Defined parameters:
M <- maximum.oxygen.uptake
m <- mass
a <- age
s <- sex
v <- as.numeric(vigorous.exercise>0)
sv <- s*v
asv <- a*s*v
as <- a*s
av <- a*v
lnm=log(m)
lnms <- log(m)*s
lnmv <- log(m)*v
lnmsv <- log(m)*s*v
y <- M/m^(2/3)
I fit an nls model successfully using:
nls.full <- nls(M ~ (m ^ (alpha0 + alpha1 * s + alpha2 * v + alpha3 * s * v)) *
(beta0 + beta1 * s + beta2 * v + beta3 * sv +
a * gamma0 + gamma1 * as + gamma2 * av + gamma3 * asv),
trace=TRUE,
start=list(alpha0=2/3, alpha1=0, alpha2=0, alpha3=0,
beta0=est[1], beta1=est[2], beta2=est[3],beta3=est[4],
gamma0=est[5],gamma1=est[6],gamma2=est[7],gamma3=est[8]))
PROBLEM: CAN'T PLOT PREDICTION
xpredict <- seq(10,120,length.out=300)
data1 <- data.frame(a=35,s=0,v=1,m=seq(10,120,length.out=300))
ypredict <- predict(nls.full, newdata=data1, type="response")
plot(log(maximum.oxygen.uptake) ~ log(mass), subset = (s=='0' & v=='1'))
lines(xpredict,ypredict)
lengths of y and x differ.
I don't see why it should, I defined a new data frame with 300 variables, I should only have 300 results in the y predict.
Your question adds an important case study on the use of predict, which is currently missing on this site (as far as I know), hence I did not close it as a duplicate as I would usually do.
This simple example is sufficient to illustrate what your problem is:
set.seed(0)
x <- runif(50)
y <- runif(50)
## true model
z <- exp(4 * x - x * y) + sin(0.5 * y) + rnorm(50)
We can fit a non-linear regression model by:
fit1 <- nls(z ~ exp(a * x + b * x * y) + sin(c * y),
start = list(a = 3, b = 0, c = 1))
or
xy <- x * y
fit2 <- nls(z ~ exp(a * x + b * xy) + sin(c * y),
start = list(a = 3, b = 0, c = 1))
However, be careful when making prediction with predict.
newdat <- data.frame(x = runif(2), y = runif(2))
pred1 <- predict(fit1, newdat)
# [1] 19.476569 2.870397
pred2 <- predict(fit2, newdat)
#[1] 12.205215 2.900922 16.675160 2.588310 18.466907 3.221744 21.207958
#[8] 2.478375 16.294230 2.230084 22.675165 2.741694 22.053141 2.441442
#[15] 20.378554 2.069649 20.362845 2.380586 10.570350 3.168567 11.477691
#[22] 2.438041 19.336928 2.648129 22.282448 2.899636 16.264152 3.229857
#[29] 19.928498 1.779721 16.563424 2.688125 14.925190 2.718176 21.853093
#[36] 1.856641 20.213350 1.957830 22.960452 2.767944 21.890656 2.719899
#[43] 22.370200 2.066384 14.061771 2.237771 12.102094 3.232742 18.985547
#[50] 1.909355
predict.nls does not issue any warning like what predict.lm and predict.glm do (Getting Warning: “ 'newdata' had 1 row but variables found have 32 rows” on predict.lm in R). Basically, you have to provide all variables used in your fitting formula. Be aware, xy is also a variable:
newdat$xy <- with(newdat, x * y)
pred2 <- predict(fit2, newdat)
# [1] 19.476569 2.870397