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I am aware of a variety of methods for simultaneously predicting multiple outcomes known sometimes as multivariate regression/analysis. However, my situation is a little more special. I am trying to predict a vector of values and where each part ranges from 0-1, and the sum of the vector must be equal to 1. A typical example of this would be population fractions of exclusive groups.

The most simple approach to this I can think of is to use OLS and rescale the predictions so they do not violate data structure. Here's an example using R and US state population data.

> #packages
> suppressPackageStartupMessages(library(tidyverse))

> suppressPackageStartupMessages(library(poliscidata))

> suppressPackageStartupMessages(library(rms))

> suppressPackageStartupMessages(library(magrittr))

> d = states

> #recode
> d$black = d$blkpct10 / 100

> d$hispanic = d$hispanic10 / 100

> d$white = 1 - d$black - d$hispanic

> #test sums
> rowSums(d %>% select(black, hispanic, white))
 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 
46 47 48 49 50 
 1 1 1 1 1 

> #regression
> #using 4 variables
> #OLS
> (ols_black = ols(black ~ abortion_rank12 + ba_or_more + cig_tax12 + conserv_advantage, data = d))
Linear Regression Model

 ols(formula = black ~ abortion_rank12 + ba_or_more + cig_tax12 + 
 conserv_advantage, data = d)

 Model Likelihood Discrimination 
 Ratio Test Indexes 
 Obs 50 LR chi2 4.81 R2 0.092 
 sigma0.0958 d.f. 4 R2 adj 0.011 
 d.f. 45 Pr(> chi2) 0.3068 g 0.033 

 Residuals

 Min 1Q Median 3Q Max 
 -0.11768 -0.06635 -0.03056 0.05120 0.25075 


 Coef S.E. t Pr(>|t|)
 Intercept 0.2077 0.1505 1.38 0.1744 
 abortion_rank12 -0.0016 0.0013 -1.28 0.2063 
 ba_or_more 0.0000 0.0042 0.00 0.9970 
 cig_tax12 -0.0209 0.0211 -0.99 0.3272 
 conserv_advantage -0.0014 0.0024 -0.56 0.5803 


> (ols_hispanic = ols(hispanic ~ abortion_rank12 + ba_or_more + cig_tax12 + conserv_advantage, data = d))
Linear Regression Model

 ols(formula = hispanic ~ abortion_rank12 + ba_or_more + cig_tax12 + 
 conserv_advantage, data = d)

 Model Likelihood Discrimination 
 Ratio Test Indexes 
 Obs 50 LR chi2 3.11 R2 0.060 
 sigma0.1010 d.f. 4 R2 adj -0.023 
 d.f. 45 Pr(> chi2) 0.5400 g 0.028 

 Residuals

 Min 1Q Median 3Q Max 
 -0.13098 -0.05541 -0.02833 0.02041 0.34087 


 Coef S.E. t Pr(>|t|)
 Intercept 0.1112 0.1586 0.70 0.4869 
 abortion_rank12 0.0011 0.0013 0.87 0.3904 
 ba_or_more 0.0001 0.0044 0.01 0.9899 
 cig_tax12 -0.0069 0.0222 -0.31 0.7558 
 conserv_advantage -0.0014 0.0026 -0.56 0.5764 


> (ols_white = ols(white ~ abortion_rank12 + ba_or_more + cig_tax12 + conserv_advantage, data = d))
Linear Regression Model

 ols(formula = white ~ abortion_rank12 + ba_or_more + cig_tax12 + 
 conserv_advantage, data = d)

 Model Likelihood Discrimination 
 Ratio Test Indexes 
 Obs 50 LR chi2 1.29 R2 0.025 
 sigma0.1344 d.f. 4 R2 adj -0.061 
 d.f. 45 Pr(> chi2) 0.8635 g 0.024 

 Residuals

 Min 1Q Median 3Q Max 
 -0.29197 -0.11366 0.02988 0.08802 0.19163 


 Coef S.E. t Pr(>|t|)
 Intercept 0.6811 0.2111 3.23 0.0023 
 abortion_rank12 0.0005 0.0018 0.26 0.7938 
 ba_or_more -0.0001 0.0059 -0.01 0.9903 
 cig_tax12 0.0278 0.0296 0.94 0.3517 
 conserv_advantage 0.0028 0.0034 0.82 0.4167 


