Let us start today our series on classification from scratch…
The logistic regression is based on the assumption that given covariates \mathbf{x}, Y has a Bernoulli distribution,Y|\mathbf{X}=\mathbf{x}\sim\mathcal{B}(p_{\mathbf{x}}),~~~~p_\mathbf{x}=\frac{\exp[\mathbf{x}^T\mathbf{\beta}]}{1+\exp[\mathbf{x}^T\mathbf{\beta}]}The goal is to estimate parameter \mathbf{\beta}.
Recall that the heuristics for the use of that function for the probability is that\log[\text{odds}(Y=1)]=\log\frac{\mathbb{P}[Y=1]}{\mathbb{P}[Y=0]}=\mathbf{x}^T\mathbf{\beta}
Maximimum of the (log)-likelihood function
The log-likelihood is here\log\mathcal{L} = \sum_{i=1}^n y_i\log p_i+(1-y_i)\log (1-p_i) where p_{i}=(1+\exp[-\mathbf{x}_i^T\mathbf{\beta}])^{-1}. Numerical techniques are based on (numerical) gradient descent to compute the maximum of the likelihood function. The (negative) log-likelihood is the following function
y = myocarde$PRONO X = cbind(1,as.matrix(myocarde[,1:7])) negLogLik = function(beta){ -sum(-y*log(1 + exp(-(X%*%beta))) - (1-y)*log(1 + exp(X%*%beta))) } |
We use the minus sign since standard optimization routines compute minima, not maxima. Now, to find the minimum of that function, we need a starting point to initiate the algorithm
beta_init = lm(PRONO~.,data=myocarde)$coefficients |
Why not start with the parameter of the OLS. Somehow, we might think that at least, sign should be ok for instance. Anyway, we need a starting point, and let us use that one.
logistic_opt = optim(par = beta_init, negLogLik, hessian=TRUE, method = "BFGS", control=list(abstol=1e-9)) |
Here, we obtain
logistic_opt$par (Intercept) FRCAR INCAR INSYS 1.656926397 0.045234029 -2.119441743 0.204023835 PRDIA PAPUL PVENT REPUL -0.102420095 0.165823647 -0.081047525 -0.005992238 |
Let us verify here that this output is valid. For instance, what if we change the value of the starting point (randomly)
simu = function(i){ logistic_opt_i = optim(par = rnorm(8,0,3)*beta_init, negLogLik, hessian=TRUE, method = "BFGS", control=list(abstol=1e-9)) logistic_opt_i$par[2:3] } v_beta = t(Vectorize(simu)(1:1000)) plot(v_beta) par(mfrow=c(1,2)) hist(v_beta[,1],xlab=names(myocarde)[1]) hist(v_beta[,2],xlab=names(myocarde)[2]) |
Ooops. There is a problem here. Clearly, we cannot rely on numerical optimization here. We can think about using another optimization routine
library(optimx) logit = function(mX, vBeta) { exp(mX %*% vBeta)/(1+ exp(mX %*% vBeta)) } logLikelihoodLogitStable = function(vBeta, mX, vY) { -sum(vY*(mX %*% vBeta - log(1+exp(mX %*% vBeta))) + (1-vY)*(-log(1 + exp(mX %*% vBeta)))) } likelihoodScore = function(vBeta, mX, vY) { return(t(mX) %*% (logit(mX, vBeta) - vY) ) } optimLogitLBFGS = optimx(beta_init, logLikelihoodLogitStable, method = 'L-BFGS-B', gr = likelihoodScore, mX = X, vY = y, hessian=TRUE) |
The optimum is here
attr(optimLogitLBFGS, "details")[[2]] [,1] 0.066680272 FRCAR 0.003080542 INCAR 0.079031364 INSYS -0.001586194 PRDIA 0.040500697 PAPUL -0.041870705 PVENT -0.014162756 REPUL 0.195632244 |
Let’s be honest here, I do not feel confortable with those techniques. So, what happened here ?
Here, the technique we use is based on the following idea,\mathbf{\beta}_{new}=\mathbf{\beta}_{old} -\left(\frac{\partial^2\log\mathcal{L}(\mathbf{\beta}_{old})}{\partial\mathbf{\beta}\partial\mathbf{\beta}^T}\right)^{-1}\cdot \frac{\partial\log\mathcal{L}(\mathbf{\beta}_{old})}{\partial\mathbf{\beta}}The problem is that my computer does not know this first and second derivatives. So it will compute them using approximation techniques.
