Enhance your (Windows) remote terminal experience with MobaXterm

Jazmin and Julie recently introduced me to a helpful program for Windows called “MobaXterm” that has significantly sped up my workflow when running remotely on the Cube (our cluster here at Cornell). MobaXterm bills itself as an “all in one” toolbox for remote computing. The program’s interface includes a terminal window as well as a graphical SFTP browser. You can link the terminal to the SFTP browser so that as you move through folders on the terminal the browser follows you. The SFTP browser allows you to view and edit files using your text editor of choice on your windows desktop, a feature that I find quite helpful for making quick edits to shell scripts or pieces of code as go.

A screenshot of the MobaXterm interface. The graphical SFTP browser is on the left, while the terminal is on the right (note the checked box in the center of the left panel that links the browser to the terminal window).

 

You can set up a remote Cube session using MobaXterm with the following steps:

1. Download MobaXterm using this link
2.  Follow the installation instructions
3. Open MobaXterm and select the “Session” icon in the upper left corner.
4. In the session popup window, select a new SSH session in the upper left, enter “thecube.cac@cornell.edu” as the name of the remote host and enter your username.
5. When the session opens, check the box below the SFTP browser on the left to link the browser to your terminal
6. Run your stuff!

Note that for a Linux system, you can simply link your file browser window to your terminal window and get the same functionality as MobaXterm. MobaXterm is not available for Mac, but Cyberduck and Filezilla are decent alternatives. An alternative graphical SFTP browser for Windows is WinSCP, though I prefer MobaXterm because of its linked terminal/SFTP interface.

For those new to remote computing, ssh or UNIX commands in general, I’d recommend checking out the following posts to get familiar with running on a remote cluster:

• Introduction to remote computing and basic UNIX commands
• Improving SSH (for use in a terminal program like cygwin)
• Useful UNIX commands for quickly editing data files
• Tips for Bash submission scripts
• Barney’s Series on parallelization using MPI

 

 

 

Introduction To Econometrics: Part II- Violations of OLS Assumptions & Methods for Fixing them

Regression is the primary tool used in econometrics to infer relationships between a group of explanatory variables, X and a dependent variable, y. My previous post focused on the mechanics of Ordinary Least Squares (OLS) Regression and outlined key assumptions that, if true, make OLS estimates the Best Linear Unbiased Estimator (BLUE) for the coefficients in the regression:

This post will discuss three common violations of OLS assumptions, and explain tools that have been developed for dealing with these violations. We’ll start with a violation of the assumption of a linear relationship between X and y, then discuss heteroskedasticity in the error terms and the issue of endogeniety.

Linearity

If the relationship between X and y is not linear, OLS can no longer be used to estimate beta. A nonlinear regression of y on X has the form:

Where  g(X\beta) is the functional form of the nonlinear relationship between X and y and epsilon is the error term. Beta can be estimated using Nonlinear Least Squares regression (NLS). Similar  to OLS regression, NLS seeks to minimize the sum of the square error term.

To solve for beta, we again take the derivative and set it equal to zero, but for the nonlinear system there is no closed form solution, so the estimators have to be found using numerical optimization techniques.

The variance of a NLS estimator is:

Where G is a matrix of partial derivatives of g with respect to each Beta.

Modern numerical optimization techniques can solve many NLS equations quite easily making NLS a common alternative to OLS regression especially when there is a hypothesized functional form for the relationship between X and y.

Heteroskedasticity

Heteroskedasticity arises within a data set when the errors do not have a constant variance with respect to X. In equation form, under heteroskedasticity:

The presence of heteroskedasticity  increases the variance of Beta estimators found using OLS regression, reducing the efficiency of the estimator and causing it to no longer be the BLUE. As put by Allison (2012), OLS on heteroskedastic data puts “equal weight on all observations when, in fact, observations with larger disturbances contain less information”.