> #get predictions
> d$ols_black = predict(ols_black)

> d$ols_hispanic = predict(ols_hispanic)

> d$ols_white = predict(ols_white)

> #inspect predictions
> d %>% select(starts_with("ols")) %>% mutate(sum = rowSums(.))
 ols_black ols_hispanic ols_white sum
1 0.08129250 0.10802760 0.8106799 1
2 0.11823321 0.08031043 0.8014564 1
3 0.14136889 0.07023172 0.7883994 1
4 0.13189772 0.07624972 0.7918526 1
5 0.10280396 0.15374533 0.7434507 1
6 0.12930893 0.11325306 0.7574380 1
7 0.06261715 0.13842026 0.7989626 1
8 0.11806119 0.12687147 0.7550673 1
9 0.11508647 0.10817106 0.7767425 1
10 0.15097533 0.08315063 0.7658740 1
11 0.06825851 0.13257364 0.7991678 1
12 0.09290831 0.11626503 0.7908267 1
13 0.12003177 0.09022122 0.7897470 1
14 0.09482377 0.12513629 0.7800399 1
15 0.14432113 0.07970917 0.7759697 1
16 0.14472842 0.08870156 0.7665700 1
17 0.14109725 0.09872039 0.7601824 1
18 0.15769332 0.06708056 0.7752261 1
19 0.09469234 0.14480432 0.7605033 1
20 0.09043738 0.14094802 0.7686146 1
21 0.10129247 0.11791403 0.7807935 1
22 0.12064080 0.10132318 0.7780360 1
23 0.11602388 0.11910634 0.7648698 1
24 0.16377700 0.09077838 0.7454446 1
25 0.12524587 0.07730750 0.7974466 1
26 0.07060243 0.10921933 0.8201782 1
27 0.12703322 0.11021081 0.7627560 1
28 0.13367696 0.07430030 0.7920227 1
29 0.15029178 0.07798233 0.7717259 1
30 0.10685246 0.12199943 0.7711481 1
31 0.07058313 0.13901865 0.7903982 1
32 0.09142246 0.12212662 0.7864509 1
33 0.10757628 0.12890212 0.7635216 1
34 0.03493501 0.13189804 0.8331670 1
35 0.13176324 0.09598102 0.7722557 1
36 0.14334473 0.06494498 0.7917103 1
37 0.10788090 0.14958898 0.7425301 1
38 0.14930675 0.08299583 0.7676974 1
39 0.09010154 0.12273524 0.7871632 1
40 0.12960586 0.09186749 0.7785267 1
41 0.12586666 0.08545994 0.7886734 1
42 0.12252536 0.09645709 0.7810176 1
43 0.12473591 0.08529285 0.7899712 1
44 0.09099896 0.08246437 0.8265367 1
45 0.16097493 0.09583502 0.7431901 1
46 0.07564659 0.14598479 0.7783686 1
47 0.05854270 0.14244206 0.7990152 1
48 0.09670796 0.09239404 0.8108980 1
49 0.10933095 0.10965351 0.7810155 1
50 0.09307565 0.09622426 0.8107001 1

> #fit accuracies
> d %>% select(starts_with("ols"), black, hispanic, white) %>% cor() %>% .[1:3, 4:6]
 black hispanic white
ols_black 0.3029909 -0.17497676 -0.08992821
ols_hispanic -0.2159748 0.24547482 -0.02824165
ols_white -0.1708902 -0.04347992 0.15944403

> #multivariate OLS
> (ols_joint = ols(cbind(black, hispanic, white) ~ abortion_rank12 + ba_or_more, data = d))
Linear Regression Model

 ols(formula = cbind(black, hispanic, white) ~ abortion_rank12 + 
 ba_or_more, data = d)