Actually, it is possible to use functions dedicated to such computation
library(numDeriv) library(MASS) logit = function(x){1/(1+exp(-x))} logLik = function(beta, X, y){ -sum(y*log(logit(X%*%beta)) + (1-y)*log(1-logit(X%*%beta))) } optim_second = function(beta, num_iter){ LL = vector() for(i in 1:num_iter){ grad = (t(X)%*%(logit(X%*%beta) - y)) H = hessian(logLik, beta, method = "complex", X = X, y = y) beta = beta - ginv(H)%*%grad LL[i] = logLik(beta, X, y) } result = list(beta, H) return(result) } |
With our OLS starting point, we obtain
opt0 = optim_second(beta_init,500) opt0[[1]] [,1] [1,] 0.951074420 [2,] 0.018860280 [3,] 0.275428978 [4,] 0.144803636 [5,] -0.058535606 [6,] 0.001182178 [7,] -0.108651776 [8,] -0.002940315 |
But if we try with another starting point
opt1 = optim_second(beta_init*runif(8),500) opt1[[1]] [,1] [1,] 0.052894794 [2,] 0.024718435 [3,] 0.167953661 [4,] 0.171662947 [5,] -0.057458066 [6,] -0.011361034 [7,] -0.107532114 [8,] -0.002679064 |
Clearly, some coefficients are rather close. But other aren’t. From my point of viezw, that is a major problem (keep in mind that we do not deal here with massive data ! There are only 7 explanatory variables, and only 71 observations).
Why not try to be clever, and use the analytical values of those derivatives ? Even if some people claim the oppositive, sometimes, it can actually be usefull to do the maths, instead of considering only numerical values.
Newton (or Fisher) Algorithm
If you open any Econometrics textbooks (one can also try to derive it), you will get \frac{\partial\log\mathcal{L}(\mathbf{\beta}_{old})}{\partial\mathbf{\beta}}=\mathbf{X}^T(\mathbf{y}-\mathbf{p}_{old})
while\frac{\partial^2\log\mathcal{L}(\mathbf{\beta}_{old})}{\partial\mathbf{\beta}\partial\mathbf{\beta}^T}=-\mathbf{X}^T\mathbf{\Delta}_{old}\mathbf{X}
Y=myocarde$PRONO X=cbind(1,as.matrix(myocarde[,1:7])) colnames(X)=c("Inter",names(myocarde[,1:7])) beta=as.matrix(lm(Y~0+X)$coefficients,ncol=1) for(s in 1:9){ pi=exp(X%*%beta[,s])/(1+exp(X%*%beta[,s])) gradient=t(X)%*%(Y-pi) omega=matrix(0,nrow(X),nrow(X));diag(omega)=(pi*(1-pi)) Hessian=-t(X)%*%omega%*%X beta=cbind(beta,beta[,s]-solve(Hessian)%*%gradient)} |
Observe that here, I use only ten iterations of the algorithm !
beta[,8:10] [,1] [,2] [,3] XInter -10.187641685 -10.187641696 -10.187641696 XFRCAR 0.138178119 0.138178119 0.138178119 XINCAR -5.862429035 -5.862429037 -5.862429037 XINSYS 0.717084018 0.717084018 0.717084018 XPRDIA -0.073668171 -0.073668171 -0.073668171 XPAPUL 0.016756506 0.016756506 0.016756506 XPVENT -0.106776012 -0.106776012 -0.106776012 XREPUL -0.003154187 -0.003154187 -0.003154187 |
The thing is that is seems to converge extremely fast. And it is rather robust ! Look at what we get if we change our starting point
beta=as.matrix(lm(Y~0+X)$coefficients,ncol=1)*runif(8) for(s in 1:9){ pi=exp(X%*%beta[,s])/(1+exp(X%*%beta[,s])) gradient=t(X)%*%(Y-pi) omega=matrix(0,nrow(X),nrow(X));diag(omega)=(pi*(1-pi)) Hessian=-t(X)%*%omega%*%X beta=cbind(beta,beta[,s]-solve(Hessian)%*%gradient)} beta[,8:10] [,1] [,2] [,3] XInter -10.187641586 -10.187641696 -10.187641696 XFRCAR 0.138178118 0.138178119 0.138178119 XINCAR -5.862429017 -5.862429037 -5.862429037 XINSYS 0.717084013 0.717084018 0.717084018 XPRDIA -0.073668172 -0.073668171 -0.073668171 XPAPUL 0.016756508 0.016756506 0.016756506 XPVENT -0.106776012 -0.106776012 -0.106776012 XREPUL -0.003154187 -0.003154187 -0.003154187 |
Nice, isn’t it? Looks like we got our winner, don’t we? And one can use the inverse of the Hessian matrix to get standard deviations.
