# Estimating Logistic Regression coefficients From Scratch in R

Fitting logistic regression models with iterative reweighted least squares in R
Statistical Modeling
R
Published

March 2, 2024

In this post, we highlight the parameter estimation routines called behind the scenes upon invocation of R’s glm function. Specifically, we’ll focus on how parameters of a logistic regression model are estimated when fit to data having a binary response.

R’s glm function is used to fit generalized linear models, specified by giving a symbolic description of the linear predictor and a description of the error distribution. This function conceals a good deal of the complexity behind a simple interface, making it easy to overlook the calculations that estimate a model’s coefficients. The goal of this post is to shed some light on the mechanics of those calculations.

### Background

In a generalized linear model the response may follow any distribution from the exponential family, and rather than assuming the mean is a linear function of the explanatory variables, we assume that a function of the mean (the link function) is a linear function of the explanatory variables.

Logistic regression is used for modeling data with a categorical response. Although it’s possible to model multinomial data using logistic regression, this article focuses only on fitting data having a dichotomous response (‘Yes/No’, ‘True/False’, ‘1/0’, ‘Good/Bad’).

The logistic regression model is a generalized linear model whose canonical link is the logit, or log-odds:

$\mathrm{Ln} \Big(\frac{\pi_{i}}{1 - \pi_{i}} \Big) = \beta_{0} + \beta_{1}{x}_{i1} + \dots + \beta_{p}{x}_{ip}, \quad i = (1, \dots , n).$

Solving the logit for $$\pi_{i}$$, which represents the predicted probability for a set of features $$x_{i}$$, yields

$\pi_{i} = \frac {e^{\beta_{0} + \beta_{1}{x}_{i1} + \dots + \beta_{p}{x}_{ip}}}{1 + e^{\beta_{0} + \beta_{1}{x}_{i1} + \dots + \beta_{p}{x}_{ip}}} = \frac {1}{1 + e^{-(\beta_{0} + \beta_{1}{x}_{i1} + \dots + \beta_{p}{x}_{ip})}},$

Where $$-\infty < x_{i} < \infty$$ and $$0<\pi_{i}<1$$.

In other words, the expression for $$\pi_{i}$$ maps any real-valued $$x_{i}$$ to a positive probability between 0 and 1.

### Parameter Estimation

Maximum Likelihood Estimation can be used to determine the parameters of a Logistic Regression model, which entails finding the set of parameters for which the probability of the observed data is greatest. The objective is to estimate the $$(p+1)$$ unknown $$\beta_{0}, \dots, \beta_{p}$$.

Let $$Y_{i}$$ represent independent, dichotomous response values for each of $$n$$ observations, where $$Y_{i}=1$$ denotes a success and $$Y_{i}=0$$ denotes a failure. The density function of a single observation $$Y_{i}$$ can be expressed as

$p(y_{i}) = \pi_{i}^{y_{i}}(1-\pi_{i})^{1-y_{i}},$

From which we obtain the likelihood function:

$L(\beta) = \prod_{i=1}^{n} \pi_{i}^{y_{i}}(1-\pi_{i})^{1-y_{i}}.$

Taking the natural log of the maximum likelihood estimate results in the log-likelihood function:

\begin{align*} l(\beta) &= \mathrm{Ln}(L(\beta)) = \mathrm{Ln} \Big(\prod_{i=1}^{n} \pi_{i}^{y_{i}}(1-\pi_{i})^{1-y_{i}} \Big) \\ &= \sum_{i=1}^{n} y_{i} \mathrm{Ln}(\pi_{i}) + (1-y_{i})\mathrm{Ln}(1-\pi_{i}) \\ &= \sum_{i=1}^{n} y_{i}(\beta_{0} + \beta_{1}{x}_{i1} + \dots + \beta_{p}{x}_{ip}) - \mathrm{Ln}(1 + e^{\beta_{0} + \beta_{1}{x}_{i1} + \dots + \beta_{p}{x}_{ip}}) \end{align*}

The first-order partial derivatives of the log-likelihood are calculated and set to zero for each $$\beta_{k}, k = 0, 1, \dots, p$$:

$\frac {\partial l(\beta)}{\partial \beta_{k}} = \sum_{i=1}^{n} y_{i}x_{ik} - \pi_{i}x_{ik} = \sum_{i=1}^{n} x_{ik}(y_{i} - \pi_{i}) = 0,$

which can be represented in matrix form as

$\frac {\partial l(\beta)}{\partial \beta} = X^{T}(y - \pi),$

Where $$X^{T}$$ is a (p+1)xn matrix and $$(y - \pi)$$ a nx1 vector.

