# 15.1.250. tablet_qt/maths/logisticdescriptives.h¶

```
/*
Copyright (C) 2012, University of Cambridge, Department of Psychiatry.
Created by Rudolf Cardinal (rnc1001@cam.ac.uk).
This file is part of CamCOPS.
CamCOPS is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
CamCOPS is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with CamCOPS. If not, see <https://www.gnu.org/licenses/>.
*/
#pragma once
#include <QVector>
#include "maths/include_eigen_dense.h"
class LogisticDescriptives
{
// Deals with multiple ways of specifying a simple logistic regression
// equation with a SINGLE predictor.
//
// This class just stores the coefficients. It can be initialized directly
// with them, or given data with which to perform the regression (via
// the LogisticRegression class).
public:
// "Invalid" initialization:
LogisticDescriptives();
// Initialize from coefficients:
LogisticDescriptives(const Eigen::VectorXd& coefficients);
LogisticDescriptives(const QVector<qreal>& coefficients);
LogisticDescriptives(double intercept, double slope);
// Initialize from data:
LogisticDescriptives(const QVector<qreal>& x, const QVector<int>& y,
bool verbose = false);
// OK?
bool ok() const;
// Values:
double b0() const;
double b1() const;
double intercept() const;
double slope() const;
double k() const;
double theta() const;
double x50() const;
// Prediction:
double p(double x) const;
double x(double p) const;
protected:
void setFromGlmCoefficients(double b0, double b1);
protected:
bool m_ok;
double m_b0;
double m_b1;
/*
1. These parameters are the GLM versions. We start with these.
The GLM, ignoring error terms, is:
Y = logistic(Xb) = logistic(b0 + b1*x)
logit(Y) = Xb = b0 + b1*x
The Y value, here, is the probability p, so:
p = logistic(b0 + b1*x)
= 1 / (1 + exp(-[b0 + b1*x]));
logit(p) = b0 + b1*x
Reversing:
x = (logit(p) - b0) / b1
Or, reversing in full:
P = 1 / (1 + exp(-intercept - slope * X))
1 = P + P * exp(-intercept - slope * X)
-intercept - slope*X = ln((1 - P) / P)
intercept + slope * X = ln(P / (1 - P))
X = (ln(P / (1 - P)) - intercept) / slope
2. From an alternative formulation:
These parameters define a linear equation in logits,
L(X) = intercept + slope * X
The logistic function itself is
P = plogis(L) = 0.5 * (1 + tanh(L/2)) = 1 / (1 + exp(-L))
So that's
P = 1 / (1 + exp(-intercept - slope * X))
Comparing to Lecluyse & Meddis (2009)'s function,
p = 1 / (1 + exp(-k(X - theta)))
= 1 / (1 + exp(-k*X + k*theta))),
we have
k = slope
and
theta = -intercept/k = -intercept/slope
Comparing back to the GLM function:
intercept = b0
slope = b1
k = slope = b1
theta = -intercept/k = -b0/b1 [... since k*theta = -intercept]
3. We will have p = 0.5 when
0.5 = 1 / (1 + exp(-[b0 + b1*x]))
1 + exp(-[b0 + b1*x]) = 2
exp(-[b0 + b1*x]) = 1
-[b0 + b1*x] = 0
b0 + b1*x = 0
x = -b0/b1
= theta
*/
};
```