SAMM: Some Algoritms for Mixed Models
In SAMM: Some Algorithms for Mixed Models
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
The LMM model of focus can be expressed as
Y=XB+∑_{j=1}^k Z_j G_j+WE,
where Y is the n \times d response variable, X is the n \times q design matrix of q \times d the fixed effects B, Z_j for j=1,2,…,k (k≥q 1) are the n \times q_j design matrices of the q_j \times d random effects G_j, and W is the n \times t design matrix of t \times d residual effecs E. The random effects and the residual are independently distributed, and have matrix variate distributions (G_j\sim N_{q_j \times d}(0_{q_j \times d}, K_j,Σ_j) for j=1,2,…,k and E\sim N_{t \times d}(0_{t \times d}, R,Σ_E)). The matrices K_j, R, Σ_j, Σ_E might be further parametrized. When the response is univariate (d=1) there is no need to specify a structure for Σ_j's andΣ_E. On the other hand, the K_j and R are kernel matrices (covariance matrices with some standardization over the diagonals) and they need to be provided by the user.
Please refer to the examples below and the other help files for more details about the kernel and covariance structures.
Usage
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Arguments
Y
Y is the n \times d response variable
X
n \times q design matrix of q \times d the fixed effects B
Zlist
a list object containing Z_j for j=1,2,…,k (k≥q 1), the n \times q_j design matrices of the q_j \times d random effects G_j,
Klist
a list to specify the kernel matrices K_j for j=1,2,…,k. For each j, the user needs to provide a constant matrix, or a list specifying the kernel structure
lambda
a scalar shrinkage parameter for shrinkage of variance components (only works with the choice mmalg=”mmmk_ml”).
W
W is the n \times t design matrix of t \times d residual effecs E
R
a list to specify the kernel matrix R,the user needs to provide a constant matrix, or a list specifying the kernel structure
Siglist
a list to specify the covariance structures Σ_j's andΣ_E
corfunc
a boolian vector specifying whether K_j for j=1,2,…,k and R are functions or given matrices (TRUE for functions)
corfuncfixed
a boolian vector specifying whether K_j for j=1,2,…,k and R are fixed at the initial parameter values specified
sigfunc
a boolian vector specifying whether Σ_j for j=1,2,…,k, E are functions or unstructured (TRUE for functions)
mmalg
The mixed model solving algorithm
tolparconv
convergence criteria
tolparinv
a small scalar to add to the diagonals of positive semidefinite matrices for inversion or for calculating the Cholesky decompositions
maxiter
Maximum number of iterations
geterrors
TRUE or FALSE, if true prediction error variances for the random effects are supplied in the output
Hinv
TRUE or FALSE, if TRUE the inverse of the
Details
This might change with respect to the algorithm or analysis.
Value
This might change with respect to the algorithm or analysis.
Author(s)
Deniz Akdemir
Examples
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## Not run:
library(SAMM)
#set.seed(12345)
n=120
ntrain=100
M1<-matrix(rbinom(n*180,2,.3)-1, nrow=n)
K<-relmatcov_cppforR(c(0), M1)
K[1:5,1:5]
det(K)
K=K+1e-3*diag(n)
mean(diag(K))
covY<-2*K+1*diag(n)
Y<-10+crossprod(chol(covY),rnorm(n))
#training set
Trainset<-sample(1:n,ntrain,replace=(ntrain>n))
y=Y[Trainset]
X=matrix(rep(1, n)[Trainset], ncol=1)
Z=diag(n)[Trainset,]
X=X
y=y
########1
samout1<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z), Klist=list(K),
lambda=0, W=diag(ntrain),R=list(diag(ntrain)),Siglist=list(),
corfunc=c(F,F), corfuncfixed=c(F,F), sigfunc=c(F),mmalg="emm_ml",
tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
samout2<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z), Klist=list(K),
lambda=0, W=diag(ntrain),R=list(diag(ntrain)),Siglist=list(),
corfunc=c(F,F), corfuncfixed=c(F,F), sigfunc=c(F),mmalg="emm_reml",
tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
samout3<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(list("Const",c(0),K)), lambda=0,
W=diag(ntrain),R=list(diag(ntrain)),Siglist=list(),
corfunc=c(F,F), corfuncfixed=c(F,F), sigfunc=c(F),
mmalg="dmm_ml", tolparconv=1e-10, tolparinv=1e-10,
maxiter=1000,geterrors=F)
