# Calculate coefficients in a logistic regression with R

In a multiple linear regression it is possible to find out the coeffient with the following formula.

$b = (X’X)^{-1}(X’)Y$

beta = solve(t(X) %*% X) %*% (t(X) %*% Y) ; beta


For instance:

> y <- c(9.3, 4.8, 8.9, 6.5, 4.2, 6.2, 7.4, 6, 7.6, 6.1)
> x0 <- c(1,1,1,1,1,1,1,1,1,1)
> x1 <-  c(100,50,100,100,50,80,75,65,90,90)
> x2 <- c(4,3,4,2,2,2,3,4,3,2)
> Y <- as.matrix(y)
> X <- as.matrix(cbind(x0,x1,x2))

> beta = solve(t(X) %*% X) %*% (t(X) %*% Y);beta
[,1]
x0 -0.8687015
x1  0.0611346
x2  0.9234254
> model <- lm(y~+x1+x2) ; modelcoefficients (Intercept) x1 x2 -0.8687015 0.0611346 0.9234254  I would like how to calculate in the same “manual” way the beta for a logistic regression. Where of course the y would be 1 or 0. Assuming I’m using the binomial family with a logit link. ## Answer The OLS estimator in the linear regression model is quite rare in having the property that it can be represented in closed form, that is without needing to be expressed as the optimizer of a function. It is, however, an optimizer of a function — the residual sum of squares function — and can be computed as such. The MLE in the logistic regression model is also the optimizer of a suitably defined log-likelihood function, but since it is not available in a closed form expression, it must be computed as an optimizer. Most statistical estimators are only expressible as optimizers of appropriately constructed functions of the data called criterion functions. Such optimizers require the use of appropriate numerical optimization algorithms. Optimizers of functions can be computed in R using the optim() function that provides some general purpose optimization algorithms, or one of the more specialized packages such as optimx. Knowing which optimization algorithm to use for different types of models and statistical criterion functions is key. ## Linear regression residual sum of squares The OLS estimator is defined as the optimizer of the well-known residual sum of squares function: \begin{align} \hat{\boldsymbol{\beta}} &= \arg\min_{\boldsymbol{\beta}}\left(\boldsymbol{Y} – \mathbf{X}\boldsymbol{\beta}\right)’\left(\boldsymbol{Y} – \mathbf{X}\boldsymbol{\beta}\right) \\ &= (\mathbf{X}’\mathbf{X})^{-1}\mathbf{X}’\boldsymbol{Y} \end{align} In the case of a twice differentiable, convex function like the residual sum of squares, most gradient-based optimizers do good job. In this case, I will be using the BFGS algorithm. #================================================ # reading in the data & pre-processing #================================================ urlSheatherData = "http://www.stat.tamu.edu/~sheather/book/docs/datasets/MichelinNY.csv" dfSheather = as.data.frame(read.csv(urlSheatherData, header = TRUE)) # create the design matrices vY = as.matrix(dfSheather['InMichelin']) mX = as.matrix(dfSheather[c('Service','Decor', 'Food', 'Price')]) # add an intercept to the predictor variables mX = cbind(1, mX) # the number of variables and observations iK = ncol(mX) iN = nrow(mX) #================================================ # compute the linear regression parameters as # an optimal value #================================================ # the residual sum of squares criterion function fnRSS = function(vBeta, vY, mX) { return(sum((vY - mX %*% vBeta)^2)) } # arbitrary starting values vBeta0 = rep(0, ncol(mX)) # minimise the RSS function to get the parameter estimates optimLinReg = optim(vBeta0, fnRSS, mX = mX, vY = vY, method = 'BFGS', hessian=TRUE) #================================================ # compare to the LM function #================================================ linregSheather = lm(InMichelin ~ Service + Decor + Food + Price, data = dfSheather)  This yields: > print(cbind(coef(linregSheather), optimLinRegpar))
[,1]         [,2]
(Intercept) -1.492092490 -1.492093965
Service     -0.011176619 -0.011176583
Decor        0.044193000  0.044193023
Food         0.057733737  0.057733770
Price        0.001797941  0.001797934


## Logistic regression log-likelihood

The criterion function corresponding to the MLE in the logistic regression model is
the log-likelihood function.

\begin{align} \log L_n(\boldsymbol{\beta}) &= \sum_{i=1}^n \left(Y_i \log \Lambda(\boldsymbol{X}_i’\boldsymbol{\beta}) + (1-Y_i)\log(1 – \Lambda(\boldsymbol{X}_i’\boldsymbol{\beta}))\right) \end{align}
where $\Lambda(k) = 1/(1+ \exp(-k))$ is the logistic function. The parameter estimates are the optimizers of this function
$$\hat{\boldsymbol{\beta}} = \arg\max_{\boldsymbol{\beta}}\log L_n(\boldsymbol{\beta})$$

I show how to construct and optimize the criterion function using the optim() function
once again employing the BFGS algorithm.

#================================================
# compute the logistic regression parameters as
#   an optimal value
#================================================
# define the logistic transformation
logit = function(mX, vBeta) {
return(exp(mX %*% vBeta)/(1+ exp(mX %*% vBeta)) )
}

# stable parametrisation of the log-likelihood function
# Note: The negative of the log-likelihood is being returned, since we will be
# /minimising/ the function.
logLikelihoodLogitStable = function(vBeta, mX, vY) {
return(-sum(
vY*(mX %*% vBeta - log(1+exp(mX %*% vBeta)))
+ (1-vY)*(-log(1 + exp(mX %*% vBeta)))
)
)
}

# initial set of parameters
vBeta0 = c(10, -0.1, -0.3, 0.001, 0.01) # arbitrary starting parameters

# minimise the (negative) log-likelihood to get the logit fit
optimLogit = optim(vBeta0, logLikelihoodLogitStable,
mX = mX, vY = vY, method = 'BFGS',
hessian=TRUE)

#================================================
# test against the implementation in R
# NOTE glm uses IRWLS:
# http://en.wikipedia.org/wiki/Iteratively_reweighted_least_squares
# rather than the BFGS algorithm that we have reported
#================================================
logitSheather = glm(InMichelin ~ Service + Decor + Food + Price,
data = dfSheather,
family = binomial, x = TRUE)


This yields

> print(cbind(coef(logitSheather), optimLogit\$par))
[,1]         [,2]
(Intercept) -11.19745057 -11.19661798
Service      -0.19242411  -0.19249119
Decor         0.09997273   0.09992445
Food          0.40484706   0.40483753
Price         0.09171953   0.09175369


As a caveat, note that numerical optimization algorithms require careful use or you can end up with
all sorts of pathological solutions. Until you understand them well, it is best to
use the available packaged options that allow you to concentrate on specifying the model
rather than worrying about how to numerically compute the estimates.