HOOVS: High Dimensional Ordinal Outcome Variable Selection

Ben Bodek, Brian Chen, Forrest Hurley, Brian Richardson, Emmanuel Rockwell

Description

The HOOVS package (“High-Dimensional Ordinal Outcome Variable Selection”) is for a group project for Bios 735 (statistical computing). The goal of the project is to develop two methods to perform variable selection on a high-dimensional data set with an ordinal outcome. The first method is a LASSO-penalized ordinal regression model and the second is a random forest model.

Installation

Installation of the HOOVS from GitHub requires the devtools package and can be done in the following way.

# Install the package
devtools::install_github("brian-d-richardson/HOOVS")

# Then load it
library(HOOVS)

Other packages used in this README can be loaded in the following chunk.

suppressPackageStartupMessages(if (!require(dplyr)) {install.packages("dplyr")})
suppressPackageStartupMessages(if (!require(tidyr)) {install.packages("tidyr")})
suppressPackageStartupMessages(if (!require(ggplot2)) {install.packages("ggplot2")})
suppressPackageStartupMessages(if (!require(ordinalNet)) {install.packages("ordinalNet")})
suppressPackageStartupMessages(if (!require(foreign)) {install.packages("foreign")})
suppressPackageStartupMessages(if (!require(devtools)) {install.packages("devtools")})
suppressPackageStartupMessages(if (!require(tictoc)) {install.packages("tictoc")})
suppressPackageStartupMessages(if (!require(psych)) {install.packages("psych")})

load_all()
#> ℹ Loading HOOVS

#For reproducibility
set.seed(1)

To build the final report document render final_presentation.Rmd.

Method 1: LASSO-Penalized Ordinal Regression Model

We begin with a theoretical introduction of the ordinal regression model and how the HOOVS package calculates parameter estimates.

Ordinal Regression Model Setup

Suppose that, for observation $i = 1, \dots, n$ , the ordinal outcome given the covariate vector $\pmb{x}_i$ has a multinomial distribution with outcome categories and probabilities of success $\pi_1(\pmb{x}_i), \dots, \pi_J(\pmb{x}_i)$ . That is,

$Y_i | \pmb{x}_i \sim \text{multinomial} \{ 1; \pi_1(\pmb{x}_i), \dots, \pi_J(\pmb{x}_i) \}.$

The cumulative probability for subject and ordinal outcome category is $P(Y_i \leq j | \pmb{x}_i) = \sum_{k=1}^j \pi_k(\pmb{x}_i)$ . Note that by definition $P(Y_i \leq J | \pmb{x}_i) = 1$ .

The following proportional odds model relates the cumulative probability for subject and ordinal outcome category to the covariates $\pmb{x}_i$ via the parameters $\pmb{\alpha} = (\alpha_1, \dots, \alpha_{J-1})^T$ and $\pmb{\beta} = (\beta_1, \dots, \beta_p)^T$ with a logit link function.

$\text{logit}\{ P(Y_i \leq j | \pmb{x}_i) \} = \alpha_j + \pmb{x}_i^T \pmb{\beta}.$

In this model, $\alpha_1, \dots, \alpha_{J-1}$ are outcome category-specific intercepts for the first ordinal outcome categories and $\beta_1, \dots, \beta_p$ are the slopes corresponding to the covariates. Since the cumulative probabilities must be increasing in , i.e., $P(Y_i \leq j | \pmb{x}_i) < P(Y_i \leq j+1 | \pmb{x}_i)$ , we require that $\alpha_1 < \dots < \alpha_{J-1}$ .

The likelihood function for the ordinal regression model is

$L_n(\pmb{\alpha}, \pmb{\beta}) = \prod_{i=1}^n \prod_{j=1}^J \left\{ \text{logit}^{-1}(\alpha_j + \pmb{x}_i^T\pmb{\beta}) - \text{logit}^{-1}(\alpha_{j-1} + \pmb{x}_i^T\pmb{\beta} ) \right\} ^ {_(y_i = j)}.$

LASSO Penalization

Let $l(\pmb{\alpha}, \pmb{\beta}) = \frac{-1}{n} \log L(\pmb{\alpha}, \pmb{\beta})$ be the standardized log-likelihood.