samout4<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(list("Const",c(0),K)), lambda=0,
W=diag(ntrain),R=list(diag(ntrain)),Siglist=list(),
corfunc=c(F,F), corfuncfixed=c(F,F), sigfunc=c(F),
mmalg="dmm_reml", tolparconv=1e-10, tolparinv=1e-10,
maxiter=1000,geterrors=F)
samout5<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),
R=list(diag(ntrain)),Siglist=list(), corfunc=c(F,F),
corfuncfixed=c(F,F), sigfunc=c(F),mmalg="mm_ml",
tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
samout6<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(diag(ntrain)),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="dermm_reml1", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout7<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(diag(ntrain)),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="dermm_reml2", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout8<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(diag(ntrain)),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="mmmk_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout9<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(diag(ntrain)),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="emmmk_reml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout10<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(diag(ntrain)),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="emmmk_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout11<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(diag(ntrain)),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F), sigfunc=c(F),
mmalg="emmmv_ml", tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
###########2
R<-diag(ntrain)
diag(R)<-diag(R)+.01*rnorm(nrow(R))
samout12<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(R),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="mmmk_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout13<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(R),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="mm_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout14<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(R),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="mmmv_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout15<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(R),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="mmmkmv_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout16<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(R),Siglist=list(),
corfunc=c(F,F), corfuncfixed=c(F,F), sigfunc=c(F),
mmalg="dermm_reml1", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout17<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(R),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="dermm_reml2", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout18<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(list("Const",c(0),K)), lambda=0, W=diag(ntrain),
R=list(R),Siglist=list(), corfunc=c(F,F),
corfuncfixed=c(F,F), sigfunc=c(F),mmalg="dmm_ml",
tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
samout19<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(list("Const",c(0),K)), lambda=0, W=diag(ntrain),
R=list(R),Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="dmm_reml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
##3
samout20<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(list("Const",c(0),K)), lambda=0, W=diag(ntrain),
R=list(R),Siglist=list(), corfunc=c(T,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="dmm_reml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout21<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(list("ar1",c(0),matrix(n,1,1))), lambda=0,
W=diag(ntrain),R=list(R),Siglist=list(), corfunc=c(T,F),
corfuncfixed=c(F,F), sigfunc=c(F),mmalg="dmm_reml",
tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
samout22<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(list("ar1het",rep(0,n),matrix(n,1,1))), lambda=0,
W=diag(ntrain),R=list(R),Siglist=list(), corfunc=c(T,F),
corfuncfixed=c(F,F), sigfunc=c(F),mmalg="dmm_reml",
tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
samout1$Vu
samout2$Vu
