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glhHmat

Total and Effect Deviation Matrices for General Linear Hypothesis


Description

Computes total and effect matrices of Sums of Squares and Cross-Product (SSCP) deviations for a general multivariate effect characterized by the violation of a linear hypothesis. These matrices may be used as input to the variable selection search routines anneal, genetic improve or eleaps.

Usage

## Default S3 method:
glhHmat(x,A,C,...)

## S3 method for class 'data.frame'
glhHmat(x,A,C,...)

## S3 method for class 'formula'
glhHmat(formula,C,data=NULL,...)

Arguments

x

A matrix or data frame containing the variables for which the SSCP matrix is to be computed.

A

A matrix or data frame containing a design matrix specifying a linear model in which x is the response.

C

A matrix or vector containing the coefficients of the reference hypothesis.

formula

A formula of the form 'x ~ A1 + A2 + ...' That is, the response is the set of variables whose subsets are to be compared and the right hand side specifies the columns of the design matrix.

data

Data frame from which variables specified in 'formula' are preferentially to be taken.

...

further arguments for the method.

Details

Consider a multivariate linear model x = A Ψ + U and a reference hypothesis H0: C Ψ = 0, with Ψ being a matrix of unknown parameters and C a known coefficient matrix with rank r. It is well known that, under classical Gaussian assumptions, H_0 can be tested by several increasing functions of the r positive eigenvalues of a product T^{-1} H, where T and H are total and effect matrices of SSCP deviations associated with H_0. Furthermore, whether or not the classical assumptions hold, the same eigenvalues can be used to define descriptive indices that measure an "effect" characterized by the violation of H_0 (see reference [1] for further details). Those SSCP matrices are given by T = x'(I - P_{ω}) x and H = x'(P_{Ω} - P_{ω}) x, where I is an identity matrix and P_{Ω} = A(A'A)^-A' ,

P_{ω} = A(A'A)^-A' - A(A'A)^-C'[C(A'A)^-C']^-C(A'A)^-A'

are projection matrices on the spaces spanned by the columns of A (space Ω) and by the linear combinations of these columns that satisfy the reference hypothesis (space ω). In these formulae M' denotes the transpose of M and M^- a generalized inverse. glhHmat computes the T and H matrices which then can be used as input to the search routines anneal, genetic improve and eleaps that try to select subsets of x according to their contribution to the violation of H_0.

Value

A list with four items:

mat

The total SSCP matrix

H

The effect SSCP matrix

r

The expected rank of the H matrix which equals the rank of C. The true rank of H can be different from r if the x variables are linearly dependent.

call

The function call which generated the output.

References

[1] Duarte Silva. A.P. (2001). Efficient Variable Screening for Multivariate Analysis, Journal of Multivariate Analysis, Vol. 76, 35-62.

See Also

Examples

##----------------------------------------------------------------------------

## The following examples create T and H matrices for different analysis 
## of the MASS data set "crabs". This data records physical measurements 
## on 200 specimens of Leptograpsus variegatus crabs observed on the shores 
## of Western Australia. The crabs are classified by two factors, sex and sp 
## (crab species as defined by its colour: blue or orange), with two levels 
## each. The measurement variables include the carapace length (CL), 
## the carapace width (CW), the size of the frontal lobe (FL) and the size of 
## the rear width (RW). In the  analysis provided, we assume that there is 
## an interest in comparing the subsets of these variables measured in their 
## original and logarithmic scales.        

library(MASS)
data(crabs)
lFL <- log(crabs$FL)
lRW <- log(crabs$RW)
lCL <- log(crabs$CL)
lCW <- log(crabs$CW)

# 1)  Create the T and H matrices associated with a linear
# discriminant analysis on the groups defined by the sp factor.  
# This call is equivalent to ldaHmat(sp ~ FL + RW + CL + CW  + lFL +
# lRW + lCL + lCW,crabs) 