To fix this problem, econometric literature provides two options which both use a form of weighting to correct for differences in variance amongst the error terms:

1. Use the OLS estimate for beta, but calculate the variance of beta with a robust variance-covariance matrix .
2. Estimate Beta using Feasible Generalized Least Squares (FGLS)

Let’s begin with the first strategy, using OLS beta estimates with a robust variance-covariance matrix. The robust variance-covariance matrix can be derived using the Generalized Method of Moments (GMM) for the sake of brevity, I’ll omit the derivation here and skip to the final result:

Where is a matrix of square residuals from the OLS regression:

The second strategy, estimation using FGLS, requires a more involved process for estimating beta. FGLS can be accomplished through 3 steps:

1. Use OLS to find OLS estimate for beta and calculate the residuals:

2. Regress the error term on a subset of X, which we will call Z, to get an estimate of a new parameter, theta (denoted with a tilde, but wordpress makes it difficult for me to add this in the middle of a paragraph). We then use this parameter to estimate the variance of the error term, sigma squared,  for each observation:

A diagonal matrix, D (different than the D used for the robust variance-covariance matrix), is then constructed using these variance estimates.

3. Finally, we use the matrix D to find our FGLS estimator for beta:

The variance of of the FGLS beta etimate is then defined as:

Endogeneity

Endogeneity arises when explanatory variables are correlated with the error term in a regression. This may be a result of simultaneity, when errors and explanatory variables are effected by the same exogenous influences, omitted variable bias,  when an important variable is left out of a regression, causing the over- or underestimation of the effect of other explanatory variables and the error term, measurement error or a lag in the dependent variable. Endogeniety can be hard to detect and may cause regression large errors in regression results.

A common way of correcting for endogeniety is through Instrumental Variables (IVs). Instrumental variables are explanatory variables that are highly correlated with variables that cause endogeniety but are exogenous to the system. Examples include using proximity to cardiac care centers as an IV for heart surgery when modeling health or state cigarette taxes as an IV for maternal smoking rate when modeling infant birth weight (Angrist and Kruger, 2001). For an expansive but accessible overview of IVs and their many applications, see Angrist and Kruger (2001).

A common technique for conducting a regression using IVs is 2 Stage Least Squares (2SLS) regression. The two stages of 2SLS are as follows:

1. Define Z as a new set of explanatory variables, which omits the endogenous variables and includes the IVs (which are usually not included in the original OLS regression).
2. Project Z onto the column space of X.
3. Estimate the 2SLS using this projection:

Using 2SLS regression to correct for endogeneity is fairly simple, however identifying good IVs for an endogenous variable can be extremely difficult. Finding a good IV (or set of IVs) can be enough to get one published in an economics journal (at least that’s what my economist friend told me).

Concluding thoughts

These two posts have constituted an extremely brief introduction to the field of econometrics meant for engineers who may be interested in learning about common empirical tools employed by economists. We covered the above methods in much more detail in class and also covered other topics such as panel data, Generalize Method Of Moments estimation, Maximum Likelihood Estimation, systems of equations in regression and discrete choice modeling. Overall, I found the course (AEM 7100) to be a useful introduction to a field that I hope to learn more about over the course of my PhD.

References:

Allison, Paul D. (2012). “Multiple regression: a primer“. Pine Forge. Thousand Oaks, CA: Press Print.

Angrist, J.; Krueger, A. (2001). “Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments”. Journal of Economic Perspectives. 15 (4): 69–85. doi:10.1257/jep.15.4.69.

An Introduction To Econometrics: Part 1- Ordinary Least Squares Regression

I took a PhD level econometrics course this semester in the Applied Economics and Management department here at Cornell and I thought I’d share some of what I learned. Overall, I enjoyed the course and learned a great deal. It was very math and theory heavy, but the Professor Shanjun Li did a nice job keeping the class lively and interesting. I would recommend the class to future EWRS students who may be looking for some grounding in econometrics, provided they’ve taken some basic statics and linear algebra courses.

So lets start with the basics, what does the term “econometrics” even mean? Hansen (2010) defined econometrics as “the unified study of economic models, mathematical statistics and economic data”. After taking this introductory course, I’m inclined to add my own definition: econometrics is “a study of the problems with regression using Ordinary Least Squares (OLS) and how to solve them”. This is obviously a gross oversimplification of the field, however, regression through OLS was the primary tool used for finding insights and patterns within data, and we spent the vast majority of the course examining it. In this post I’ll briefly summarize OLS mechanics and classical OLS assumptions. In my next post, I’ll detail methods for dealing with violations of OLS assumptions. My hope is that reading this may help you understand some key terminology and the reasoning behind why certain econometric tools are employed.

