# Dan Foreman-Mackey

# Fitting a plane to data

June 01, 2017

This post was generated from an IPython notebook that can be downloaded here.

Who knew that it was possible to say more about how to fit a line to data? Recently, Megan Bedell asked about how to generalize the method described in section 7 of the bible for fitting a line to data with 2-dimensional error elipses to higher dimensional problems, and how to include intrinsic scatter in that relation. While the correct generalization might be obvious to some of you, I wanted to go through the full derivation to make sure that I knew what was going on. As a result, we discovered a mistake in the aforementioned paper (discussed in this GitHub issue — they are going to fix the mistake in an updated version of the paper) and I learned a few things about Gaussian integrals, so I wanted to post this here for posterity. I'll start with a discussion of how to correct the method from Hogg, Bovy, & Lang (2010), then talk about including intrinsic scatter, and, finally, generalize to N dimensions. I'm sure that this is written somewhere else in the literature with different notation, but I never came across it so I thought that it might be worth writing it down.

To start, we'll need a dataset to work with:

```
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
# Reproducible!
np.random.seed(42)
# A helper function to make the plots with error ellipses
def plot_error_ellipses(ax, X, S, color="k"):
for n in range(len(X)):
vals, vecs = np.linalg.eig(S[n])
theta = np.degrees(np.arctan2(*vecs[::-1, 0]))
w, h = 2 * np.sqrt(vals)
ell = Ellipse(xy=X[n], width=w, height=h,
angle=theta, color=color, lw=0.5)
ell.set_facecolor("none")
ax.add_artist(ell)
ax.plot(X[:, 0], X[:, 1], ".", color=color, ms=4)
# Generate the true coordinates of the data points.
N = 10
m_true = 1.2
b_true = -0.1
X_true = np.empty((N, 2))
X_true[:, 0] = np.random.uniform(0, 10, N)
X_true[:, 1] = m_true * X_true[:, 0] + b_true
X = np.empty((N, 2))
# Generate error ellipses and add uncertainties to each point.
S = np.zeros((N, 2, 2))
for n in range(N):
L = np.zeros((2, 2))
L[0, 0] = np.exp(np.random.uniform(-1, 1))
L[1, 1] = np.exp(np.random.uniform(-1, 1))
L[1, 0] = 0.5 * np.random.randn()
S[n] = np.dot(L, L.T)
X[n] = np.random.multivariate_normal(X_true[n], S[n])
# Plot the simulated dataset.
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
x0 = np.array([-2, 12])
ax.plot(x0, m_true*x0 + b_true, lw=1)
plot_error_ellipses(ax, X, S)
ax.set_xlim(-2, 12)
ax.set_ylim(-2, 12)
ax.set_xlabel("x")
ax.set_ylabel("y");
```

In this figure, you're seeing the simulated dataset with its error ellipses shown in black. The true linear relation that was used to simulate the data is shown in blue.

## Naïve sampling¶

The simplest way to fit this line to the data is to invoke $N$ new parameters $\hat{x}_n$ giving the "true" independent coordinate of each data point. In this case, the likelihood function is:

$$ p(\{y_n,\,x_n\}\,|\,m,\,b,\,\{\hat{x}_n,\,S_n\}) = \prod_{n=1}^N \frac{1}{2\,\pi\,\sqrt{\det S_n}} \exp\left(-\frac{1}{2}\,{\vec{r}_n}^\mathrm{T}\,{S_n}^{-1}\,{\vec{r}_n}\right) $$where $m$ and $b$ are the usual slope and intercept of the line, $x_n$ and $y_n$ are the observations, $S_n$ is the uncertainty tensor of the $n$-th data point:

$$ S_n = \left(\begin{array}{cc} {\sigma_{x,n}}^2 & {\sigma_{xy,n}} \\ {\sigma_{xy,n}} & {\sigma_{y,n}}^2 \\ \end{array}\right) \quad, $$and $\vec{r}_n$ is the residual vector:

$$ \vec{r}_n = \left( x_n - \hat{x}_n \quad y_n - m\,\hat{x}_n - b \right)^\mathrm{T} \quad. $$Now, let's choose some priors for $m$, $b$, and $\hat{x}_n$ and sample the posterior

$$ p(m,\,b,\,\{\hat{x}_n\}\,|\,\{y_n,\,x_n,\,S_n\}) \propto p(\{y_n,\,x_n\}\,|\,m,\,b,\,\{\hat{x}_n,\,S_n\}) \, p(m,\,b,\,\{\hat{x}_n\}) \quad. $$Following Jake VanderPlas, let's use the prior:

$$ p(m,\,b,\,\{\hat{x}_n\}) \propto \left(1+m^2\right)^{-3/2} \quad. $$Formally, this prior is improper, but it'll do the trick for today. Here's the model in code:

```
def log_prior(params):
m = params[0]
return -1.5 * np.log(1 + m*m)
# Pre-compute the inverses for speed.
Sinv = np.array([np.linalg.inv(Sn) for Sn in S])
def log_prob_naive(params):
# Compute the "true" model predictions.
m, b = params[:2]
xhat = params[2:]
yhat = m * xhat + b
# Compute the residuals.
r = np.array(X)
r[:, 0] -= xhat
r[:, 1] -= yhat
# Use some numpy magic to compute the likelihood (up to a constant).
ll = -0.5*np.einsum("n...i,nij,n...j", r, Sinv, r)
return ll + log_prior(params)
```

Let's use emcee to sample this density:

```
import emcee
nwalkers = 64
xhat0 = np.array(X[:, 0])
p0 = np.append([m_true, b_true], xhat0)
p0 = p0 + 1e-4 * np.random.randn(nwalkers, len(p0))
sampler_naive = emcee.EnsembleSampler(nwalkers, p0.shape[1], log_prob_naive)
pos, _, _ = sampler_naive.run_mcmc(p0, 5000)
sampler_naive.reset()
sampler_naive.run_mcmc(pos, 10000)
samples_naive = sampler_naive.flatchain[:, :2]
```

We'll look at the integrated autocorrelation time to judge convergence:

```
tau = sampler_naive.get_autocorr_time(c=4)
nsamples = len(samples_naive)
print("{0:.0f} independent samples of m".format(nsamples / tau[0]))
print("{0:.0f} independent samples of b".format(nsamples / tau[1]))
```

And plot the posterior constraints on $m$ and $b$:

```
import corner
rng = [(x.min(), x.max()) for x in samples_naive.T]
corner.corner(samples_naive, labels=["m", "b"], truths=[m_true, b_true],
range=rng);
```

Cool. That looks fine, but the problem here is that this won't really scale well to large datasets because we'll need a parameter $\hat{x}_n$ for each data point. It turns out (thanks to the magic of Gaussians) that we can analytically marginalize over these nuisance parameters and reduce the problem back down to 2-D.

## Marginalizing over the true coordinates¶

To start, let's look at a single data point $n$ and marginalize over $\hat{x}_n$ for that point. The integral that we need to compute is:

$$ \begin{eqnarray} p(y_n,\,x_n\,|\,m,\,b,\,S_n) &=& \int_{-x_\mathrm{min}}^{x_\mathrm{max}} p(\hat{x}_n) \, p(y_n,\,x_n\,|\,m,\,b,\,\hat{x}_n,\,S_n)\,\mathrm{d}\hat{x}_n \\ &=& \frac{1}{x_\mathrm{max} - x_\mathrm{min}} \int_{-x_\mathrm{min}}^{x_\mathrm{max}} p(y_n,\,x_n\,|\,m,\,b,\,\hat{x}_n,\,S_n)\,\mathrm{d}\hat{x}_n \end{eqnarray} $$In order to simplify the math, I'll be a little sloppy here and send $x_\mathrm{min} \to -\infty$ and $x_\mathrm{max} \to \infty$, but ignore the fact that this makes the constant $1/(x_\mathrm{max} - x_\mathrm{min})$ zero. This inconsistency is caused by the fact that we used an improper prior above and there are more rigorous ways to derive and interpret this result—by putting a broad Gaussian prior on $\hat{x}_n$, for example—but this won't change the results. If you're willing to roll with me here (and if you aren't, too bad!) then the integral that we need to compute becomes:

$$ p(y_n,\,x_n\,|\,m,\,b,\,S_n) \propto \int_{-\infty}^{\infty} \frac{1}{2\,\pi\,\sqrt{\det S_n}} \exp\left(-\frac{1}{2}\,{\vec{r}_n}^\mathrm{T}\,{S_n}^{-1}\,{\vec{r}_n}\right) \,\mathrm{d}\hat{x}_n $$where $r_n$ and $S_n$ are defined above. We'll go through the pain of solving this integral in the general case below, but for now, I just tossed this into Mathematica and found:

$$ p(y_n,\,x_n\,|\,m,\,b,\,S_n) \propto \frac{1}{\sqrt{2\,\pi\,{\Sigma_n}^2}}\, \exp\left(-\frac{{\Delta_n}^2}{2\,{\Sigma_n}^2}\right) $$where $\Delta_n = y_n - m\,x_n - b$ and ${\Sigma_n}^2 = (\sigma_x\,m)^2 - 2\,\sigma_{xy}\,m + {\sigma_y}^2$ or, equivalently ${\Sigma_n}^2 = \vec{v}^\mathrm{T}\,S_n\,\vec{v}$ where $\vec{v}^\mathrm{T} = (-m,\,1)$.
This likelihood function is similar to Equation (32) in Hogg, Bovy, & Lang (2010).
You can convince yourself that the version in that paper isn't quite right by checking the limiting case where $\sigma_x = 0$ and $\sigma_{xy} = 0$, and feel free to work the math to convince youself that this one is right.
Note: Jo Bovy came to the same result with a different derivation that you can read about here and I think that they are planning to update the document on ArXiv to fix this section.
The main thing to note about this equation is that you *must* include the pre-factor $1/\sqrt{2\,\pi\,{\Sigma_n}^2}$ because $\Sigma_n$ is a function of $m$.

Now that we've derived the marginalized likelihood function, let's use emcee to sample from the 2-D problem with the same prior $p(m,\,b)$ and confirm that we get the same results.

```
def log_marg_prob(params):
m, b = params
v = np.array([-m, 1.0])
# Compute \Sigma^2 and \Delta.
# You can probably convince yourself that this formulation of
# \Sigma^2 is equivalent to the one in the text.
Sigma2 = np.dot(np.dot(S, v), v)
Delta = m * X[:, 0] + b - X[:, 1]
# Compute the log likelihood up to a constant (zero, oops...).
ll = -0.5 * np.sum(Delta**2 / Sigma2 + np.log(Sigma2))
return ll + log_prior(params)
# Run the MCMC with the same initialization as above.
sampler_marg = emcee.EnsembleSampler(nwalkers, 2, log_marg_prob)
pos, _, _ = sampler_marg.run_mcmc(p0[:, :2], 100)
sampler_marg.reset()
sampler_marg.run_mcmc(pos, 1000)
samples_marg = sampler_marg.flatchain
tau = sampler_marg.get_autocorr_time(c=4)
nsamples = len(samples_marg)
print("{0:.0f} independent samples of m".format(nsamples / tau[0]))
print("{0:.0f} independent samples of b".format(nsamples / tau[1]))
# Plot the posterior constraints for the naive sampling.
fig = corner.corner(samples_naive, labels=["m", "b"], truths=[m_true, b_true],
range=rng)
# Plot the constraints from the marginalized sampling.
# We need to re-weight the samples because we didn't need to run
# as many steps to converge in 2 dimensions as we did in 12.
w = len(samples_naive) / len(samples_marg) + np.zeros(len(samples_marg))
corner.corner(samples_marg, range=rng, color="g", fig=fig, weights=w);
```

In this figure, you see the same results from before (in black) when we sampled in $m$, $b$, and $\hat{x}_n$. The green contours are what we got when we sampled the analytically marginalized probability density. As expected, these results are indistinguishable within the sampling error, but the latter is more scalable and easier to sample to convergence (we evaluated the model an order of magnitude fewer times to get the same number of independent samples).

To finish this section, let's make one more plot of the results:

```
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
x0 = np.array([-2, 12])
# Plot posterior predictions for a few samples.
for m, b in samples_marg[np.random.randint(len(samples_marg), size=100)]:
ax.plot(x0, m*x0 + b, lw=1, alpha=0.1, color="g")
ax.plot(x0, m_true*x0 + b_true, "k", lw=2)
plot_error_ellipses(ax, X, S)
ax.set_xlim(-2, 12)
ax.set_ylim(-2, 12)
ax.set_xlabel("x")
ax.set_ylabel("y");
```

In this figure, the true model is shown as the black line and several posterior samples are shown as light green lines.

## Intrinsic scatter¶

Now, let's generalize our derivation from above to include an intrinsic width to the line. Another way of saying this is that, instead of $\hat{y}_n = m\,\hat{x}_n + b$, our model is defined by

$$ \left(\begin{array}{c} \hat{x}_n \\ \hat{y}_n \end{array}\right) \sim \mathcal{N}\left( \left(\begin{array}{c} \tilde{x}_n \\ m\,\tilde{x}_n + b \end{array}\right),\,\Lambda \right) $$where $\Lambda$ is a 2x2 tensor describing the width of the line, and we have redefined $\tilde{x}_n$ as the "true" input coordinate. In this case, we can multiply this Gaussian by the Gaussian defined in the first equation in the first section and integrate out $\hat{x}_n$ and $\hat{y}_n$ (Section 8.1.8 of this document might come in handy) to find:

$$ \begin{eqnarray} &&p(\{y_n,\,x_n\}\,|\,m,\,b,\,\{\tilde{x}_n,\,S_n\},\,\Lambda) \\ &=& \prod_{n=1}^N \frac{1}{2\,\pi\,\sqrt{\det (S_n+\Lambda)}} \exp\left(-\frac{1}{2}\,{\tilde{r}_n}^\mathrm{T}\,({S_n} + \Lambda)^{-1}\,{\tilde{r}_n}\right) \end{eqnarray} $$where

