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Someone has probably already written code to do this. But I couldn’t find it in CRAN, so here goes.

Oh no lm() won’t take complex numbers! (or rather, it’ll take them, but it’ll discard the imaginary part.)

Easy enough fix. Split into real.

```y = a*x
y1 + y2*i = (a1 + a2*i) * (x1 + x2*i)
y1 = a1*x1 - a2*x2
y2 = a1*x2 + a2*x1```

So, if we’re looking to discover the coefficients a1 and a2, we can split the vectors y and x like

```Re(y1)  Re(x1)  -Im(x1)
Im(y1)  Im(x1)   Re(x1)
...     ...      ...
break1 = function(X) {
do.call(c, lapply(X, function(x) { c(Re(x), Im(x)) }))
}

break2 = function(X) {
do.call(c, lapply(X, function(x) { c(-Im(x), Re(x)) }))
}```

So if we have a function to do a complex fit, the first thing is to make new variables based on the inputs.

```fit.complex = function(Y, X.List) {

# Split into real variables
YF = break1(Y)
XF.List = do.call(c, lapply(X.List,
function(x) { list(break1(x), break2(x)) } ))

# ...
}```

Then put those into a data.frame and make an appropriate formula.

```        # Make the data.fram
Data = data.frame(Y = YF)
X.Names = paste('X', 1:length(XF.List), sep='')

for (N in seq_along(XF.List)) {
Data[[ X.Names[[N]] ]] = XF.List[[N]]
}

Formula = paste("Y ~ ", paste(X.Names, collapse='+'), "-1")```

It’s important to put the “-1″ in the formula so that lm() doesn’t include a constant term. (We might want a constant term, but it would have to look more like c(1, 0, 1, 0, …) because it is complex).

Then do the fit and extract the coefficients.

```        Model = lm(as.formula(Formula), data=Data)

# Make them complex again
Coeffs = sapply(seq_along(X.List),
function(N) {
( Model\$coefficients[[ X.Names[[2*N-1]] ]]
+ Model\$coefficients[[ X.Names[[2*N]] ]]*1i )
})
names(Coeffs) = names(X.List)

Model\$coefficients.complex = Coeffs```

The whole function looks like

```fit.complex = function(Y, X.List) {

# Split into real variables
YF = break1(Y)
XF.List = do.call(c, lapply(X.List,
function(x) { list(break1(x), break2(x)) } ))

# Make the data.fram
Data = data.frame(Y = YF)
X.Names = paste('X', 1:length(XF.List), sep='')

for (N in seq_along(XF.List)) {
Data[[ X.Names[[N]] ]] = XF.List[[N]]
}

# Formula + Model
Formula = paste("Y ~ ", paste(X.Names, collapse='+'), "-1")
Model = lm(as.formula(Formula), data=Data)

# Make them complex again
Coeffs = sapply(seq_along(X.List),
function(N) {
( Model\$coefficients[[ X.Names[[2*N-1]] ]]
+ Model\$coefficients[[ X.Names[[2*N]] ]]*1i )
})
names(Coeffs) = names(X.List)

Model\$coefficients.complex = Coeffs

Model
}```

Now test it

```Beta0 = 1 + 3i
Beta1 = 3 - 2i

X = runif(15, 0, 10)
Y = (Beta0 + Beta1*X +
rnorm(length(X), 0, 0.7) * exp(1i*runif(length(X), 0, 2*pi))
)

Model = fit.complex(Y, list(
const = 0*X+1,
linear = X
))

Beta0.Est = Model\$coefficients.complex[]
Beta1.Est = Model\$coefficients.complex[]
> Beta0.Est
 1.090385+3.017922i
> Beta1.Est
 2.912617-2.030427i```

Excellent.        