The LASSO-penalized ordinal regression model is fit by minimizing the following objective function with respect to $\pmb{\alpha}$ and $\pmb{\beta}$ .

$f(\pmb{\alpha}, \pmb{\beta}) = l(\pmb{\alpha}, \pmb{\beta}) + \lambda\sum_{j=1}^p|\beta_j|.$

Proximal Gradient Descent Algorithm

The objective function can be minimized using a proximal gradient descent (PGD) algorithm.

Fix the following initial parameters for the PGD algorithm: (the initial step size), $a \in (0, 1)$ (the step size decrement value), and $\epsilon > 0$ (the convergence criterion).

The proximal projection operator for the LASSO penalty (applied to $\pmb{\beta}$ but not to $\pmb{\alpha}$ ) is

$\text{prox}_{\lambda m}(\pmb{w}, \pmb{z}) = \text{argmin}_{\pmb{\alpha}, \pmb{\beta}} \frac{1}{2m} \left(||\pmb{w} - \pmb{\alpha} ||_2^2 + ||\pmb{z} - \pmb{\beta} ||_2^2 \right) + \lambda\sum_{j=1}^p|\beta_j| = \left\{ \pmb{w}, \text{sign}(\pmb{z})(\pmb{z} - m \lambda)_+ \right\}$

Given current estimates $\pmb{\theta}^{(k)} = (\pmb{\alpha}^{(k)}, \pmb{\beta}^{(k)})^T$ , search for updated estimates $\pmb{\theta}^{(k+1)}$ by following the steps:

propose a candidate update $\pmb{\theta} = \text{prox}_{\lambda m}\left\{\pmb{\theta}^{(k)} - \frac{1}{m} \nabla l(\pmb{\theta}^{(k)})\right\}$ ,
if the condition $l(\pmb{\theta}) \leq l(\pmb{\theta}^{(k)}) + \nabla l(\pmb{\theta}^{(k)})^T(\pmb{\theta} - \pmb{\theta}^{(k)}) + \frac{1}{2m} (\pmb{\theta} - \pmb{\theta}^{(k)})^T (\pmb{\theta} - \pmb{\theta}^{(k)})$ is met, then make the the update $\pmb{\theta}^{(k+1)} = \pmb{\theta}$ ,
else decrement the step size and returning to step 2.

Continue updating $\pmb{\theta}^{(k)}$ until convergence, i.e., until $\left|\frac{f(\pmb{\theta}^{(k+1)}) - f(\pmb{\theta}^{(k)})}{f(\pmb{\theta}^{(k)})}\right| < \epsilon$ .

A technical note is that the $\pmb{\alpha}$ parameters are constrained by $\alpha_1 < \dots < \alpha_{J-1}$ . We can reparametrize the model with $\pmb{\zeta} = (\zeta_1, \dots, \zeta_{J-1})^T$ , where ![\zeta_1

\alpha_1](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Czeta_1%20%3D%20%5Calpha_1 "\zeta_1 = \alpha_1") and $\zeta_j = \log(\alpha_j - \alpha_{j-1})$ for $j = 2, \dots, J-1$ . Then $\pmb{\zeta} \in \mathbb{R}^{J-1}$ have no constraints. So we can follow the above procedure to minimize the above objective function with respect to $\pmb{\zeta}$ and $\pmb{\beta}$ , then back-transform to obtain estimates for $\pmb{\alpha}$ .

Data Generation

The HOOVS package allows the user to simulate their own data with the simulate.data() function. For subjects, we generate covariates from independent standard normal distributions[1]. Given true parameters $\pmb{\alpha}_0$ and $\pmb{\beta}_0$ , we compute the multinomial probabilities for the outcome for each individual and simulate accordingly.

