152, 20.2704921282476,
13.3441156357956, 9.13092569988769, 1781.08346869568, 71.2690023512206,
80.2376892286713, 344.114362037227, 208.830841645638, 91.1778810401913,
2220.0120768657, 41.4820962277111, 16.5730025748281, 32.30173229022,
108.703930214512, 51.6770035143256, 709.071405759588, 87.9618878732223,
10.4198968123037, 34.4951840238729, 57.8603720445067, 72.3289197551429,
30.2366643066749, 23.8696161364716, 270.014690419247, 13.8170113452005,
39.5159584479013, 27.764841260433, 18.0311836472615, 40.5709477295999,
33.1888820958952, 9.03112843931787, 4.63738971549635, 12.7591169313099,
4.7998894219979, 8.93458248803248, 7.33904760386628, 12.0940344070925,
7.17364602165948, 6.514191844409, 9.69911157978057, 6.57874454980745,
7.90556524435596), AM_leafP = c(0.02840637, 0.230728821, 0.2630533,
0.099628272, 0.28381032, 0.12414402, 0.45059978, 0.00400018,
0.00719533500000001, 0.014715103086687, 0.118637201789886, 0.022690875,
0.254707825, 0.54923913, 0.470708088, 0.113126176837872, 0.22928510745,
0.118697847481752, 0.219730100850697, 0.38583864, 0.4159485,
0.24666396693114, 0.374696992776912, 0.547589605297248, 0.00280728,
0.070156727820596, 0.062314855376136, 0.01453005323695, 0.02394282358199,
0.0548378613646, 0.00225224, 0.120533928, 0.301695482, 0.15479046,
0.045996497, 0.096754836, 0.00705147600000001, 0.0568428, 0.47985120103071,
0.14494777, 0.08641493, 0.069128642, 0.165362156, 0.20947132,
0.018517511, 0.36187275779699, 0.126531158458224, 0.180083867690804,
0.277297380904852, 0.1232408972382, 0.15065285976048, 0.55364067,
1.07819275643191, 0.39187665, 1.04169066418176, 0.55962324, 0.68128731,
0.41342697, 0.69699564, 0.76755492, 0.515511133042674, 0.760023430328564,
0.632465844687028)), class = "data.frame", row.names = c(NA,
-63L))