A quick tour of msir

Luca Scrucca

14 Mar 2019

Introduction

Model-based Sliced Inverse Regression (MSIR) is a dimension reduction method based on Gaussian finite mixture models which provides an extension to sliced inverse regression (SIR).

The basis of the MSIR subspace is estimated by modeling the inverse distribution within slice using Gaussian finite mixtures with number of components and covariance matrix parameterization selected by BIC or defined by the user.

The msir package implements the methodology described in Scrucca (2011).

This vignette is written in R Markdown using the knitr package for production.

library(msir)
## Package 'msir' version 1.3.2
## Type 'citation("msir")' for citing this R package in publications.

Example: 1-dimensional nonlinear response curve

n <- 200
p <- 5
b <- as.matrix(c(1,-1,rep(0,p-2)))
x <- matrix(rnorm(n*p), nrow = n, ncol = p)
y <- exp(0.5 * x%*%b) + 0.1*rnorm(n)
MSIR <- msir(x, y)
summary(MSIR)
## -------------------------------------------------- 
## Model-based SIR 
## -------------------------------------------------- 
## 
## Slices:
##           1   2    3   4   5   6  
## GMM       XXI EEI  XXX XXX XXX XII
## Num.comp. 1   2    1   1   1   1  
## Num.obs.  33  9|24 33  33  33  35 
## 
## Estimated basis vectors:
##         Dir1      Dir2     Dir3      Dir4      Dir5
## x1  0.719233  0.665189 -0.35087  0.088328  0.289256
## x2 -0.693391  0.578066 -0.33561  0.195841  0.141172
## x3  0.027810  0.215261  0.30793  0.626084 -0.655309
## x4 -0.019198 -0.013048 -0.52209 -0.425878 -0.678608
## x5 -0.027765  0.420542  0.62996 -0.616840 -0.080389
## 
##                 Dir1     Dir2     Dir3     Dir4       Dir5
## Eigenvalues  0.89038  0.12676  0.04031  0.00887 1.6342e-03
## Cum. %      83.37250 95.24195 99.01642 99.84698 1.0000e+02
plot(MSIR, type = "evalues")

plot(MSIR, type = "coefficients", which = 1)

plot(MSIR, type = "2Dplot")

Example: 1-dimensional symmetric response curve

n <- 200
p <- 5
b <- as.matrix(c(1,-1,rep(0,p-2)))
x <- matrix(rnorm(n*p), nrow = n, ncol = p)
y <- (0.5 * x%*%b)^2 + 0.1*rnorm(n)
MSIR <- msir(x, y)
summary(MSIR)
## -------------------------------------------------- 
## Model-based SIR 
## -------------------------------------------------- 
## 
## Slices:
##           1   2   3   4   5     6    
## GMM       XXX XXI XII XII EEV   EII  
## Num.comp. 1   1   1   1   2     2    
## Num.obs.  33  33  33  33  13|20 16|19
## 
## Estimated basis vectors:
##          Dir1        Dir2     Dir3     Dir4     Dir5
## x1  0.7032497 -0.32265327  0.17850  0.50213  0.48315
## x2 -0.7097760 -0.20051289  0.26844  0.34153  0.51756
## x3  0.0207609 -0.64666898  0.42755 -0.59757  0.03875
## x4  0.0020883 -0.00061766  0.69106  0.35396 -0.60040
## x5 -0.0349652 -0.66144409 -0.48550  0.38580 -0.36976
## 
##                 Dir1      Dir2      Dir3     Dir4       Dir5
## Eigenvalues  0.76068  0.048494  0.027907  0.01981 2.6055e-03
## Cum. %      88.50291 94.145085 97.392035 99.69686 1.0000e+02
plot(MSIR, type = "evalues")

plot(MSIR, type = "coefficients", which = 1)

plot(MSIR, type = "2Dplot")

Example: 2-dimensional response curve

n <- 300
p <- 5
b1 <- c(1, 1, 1, rep(0, p-3))
b2 <- c(1,-1,-1, rep(0, p-3))
b <- cbind(b1,b2)
x <- matrix(rnorm(n*p), nrow = n, ncol = p)
y <- x %*% b1 + (x %*% b1)^3 + 4*(x %*% b2)^2 + rnorm(n)
MSIR <- msir(x, y)
summary(MSIR)
## -------------------------------------------------- 
## Model-based SIR 
## -------------------------------------------------- 
## 
## Slices:
##           1   2     3     4        5   6     7   8  
## GMM       XXI VVE   EEV   EVE      XII EEV   XII XXI
## Num.comp. 1   2     2     3        1   2     1   1  
## Num.obs.  42  12|30 16|26 18|12|12 42  25|17 42  6  
## 
## Estimated basis vectors:
##         Dir1      Dir2      Dir3      Dir4     Dir5
## x1  0.320287  0.944245 -0.019421 -0.047184 -0.14173
## x2  0.635841 -0.256139  0.464958  0.182138 -0.50389
## x3  0.695964 -0.194734 -0.298009 -0.145248  0.62064
## x4 -0.092271  0.065128  0.818368 -0.361349  0.42430
## x5  0.015541 -0.025113 -0.157804 -0.901626 -0.40099
## 
##                 Dir1     Dir2      Dir3      Dir4       Dir5
## Eigenvalues  0.65327  0.36881  0.054035  0.025591   0.013548
## Cum. %      58.57625 91.64546 96.490546 98.785167 100.000000
plot(MSIR, type = "evalues")

plot(MSIR, type = "coefficients", which = 1:2)

plot(MSIR, which = 1:2)

plot(MSIR, which = 1, type = "2Dplot", span = 0.7)

plot(MSIR, which = 2, type = "2Dplot", span = 0.7)

To obtain rotating 3D spinplot use:

plot(MSIR, type = "spinplot")
plot(MSIR, type = "spinplot", span = 0.75)

General usage of spinplot() function

x1 <- rnorm(100)
x2 <- rnorm(100)
y  <- 2*x1 + x2^2 + 0.5*rnorm(100)
spinplot(x1, y, x2)
spinplot(x1, y, x2, scaling="aaa")
spinplot(x1, y, x2, rem.lin.trend = "TRUE")
spinplot(x1, y, x2, fit.smooth = TRUE)