Creating Dendrograms in R

A dendrogram is an effective way of visualizing results from hierarchical clustering. The purpose of this post is to show how to make a basic dendrogram in R and illustrate the ways in which one can add colors to dendrogram labels and branches to help identify key clustering drivers. Making dendrograms in R is quite straightforward. However, customizing a dendrogram is not so straightforward, so this post shows some tricks that I learned and should help expedite the process!

First and foremost, your data must be in an appropriate from for hierarchical clustering to be conducted. Table 1 shows an example of how your data can be set up. Four different spatial temperatures projected by CMIP5 models are shown along with various attributes that could be potential driving forces behind clustering: the institution at which the model comes from, the RCP (radiative forcing scenario) used in the model, and the initial conditions with which the model was run.

Table 1: Model Attributes

At this point, it is helpful to add the model names as the row names (shown in the leftmost column) of your data frame, otherwise the dendrogram function will use the row number as a label on the dendrogram which can make it hard to interpret the clustering results.

Next, create a distance matrix, which will be composed of Euclidean distances between pairs of model projections. This is what clustering will be based on. We first create a new data frame composed of just the temperature values (shown below) by removing columns from the Model Attributes table.


Table 2: Temperature Projections

The following code can be used to create Table 2 from the original table and then the distance matrix.

#Create a new data frame with just temperature values

just_temperature=Model_Attributes[ -c(1:4) ]

#Create a distance matrix


Now, one can make the clustering diagram. Here I chose to use complete linkage clustering as the agglomeration method and wanted my dendrogram to be horizontal.

#Perform clustering


#Adjust dimensions of dendrogram so that it fits in plotting window


plot(complete_linkage_cluster,horiz =TRUE)

And that’s it! Here is the most basic dendrogram.


Figure 1: Dendrogram

Now for customization. You will first need to install the “dendextend” library in R.

We have 11 institutions that the models can come from and we want to visualize if institution has some impact on clustering, by assigning a color to the label. Here we use the rainbow color palette to assign each model a color and then replot the dendrogram.


#Create a vector of colors with one color for each institution


#Add colors to the ordered dendrogram
labels_colors(complete_linkage_cluster)= col[Model_Attributes$Institution][order.dendrogram(complete_linkage_cluster)]

#Replot the dendrogram

par(mar=c(3,4,1,15)) #Dendrogram parameters
plot(complete_linkage_cluster,horiz =TRUE)


Figure 2: Dendrogram with Colored Labels

Now suppose we wanted to change the branch colors to show what RCP each model was run with. Here, we assign a color from the rainbow palette to each of the four RCPs and add it to the dendrogram.


col_branches= col[Model_Attributes$RCP][order.dendrogram(complete_linkage_cluster)]

plot(colored_dendrogram,horiz =TRUE)


Figure 3: Dendrogram with Colored Labels and Colored Branches

Now finally, we can change the node shapes to reflect the initial condition. There are 10 total initial conditions, so we’re going to use the first 10 standard pch (plot character) elements to represent the individual nodes.

nodePar = list(lab.cex = 0.6, pch = c(NA,19),cex = 0.7, col = "black") #node parameters

dend1 = colored_dendrogram %>% set("leaves_pch", c(nodes))

plot(dend1,horiz =TRUE)


Figure 4: Dendrogram with Colored Labels, Colored Branches, and Node Shapes

And that’s how you customize a dendrogram in R!

Creating parallel axis plots with multiple datasets, color gradients, and brushing in Python

Parallel axis plots (here is a good description of what they are) are a relatively recent development in the plotting world, so it is no surprise that there is no implementations of it with more than basic functionalities in the major plotting packages available online. Over the past couple of days I then created my own implementation of parallel axis plots in Python using Matplotlib Pandas’ and’s implementation get cumbersome when the user tries to apply brushing and multiple color gradients  to create versatile, high-resolution and story-telling plots for my next papers and presentations. This implementation allows for:

  • Plotting multiple datasets,
  • Displaying dataset names,
  • Choosing columns to be plot,
  • Coloring each dataset based on a column and a different Matplotlib color map,
  • Specifying ranges to be plotted,
  • Inverting multiple axis,
  • Brushing by intervales in multiple axis,
  • Choosing different fonts for title and rest of the plot, and
  • Export result as a figure file or viewing plot in Matplotlib’s interactive window.