OLS mechanics

Our primary interest when creating an econometric model is to estimate some dependent variable, y, using a observations from a set of independent variables, X. Usually y is a vector of length n, where n is the number of observations, and X is a matrix of size (n x k) where k is the number of explanatory variables (you can think of X as a table of observations, where each column contains a different variable and each row represents an observation of that variable). The goal of OLS regression is to estimate the coefficients, beta, for the model:

Where beta is a k by 1 vector of coefficients on X and epsilon is a k by 1 vector of error terms.

OLS regression estimates beta by minimizing the sum of the square error term (hence the name “least squares”). Put in matrix notation, OLS estimates beta using the equation:

The optimal beta estimate can be found through the following equations:

Taking the derivative and setting it equal to zero:

Then solving for the beta estimate:

 

Estimation of y using OLS regression can be visualized as the orthogonal projection of the vector y onto the column space of X. The estimated error term, epsilon, is the orthogonal distance between the projection and the true vector y.  Figure 1 shows this projection for a y that is regressed on two explanatory variables, X1 and X2.

Figure 1: OLS regression as an orthogonal projection of vector y onto the column space of matrix X. The error term, , is the orthogonal distance between y and . (Image source: Wikipedia commons)

 Assumptions and properties of OLS regression

The Gauss-Markov Theorem states that under a certain set of assumptions, the OLS estimator is the Best Linear Unbiased Estimator (BLUE) for vector y.

To understand the full meaning of the Gauss-Markov theorem, it’s important to define two fundamental properties that can be used to describe estimators, consistency and efficiency. An estimator is consistent if its value will converge to the true parameter value as the number of observations goes to infinity. An estimator is efficient if its asymptotic variance is no larger than the asymptotic variance of any other possible consistent estimator for the parameter. In light of these definitions, the Gauss-Markov Theorem can be restated as: estimators found using OLS will be the most efficient consistent estimator for beta as long as the classical OLS assumptions hold. The remainder of this post will be devoted to describing the necessary assumptions for the OLS estimator to be the BLUE and detailing fixes for when these assumptions are violated.

The four classical assumptions for OLS to be the BLUE are:

1. Linearity: The relationship between X and y is linear, following the functional form:

.

2. Strict exogeneity: The error terms should be independent of the value of the explanatory variables, X. Put in equation form, this assumption requires:

3.  No perfect multicollinearity: columns of X should not be correlated with each other (see my earlier post on dealing with mulitcollinearity for fixes for violations of this assumption).

4. Spherical Error: Error terms should be homoskedastic, meaning they are evenly distributed around the X values. Put in equation form:

Where is a constant value.

Using assumption 4, we can define the variance of  as:

If assumptions 1-4 hold, then the OLS estimate for beta is the BLUE, if however, any of the assumptions are broken, we must employ other methods for estimating our regression coefficients.

In my next post I’ll detail the methods econometricians use when these assumptions are violated.

 References:

Hansen, Bruce. “Econometrics”. 2010. University of Wisconsin

http://www.ssc.wisc.edu/~bhansen/econometrics/Econometrics2010.pdf

A visual introduction to data compression through Principle Component Analysis

Principle Component Analysis (PCA) is a powerful tool that can be used to create parsimonious representations of a multivariate data set. In this post I’ll code up an example from Dan Wilks’ book Statistical Methods in the Atmospheric Sciences to visually illustrate the PCA process. All code can be found at the bottom of this post.

As with many of the examples in Dr. Wilks’ excellent textbook, we’ll be looking at minimum temperature data from Ithaca and Canandaigua, New York  (if anyone is interested, here is the distance between the two towns).  Figure 1 is a scatter plot of the minimum temperature anomalies at each location for the month of January 1987.