samout3$Vu
samout4$Vu
samout5$Vu
samout6$Vu
samout7$Vu
samout8$Vu
samout9$Vu
samout10$Vu
samout11$Vu
samout12$Vu
samout13$Vu
samout14$sigmahatlist[[1]]
samout15$sigmahatlist[[1]]
samout16$Vu
samout17$Vu
samout18$Vu
samout19$Vu
samout20$Ve
str(samout19)
str(samout20)
str(samout21)
str(samout22)
###
samout1$Vu
samout2$Vu
samout3$Vu
samout4$Vu
samout5$Vu
samout6$Vu
samout7$Vu
samout8$Vu
samout9$Vu
samout10$Vu
samout11$Vgt
samout12$Vu
samout13$Vu
samout14$sigmahatlist[[1]]
samout15$sigmahatlist[[1]]
samout16$Vu
samout17$Vu
samout18$Vu
samout19$Vu
samout20$Ve
str(samout19)
str(samout20)
str(samout21)
str(samout22)
###############################
n=100
nsample=80
rhotrans=5
rho=tan((pi/2)*print((2/pi)*atan(rhotrans)))
rho
M1<-matrix(rbinom(n*300, 2, .2)-1, nrow=n)
K1<-relmatcov_cppforR(c(.01), M1)
M2<-matrix(rbinom(n*300, 2, .2)-1, nrow=n)
K2<-relmatcov_cppforR(c(-0.3), M2)
##K2<-ar1_cppforR(c(20), matrix(n))
W=(diag(5)[sample(1:5,n, replace=T),])
covY<-3*K1+5*K2+10*(W%*%ar1cov_cppforR(c(rhotrans),matrix(5))%*%t(W))
K1[1:5,1:5]
dim(W)
dim(ar1cov_cppforR(c(6),matrix(5)))
Y<-10+crossprod(chol(covY),rnorm(n))
#training set
Trainset<-sample(1:n,nsample)
ytrain=Y[Trainset]
Xtrain=matrix(rep(1, n)[Trainset], ncol=1)
Ztrain=diag(n)[Trainset,]
Wtrain=W[Trainset,]
samout27<-SAMM(Y=matrix(ytrain,ncol=1),X=Xtrain,
Zlist=list(Ztrain, Ztrain), Klist=list(K1,K2),
lambda=0, W=Wtrain,R=list(list("ar1",c(0),matrix(5,1,1))),
Siglist=list("","",""), corfunc=c(F,F,T),
corfuncfixed=c(F,F,F), sigfunc=c(F,F,F),mmalg="mmmk_ml",
tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
str(samout27)
samout29<-SAMM(Y=matrix(ytrain,ncol=1),X=Xtrain,
Zlist=list(Ztrain, Ztrain), Klist=list(K1,K2),
lambda=0, W=Wtrain,R=list("ar1",c(0),matrix(5,1,1)),
Siglist=list("","",""), corfunc=c(F,F,T),
corfuncfixed=c(F,F,F), sigfunc=c(F,F,F),
mmalg="dermm_reml1", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
str(samout29)
samout30<-SAMM(Y=matrix(ytrain,ncol=1),X=Xtrain,
Zlist=list(Ztrain, Ztrain), Klist=list(K1,K2),
lambda=0, W=Wtrain,R=list("ar1",c(0),matrix(5,1,1)),
Siglist=list("","",""), corfunc=c(F,F,T), corfuncfixed=c(F,F,F),
sigfunc=c(F,F,F),mmalg="dermm_reml2", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
str(samout30)
###########################
n=100
M1<-matrix(rbinom(n*150, 2, .2)-1, nrow=n)
K1<-relmatcov_cppforR(c(0), M1)
M2<-matrix(rbinom(n*150, 2, .2)-1, nrow=n)
K2<-relmatcov_cppforR(c(0), M2)
M3<-matrix(rbinom(n*100, 2, .2)-1, nrow=n)
K3<-relmatcov_cppforR(c(0), M3)
M4<-matrix(rbinom(n*100, 2, .2)-1, nrow=n)
K4<-relmatcov_cppforR(c(0), M4)
covY<-2*K1+3*K2+1*K3+2*K4+3*diag(n)
Y<-10+crossprod(chol(covY),rnorm(n))
#training set
Trainset<-sample(1:n,80)
y=Y[Trainset]
X=matrix(rep(1, n)[Trainset], ncol=1)
Z=diag(n)[Trainset,]
X=X
y=y
samout35<-SAMM(Y=matrix(y,ncol=1),X=X,Zlist=list(Z,Z,Z,Z),
lambda=0,Klist=list(K1,K2,K3,K4), W=Z,R=list(diag(n)),
Siglist=list("","","","",""), corfunc=c(F,F,F,F,F),
corfuncfixed=c(T,T,T,T,T),sigfunc=c(F,F,F,F,F),
mmalg="mmmk_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
str(samout35)
samout36<-SAMM(Y=matrix(y,ncol=1),X=X,Zlist=list(Z,Z,Z,Z),
lambda=0.99999,Klist=list(K1,K2,K3,K4), W=Z,
R=list(diag(n)),Siglist=list("","","","",""),
corfunc=c(F,F,F,F,F), corfuncfixed=c(T,T,T,T,T),
sigfunc=c(F,F,F,F,F),mmalg="mmmk_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
str(samout36)
outmat<-c()
for (lambda in seq(0,.999999, length=30)){
samout37<-SAMM(Y=matrix(y,ncol=1),X=X,
Zlist=list(Z,Z,Z,Z),lambda=lambda,Klist=list(K1,K2,K3,K4),
W=Z,R=list(diag(n)),Siglist=list("","","","",""),
corfunc=c(F,F,F,F,F), corfuncfixed=c(T,T,T,T,T),
sigfunc=c(F,F,F,F,F),mmalg="mmmk_ml",
tolparconv=1e-10, tolparinv=1e-10,
maxiter=1000,geterrors=F)
outmat<-cbind(outmat,c(samout37$Vu*samout37$weights, samout37$Ve))
}
str(samout37)
colnames(outmat)<-seq(0,.999999, length=30)
maxmat<-max(c(outmat))
minmat<-min(c(outmat))
plot(seq(0,.999999, length=30),outmat[1,],
ylim=c(minmat-1, maxmat+1), col=2, type="l")
for (i in 2:5){
par(new=T)
plot(seq(0,.999999, length=30), outmat[i,],
axes=F, ylim=c(minmat-1, maxmat+1), col=i+1, type="l", xlab="", ylab="")
}
## End(Not run)
SAMM documentation built on May 2, 2019, 2:08 a.m.