Hmat1 <- glhHmat(cbind(FL,RW,CL,CW,lFL,lRW,lCL,lCW) ~ sp,c(0,1),crabs)
Hmat1

##$mat
##           FL        RW         CL         CW        lFL        lRW        lCL
##FL  2431.2422 1623.4509  4846.9787  5283.6093 162.718609 133.360397 158.865134
##RW  1623.4509 1317.7935  3254.5776  3629.6883 109.877182 107.287243 108.335721
##CL  4846.9787 3254.5776 10085.3040 11096.5141 326.243285 269.564742 330.912570
##CW  5283.6093 3629.6883 11096.5141 12331.5680 356.317934 300.786770 364.620761
##lFL  162.7186  109.8772   326.2433   356.3179  11.114733   9.188391  10.910730
##lRW  133.3604  107.2872   269.5647   300.7868   9.188391   8.906350   9.130692
##lCL  158.8651  108.3357   330.9126   364.6208  10.910730   9.130692  11.088706
##lCW  152.7872  106.4277   321.0253   357.0051  10.503303   8.970570  10.765175
##          lCW
##FL  152.78716
##RW  106.42775
##CL  321.02534
##CW  357.00510
##lFL  10.50330
##lRW   8.97057
##lCL  10.76517
##lCW  10.54334

##$H
##           FL         RW        CL        CW        lFL        lRW        lCL
##FL  466.34580 247.526700 625.30650 518.41650 30.7408809 19.4543206 20.5494907
##RW  247.52670 131.382050 331.89975 275.16475 16.3166234 10.3259508 10.9072444
##CL  625.30650 331.899750 838.45125 695.12625 41.2193540 26.0856066 27.5540813
##CW  518.41650 275.164750 695.12625 576.30125 34.1733106 21.6265286 22.8439819
##lFL  30.74088  16.316623  41.21935  34.17331  2.0263971  1.2824024  1.3545945
##lRW  19.45432  10.325951  26.08561  21.62653  1.2824024  0.8115664  0.8572531
##lCL  20.54949  10.907244  27.55408  22.84398  1.3545945  0.8572531  0.9055117
##lCW  15.16136   8.047335  20.32933  16.85423  0.9994161  0.6324790  0.6680840
##           lCW
##FL  15.1613582
##RW   8.0473352
##CL  20.3293260
##CW  16.8542276
##lFL  0.9994161
##lRW  0.6324790
##lCL  0.6680840
##lCW  0.4929106

##$r
##[1] 1

##$call
##glhHmat.formula(formula = cbind(FL, RW, CL, CW, lFL, lRW, lCL,
##    lCW) ~ sp, C = c(0, 1), data = crabs)


# 2)  Create the T and H matrices associated with an analysis
# of the interactions between the sp and sex factors

Hmat2 <- glhHmat(cbind(FL,RW,CL,CW,lFL,lRW,lCL,lCW) ~ sp*sex,c(0,0,0,1),crabs)
Hmat2

##$mat
##           FL         RW         CL         CW        lFL        lRW        lCL
##FL  1960.3362 1398.52890  4199.1581  4747.5409 131.651804 115.607172 137.663744
##RW  1398.5289 1074.36105  3034.2793  3442.0233  95.176151  88.529040 100.659912
##CL  4199.1581 3034.27925  9135.6987 10314.2389 283.414814 251.877591 300.140005
##CW  4747.5409 3442.02325 10314.2389 11686.9387 320.883015 285.744945 339.253367
##lFL  131.6518   95.17615   283.4148   320.8830   9.065041   8.027569   9.509543
##lRW  115.6072   88.52904   251.8776   285.7449   8.027569   7.460222   8.516618
##lCL  137.6637  100.65991   300.1400   339.2534   9.509543   8.516618  10.090003
##lCW  137.2059  100.46203   298.6227   338.5254   9.473873   8.494741  10.037059
##           lCW
##FL  137.205863
##RW  100.462028
##CL  298.622747
##CW  338.525352
##lFL   9.473873
##lRW   8.494741
##lCL  10.037059
##lCW  10.011755