Figure 1: Minimum temperature anomalies in Ithaca and Canandaigua, New York in January 1987

As you can observe from Figure 1, the two data sets are highly correlated, in fact, they have a Pearson correlation coefficient of 0.924. Through PCA, we can identify the primary mode of variability within this data set (its largest “principle component”) and use it to create a single variable which describes the majority of variation in both locations. Let x define the matrix of our minimum temperature anomalies in both locations. The eigenvectors (E) of the covariance matrix of x describe the primary modes variability within the data set. PCA uses these eigenvectors to  create a new matrix, u,  whose columns contain the principle components of the variability in x.

Each element in u is a linear combination of the original data, with eigenvectors in E serving as a kind of weighting for each data point. The first column of u corresponds to the eigenvector associated with the largest eigenvalue of the covariance matrix. Each successive column of u represents a different level of variability within the data set, with u1 describing the direction of highest variability, u2 describing the direction of the second highest variability and so on and so forth. The transformation resulting from PCA can be visualized as a rotation of the coordinate system (or change of basis) for the data set, this rotation is shown in Figure 2.

Figure 2: Geometric interpretation of PCA

As can be observed in Figure 2, each data point can now be described by its location along the newly rotated axes which correspond to its corresponding value in the newly created matrix u. The point (16, 17.8), highlighted in Figure 2, can now be described as (23, 6.6) meaning that it is 23 units away from the origin in the direction of highest variability and 6.6 in the direction of second highest variability. As shown in Figure 2, the question of “how different from the mean” each data point is can mostly be answered by looking at its  corresponding u1 value.

Once transformed, the original data can be recovered through a process known as synthesis. Synthesis  can be thought of as PCA in reverse. The elements in the original data set x, can be approximated using the eigenvalues of the covariance matrix and the first principle component, u1.

Where:

For our data set, these reconstructions seem to work quite well, as can be observed in Figure 3.

 

 

Data compression through PCA can be a useful alternative tolerant methods for dealing with multicollinearity, which I discussed in my previous post. Rather than running a constrained regression, one can simply compress the data set to eliminate sources of multicollinearity. PCA can also be a helpful tool for identifying patterns within your data set or simply creating more parsimonious representations of a complex set of data. Matlab code used to create the above plots can be found below.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Ithaca_Canandagua_PCA
% By: D. Gold
% Created: 3/20/17
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% This script will perform Principle Component analysis on minimum
% temperature data from Ithaca and Canadaigua in January, 1987 provided in 
% Appendix A of Wilks (2011). It will then estimate minimum temperature
% values of both locations using the first principle component.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% create data sets
clear all

% data from appendix Wilks (2011) Appendix A.1
Ith = [19, 25, 22, -1, 4, 14, 21, 22, 23, 27, 29, 25, 29, 15, 29, 24, 0,... 
 2, 26, 17, 19, 9, 20, -6, -13, -13, -11, -4, -4, 11, 23]';

Can = [28, 28, 26, 19, 16, 24, 26, 24, 24, 29, 29, 27, 31, 26, 38, 23,... 
 13, 14, 28, 19, 19, 17, 22, 2, 4, 5, 7, 8, 14, 14, 23]';

%% center the data, plot temperature anomalies, calculate covariance and eigs

% center the data
x(:,1) = Ith - mean(Ith);
x(:,2) = Can - mean(Can);

% plot anomalies
figure
scatter(x(:,1),x(:,2),'Filled')
xlabel('Ithaca min temp anomaly ({\circ}F)')
ylabel('Canandagua min temp anomaly ({\circ}F)')

% calculate covariance matrix and it's corresponding Eigenvalues & vectors
S = cov(x(:,1),x(:,2));
[E, Lambda]=eigs(S);

% Identify maximum eigenvalue, it's column will be the first eigenvector
max_lambda = find(max(Lambda)); % index of maximum eigenvalue in Lambda
idx = max_lambda(2); % column of max eigenvalue

%% PCA
U = x*E(:,idx);

%% synthesis
x_syn = E(:,idx)*U'; % reconstructed values of x

% plot the reconstructed values against the original data
figure
subplot(2,1,1)
plot(1:31,x(:,1) ,1:31, x_syn(1,:),'--')
xlim([1 31])
xlabel('Time (days)')
ylabel('Ithaca min. temp. anomalies ({\circ}F)')
legend('Original', 'Reconstruction')
subplot(2,1,2)
plot(1:31, x(:,2),1:31, x_syn(2,:)','--')
xlim([1 31])
xlabel('Time (days)')
ylabel('Canadaigua min. temp. anomalies ({\circ}F)')
legend('Original', 'Reconstruction')

 

Sources:

Wilks, D. S. (2011). Statistical methods in the atmospheric sciences. Amsterdam: Elsevier Academic Press.

