We show three methods for calculating the coefficients in the multinomial logistic model, namely: (1) using the coefficients described by the r binary models, (2) using Solver and (3) using Newton’s method. On this webpage we review the first of these methods. See Finding Multinomial Logistic Regression Coefficients using Solver and Finding Multinomial Logistic Regression Coefficients using Newton’s Method.
Example 1: A new drug was tested for the treatment of certain types of cancer patients. Figure 1 shows the data for a sample of 860 patients, 449 male (Gender = 0) and 411 women (Gender = 1) given the cancer treatment at various dosages. Three outcomes were measured after 5 years: the patient was cured (i.e. cancer free after 5 years), the patient died or the patient was alive but still had cancer.
Build a multinomial logistic regression model based on this data and use it to predict the probability of the three outcomes for men and women at a dosages of 24 mg and 24.5 mg.
Figure 1 – Data for Multinomial Logistic Regression
We use Dead as the reference outcome. Generally it is best to use the outcome with the largest sample size (400 for Dead), although the end result will be the same if another choice is made. The key components of the model are shown in Figure 2.
Figure 2 – Multinomial logistic regression model (part 1)
The coefficients are derived from the two binary models: Cured + Dead and Sick + Dead, i.e. the binary logistic regression model based on the data in A5:D16 and the binary logistic regression model based on the data in the range A5:C5 + E5:E16. The fact that the data range for the second model is not contiguous is not a problem since we will be using the MLogitExtract supplemental function to extract the correct outcomes from the original data range.
Real Statistics Function: The Real Statistics Resource Pack provides the following array function where R1 is a summary data range for multinomial logistic regression with outcomes for the dependent variable of 0, …, r.
MLogitExtract(R1, r, s, head): fill the highlighted range with the columns defined by string s from the data from R1. The string s takes the form of a comma delimited list of numbers 0, …, r. If head = True (default) then R1 includes column headings, while if head = False then R1 does not include column headings. Also the output will contain column headings if head = True and it will contain only data if head = False.
For the Cured + Dead binary model we use the data range MLogitExtract(A4:E16,2,”1,0”) or MLogitExtract(A5:E16,2,”1,0”,False). The first formula includes column headings and the second does not. Here “1,0” means that the data for outcome 1 (column J) is used followed by the data for outcome 0 (column I). The second argument has value 2 since the outcomes are 0, 1, 2. It is important that the reference outcome (i.e. 0) is listed second so that “success” in the binary logistic regression model is for the non-reference outcome.
For the Sick + Dead binary model we use the data range MLogitExtract(A4:E16,2,”2,0”) or MLogitExtract(A5:E16,2,”2,0”,False). The first formula includes column headings and the second does not. The output for MLogitExtract(A4:E16,2,”2,0”) is shown in Figure 3.
Figure 3 – Use of MLogitExtract function
In fact for our purposes here we don’t need to explicitly display the results of the MLogitExtract function. Instead we use the MLogitExtract formula as an argument in the LogitCoeff Real Statistics formula (see Real Statistics Functions for Multinomial Logistic Regression), which calculates the coefficients for binary logistic regression. In particular, we insert the following array formula in range X6:X8 of Figure 1 to calculate the binary logistic regression coefficients for the Cured + Dead model.
Similarly, we insert the following array formula in range Y6:Y9 of Figure 1 to calculate the binary logistic regression coefficients for the Sick + Dead model.
The remaining formulas in Figure 1 are calculated as described in Basic Concepts of Multinomial Logistic Regression. E.g. the formulas used for the cells in row 5 are as shown in Figure 4.
Figure 4 – Key formulas from Figure 1
In calculating cell V5 we use the fact that n! = Γ(n+1) where Γ is the gamma function (per Property 1c of ). Thus Ln n! = GAMMALN(n+1).
The values of L0, the various pseudo-R2 statistics as well as the chi-square test for the significance of the multinomial logistic regression model are displayed in Figure 5.
Figure 5 – Multinomial logistic regression model (part 2)
The significance of the two sets of coefficients are displayed in Figure 6.
Figure 5 – Multinomial logistic regression model (part 3)
Here the ranges H22:N24 and H29:N31 can be calculated by the Real Statistics array formulas
The forecasted probabilities, based on the above multinomial logistic regression model, of the three outcomes for men and women at a dosages of 24 mg and 24.5 mg is displayed in Figure 6.
That exp(bgender) = 1.116 for Cured + Dead means that for any given dosage women are 11.5% more likely than men to be cured rather than dead. That exp(bgender) = .451 for Sick + Dead means that for any given dosage men are 2.22 (= 1/.451) more likely than women to be sick rather than dead. Since 1.116 – .451 = .665 and 1/.665 = 1.5, for any given dosage men are 50% more likely than women to be cured rather than sick.
That exp(bdosage) = 2.68 for Cured + Dead means one additional mg of medication increases the likelihood of being cured rather than dead 2.68 fold.
Figure 6 – Forecasted probabilities
From Figure 6, we see that the multinomial logistic regression model described on this webpage forecasts that 22.7% of women who receive a dosage of 24 mg will die, 64.1% will be cured and 13.2% will be sick. This compares with 12/63 = 19% of the sample women who receive a dosage will die, 15/63 = 24% who will be cured and 36/63 = 57% who will be sick. Even though we have no sample data for 24.5 mg the model produces the forecast shown in Figure 6.
The following formulas are used to create the first row of data in Figure 6:
Figure 7 – Key formulas for Figure 6