##$H
##            FL         RW        CL        CW        lFL        lRW        lCL
##FL   80.645000  68.389500 153.73350 191.57950  5.4708199  5.1596883  5.2140868
##RW   68.389500  57.996450 130.37085 162.46545  4.6394276  4.3755782  4.4217098
##CL  153.733500 130.370850 293.06205 365.20785 10.4290197  9.8359098  9.9396095
##CW  191.579500 162.465450 365.20785 455.11445 12.9964281 12.2573068 12.3865353
##lFL   5.470820   4.639428  10.42902  12.99643  0.3711311  0.3500245  0.3537148
##lRW   5.159688   4.375578   9.83591  12.25731  0.3500245  0.3301182  0.3335986
##lCL   5.214087   4.421710   9.93961  12.38654  0.3537148  0.3335986  0.3371158
##lCW   5.584150   4.735535  10.64506  13.26565  0.3788193  0.3572754  0.3610421
##           lCW
##FL   5.5841501
##RW   4.7355352
##CL  10.6450610
##CW  13.2656543
##lFL  0.3788193
##lRW  0.3572754
##lCL  0.3610421
##lCW  0.3866667

##$r
##[1] 1

##$call
##glhHmat.formula(formula = cbind(FL, RW, CL, CW, lFL, lRW, lCL,
##    lCW) ~ sp * sex, C = c(0, 0, 0, 1), data = crabs)

## 3)  Create the T and H matrices associated with an analysis
## of the effect of the sp factor after controlling for sex

C <- matrix(0.,2,4)
C[1,3] = C[2,4] = 1.
C

##       [,1] [,2] [,3] [,4]
##  [1,]    0    0    1    0
##  [2,]    0    0    0    1

Hmat3 <- glhHmat(cbind(FL,RW,CL,CW,lFL,lRW,lCL,lCW) ~ sp*sex,C,crabs)
Hmat3

##$mat
##           FL         RW         CL         CW        lFL        lRW        lCL
##FL  1964.8964 1375.92420  4221.6722  4765.1928 131.977728 113.906076 138.315643
##RW  1375.9242 1186.41150  2922.6779  3354.5236  93.560559  96.961292  97.428477
##CL  4221.6722 2922.67790  9246.8527 10401.3878 285.023931 243.479136 303.358489
##CW  4765.1928 3354.52360 10401.3878 11755.2667 322.144623 279.160241 341.776779
##lFL  131.9777   93.56056   285.0239   322.1446   9.088336   7.905989   9.556135
##lRW  113.9061   96.96129   243.4791   279.1602   7.905989   8.094783   8.273439
##lCL  138.3156   97.42848   303.3585   341.7768   9.556135   8.273439  10.183194
##lCW  137.6258   98.38041   300.6960   340.1509   9.503886   8.338091  10.097091
##           lCW
##FL  137.625801
##RW   98.380414
##CL  300.696018
##CW  340.150874
##lFL   9.503886
##lRW   8.338091
##lCL  10.097091
##lCW  10.050426

##$H
##            FL         RW         CL         CW        lFL         lRW
##FL   85.205200  45.784800 176.247600 209.231400  5.7967443  3.45859277
##RW   45.784800 170.046900  18.769500  74.965800  3.0238356 12.80782993
##CL  176.247600  18.769500 404.216100 452.356800 12.0381364  1.43745463
##CW  209.231400  74.965800 452.356800 523.442500 14.2580360  5.67260253
##lFL   5.796744   3.023836  12.038136  14.258036  0.3944254  0.22844463
##lRW   3.458593  12.807830   1.437455   5.672603  0.2284446  0.96467943
##lCL   5.865986   1.190274  13.158093  14.909948  0.4003070  0.09041999
##lCW   6.004088   2.653921  12.718332  14.891177  0.4088329  0.20062548
##            lCL        lCW
##FL   5.86598627  6.0040883
##RW   1.19027431  2.6539211
##CL  13.15809339 12.7183319
##CW  14.90994753 14.8911765
##lFL  0.40030704  0.4088329
##lRW  0.09041999  0.2006255
##lCL  0.43030750  0.4210740
##lCW  0.42107404  0.4253378

##$r
##[1] 2

##$call
##glhHmat.formula(formula = cbind(FL, RW, CL, CW, lFL, lRW, lCL,
##    lCW) ~ sp * sex, C = C, data = crabs)

subselect

Selecting Variable Subsets

v0.15.2
GPL (>= 2)
Authors
Jorge Orestes Cerdeira [aut], Pedro Duarte Silva [aut], Jorge Cadima [aut, cre], Manuel Minhoto [aut]
Initial release
2020-03-04

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