Dealing With Multicollinearity: A Brief Overview and Introduction to Tolerant Methods

This semester I’m taking a Multivariate statistics course taught by Professor Scott Steinschneider in the BEE department at Cornell. I’ve been really enjoying the course thus far and thought I would share some of what we’ve covered in the class with a blog post. The material below on multicollinearity is from Dr. Steinschneider’s class, presented in my own words.

What is Multicollinearity?

Multicollinearity is the condition where two or more predictor variables in a statistical model are linearly related (Dormann et. al. 2013). The existence of multicollinearity in your data set can result in an increase of the variance of regression coefficients leading to unstable estimation of parameter values. This in turn can lead to erroneous identification of relevant predictors within a regression and detracts from a model’s ability to extrapolate beyond the range of the sample it was constructed with. In this post, I’ll explain how multicollinearity causes problems for linear regression by Ordinary Least Squares (OLS), introduce three metrics for detecting multicollinearity and detail two “Tolerant Methods” for dealing with multicollinearity within a data set.

How does multicollinearity cause problems in OLS regression?

To illustrate the problems caused by multicollinearity, let’s start with a linear regression:

Where:

The Gauss-Markov theorem states that the Best Linear Unbiased Estimator (BLUE) for each  coefficient can be found using OLS:

This  estimate will have a variance defined as:

Where:

If you dive into the matrix algebra, you will find that the term (x^Tx) is equal to a matrix with ones on the diagonals and the pairwise Pearson’s correlation coefficients (ρ) on the off-diagonals:

As the correlation values increase, the values within (x^Tx)^-1 also increase. Even with a low residual variance, multicollinearity can cause large increases in estimator variance. Here are a few examples of the effect of multicollinearity using a hypothetical regression with two predictors:

So why should you care about the variance of your coefficient estimators? The answer depends on what the purpose of your model is. If your only goal is to obtain an accurate measure of the predictand, the presence of multicollinearity in your predictors might not be such a problem. If, however, you are trying to identify the key predictors that effect the predictand, multicollinearity is a big problem.

OLS estimators with large variances are highly unstable, meaning that if you construct estimators from different data samples you will potentially get wildly different estimates of your coefficient values (Dormann et al. 2013). Large estimator variance also undermines the trustworthiness of hypothesis testing of the significance of coefficients. Recall that the t value used in hypothesis testing for an OLS regression coefficient is a function of the sample standard deviation (the square root of the variance) of the  OLS estimator.

An estimator with an inflated standard deviation, , will thus yield a lower t value, which could lead to the false rejection of a significant predictor (ie. a type II error). See Ohlemüller et al. (2008) for some examples where hypothesis testing results are undermined by multicollinearity.

Detecting Multicollinearity within a data set

Now we know how multicollinearity causes problems in our regression, but how can we tell if there is multicollinearity within a data set? There are several commonly used metrics for which basic guidelines have been developed to determine whether multicollinearity is present.

The most basic metric is the pairwise Pearson Correlation Coefficient between predictors, r. Recall from your intro statistics course that the Pearson Correlation Coefficient is a measure of the linear relationship between two variables, defined as:

A common rule of thumb is that multicollinearity may be a problem in a data set if any pairwise |r| > 0.7 (Dormann et al. 2013).

Another common metric is known as the Variance Inflation Factor (VIF). This measure is calculated by regressing each predictor on all others being used in the regression.

Where R_j² is the R² value generated by regressing predictor x_j on all other predictors. Multicollinearity is thought to be a problem if VIF > 10 for any given predictor (Dormann et al. 2012).