The vector of first-order partial derivatives of the log-likelihood function is referred to as the score function, and is typically represented as $$U$$.

These $$(p+1)$$ equations are solved simultaneously to obtain the parameter estimates $$\beta_{0}, \dots, \beta_{p}$$. Each solution specifies a critical-point which will be either a maximum or a minimum. The critical point will be a maximum if the matrix of second partial derivatives is negative definite (which means every element on the diagonal of the matrix is less than zero).

The matrix of second partial derivatives can be expressed as

$\frac{\partial^{2} l(\beta)}{{\partial \beta_{k}}{\partial \beta_{k}}^{T}} = - \sum_{i=1}^{n} x_{ik}\pi_{i}(1-\pi_{i}){x_{ik}}^{T},$

which can be represented as

$\frac{\partial^{2} l(\beta)}{{\partial \beta}{\partial \beta}^{T}} = -X^{T}WX,$

where $$W$$ is an nxn diagonal matrix of weights with each element equal to $$\pi_{i}(1 - \pi_{i})$$ for logistic regression models. In general, the weight matrix $$W$$ will have entries inversely proportional to the variance of the response.

Since no closed-form solution exists for determining logistic regression coefficients, iterative techniques must be employed.

### Fitting the Model

Two distinct but related iterative methods can be utilized in determining model coefficients: the Newton-Raphson method and Fisher Scoring. The Newton-Raphson method relies on the matrix of second partial derivatives, also known as the Hessian. The Newton-Raphson update expression is given by:

$\beta^{(t+1)} = \beta^{(t)} - (H^{(t)})^{-1}U^{(t)},$

where:

• $$\beta^{(t+1)}$$ = the vector of updated coefficient estimates.
• $$\beta^{(t)}$$ = the vector of coefficient estimates from the previous iteration.
• $$(H^{(t)})^{-1}$$ = the inverse Hessian, $$\Big(\frac{\partial^{2} l(\beta)}{{\partial \beta}{\partial \beta}^{T}}\Big)^{-1}$$.
• $$U^{(t)}$$ = the vector of first-order partial derivatives of the log-likelihood function, $$\frac {\partial l(\beta)}{\partial \beta} = X^{T}(y - \pi)$$.

The Newton-Raphson method starts with an initial guess for the solution, and obtains a second guess by approximating the function to be maximized in a neighborhood of the initial guess by a second-degree polynomial, and then finding the location of that polynomial’s maximum value. This process continues until it converges to the actual solution. The convergence of $$\beta^{(t)}$$ to $$\hat{\beta}$$ is usually fast, with adequate convergence usually realized in fewer than 20 iterations.

Fisher Scoring utilizes the expected information, $$-E\Big(\frac{\partial^{2} l(\beta)}{{\partial \beta}{\partial \beta}^{T}}\Big)$$. Let $$\mathcal{I}$$ serve as a stand-in for the expected value of the information:

$\mathcal{I} = -E\Big(\frac{\partial^{2} l(\beta)}{{\partial \beta}{\partial \beta}^{T}}\Big).$

The Fisher Scoring update step replaces $$-H^{(t)}$$ from Newton-Raphson with $$\mathcal{I}^{(t)}$$:

\begin{align*} \beta^{(t+1)} &= \beta^{(t)} + (\mathcal{I}^{(t)})^{-1}U^{(t)} \\ &= \beta^{(t)} + (X^{T}WX)^{-1}X^{T}(y - \pi), \end{align*}

where:

• $$\beta^{(t+1)}$$ = the vector of updated coefficient estimates.
• $$\beta^{(t)}$$ = the vector of coefficient estimates from the previous iteration.
• $$(\mathcal{I}^{(t)})^{-1}$$ = the inverse of the expected information matrix, $$-E \Big(\frac{\partial^{2} l(\beta)}{{\partial \beta}{\partial \beta}^{T}}\Big)^{-1}$$.
• $$U^{(t)}$$ = the vector of first-order partial derivatives of the log-likelihood function, $$\frac {\partial l(\beta)}{\partial \beta} = X^{T}(y - \pi)$$.

