I tried to search this online, but couldn't exactly figure out what my issue was. Here is my code:
n = 10000
x1 <- runif(n,0,100)
x2 <- runif(n,0,100)
y1 <- 10*sin(x1/10) + 10 + rnorm(n, sd = 1)
y2 <- x2 * cos(x2) - 2 * rnorm(n, sd = 2)
x <- c(x1, x2)
y <- c(x1, x2)
start1 = list(a = 10, b = 5)
start2 = list(a = 30, b = 5)
library(flexmix)
library(flexmixNL)
modelNL <- flexmix(y~x, k =2,
model = FLXMRnlm(formula = y ~ a*x/(b+x),
family = "gaussian",
start = list(start1, start2)))
plot(x, y, col = clusters(modelNL))
and before the plot, it gives me this error:
Error in matrix(1, nrow = sum(groups$groupfirst)) : data is too long
I checked google for similar errors, but I don't quite understand what is wrong with my own code that results in this error.
As you can already tell, I am very new to R, so please explain it in the most layman terms possible. Thank you in advance.
Ironically (in the context of an error message saying data is "too long") I think the proximate cause of that error is no data argument. If you give it the data in the form of a dataframe, you still get an error but its not the same one as you are experiencing. When you plot the data, you get a rather bizarre set of values at least from a statistical distribution standpoint and it's not clear why you are trying to model this with this formula. Nonetheless, with those starting values and a dataframe argument to data, one sees results.
> modelNL <- flexmix(y~x, k =2, data=data.frame(x=x,y=y),
+ model = FLXMRnlm(formula = y ~ a*x/(b+x),
+ family = "gaussian",
+ start = list(start1, start2)))
> modelNL
Call:
flexmix(formula = y ~ x, data = data.frame(x = x, y = y), k = 2, model = FLXMRnlm(formula = y ~
a * x/(b + x), family = "gaussian", start = list(start1, start2)))
Cluster sizes:
1 2
6664 13336
convergence after 20 iterations
> summary(modelNL)
Call:
flexmix(formula = y ~ x, data = data.frame(x = x, y = y), k = 2, model = FLXMRnlm(formula = y ~
a * x/(b + x), family = "gaussian", start = list(start1, start2)))
prior size post>0 ratio
Comp.1 0.436 6664 20000 0.333
Comp.2 0.564 13336 16306 0.818
'log Lik.' -91417.03 (df=7)
AIC: 182848.1 BIC: 182903.4
Most R regression functions first check for the matchng names in formulae within the data= argument. Apparently this function fails when it needs to go out to the global environment to match formula tokens.
I tried a formula suggested by the plot of the data and get convergent results:
> modelNL <- flexmix(y~x, k =2, data=data.frame(x=x,y=y),
+ model = FLXMRnlm(formula = y ~ a*x*cos(x+b),
+ family = "gaussian",
+ start = list(start1, start2)))
> modelNL
Call:
flexmix(formula = y ~ x, data = data.frame(x = x, y = y), k = 2, model = FLXMRnlm(formula = y ~
a * x * cos(x + b), family = "gaussian", start = list(start1, start2)))
Cluster sizes:
1 2
9395 10605
convergence after 17 iterations
> summary(modelNL)
Call:
flexmix(formula = y ~ x, data = data.frame(x = x, y = y), k = 2, model = FLXMRnlm(formula = y ~
a * x * cos(x + b), family = "gaussian", start = list(start1, start2)))
prior size post>0 ratio
Comp.1 0.521 9395 18009 0.522
Comp.2 0.479 10605 13378 0.793
'log Lik.' -78659.85 (df=7)
AIC: 157333.7 BIC: 157389
The reduction in AIC seems huge compare to the first formula.