A third metric for detecting multicollinearity in a data set is the Condition Number (CN) of the predictor matrix defined as the square root of the ratio of the largest and smallest eigenvalues in the predictor matrix:

CN> 15 indicates the possible presence of multicollinearity, while a CN > 30 indicates serious multicollinearity problems (Dormann et al. 2013).

Dealing with Multicollinearity using Tolerant Methods

In a statistical sense, there is no way to “fix” multicollinearity. However, methods have been developed to mitigate its effects. Perhaps the most effective way to remedy multicollinearity is to make a priori judgements about the relationship between predictors and remove or consolidate predictors that have known correlations. This is not always possible however, especially when the true functional forms of relationships are not known (which is often why regression is done in the first place). In this section I will explain two “Tolerant Methods” for dealing with multicollinearity.

The purpose of Tolerant Methods is to reduce the sensitivity of regression parameters to multicollinearity. This is accomplished through penalized regression. Since multicollinearity can result in large and opposite signed  estimator values for correlated predictors, a penalty function is imposed to keep the value of predictors below a pre-specified value.

Where c is the predetermined value representing model complexity, p is the number of predictors and l is either 1 or 2 depending on the type of tolerant method employed (more on this below).

Ridge Regression

Ridge regression uses the L₂ norm, or Euclidean distance, to constrain model coefficients (ie. l = 2 in the equation above). The coefficients created using ridge regression are defined as:

Ridge regression adds a constant, λ, to the term x^Tx to construct the estimator. It should be noted that both x and y should be standardized before this estimator is constructed. The Ridge regression coefficient is the result of a constrained version of the ordinary least squares optimization problem. The objective is to minimize the sum of square errors for the regression while meeting the complexity constraint.

To solve the constrained optimization, Lagrange multipliers can be employed. Let z equal the Residual Sum of Squares (RSS) to be minimized:

This can be rewritten in terms of the L₂ norm of β:

Taking the derivative with respect to β and solving:

Remember that the Gauss-Markov theorem states that the OLS estimate for regression coefficients is the BLUE, so by using ridge regression, we are sacrificing some benefits of OLS estimators in order to constrain estimator variance. Estimators constructed using ridge regression are in fact biased, this can be proven by calculating the expected value of ridge regression coefficients.

For a scenario with two predictors, the tradeoff between reduced model complexity and increase bias in the estimators can be visualized graphically by plotting the estimators of the two beta values against each other. The vector of beta values estimated by regression are represented as points on this plot  .  In Figure 1, is plotted in the upper right quadrant and represents estimator that produces the smallest RSS possible for the model. The ellipses centered around  are representations of the increasing RSS resulting from the combination of β1 and β2  values, each RSS is a function of a different lambda value added to the regression.  The circle centered around the origin represents the chosen level of model complexity that is constraining the ridge regression. The ridge estimator is the point where this circle intersects a RSS ellipse. Notice that as the value of c increases, the error introduced into the estimators decreases and vice versa.

Figure 1: Geometric Interpretation of a ridge regression estimator. The blue dot indicates the OLS estimate of Beta, ellipses centered around the OLS estimates represent RSS contours for each Beta 1, Beta 2 combination (denoted on here as z from the optimization equation above). The model complexity is constrained by distance c from the origin. The ridge regression estimator of Beta is shown as the red dot, where the RSS contour meets the circle defined by c.

The c value displayed in Figure 1 is only presented to explain the theoretical underpinnings of ridge regression. In practice, c is never specified, rather, a value for λ is chosen a priori to model construction. Lambda is usually chosen through a process known as k-fold cross validation, which is accomplished through the following steps:

1. Partition data set into K separate sets of equal size
2. For each k = 1 …k, fit model with excluding the kth set.
3. Predict for the kth set
4. Calculate the cross validation error (CV_error)for kth set: 
5. Repeat for different values of , choose a that minimizes   

Lasso Regression

Another Tolerant Method for dealing with multicollinearity known as Least Absolute Shrinkage and Selection Operator (LASSO) regression, solves the same constrained optimization problem as ridge regression, but uses the L1 norm rather than the L2 norm as a measure of complexity.