For GLM’s with a canonical link, the observed and expected information are the same. When the response follows an exponential family distribution and the canonical link function is employed, observed and expected Information coincide so that Fisher Scoring produces the same estimates as Newton-Raphson.

When the canonical link is used, the second partial derivatives of the log-likelihood do not depend on the observations $$y_{i}$$, and therefore

$\frac{\partial^{2} l(\beta)}{{\partial \beta}{\partial \beta}^{T}} = E \Big(\frac{\partial^{2} l(\beta)}{{\partial \beta}{\partial \beta}^{T}} \Big).$

Fisher scoring has the advantage that it produces the asymptotic covariance matrix as a by-product. To summarize:

• The Hessian is the matrix of second partial derivatives of the log-likelihood with respect to the parameters: $$H = \frac{\partial^{2} l(\beta)}{{\partial \beta}{\partial \beta}^{T}}$$.
• The observed information is $$-\frac{\partial^{2} l(\beta)}{{\partial \beta}{\partial \beta}^{T}}$$.
• The expected information is $$\mathcal{I} = E\Big(-\frac{\partial^{2} l(\beta)}{{\partial \beta}{\partial \beta}^{T}}\Big)$$.
• The asymptotic covariance matrix is $$mathrm{Var}(\hat{\beta}) = E\Big(-\frac{\partial^{2} l(\beta)}{{\partial \beta}{\partial \beta}^{T}}\Big)^{-1} = (X^{T}WX)^{-1}$$.

For models employing a canonical link function:

• The observed and expected information are the same: $$\frac{\partial^{2} l(\beta)}{{\partial \beta}{\partial \beta}^{T}} = E\Big(\frac{\partial^{2} l(\beta)}{{\partial \beta}{\partial \beta}^{T}}\Big)$$.
• $$H = -\mathcal{I}$$, or $$\frac{\partial^{2} l(\beta)}{{\partial \beta}{\partial \beta}^{T}} = E\Big(-\frac{\partial^{2} l(\beta)}{{\partial \beta}{\partial \beta}^{T}}\Big)$$.
• The Newton-Raphson and Fisher Scoring algorithms yield identical results.

### Fisher Scoring in R

The data used for our sample calculation can be obtained here. This data represents O-ring failures in the 23 pre-Challenger space shuttle missions. In this dataset, TEMPERATURE serves as the single explanatory variable which will be used to predict “O_RING_FAILURE”, which is 1 if a failure occurred, 0 otherwise.

Once the parameters have been determined, the model estimate of the probability of success for a given observation can be calculated via:

$\hat\pi_{i} = \frac {e^{\hat\beta_{0} + \hat\beta_{1}{x}_{i1} + \dots + \hat\beta_{p}{x}_{ip}}}{1 + e^{\hat\beta_{0} + \hat\beta_{1}{x}_{i1} + \dots + \hat\beta_{p}{x}_{ip}}}$

getCoefficients returns the estimated model coefficients as a (p+1)x1 matrix. In addition, the function returns the number of scoring iterations, fitted values and resulting variance-covariance matrix.


getCoefficients = function(design_matrix, response_vector, epsilon=.0001) {
# =========================================================================
# design_matrix      X     => n-by-(p+1)                                |
# response_vector    y     => n-by-1                                    |
# probability_vector p     => n-by-1                                    |
# weights_matrix     W     => n-by-n                                    |
# epsilon                    => threshold above which iteration continues |
# =========================================================================
# n                          => # of observations                         |
# (p + 1)                    => # of parameters, +1 for intercept term    |
# =========================================================================
# U => First derivative of Log-Likelihood with respect to                 |
#      each beta_i, i.e. Score Function: X_transpose * (y - p)          |
#                                                                         |
# I => Second derivative of Log-Likelihood with respect to                |
#      each beta_i. The Information Matrix: (X_transpose * W * X)       |
#                                                                         |
# X^T*W*X results in a (p+1)-by-(p+1) matrix                              |
# X^T(y - p) results in a (p+1)-by-1 matrix                               |
# (X^T*W*X)^-1 * X^T(y - p) results in a (p+1)-by-1 matrix                |
# ========================================================================|
X = as.matrix(design_matrix)
y = as.matrix(response_vector)