 Model Likelihood Discrimination 
 Ratio Test Indexes 
 Obs 150 LR chi2 342.38 R2 0.898 
 sigma0.1911 d.f. 8 R2 adj 0.892 
 d.f. 141 Pr(> chi2) 0.0000 g 0.314 

 Residuals

 black hispanic white
 Min -0.12063 -0.12726 -0.28038
 1Q -0.06388 -0.05599 -0.10255
 Median -0.03327 -0.02423 0.04949
 3Q 0.05821 0.01528 0.09565
 Max 0.24543 0.34242 0.18706


 Coef S.E. t Pr(>|t|)
 [1,] 0.1552 0.1636 0.95 0.3442 
 [2,] -0.0018 0.0022 -0.82 0.4133 
 [3,] 0.0001 0.0067 0.02 0.9868 
 [4,] 0.0384 0.1636 0.23 0.8146 
 [5,] 0.0013 0.0022 0.62 0.5392 
 [6,] 0.0012 0.0067 0.18 0.8549 
 [7,] 0.8063 0.1636 4.93 <0.0001 
 [8,] 0.0004 0.0022 0.21 0.8378 
 [9,] -0.0013 0.0067 -0.20 0.8419 


> #predictions
> d$joint_ols_black = predict(ols_joint)[, 1]

> d$joint_ols_hispanic = predict(ols_joint)[, 2]

> d$joint_ols_white = predict(ols_joint)[, 3]

> #inspect predictions
> d %>% select(starts_with("joint_ols")) %>% mutate(sum = rowSums(.))
 joint_ols_black joint_ols_hispanic joint_ols_white sum
1 0.09555303 0.11815034 0.7862966 1
2 0.12188779 0.09234947 0.7857627 1
3 0.15017948 0.06705244 0.7827681 1
4 0.14913609 0.07664226 0.7742216 1
5 0.07086325 0.14100841 0.7881283 1
6 0.11448727 0.11617229 0.7693404 1
7 0.07865753 0.14265444 0.7786880 1
8 0.10473606 0.11402244 0.7812415 1
9 0.11151654 0.10446698 0.7840165 1
10 0.14218850 0.08435132 0.7734602 1
11 0.08335854 0.13124113 0.7854003 1
12 0.09180628 0.11898908 0.7892046 1
13 0.11851983 0.09737339 0.7841068 1
14 0.09420884 0.12441664 0.7813745 1
15 0.14521110 0.07551151 0.7792774 1
16 0.13883168 0.08949833 0.7716700 1
17 0.12714583 0.08709083 0.7857633 1
18 0.15582745 0.06610206 0.7780705 1
19 0.08789627 0.13914201 0.7729617 1
20 0.08224830 0.14009239 0.7776593 1
21 0.10274571 0.11314933 0.7841050 1
22 0.12575708 0.09286476 0.7813782 1
23 0.10862763 0.11478390 0.7765885 1
24 0.14372208 0.08017760 0.7761003 1
25 0.13056949 0.08268238 0.7867481 1
26 0.08490326 0.12719051 0.7879062 1
27 0.10986041 0.10728667 0.7828529 1
28 0.13662966 0.08628645 0.7770839 1
29 0.14754681 0.08020051 0.7722527 1
30 0.10152407 0.12076966 0.7777063 1
31 0.07674517 0.14264299 0.7806118 1
32 0.09003875 0.12057784 0.7893834 1
33 0.08785901 0.11761214 0.7945288 1
34 0.07293158 0.14274317 0.7843252 1
35 0.12928101 0.08956415 0.7811548 1
36 0.15418246 0.06904484 0.7767727 1
37 0.07973434 0.13343390 0.7868318 1
38 0.15280485 0.07494186 0.7722533 1
39 0.10672641 0.11489554 0.7783781 1
40 0.12393384 0.09383805 0.7822281 1
41 0.13476186 0.08676737 0.7784708 1
42 0.11662975 0.09760812 0.7857621 1
43 0.13301661 0.08860231 0.7783811 1
44 0.11545267 0.10572082 0.7788265 1
45 0.14112283 0.09369495 0.7651822 1
46 0.07479938 0.14226225 0.7829384 1
47 0.06919598 0.14370501 0.7870990 1
48 0.12051018 0.09824650 0.7812433 1
49 0.09809657 0.10401752 0.7978859 1
50 0.09703090 0.11336115 0.7896079 1