LASSO regression can be visualized similarly to ridge regression, but since c is defined by the sum of absolute values of beta, rather than sum of squares, the area it constrains is diamond shaped rather than circular.  Figure 2 shows the selection of the beta estimator from LASSO regression. As you can see, the use of the L₁ norm means LASSO regression selects one of the predictors and drops the other (weights it as zero). This has been argued to provide a more interpretable estimators (Tibshirani 1996).

Figure 2: Geometric interpretation of Lasso Regression Estimator. The blue dot indicates the OLS estimate of Beta, ellipses centered around the OLS estimate represents RSS contours for each Beta 1, Beta 2 combination (denoted as z from the optimization equation). The mode complexity is constrained by the L1 norm representing model complexity. The Lasso estimator of Beta is shown as the red dot, the location where the RSS contour intersects the diamond defined by c.

Final thoughts

If you’re creating a model with multiple predictors, it’s important to be cognizant of potential for multicollinearity within your data set. Tolerant methods are only one of many possible remedies for multicollinearity (other notable techniques include data clustering and Principle Component Analysis) but it’s important to remember that no known technique can truly “solve” the problem of multicollinearity. The method chosen to deal with multicollinearity should be chosen on a case to case basis and multiple methods should be employed if possible to help identify the underlying structure within the predictor data set (Dormann et. al. 2013)

Citations

Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J., Münkemüller, T., McClean, C., Osborne, P. E., Reineking, B., Schröder, B., Skidmore, A. K., Zurell, D. and Lautenbach, S. 2013, “Collinearity: a review of methods to deal with it and a simulation study evaluating their performance.” Ecography, 36: 27–46. doi:10.1111/j.1600-0587.2012.07348.x

Ohlemüller, R. et al. 2008. “The coincidence of climatic and species rarity: high risk to small-range species from climate change.” Biology Letters. 4: 568 – 572.

Tibshirani, Robert 1996. “Regression shrinkage and selection via the lasso.” Journal of the Royal Statistical Society. Series B (Methodological): 267-288.

 

 

 

Plotting Probability Ellipses for Bivariate Normal Distributions

Plotting probability ellipses can be a useful way to visualize bivariate normal distributions. A probability ellipse represents a contour of constant probability within which a certain percentage of the distribution lies. The width and orientation of probability ellipses can yield information about the correlation between the two data points of interest.

To plot probability ellipses of a bivariate normal distribution, you need to have a vector containing the means of both data sets of interest as well as the covariance matrix for the two data sets. Each probability ellipse is centered around the means of the two data sets and oriented in the direction of the first eigenvector of the covariance matrix. The length of the primary axis of each ellipse is proportional to the value of the percentile of the Chi Squared distribution for the given percentile the ellipse represents.

Below, I’ve coded an example (using Matlab) that is presented in the very informative textbook Statistical Methods in the Atmospheric Sciences by Dan Wilks. The example uses one month of daily minimum temperature data from the towns of Ithaca and Canandaigua, New York. Notice that the 50% ellipse in the center of the plot encloses roughly half the data points, indicating that the contours were plotted correctly.

Probability Ellipses for Ithaca and Canandaigua Minimum Temperatures

 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Plotting  probability ellipses of the bivariate normal distribution
% By: D. Gold
% Created: 10/28/16
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script will plot the multivariate cumulative probability contours 
% of two data sets that are fit to a multivariate normal distribution
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%
clear all; close all; clc;

%%% Create Data Sets %%%
Ith_MinT = [19, 25, 22, -1, 4, 14, 21, 22, 23, 27, 29, 25, 29, 15, 29, 24, 0, 2, 26, 17, 19, 9, 20, -6, -13, -13, -11, -4, -4, 11, 23];

Can_MinT = [28, 28, 26, 19, 16, 24, 26, 24, 24, 29, 29, 27, 31, 26, 38, 23, 13, 14, 28, 19, 19, 17, 22, 2, 4, 5, 7, 8, 14, 14, 23];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%% Calculate moments of the data sets %%%