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Description Usage Arguments Details Value Author(s) Examples
Description
The LMM model of focus can be expressed as
Y=XB+∑_{j=1}^k Z_j G_j+WE,
where Y is the n \times d response variable, X is the n \times q design matrix of q \times d the fixed effects B, Z_j for j=1,2,…,k (k≥q 1) are the n \times q_j design matrices of the q_j \times d random effects G_j, and W is the n \times t design matrix of t \times d residual effecs E. The random effects and the residual are independently distributed, and have matrix variate distributions (G_j\sim N_{q_j \times d}(0_{q_j \times d}, K_j,Σ_j) for j=1,2,…,k and E\sim N_{t \times d}(0_{t \times d}, R,Σ_E)). The matrices K_j, R, Σ_j, Σ_E might be further parametrized. When the response is univariate (d=1) there is no need to specify a structure for Σ_j's andΣ_E. On the other hand, the K_j and R are kernel matrices (covariance matrices with some standardization over the diagonals) and they need to be provided by the user.
Please refer to the examples below and the other help files for more details about the kernel and covariance structures.
Usage
1 2 3 4 |
Arguments
Y |
Y is the n \times d response variable |
X |
n \times q design matrix of q \times d the fixed effects B |
Zlist |
a list object containing Z_j for j=1,2,…,k (k≥q 1), the n \times q_j design matrices of the q_j \times d random effects G_j, |
Klist |
a list to specify the kernel matrices K_j for j=1,2,…,k. For each j, the user needs to provide a constant matrix, or a list specifying the kernel structure |
lambda |
a scalar shrinkage parameter for shrinkage of variance components (only works with the choice mmalg=”mmmk_ml”). |
W |
W is the n \times t design matrix of t \times d residual effecs E |
R |
a list to specify the kernel matrix R,the user needs to provide a constant matrix, or a list specifying the kernel structure |
Siglist |
a list to specify the covariance structures Σ_j's andΣ_E |
corfunc |
a boolian vector specifying whether K_j for j=1,2,…,k and R are functions or given matrices (TRUE for functions) |
corfuncfixed |
a boolian vector specifying whether K_j for j=1,2,…,k and R are fixed at the initial parameter values specified |
sigfunc |
a boolian vector specifying whether Σ_j for j=1,2,…,k, E are functions or unstructured (TRUE for functions) |
mmalg |
The mixed model solving algorithm |
tolparconv |
convergence criteria |
tolparinv |
a small scalar to add to the diagonals of positive semidefinite matrices for inversion or for calculating the Cholesky decompositions |
maxiter |
Maximum number of iterations |
geterrors |
TRUE or FALSE, if true prediction error variances for the random effects are supplied in the output |
Hinv |
TRUE or FALSE, if TRUE the inverse of the |
Details
This might change with respect to the algorithm or analysis.
Value
This might change with respect to the algorithm or analysis.