Weighted Least-Squares
Let us go one step further. We’ve seen that we want to compute something like\mathbf{\beta}_{new} =(\mathbf{X}^T\mathbf{\Delta}_{old}\mathbf{X})^{-1}\mathbf{X}^T\mathbf{\Delta}_{old}\mathbf{z}(if we do substitute matrices in the analytical expressions) where \mathbf{z}=\mathbf{X}\mathbf{\beta}_{old}+\mathbf{\Delta}_{old}^{-1}[\mathbf{y}-\mathbf{p}_{old}]. But actually, that’s simply a standard least-square problem\mathbf{\beta}_{new} = \text{argmin}\left\lbrace(\mathbf{z}-\mathbf{X}\mathbf{\beta})^T\mathbf{\Delta}_{old}^{-1}(\mathbf{z}-\mathbf{X}\mathbf{\beta})\right\rbraceThe only problem here is that weights \mathbf{\Delta}_{old} are functions of unknown \mathbf{\beta}_{old}. But actually, if we keep iterating, we should be able to solve it : given the \mathbf{\beta} we got the weights, and with the weights, we can use weighted OLS to get an updated \mathbf{\beta}. That’s the idea of iteratively reweighted least squares.
The algorithm will be
df = myocarde beta_init = lm(PRONO~.,data=df)$coefficients X = cbind(1,as.matrix(myocarde[,1:7])) beta = beta_init for(s in 1:1000){ p = exp(X %*% beta) / (1+exp(X %*% beta)) omega = diag(nrow(df)) diag(omega) = (p*(1-p)) df$Z = X %*% beta + solve(omega) %*% (df$PRONO - p) beta = lm(Z~.,data=df[,-8], weights=diag(omega))$coefficients } |
and the output is here
beta (Intercept) FRCAR INCAR INSYS PRDIA -10.187641696 0.138178119 -5.862429037 0.717084018 -0.073668171 PAPUL PVENT REPUL 0.016756506 -0.106776012 -0.003154187 |
which is almost what we’ve obtained before. Nice isn’t it ? Actually, here we also have standard deviations of estimators
summary( lm(Z~.,data=df[,-8], weights=diag(omega))) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -10.187642 10.668138 -0.955 0.343 FRCAR 0.138178 0.102340 1.350 0.182 INCAR -5.862429 6.052560 -0.969 0.336 INSYS 0.717084 0.503527 1.424 0.159 PRDIA -0.073668 0.261549 -0.282 0.779 PAPUL 0.016757 0.306666 0.055 0.957 PVENT -0.106776 0.099145 -1.077 0.286 REPUL -0.003154 0.004386 -0.719 0.475 |
The standard glm function
Of course, it is possible to use an R built-in function to get our estimate
summary(glm(PRONO~.,data=myocarde,family=binomial(link = "logit"))) Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -10.187642 11.895227 -0.856 0.392 FRCAR 0.138178 0.114112 1.211 0.226 INCAR -5.862429 6.748785 -0.869 0.385 INSYS 0.717084 0.561445 1.277 0.202 PRDIA -0.073668 0.291636 -0.253 0.801 PAPUL 0.016757 0.341942 0.049 0.961 PVENT -0.106776 0.110550 -0.966 0.334 REPUL -0.003154 0.004891 -0.645 0.519 |
Application and visualisation
Let us visualize the prediction obtained from the logistic regression, on our second dataset
x = c(.4,.55,.65,.9,.1,.35,.5,.15,.2,.85) y = c(.85,.95,.8,.87,.5,.55,.5,.2,.1,.3) z = c(1,1,1,1,1,0,0,1,0,0) df = data.frame(x1=x,x2=y,y=as.factor(z)) reg = glm(y~x1+x2,data=df,family=binomial(link = "logit")) u = seq(0,1,length=101) p = function(x,y) predict.glm(reg,newdata=data.frame(x1=x,x2=y),type="response") v = outer(u,u,p) image(u,u,v,xlab="Variable 1",ylab="Variable 2",col=clr10,breaks=(0:10)/10) points(x,y,pch=19,cex=1.5,col="white") points(x,y,pch=c(1,19)[1+z],cex=1.5) contour(u,u,v,levels = .5,add=TRUE) |
Here level curves – or iso-probabilities – are linear, so the space is divided in two (0 and 1, survival and death, white and black) by a straight line (or an hyperplane in higher dimension). Furthermore, since we have a linear model, if we change the cutoff (the threshold used to create the two classes), we obtain another straight line (or hyperplane) parallel to the first one.
Next time, we will introduce splines to smooth those continuous covariates… to be continued.
OpenEdition suggests that you cite this post as follows:
Arthur Charpentier (May 30, 2018). Classification from scratch, logistic regression 1/8. Freakonometrics. Retrieved October 10, 2024 from https://doi.org/10.58079/ovan
Thank you so much on the detailed steps in achieving a Logistic regression. Please the PRONO column being used, is it a factor or numeric?
Apart from the glm approach, I was not about to obtain results for every other methods. I’ll be glad if my request is considered
Hi, great post thank’s for sharing.
My comment (or question) may be a little out scope here but still… I was wondering about colinearity problems and their diagnostic for logistic regression . For linear models, Variance Inflation Factor (VIF) can be used and they are well studied. I have seen here and there people using them in logistic regression estimation contexts, but I was rather unsatisfied because I couldn’t find any single convincing academic study on the properties of VIF in this context. Are you aware of any reference on this matter ?
Best regards