% Observed
Ith_mean = mean(Ith_MinT); % T mean
Can_mean= mean(Can_MinT); % Td mean
CV = cov(Ith_MinT,Can_MinT); % covariance of T and Td
[Evec, Eval]=eig(CV); % Eigen values and vectors of covariance matrix
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%% Plot observed multivariate contours %%

% Observed data
xCenter = Ith_mean; % ellipses centered at sample averages
yCenter = Can_mean;
theta = 0 : 0.01 : 2*pi; % angles used for plotting ellipses

% compute angle for rotation of ellipse
% rotation angle will be angle between x axis and first eigenvector
x_vec= [1 ; 0]; % vector along x-axis
cosrotation =dot(x_vec,Evec(:,1))/(norm(x_vec)*norm(Evec(:,1))); 
rotation =pi/2-acos(cosrotation); % rotation angle
R  = [sin(rotation) cos(rotation); ...
      -cos(rotation)  sin(rotation)]; % create a rotation matrix

% create chi squared vector
chisq = [1.368 4.605 3.2188  5.991]; % percentiles of chi^2 dist df=2

% size ellipses for each quantile
for i = 1:length(chisq)
    % calculate the radius of the ellipse
    xRadius(i)=(chisq(i)*Eval(1,1))^.5; % primary
    yRadius(i)=(chisq(i)*Eval(2,2))^.5; % secondary
    % lines for plotting ellipse
    x{i} = xRadius(i)* cos(theta);
    y{i} = yRadius(i) * sin(theta);
    % rotate ellipse
    rotated_Coords{i} = R*[x{i} ; y{i}];
    % center ellipse
    x_plot{i}=rotated_Coords{i}(1,:)'+xCenter;
    y_plot{i}=rotated_Coords{i}(2,:)'+yCenter;
end

% Set up plot
figure
xlabel('Ithaca Minimum Temperature (F)')
ylabel('Canandagua Minimum Temperature (F)(F)')
hold on

% Plot data points
plot(Ith_MinT,Can_MinT,'o');

% Plot contours
for j = 1:length(chisq)
    plot(x_plot{j},y_plot{j})
end
legend('Data points','50%', '80%', '90%', '99%')

 

A simple command for plotting autocorrelation functions in Matlab

Autocorrelation is a measure of persistence within a data set, which can be defined as the tendency for successive data points to be similar (Wilks, 2011).  In atmospheric science temporal autocorrelation can be a helpful tool for model evaluation. Temporal autocorrelation is also a fundamental concept for synthetic weather generation (for more detail see Julie’s fantastic series of blog posts on synthetic weather generation here).  Calculating autocorrelation within a sample data set can also be a helpful for assessing the applicability of classical statistical methods requiring independence of data points within a sample. Should a data set prove to be strongly persistent, such methods will likely yield inaccurate results.

Autocorrelation is commonly computed by making a copy of the original data set, shifting the copy k points forward (where k is the lag over which you would like to compute the autocorrelation)  and computing the Pearson correlation coefficient between the original data set and the copy.

Where:

The calculation of autocorrelation for a number of different lags at once is known as the autocorrelation function. Plotting the autocorrelation graphically can be a helpful tool for quickly assessing the presence of autocorrelation within a data set.

You can generate such plots in Matlab using the simple command shown below:

autocorr(T,k) 
% T is your data set and k is the number of lags you would like to compute

The command generates a plot of the autocorrelation function. Below are two examples, the first is the autocorrelation function of a set of observed temperature values in Des Moines Iowa, the second is autocorrelation function of the temperature values at the same location as modeled by the MM5I regional climate model:

Figure 1: Temporal autocorrelation function of temperature observations from Des Moines Iowa (temperatures reported at 3 hour intervals)

 

Figure 2: Temporal autocorrelation function of temperature produced by the MM5I regional climate model for Des Moines Iowa (temperatures reported at 3 hour intervals)

Note the cyclical nature of the autocorrelation functions, this is a reflection of the daily temperature cycle. The autocorrelations function of the maximum or minimum temperatures would show more constant persistence.

Sources:

Wilks, D. S. (2011). Statistical methods in the atmospheric sciences. Burlington, MA: Academic Press.