The source code can be found here, and below is an example of how to use it:

import numpy as np
from plotting.parallel_axis import paxis_plot
from matplotlib.colors import LinearSegmentedColormap
from matplotlib import cm

bu_cy = LinearSegmentedColormap.from_list('BuCy', [(0, 0, 1), (0, 1, 1)])
bu_cy_r = bu_cy.reversed()

data1 = np.random.normal(size=(100, 8))
data2 = np.random.normal(size=(100, 8))
columns_to_plot = [0, 1, 3, 5, 7]
color_column = 0
axis_labels = ['axes ' + str(i) for i in range(8)]
dataset_names = ['Data set 1', 'Data set 2']
plot_ranges = [[-3.5, 3.5]] * 3 + [[-2.9, 3.1]] + [[-3.5, 3.5]] * 4
axis_to_invert = [1, 5]
brush_criteria = {1: [-10., 0.], 7: [10., 0.]}

paxis_plot((data1, data2),
           [bu_cy_r, cm.get_cmap('autumn_r')],
           'Title Here',
           fontname_title='Gill Sans MT',
           fontname_body='CMU Bright',

The output of this script should be a file named “test.png” that looks similar to the plot below:


Logistic Regression for Scenario Discovery

As most of you probably know, scenario discovery is an exploratory modeling approach [Bankes, 1993] that involves stress-testing proposed policies over plausible future “states of the world” (SOWs) to discover conditions under which those policies would fail to meet performance goals [Bryant and Lempert, 2010]. The scenario discovery process is therefore an exercise in statistical classification. Two commonly used methods used for the scenario discovery process are the Patient Rule Induction Method (PRIM; Friedman and Fisher [1999]) and Classification and Regression Trees (CART; Breiman et al. [1984]), both of which are included in the OpenMORDM R package and Rhodium Python package.

Another commonly used method in classification that hasn’t been given much attention in the scenario discovery literature is logistic regression. Logistic regression models estimate the probability that an event is classified as a success (1) as opposed to a failure (0) as a function of different covariates. This allows for the definition of “safe operating spaces,” or factor combinations leading to success, based on the probability with which one would like to be able to achieve the specified performance goal(s). We may not know the probability that a particular SOW will occur, but through the logistic regression we can estimate the probability of success in that SOW should it occur. The logistic regression can also identify which factors most influence a policy’s ability to meet those performance goals.

This blog post will illustrate how to build logistic regression models in Python for scenario discovery using the Red River basin as an example. Here we are interested in determining under what streamflow and demand characteristics reservoir operating policies are unable to protect Hanoi from the 100-yr flood. We assume operators want to ensure protection to this event with at least 95% reliability and use logistic regression to estimate under what combination of streamflow and demand characteristics they will be able to do so.

The form of the logistic regression model is given by Equation 1, where pi represents the probability that performance in the ith SOW is classified as a success and Xi represents a vector of covariates (in this case, streamflow and demand characteristics) describing the ith SOW:

1) \ln\Bigg(\frac{p_i}{1-p_i}\Bigg) = \mathbf{X_i^\intercal}\mathbf{\beta}.

The coefficients, \mathbf{\beta}, on the covariates are estimated using Maximum Likelihood Estimation.

To determine which streamflow and demand characteristics are most important in explaining successes and failures, we can compare the McFadden’s pseudo-R2 values associated with different models that include different covariates. McFadden’s pseudo-R2, R_{McFadden}^2, is given by Equation 2:

2) R_{McFadden}^2 = 1 - \frac{\ln \hat{L}(M_{Full})}{\ln \hat{L}(M_{Intercept})}

where \ln \hat{L}(M_{Full}) is the log-likelihood of the full model and \ln \hat{L}(M_{Intercept}) is the log-likelihood of the intercept model, i.e. a model with no covariates beyond the intercept. The intercept model therefore predicts the mean probability of success across all SOWs. R_{McFadden}^2 is a measure of improvement of the full model over the intercept model.

A common approach to fitting regression models is to add covariates one-by-one based on which most increase R2 (or in this case, R_{McFadden}^2), stopping once the increase of an additional covariate is marginal. The covariate that by itself most increases R_{McFadden}^2 is therefore the most important in predicting a policy’s success. To do this in Python, we will use the library statsmodels.

Imagine we have a pandas dataframe, dta that includes n columns of streamflow and demand characteristics describing different SOWs (rows) and a final column of 0s and 1s representing whether or not the policy being evaluated can provide protection to the 100-yr flood in that SOW (0 for no and 1 for yes). Assume the column of 0s and 1s is the last column and it is labeled Success. We can find the value of R_{McFadden}^2 for each covariate individually by running the following code:

import pandas as pd
import statsmodels.api as sm
from scipy import stats

# deal with fact that calling result.summary() in statsmodels.api
# calls scipy.stats.chisqprob, which no longer exists
stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)

def fitLogit(dta, predictors):
    # concatenate intercept column of 1s
    dta['Intercept'] = np.ones(np.shape(dta)[0])

    # get columns of predictors
    cols = dta.columns.tolist()[-1:] + predictors

    #fit logistic regression
    logit = sm.Logit(dta['Success'], dta[cols])
    result =

    return result

dta = pd.read_csv('SampleData.txt')
n = len(dta.columns) - 1
for i in range(n):
    predictors = dta.columns.tolist()[i:(i+1)]
    result = fitLogit(dta, predictors)

A sample output for one predictor, Col1 is shown below. This predictor has a pseudo-R2 of 0.1138.