# Initialize logistic function used for Scoring calculations.
pi_i = function(v) return(exp(v) / (1 + exp(v)))

# Initialize beta_0, p_0, W_0, I_0 & U_0.
beta_0 = matrix(rep(0, ncol(X)), nrow=ncol(X), ncol=1, byrow=FALSE, dimnames=NULL)
p_0 = pi_i(X %*% beta_0)
W_0 = diag(as.vector(p_0*(1 - p_0)))
I_0 = t(X) %*% W_0 %*% X
U_0 = t(X) %*% (y - p_0)

# Initialize variables for iteration.
beta_old = beta_0
iter_I = I_0
iter_U = U_0
iter_p = p_0
iter_W = W_0
fisher_scoring_iterations = 0

# Iterate until difference between abs(beta_new - beta_old) < epsilon.
while(TRUE) {
fisher_scoring_iterations = fisher_scoring_iterations + 1
beta_new = beta_old + solve(iter_I) %*% iter_U

if (all(abs(beta_new - beta_old) < epsilon)) {
model_parameters = beta_new
fitted_values = pi_i(X %*% model_parameters)
covariance_matrix = solve(iter_I)
break

} else {
iter_p = pi_i(X %*% beta_new)
iter_W = diag(as.vector(iter_p*(1-iter_p)))
iter_I = t(X) %*% iter_W %*% X
iter_U = t(X) %*% (y - iter_p)
beta_old = beta_new
}
}

results = list(
'model_parameters'=model_parameters,
'covariance_matrix'=covariance_matrix,
'fitted_values'=fitted_values,
'number_iterations'=fisher_scoring_iterations
)

return(results)
}

A quick summary of R’s matrix operators:

• %*% is a stand-in for matrix multiplication.
• diag returns a matrix with the provided vector as the diagonal and zero off-diagonal entries.
• t returns the transpose of the provided matrix.
• solve returns the inverse of the provided matrix (if it exists).

Note that in our implementation, we solve the normal equations directly. You wouldn’t see this in practice or when using optimized numerical software packages. This is because since when confronted with solving ill-conditioned systems of equations, computing $$(X^{T}WX)^{-1}$$ effectively squares the condition number, which results in an answer with diminished accuracy. Optimized statistical computing packages instead leverage more stable methods such as the QR decomposition or SVD. But this suffices for our purposes.

We load the Challenger dataset and partition it into the design matrix and response, which will then be passed into getCoefficients:

df = read.table(
stringsAsFactors=FALSE
)

X = as.matrix(cbind(1, df['TEMPERATURE']))  # design matrix
y = as.matrix(df['O_RING_FAILURE'])         # response vector

colnames(X) = NULL
colnames(y) = NULL

# Call getCoefficients, keeping epsilon at .0001.
results = getCoefficients(X, y, epsilon=.0001)

Printing results displays the model’s estimated coefficients (model_parameters), the variance-covariance matrix of the coefficient estimates (covariance_matrix), fitted values (fitted_values) and the number of Fisher Scoring iterations (number_iterations):

> print(results)

$model_parameters [,1] [1,] 15.0429016 [2,] -0.2321627$covariance_matrix
[,1]        [,2]
[1,] 54.4442748 -0.79638682
[2,] -0.7963868  0.01171514

$fitted_values [,1] [1,] 0.43049313 [2,] 0.22996826 [3,] 0.27362105 [4,] 0.32209405 [5,] 0.37472428 [6,] 0.15804910 [7,] 0.12954602 [8,] 0.22996826 [9,] 0.85931657 [10,] 0.60268105 [11,] 0.22996826 [12,] 0.04454055 [13,] 0.37472428 [14,] 0.93924781 [15,] 0.37472428 [16,] 0.08554356 [17,] 0.22996826 [18,] 0.02270329 [19,] 0.06904407 [20,] 0.03564141 [21,] 0.08554356 [22,] 0.06904407 [23,] 0.82884484$number_iterations
[1] 6

For the Challenger dataset, our implementation of Fisher Scoring yields a $$\beta_{0}=15.0429016$$ and $$\beta_{1}=-0.2321627$$. In order to predict new probabilities of O-ring failure based on temperature, our model relies on the following formula:

$\pi = \frac {e^{15.0429016 -0.2321627 * \mathrm{Temperature}}}{1 + e^{15.0429016 -0.2321627 * \mathrm{Temperature}}}$

Negative coefficients correspond to variables that are negatively correlated with the probability of a positive outcome, the reverse being true for positive coefficients.