# sample size
n <- 1000

# number of covariates
p <- 50

# number of categories for ordinal outcome
J <- 4

# grid of lambdas
lambdas <- seq(0.2, 0, -0.02)

# set population parameters
alpha <- seq(.5, 4, length = J - 1) # category-specific intercepts
beta <- rep(0, p)                     # slope parameters
beta[1: floor(p / 2)] <- 1            # half of the betas are 0, other half are 1

# simulate data according to the above parameters
dat <- simulate.data(
  n = 1000,
  alpha = alpha,
  beta = beta)

For this example, we simulated data with = 1000 observations, = 50 covariates, = 4 ordinal outcome categories, and true parameter values of $\pmb{\alpha}_0$ = (0.5, 2.25, 4) and $\pmb{\beta}_0$ = (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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0). Note that this implies the first half of the covariates are truly associated with the outcome and the last half are not. The first 10 rows and 10 columns of the data set are shown below.

dat[1:10, 1:10] %>% 
  mutate_if(.predicate = function(x) is.numeric(x),
            .funs = function(x) round(x, digits = 2))
#>    y    X1    X2    X3    X4    X5    X6    X7    X8    X9
#> 1  3 -0.63  1.13 -0.89  0.74 -1.13 -1.52 -0.62 -1.33  0.26
#> 2  3  0.18  1.11 -1.92  0.39  0.76  0.63 -1.11  0.95 -0.83
#> 3  3 -0.84 -0.87  1.62  1.30  0.57 -1.68 -2.17  0.86 -1.46
#> 4  2  1.60  0.21  0.52 -0.80 -1.35  1.18 -0.03  1.06  1.68
#> 5  4  0.33  0.07 -0.06 -1.60 -2.03  1.12 -0.26 -0.35 -1.54
#> 6  1 -0.82 -1.66  0.70  0.93  0.59 -1.24  0.53 -0.13 -0.19
#> 7  3  0.49  0.81  0.05  1.81 -1.41 -1.23 -0.56  0.76  1.02
#> 8  1  0.74 -1.91 -1.31 -0.06  1.61  0.60  1.61 -0.49  0.55
#> 9  1  0.58 -1.25 -2.12  1.89  1.84  0.30  0.56  1.11  0.76
#> 10 1 -0.31  1.00 -0.21  1.58  1.37 -0.11  0.19  1.46 -0.42

Fitting Penalized Model

Now run our version of a LASSO-penalized ordinal regression function on the simulated data for various values of $\lambda$ : 0.2, 0.18, 0.16, 0.14, 0.12, 0.1, 0.08, 0.06, 0.04, 0.02, 0.

# test HOOVS LASSO-penalized ordinal regression function
tic("HOOVS ordreg.lasso() function")
res.ordreg <- ordreg.lasso(
  formula = y ~ .,
  data = dat,
  lambdas = lambdas
)
toc()
#> HOOVS ordreg.lasso() function: 5.943 sec elapsed

coef.ordreg <- cbind(res.ordreg$alpha, res.ordreg$beta)

We can now look at how the parameter estimates from our funciton change as the penalty parameter $\lambda$ changes

Note in the above plot that the $\pmb{\alpha}$ estimates do not shrink all the way to 0 since they are not penalized in the LASSO model. On the other hand, the $\pmb{\beta}$ estimates do shrink to 0 as $\lambda$ increases. Recall that the data were simulated according to a model where half of the $\pmb{\beta}$ values are truly 0 and the other half are truly 1. It is clear in the above plot which covariates are truly not associated with the outcome based on how fast their corresponding parameter estimates shrink to 0.

Assessing Prediction with Weighted Kappa

We can assess the predictive performance of the model with a weighted kappa statistic. The following table gives the weighted kappa values for the models fit using each supplied penalty parameter $\lambda$ .

data.frame("lambda" = lambdas,
           "Weighted Kappa" = res.ordreg$kappa)
#>             lambda Weighted.Kappa
#> lambda=0.2    0.20     0.00000000
#> lambda=0.18   0.18     0.00000000
#> lambda=0.16   0.16     0.00000000
#> lambda=0.14   0.14     0.00000000
#> lambda=0.12   0.12     0.00000000
#> lambda=0.1    0.10     0.00000000
#> lambda=0.08   0.08     0.02319807
#> lambda=0.06   0.06     0.44565785
#> lambda=0.04   0.04     0.72450154
#> lambda=0.02   0.02     0.83576007
#> lambda=0      0.00     0.90220833

Inference

Suppose we have identified a subset of relevant predictors and want to perform inference or hypothesis testing using an independent test data set. We obtain the asymptotic covariance of the parameter estimates from an ordinal regression model fit with no LASSO penalty by specifying return.cov = TRUE. Then the standard errors of the parameter estimates are the square roots of the diagonal entries of the covariance matrix.

