An Efficient State Space Approach to Estimate Univariate and Multivariate Multilevel Regression Models

Transcription

1 An Efficient State Space Approach to Estimate Univariate and Multivariate Multilevel Regression Models Fei Gu Kristopher J. Preacher Wei Wu 05/21/2013

2 Overview Introduction: estimate MLM as SEM (Bauer, 2003; Curran, 2003; Mehta & Neale, 2005) Multiple-subject state space model (SSM) Examples of the SSM approach to 1. Univariate MLM: random intercept and random slope model 2. Univariate MLM: random intercept longitudinal model 3. Multivariate MLM: random intercepts model Summary, Discussions, and Conclusions

3 Introduction The SEM approach to MLMs: transforming data set structure (long format to wide format; e.g.,bauer, 2003; Curran, 2003) Equivalence between the SEM and the SSM can give rise to the state space approach conforming the SEM approach Limitations: computationally inefficient; data management nightmare (Curran, 2003, p. 565)

4 Introduction The state space approach in the time series literature: Icaza & Jones, 1999; Jones, 1993 Mentioned in educational and psychological literature briefly by Ho, Shumway, & Ombao, 2006, pp

5 State space formulation y x, ~ MVN (0, ), i i i i i x, ~ MVN (0, ). i i1 i i i y i : p-variate data vector η i : q-variate latent state vector x i : g-variate vector containing exogenous variables ε i : p 1 vector containing measurement errors ζ i : q 1 vector containing latent state noises subscript, i, (i = 1, 2,, T) denotes the time point

6 Parameter estimation Kalman filter (Kalman, 1960) x P i i1 i1 i1 i P i i1 i1 i1 e y y y ( x ) D i i i i1 i i i1 i i P i i1 K P D 1 i i i1 i K e P D 1 i i i i1 i i i i1 i i1 i i P P K D K P P D P. 1 i i i i1 i i i i i1 i i1 i i i1 Prediction Error Decomposition (PED; Schweppe, 1965) function: 1 T 1 PED plog(2 ) log Di e id i ei. 2 i1 e

7 Multiple-subject SSM A second subscript, j, (j = 1, 2,, J) indexing different units is added to both the measurement and transition equations: y x, ~ MVN (0, ), j j j j x, ~ MVN (0, ). j j i1, j j j Total PED function: 1 J T 1 total PED pj log(2 ) log D e D e. 2 j1 i1

8 Example 1: univariate MLM (random intercept and random slope model) Level-1: Level-2: Combined equation: y 2 ~ 0, N 0j 1 jx, w u 0 j j 0 j, 1 j 10 11w1 j u1 j u0 j 0 00 ~ MVN, u 0 1 j y w x w x u u x j j 0 j 1 j.

9 Matrix form of the MLM Write the combined equation in Laird & Ware (1982) form: u0 j y 1 w1j x w1j x 1 x. u 10 1 j 11 Rearrange the terms to match the measurement equation of the SSM: 1 u 0 j w 1 1 j y x u 1 j x w1 jx

10 Transition equation and the state space form for the MLM Within a cluster/group, we have the transition equation: u0j 1 0u0j. u 0 1 u 1j 1j The state space form is obtained by define 1 u 0 j w 1j y y,, x,, 0, u 1 j x w1 jx x 2 j 0, j 1, j , j, j, j, j , and j. 0 0

11 Initializing the KF To capture the random effects from the level-2 units: , j and P0 0, j Ready to estimate the MLM using a simulated data set: 50 level-2 units; unbalanced design with level-1 units in each cluster (Poisson(20))

12 Example 1 results True MLwiN PROC MIXED PROC IML Parameter value Point SE Point SE Point SE γ γ γ γ τ τ τ *loglik

13 Computational inefficiency Estimators specifically designed for MLMs involve matrix operations at the cluster level. The dimension of the covariance matrix to be inverted is determined by the number of level- 1 units within each level-2 unit.

14 Computational efficiency In the KF, D i (or D ) is the only matrix that needs to be inverted in the (multiple-subject) Kalman recursion. The dimension of D i (or D ) corresponds to that of the data vector, p. In this example, D i (or D ) reduces to a scalar, rendering the computations very efficient.

15 Empirical comparison 50 clusters, balanced designs

16 Example 2: univariate MLM (random intercept longitudinal model) The longitudinal model (Icaza & Jones, 1999): 2 i1, j + u, where u ~ N 0, u, The state space form specification: 2 y j +, where j ~ N 0,, ~ N 0,, y y,, x 1,, 0, j 2 j 0, j 1 1, j, j, u j, j, j, and j,

17 Initializing the KF Initialized with for all j to capture the random effect and the innovation variance of the AR(1) process. Note 2 u 0 2 and P , j 0 0, j 2 2 u Var( ) Var( i 1, j )+Var( u ) Var( ) Var( i 1, j). 2 1

18 Example 2 results 100 subjects, 20 observations, balanced design True PROC MIXED PROC IML Parameter value Point SE Point SE ϕ μ u τ *loglik

19 Empirical comparison 50 subjects, balanced design

20 Example 3: multivariate MLM (random intercepts model) The combined matrix form: y1 100 u1 0 j y u20 j 2, where 2 ~ MVN 0, y u3 0 j The random effect vector: u1 0 j 0 u2 0 j ~ MVN 0,. u3 0 j 0 Kamata, Bauer, & Miyazaki (2008, Equations 10.8a 10.8g)

21 State space form Re-write the combined matrix form: Then we have y u1 0 j y u2 0 j y u j 00 y1 u1 0 j 1 0 y y2, u2 0 j, x 1, 2, 0, y3 u j , 0 1 0, 2, 21 22, , 0 1 0, 0, and j j j 00 j j j j j

22 Initializing the KF Initialized with for all j , j 0 and P 0 0, j 0

23 Example 3 results MLwiN Mplus PROC MIXED PROC IML Parameter Point SE Point SE Point SE Point SE γ γ γ τ δ δ δ δ δ δ *loglik

24 Summary The results from the state space approach are identical to those from the MLM and SEM software, except that The estimated variance of the innovation in the AR(1) process is biased from both PROC MIXED and PROC IML. The state space approach is more efficient because matrix operation is implemented at each observation, instead of at the cluster level.

25 Discussions Software: SAS/IML, R, MATLAB, C++, C, FORTRAN Further extension to multilevel CFA and multilevel SEM

26 Conclusions In general, the solution to representing the multilevel models using state space forms is to include the random effects in the latent state vector; and then, initialize the Kalman recursion by properly specifying the latent state vector (η 0 0,j ) and its associated covariance matrix (P 0 0,j ) to subsume the parameters at the cluster level.