Once the most informative predictor has been determined, additional models can be tested by adding more predictors one-by-one as described above. Suppose that through this process, one finds that the first 3 columns of dta (Col1,Col2 and Col3) are the most informative for predicting success on providing protection to the 100-yr flood, while the subsequent columns provide little additional predictive power. We can use this model to visualize the probability of success as a function of these 3 factors using a contour map. If we want to show this as a 2D projection, the probability of success can only be shown for combinations of 2 of these factors. In this case, we can hold the third factor constant at some value, say its base value. This is illustrated in the code below, which also shows a scatter plot of the SOWs. The dots are shaded light blue if the policy succeeds in providing protection to the 100-yr flood in that world, and dark red if it does not.

import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
import pandas as pd
import statsmodels.api as sm

def fitLogit(dta, predictors):
    # concatenate intercept column of 1s
    dta['Intercept'] = np.ones(np.shape(dta)[0])

    # get columns of predictors
    cols = dta.columns.tolist()[-1:] + predictors

    #fit logistic regression
    logit = sm.Logit(dta['Success'], dta[cols])
    result =
    return result

def plotContourMap(ax, result, constant, dta, contour_cmap, dot_cmap, levels, xgrid, ygrid, \
    xvar, yvar, base):

    # find probability of success for x=xgrid, y=ygrid
    X, Y = np.meshgrid(xgrid, ygrid)
    x = X.flatten()
    y = Y.flatten()
    if constant == 'x3': # 3rd predictor held constant at base value
        grid = np.column_stack([np.ones(len(x)),x,y,np.ones(len(x))*base[2]])
    elif constant == 'x2': # 2nd predictor held constant at base value
        grid = np.column_stack([np.ones(len(x)),x,np.ones(len(x))*base[1],y])
    else: # 1st predictor held constant at base value
        grid = np.column_stack([np.ones(len(x)),np.ones(len(x))*base[0],x,y])

    z = result.predict(grid)
    Z = np.reshape(z, np.shape(X))

    contourset = ax.contourf(X, Y, Z, levels, cmap=contour_cmap)
    ax.scatter(dta[xvar].values, dta[yvar].values, c=dta['Success'].values, edgecolor='none', cmap=dot_cmap)

    return contourset

# build logistic regression model with first 3 columns of predictors from dta
dta = pd.read_csv('SampleData.txt')
predictors = dta.columns.tolist()[0:3]
result = fitLogit(dta, predictors)

# define color map for dots representing SOWs in which the policy
# succeeds (light blue) and fails (dark red)
dot_cmap = mpl.colors.ListedColormap(np.array([[227,26,28],[166,206,227]])/255.0)

# define color map for probability contours
contour_cmap =‘RdBu’)

# define probability contours
contour_levels = np.arange(0.0, 1.05,0.1)

# define grid of x (1st predictor), y (2nd predictor), and z (3rd predictor) dimensions
# to plot contour map over
xgrid = np.arange(-0.1,1.1,0.01)
ygrid = np.arange(-0.1,1.1,0.01)
zgrid = np.arange(-0.1,1.1,0.01)

# define base values of 3 predictors
base = [0.5, 0.5, 0.5]

fig = plt.figure()
ax = fig.add_subplot(121)
# plot contour map when 3rd predictor ('x3') is held constant
plotContourMap(ax, result, 'x3', dta, contour_cmap, dot_cmap, contour_levels, xgrid, ygrid, \
    'Col1', 'Col2', base)
ax = fig.add_subplot(122)
# plot contour map when 2nd predictor ('x2') is held constant
contourset = plotContourMap(ax, result, 'x2', dta, contour_cmap, dot_cmap, contour_levels, xgrid, zgrid, \
    'Col1', 'Col3', base)

cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
cbar = fig.colorbar(contourset, cax=cbar_ax)
cbar_ax.set_ylabel('Probability of Success',fontsize=20)
yticklabels =,fontsize=18)

This produces the following figure:

We can also use the probability contours discovered above to define “safe operating spaces” as combinations of these 3 factors under which the evaluated policy is able to succeed in providing protection to the 100-yr flood with some reliability, say 95%. The hyperplane of factor combinations defining that 95% probability contour can be determined by setting p to 0.95 in Equation 2. Again, to plot 2-D projections of that hyperplane, the values of the other covariates can be held constant at their base values. The code below illustrates how to do this with a 95% boundary.

# define colormap for classifying boundary between failure and success
class_cmap = mpl.colors.ListedColormap(np.array([[251,154,153],[31,120,180]])/255.0)

# define probability cutoff between failure and success
class_levels = [0.0, 0.95, 1.0]

fig = plt.figure()
ax = fig.add_subplot(121)
# plot contour map when 3rd predictor ('x3') is held constant
plotContourMap(ax, result, 'x3', dta, class_cmap, dot_cmap, class_levels, xgrid, ygrid, \
    'Col1', 'Col2', base)

ax = fig.add_subplot(122)
# plot contour map when 2nd predictor ('x2') is held constant
plotContourMap(ax, result, 'x2', dta, class_cmap, dot_cmap, class_levels, xgrid, zgrid, \
    'Col1', 'Col3', base)


This produces the following figure, where the light red region is the parameter ranges in which the policy cannot provide protection to the 100-yr flood with 95% reliability, and the dark blue region is the “safe operating space” in which it can.

All code for this example can be found here.