Lets compare the results of our implementation with the output of glm using the same dataset, and specifying family=“binomial” and link=“logit”:

df = read.table(
stringsAsFactors=FALSE
)

logistic.fit = glm(
formula=O_RING_FAILURE ~ TEMPERATURE,
)

From logistic.fit, we’ll extract coefficients, fitted.values and iter, and call vcov(logistic.fit) to obtain the variance-covariance matrix of the estimated coefficients:

> logistic.fit$coefficients (Intercept) TEMPERATURE 15.0429016 -0.2321627 > matrix(logistic.fit$fitted.values)
[,1]
[1,] 0.43049313
[2,] 0.22996826
[3,] 0.27362105
[4,] 0.32209405
[5,] 0.37472428
[6,] 0.15804910
[7,] 0.12954602
[8,] 0.22996826
[9,] 0.85931657
[10,] 0.60268105
[11,] 0.22996826
[12,] 0.04454055
[13,] 0.37472428
[14,] 0.93924781
[15,] 0.37472428
[16,] 0.08554356
[17,] 0.22996826
[18,] 0.02270329
[19,] 0.06904407
[20,] 0.03564141
[21,] 0.08554356
[22,] 0.06904407
[23,] 0.82884484

> logistic.fit$fitted.iter 5 > vcov(logistic.fit) (Intercept) TEMPERATURE (Intercept) 54.4441826 -0.79638547 TEMPERATURE -0.7963855 0.01171512 Our coefficients match exactly with those generated by glm, and as would be expected, the fitted values are also identical. Notice there’s some discrepancy in the estimate of the variance-covariance matrix beginning with the 4th decimal (54.4442748 in our algorithm vrs. 54.4441826 for the variance of the Intercept term from glm). This may be due to rounding, or the loss of precision in floating point values when inverting matrices. Notice our implementation required one more Fisher Scoring iteration than glm (6 vrs. 5). Perhaps increasing the size of our epsilon will reduce the number of Fisher Scoring iterations, which in turn may lead to better agreement between the variance-covariance matrices. Calling summary(logistic.fit) prints, among other things, the standard error of the coefficient estimates: > summary(logistic.fit) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 15.0429 7.3786 2.039 0.0415 * TEMPERATURE -0.2322 0.1082 -2.145 0.0320 * The Std. Error values are the square root of the diagonal elements of the variance-covariance matrix, $$\sqrt{54.4441826} = 7.3786$$ and $$\sqrt{0.01171512} = 0.1082$$. z value is the estimated coefficient divided by Std. Error. In our example, $$15.0429/7.3786=2.039$$ and $$-0.2322/0.1082 = -2.145$$. Pr(>|z|) is the p-value, which tells us whether we should trust the estimated coefficient value. The standard rule of thumb is that coefficients with p-values less than 0.05 are reliable, although some tests require stricter thresholds. A feature of Logistic Regression is that the training data’s marginal probabilities are preserved. If you aggregate fitted values from the training set, that quantity will equal the number of positive outcomes in the response vector (this is true for all exponential family GLMs employing a canonical link function): > sum(y) 7 # Checking sum for our algorithm. > sum(mySummary$fitted_values)
7

#checking sum for glm.
> sum(logistic.fit\$fitted.values)
7

## Using The Model to Calculate Probabilities

To apply the model generated by glm to a new set of explanatory variables, use the predict function. Pass a list or data.frame of explanatory variables to predict, and for logistic regression models, be sure to set type="response" to ensure probabilities are returned. For example:

# New inputs for Logistic Regression model.
> tempsDF <- data.frame(TEMPERATURE=c(24, 41, 46, 47, 61))

> predict(logistic.fit, tempsDF, type="response")

1         2         3         4         5
0.9999230 0.9960269 0.9874253 0.9841912 0.7070241