$$ \tilde{r}_n = \left( x_n - \tilde{x}_n \quad y_n - m\,\tilde{x}_n - b \right)^\mathrm{T} \quad. $$Then we can repeat the derivation from Section 2 to find the marginalized likelihood:

$$ p(y_n,\,x_n\,|\,m,\,b,\,S_n,\,\Lambda) \propto \frac{1}{\sqrt{2\,\pi\,{\tilde{\Sigma}_n}^2}}\, \exp\left(-\frac{{\Delta_n}^2}{2\,{\tilde{\Sigma}_n}^2}\right) $$where $\Delta_n$ is defined above and ${\tilde{\Sigma}_n}^2 = \vec{v}^\mathrm{T}\,(S_n + \Lambda)\,\vec{v}$.

To be more concrete, here are a few examples of the scatter that you might expect:

- If the scatter is perpendicular to the line with some variance $\lambda^2$, then $$ \Lambda = \frac{\lambda^2}{1+m^2}\,\left(\begin{array}{cc} m^2 & -m \\ -m & 1 \end{array}\right) $$
- If the scatter is only in the $y$ direction, then $$ \Lambda = \lambda^2\,\left(\begin{array}{cc} 0 & 0 \\ 0 & 1 \end{array}\right) $$

For simplicity, let's consider the simple case where the intrinsic scatter is in the $y$ direction. In this case, $\vec{v}^\mathrm{T}\,\Lambda\,\vec{v}$ simplifies to $\lambda^2$.

```
# Reproducible!
np.random.seed(42)
# Add some scatter perpendicular to the line.
lambda_true = 2.0
X_scatter = np.array(X)
X_scatter[:, 1] += lambda_true * np.random.randn(N)
# Plot the updated dataset.
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
x0 = np.array([-2, 12])
ax.plot(x0, m_true*x0 + b_true, lw=1)
plot_error_ellipses(ax, X_scatter, S)
# Plot the displacement vectors.
for n in range(N):
ax.plot([X[n, 0], X_scatter[n, 0]],
[X[n, 1], X_scatter[n, 1]],
"k", lw=0.5)
ax.set_xlim(-2, 12)
ax.set_ylim(-2, 12)
ax.set_xlabel("x")
ax.set_ylabel("y");
```

This is the same kind of figure that we had before but now I'm indicating the extra scatter that was added to each data point with the black lines. You can see that all of these offsets are in the $y$ direction.

To sample this model, we'll need to choose a prior on $\lambda$. It's useful to choose a proper prior in this case so I'll choose (somewhat arbitrarily) a log-uniform prior between $e^{-5}$ and $e^5$:

$$ p(\ln\lambda) \propto \left\{\begin{array}{ll} 1 & \mathrm{if\,-5 < \ln\lambda < 5} \\ 0 & \mathrm{otherwise} \end{array}\right. $$```
def log_marg_prob_scatter(params):
m, b, log_lambda = params
v = np.array([-m, 1.0])
# Enforce the log-uniform prior on lambda.
if not -5.0 < log_lambda < 5.0:
return -np.inf
# Compute \Sigma^2 and \Delta.
Sigma2 = np.dot(np.dot(S, v), v) + np.exp(2*log_lambda)
Delta = m * X_scatter[:, 0] + b - X_scatter[:, 1]
# Compute the log likelihood up to a constant.
ll = -0.5 * np.sum(Delta**2 / Sigma2 + np.log(Sigma2))
return ll + log_prior(params)
# Run the MCMC.
sampler_scatter = emcee.EnsembleSampler(nwalkers, 3, log_marg_prob_scatter)
p0 = np.array([m_true, b_true, np.log(lambda_true)])
p0 = p0 + 1e-4 * np.random.randn(nwalkers, len(p0))
pos, _, _ = sampler_scatter.run_mcmc(p0, 1000)
sampler_scatter.reset()
sampler_scatter.run_mcmc(pos, 6000)
samples_scatter = sampler_scatter.flatchain
tau = sampler_scatter.get_autocorr_time(c=4)
nsamples = len(samples_scatter)
print("{0:.0f} independent samples of m".format(nsamples / tau[0]))
print("{0:.0f} independent samples of b".format(nsamples / tau[1]))
print("{0:.0f} independent samples of lambda".format(nsamples / tau[2]))
corner.corner(samples_scatter, labels=["m", "b", "$\ln\lambda$"],
truths=[m_true, b_true, np.log(lambda_true)]);
```