# simulate test data
dat <- simulate.data(
  n = 500,
  alpha = alpha,
  beta = beta)

# specify relevant parameters
# (in practice these would be selected using training data)
rel.betas.ind <- which(beta != 0)

# fit model with no penalty
res.ordreg.test <- ordreg.lasso(
  formula = y ~ .,
  data = select(dat, c("y", paste0("X", rel.betas.ind))),
  lambdas = 0,
  return.cov = T
)

# covariance matrix
#res.ordreg.test$cov

# standard error of parameter estimates
sqrt(diag(res.ordreg.test$cov))
#>    alpha1    alpha2    alpha3        X1        X2        X3        X4        X5 
#> 0.1885505 0.2302522 0.3188871 0.1518158 0.1493560 0.1437412 0.1492866 0.1425663 
#>        X6        X7        X8        X9       X10       X11       X12       X13 
#> 0.1532874 0.1598190 0.1501358 0.1533953 0.1471709 0.1463651 0.1503864 0.1473245 
#>       X14       X15       X16       X17       X18       X19       X20       X21 
#> 0.1385858 0.1596688 0.1555481 0.1417651 0.1514717 0.1404234 0.1415255 0.1427013 
#>       X22       X23       X24       X25 
#> 0.1532489 0.1518607 0.1467264 0.1558101

Method 2: Random Forest

the current version can only handle continuous covariates, and categorical variables must be created into dummy variables by hand.

Name		Name	Last commit message	Last commit date
Latest commit History 82 Commits
R		R
README_files/figure-gfm		README_files/figure-gfm
final_presentation_src		final_presentation_src
man		man
rf plots		rf plots
tests		tests
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
NAMESPACE		NAMESPACE
README.Rmd		README.Rmd
README.html		README.html
README.md		README.md
Random_Forest_real_data.Rmd		Random_Forest_real_data.Rmd
Random_Forest_real_data.html		Random_Forest_real_data.html
bios-735-project.Rproj		bios-735-project.Rproj
data_processing.R		data_processing.R
final_presentation.Rmd		final_presentation.Rmd
final_presentation.html		final_presentation.html
ordreg_lasso_real_data.Rmd		ordreg_lasso_real_data.Rmd
ordreg_lasso_real_data.html		ordreg_lasso_real_data.html
processed_data.csv		processed_data.csv

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HOOVS: High Dimensional Ordinal Outcome Variable Selection

Description

Installation

Method 1: LASSO-Penalized Ordinal Regression Model

Ordinal Regression Model Setup

LASSO Penalization

Proximal Gradient Descent Algorithm

A technical note is that the $\pmb{\alpha}$ parameters are constrained by $\alpha_1 < \dots < \alpha_{J-1}$ . We can reparametrize the model with $\pmb{\zeta} = (\zeta_1, \dots, \zeta_{J-1})^T$ , where ![\zeta_1

Data Generation

Fitting Penalized Model

Assessing Prediction with Weighted Kappa

Inference

Method 2: Random Forest

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Contributors 5

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brian-d-richardson/HOOVS

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HOOVS: High Dimensional Ordinal Outcome Variable Selection

Description

Installation

Method 1: LASSO-Penalized Ordinal Regression Model

Ordinal Regression Model Setup

LASSO Penalization

Proximal Gradient Descent Algorithm

A technical note is that the parameters are constrained by . We can reparametrize the model with , where ![\zeta_1

Data Generation

Fitting Penalized Model

Assessing Prediction with Weighted Kappa

Inference

Method 2: Random Forest

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A technical note is that the $\pmb{\alpha}$ parameters are constrained by $\alpha_1 < \dots < \alpha_{J-1}$ . We can reparametrize the model with $\pmb{\zeta} = (\zeta_1, \dots, \zeta_{J-1})^T$ , where ![\zeta_1

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