> #accuracies
> d %>% select(starts_with("joint_ols"), black, hispanic, white) %>% cor() %>% .[1:3, 4:6]
 black hispanic white
joint_ols_black 0.2679728 -0.22508994 -0.02574528
joint_ols_hispanic -0.2605606 0.23149306 0.01537504
joint_ols_white -0.1416356 0.07306988 0.04870974

Thus, in my case, as far as I understand, the OLS does not actually know the values must range from 0-1, and sum to 1, yet this somehow happens with the data when doing regressions one by one, as well as in the joint OLS (i.e. multivariate regression).

My questions are:

1. How does one generally model outcomes that have a specified range, such as 0-1? This is somewhat different from modeling binary data because while in that case the predictions must be within 0-1 (when transformed from logits), the training data is not binary in this case.


2. In general, how does one force outcome constraints such as the sum=1 in the above case?


3. How does OLS know in the above code not to output inappropriate values?

I imagine the above issues can be approached with explicit Bayesian approaches where the priors reflect the outcome ranges, though not sure how one would handle the sum constraint. Is there a frequentist approach as well?

ETA

Stephan Kolassa points to this prior question, which also concerns the issue of proportional data, also called compositional data. However, it differs from the present question in that the question concerns time series data, whereas the present does not, and that I'm explicitly interested in retaining modeling on the proportional data, whereas the approach in the answer question is to transform the data. I am essentially looking for e.g. links to R packages (or maybe Python) that enable modeling on proportional data and predictions on the same scale.

In response to you showing and asking about the results of OLS models, in general, OLS is not appropriate for truncated data, as it will not adjust the estimates of the coefficients to take into account the effect of truncation, resulting in biased coefficients. It is similar to estimating a logistic regression using OLS. But just as with logistic regression, a linear model will sometimes fit just as well, or almost as well, as a non-linear model. (That's why there is something called a linear probability model that models continuous probability values between 0 and 1 using OLS and that is sometimes used by practitioners instead of logistic regression.) The fact that you obtained "appropriate" predicted values is simply due to your data allowing for a good linear approximation of the data generating process.

You'll probably find good examples of modeling compositional data, but for reference, since you asked for general information and are using R, the censReg package allows for estimation of a censored regression model with both lower and upper limits of the dependent variable that can be any numbers, which is a generalization of the the standard Tobit model with a dependent variable left-censored at zero:

https://cran.r-project.org/web/packages/censReg/vignettes/censReg.pdf

The model will be estimated using Maximum Likelihood, as is usually done for censored/truncated/limited dependent variable models, not using OLS.

First, your OLS models automatically respect the sum-to-1 constraint (up to roundoff error) because both the models and the constraint are linear in the observations. That is, the sum of the predictions for each input is equivalent to the prediction of an OLS model fit to the sum of observations (a constant of 1). What is not guaranteed is that the OLS predictions will be between 0 and 1, although they happen to be for your data.

The multinomial logit model provides one natural way to transform between unconstrained linear-regression predictions and "probabilities" (positive fractions that sum to 1). This is the transformation $y_itmapsto log(y_it/y_0t)$ in the linked answer (where here you would take $t$ as a sample index, not time).

To make a proper fit systematically, you would need to decide on your error model, which determines the likelihood function. For example, do you imagine that the model can precisely predict probabilities from which the individuals are sampled, and the only error in the population fractions comes from the multinomial distribution based on the population size? If so, you could directly use the likelihood from the multinomial logit model, casting your observations as counts of individuals. But this is implausible for the US state example, because factors not in the model have a much greater effect than the tiny multinomial fluctuations in a state population of millions.

More likely, you just want a reasonable error model with a tractable likelihood, which could be a Gaussian error with unknown variance on the linear predictor functions (log-odds-ratios) of the multinomial logit -- i.e., OLS applied to the transformed observations.