Author(s)
Deniz Akdemir
Examples
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 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 | ## Not run:
library(SAMM)
#set.seed(12345)
n=120
ntrain=100
M1<-matrix(rbinom(n*180,2,.3)-1, nrow=n)
K<-relmatcov_cppforR(c(0), M1)
K[1:5,1:5]
det(K)
K=K+1e-3*diag(n)
mean(diag(K))
covY<-2*K+1*diag(n)
Y<-10+crossprod(chol(covY),rnorm(n))
#training set
Trainset<-sample(1:n,ntrain,replace=(ntrain>n))
y=Y[Trainset]
X=matrix(rep(1, n)[Trainset], ncol=1)
Z=diag(n)[Trainset,]
X=X
y=y
########1
samout1<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z), Klist=list(K),
lambda=0, W=diag(ntrain),R=list(diag(ntrain)),Siglist=list(),
corfunc=c(F,F), corfuncfixed=c(F,F), sigfunc=c(F),mmalg="emm_ml",
tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
samout2<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z), Klist=list(K),
lambda=0, W=diag(ntrain),R=list(diag(ntrain)),Siglist=list(),
corfunc=c(F,F), corfuncfixed=c(F,F), sigfunc=c(F),mmalg="emm_reml",
tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
samout3<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(list("Const",c(0),K)), lambda=0,
W=diag(ntrain),R=list(diag(ntrain)),Siglist=list(),
corfunc=c(F,F), corfuncfixed=c(F,F), sigfunc=c(F),
mmalg="dmm_ml", tolparconv=1e-10, tolparinv=1e-10,
maxiter=1000,geterrors=F)
samout4<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(list("Const",c(0),K)), lambda=0,
W=diag(ntrain),R=list(diag(ntrain)),Siglist=list(),
corfunc=c(F,F), corfuncfixed=c(F,F), sigfunc=c(F),
mmalg="dmm_reml", tolparconv=1e-10, tolparinv=1e-10,
maxiter=1000,geterrors=F)
samout5<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),
R=list(diag(ntrain)),Siglist=list(), corfunc=c(F,F),
corfuncfixed=c(F,F), sigfunc=c(F),mmalg="mm_ml",
tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
samout6<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(diag(ntrain)),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="dermm_reml1", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout7<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(diag(ntrain)),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="dermm_reml2", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout8<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(diag(ntrain)),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="mmmk_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout9<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(diag(ntrain)),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="emmmk_reml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout10<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(diag(ntrain)),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="emmmk_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout11<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(diag(ntrain)),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F), sigfunc=c(F),
mmalg="emmmv_ml", tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
###########2
R<-diag(ntrain)
diag(R)<-diag(R)+.01*rnorm(nrow(R))
samout12<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(R),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="mmmk_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout13<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(R),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="mm_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout14<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(R),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="mmmv_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout15<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(R),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="mmmkmv_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout16<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(R),Siglist=list(),
corfunc=c(F,F), corfuncfixed=c(F,F), sigfunc=c(F),
mmalg="dermm_reml1", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout17<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(K), lambda=0, W=diag(ntrain),R=list(R),
Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="dermm_reml2", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout18<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(list("Const",c(0),K)), lambda=0, W=diag(ntrain),
R=list(R),Siglist=list(), corfunc=c(F,F),
corfuncfixed=c(F,F), sigfunc=c(F),mmalg="dmm_ml",
tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
samout19<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(list("Const",c(0),K)), lambda=0, W=diag(ntrain),
R=list(R),Siglist=list(), corfunc=c(F,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="dmm_reml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
##3
samout20<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(list("Const",c(0),K)), lambda=0, W=diag(ntrain),
R=list(R),Siglist=list(), corfunc=c(T,F), corfuncfixed=c(F,F),
sigfunc=c(F),mmalg="dmm_reml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
samout21<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(list("ar1",c(0),matrix(n,1,1))), lambda=0,
W=diag(ntrain),R=list(R),Siglist=list(), corfunc=c(T,F),
corfuncfixed=c(F,F), sigfunc=c(F),mmalg="dmm_reml",
tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
samout22<-SAMM(Y=matrix(y,ncol=1),X=X, Zlist=list(Z),
Klist=list(list("ar1het",rep(0,n),matrix(n,1,1))), lambda=0,
W=diag(ntrain),R=list(R),Siglist=list(), corfunc=c(T,F),
corfuncfixed=c(F,F), sigfunc=c(F),mmalg="dmm_reml",
tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
samout1$Vu
samout2$Vu
samout3$Vu
samout4$Vu
samout5$Vu
samout6$Vu
samout7$Vu
samout8$Vu
samout9$Vu
samout10$Vu
samout11$Vu
samout12$Vu
samout13$Vu
samout14$sigmahatlist[[1]]
samout15$sigmahatlist[[1]]
samout16$Vu
samout17$Vu
samout18$Vu
samout19$Vu
samout20$Ve
str(samout19)
str(samout20)
str(samout21)
str(samout22)
###
samout1$Vu
samout2$Vu
samout3$Vu
samout4$Vu
samout5$Vu
samout6$Vu
samout7$Vu
samout8$Vu
samout9$Vu
samout10$Vu
samout11$Vgt
samout12$Vu
samout13$Vu
samout14$sigmahatlist[[1]]
samout15$sigmahatlist[[1]]
samout16$Vu
samout17$Vu
samout18$Vu
samout19$Vu
samout20$Ve
str(samout19)
str(samout20)
str(samout21)
str(samout22)
###############################
n=100
nsample=80
rhotrans=5
rho=tan((pi/2)*print((2/pi)*atan(rhotrans)))
rho
M1<-matrix(rbinom(n*300, 2, .2)-1, nrow=n)
K1<-relmatcov_cppforR(c(.01), M1)
M2<-matrix(rbinom(n*300, 2, .2)-1, nrow=n)
K2<-relmatcov_cppforR(c(-0.3), M2)
##K2<-ar1_cppforR(c(20), matrix(n))
W=(diag(5)[sample(1:5,n, replace=T),])
covY<-3*K1+5*K2+10*(W%*%ar1cov_cppforR(c(rhotrans),matrix(5))%*%t(W))
K1[1:5,1:5]
dim(W)
dim(ar1cov_cppforR(c(6),matrix(5)))
Y<-10+crossprod(chol(covY),rnorm(n))
#training set
Trainset<-sample(1:n,nsample)
ytrain=Y[Trainset]
Xtrain=matrix(rep(1, n)[Trainset], ncol=1)
Ztrain=diag(n)[Trainset,]
Wtrain=W[Trainset,]
samout27<-SAMM(Y=matrix(ytrain,ncol=1),X=Xtrain,
Zlist=list(Ztrain, Ztrain), Klist=list(K1,K2),
lambda=0, W=Wtrain,R=list(list("ar1",c(0),matrix(5,1,1))),
Siglist=list("","",""), corfunc=c(F,F,T),
corfuncfixed=c(F,F,F), sigfunc=c(F,F,F),mmalg="mmmk_ml",
tolparconv=1e-10, tolparinv=1e-10,maxiter=1000,geterrors=F)
str(samout27)
samout29<-SAMM(Y=matrix(ytrain,ncol=1),X=Xtrain,
Zlist=list(Ztrain, Ztrain), Klist=list(K1,K2),
lambda=0, W=Wtrain,R=list("ar1",c(0),matrix(5,1,1)),
Siglist=list("","",""), corfunc=c(F,F,T),
corfuncfixed=c(F,F,F), sigfunc=c(F,F,F),
mmalg="dermm_reml1", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
str(samout29)
samout30<-SAMM(Y=matrix(ytrain,ncol=1),X=Xtrain,
Zlist=list(Ztrain, Ztrain), Klist=list(K1,K2),
lambda=0, W=Wtrain,R=list("ar1",c(0),matrix(5,1,1)),
Siglist=list("","",""), corfunc=c(F,F,T), corfuncfixed=c(F,F,F),
sigfunc=c(F,F,F),mmalg="dermm_reml2", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
str(samout30)
###########################
n=100
M1<-matrix(rbinom(n*150, 2, .2)-1, nrow=n)
K1<-relmatcov_cppforR(c(0), M1)
M2<-matrix(rbinom(n*150, 2, .2)-1, nrow=n)
K2<-relmatcov_cppforR(c(0), M2)
M3<-matrix(rbinom(n*100, 2, .2)-1, nrow=n)
K3<-relmatcov_cppforR(c(0), M3)
M4<-matrix(rbinom(n*100, 2, .2)-1, nrow=n)
K4<-relmatcov_cppforR(c(0), M4)
covY<-2*K1+3*K2+1*K3+2*K4+3*diag(n)
Y<-10+crossprod(chol(covY),rnorm(n))
#training set
Trainset<-sample(1:n,80)
y=Y[Trainset]
X=matrix(rep(1, n)[Trainset], ncol=1)
Z=diag(n)[Trainset,]
X=X
y=y
samout35<-SAMM(Y=matrix(y,ncol=1),X=X,Zlist=list(Z,Z,Z,Z),
lambda=0,Klist=list(K1,K2,K3,K4), W=Z,R=list(diag(n)),
Siglist=list("","","","",""), corfunc=c(F,F,F,F,F),
corfuncfixed=c(T,T,T,T,T),sigfunc=c(F,F,F,F,F),
mmalg="mmmk_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
str(samout35)
samout36<-SAMM(Y=matrix(y,ncol=1),X=X,Zlist=list(Z,Z,Z,Z),
lambda=0.99999,Klist=list(K1,K2,K3,K4), W=Z,
R=list(diag(n)),Siglist=list("","","","",""),
corfunc=c(F,F,F,F,F), corfuncfixed=c(T,T,T,T,T),
sigfunc=c(F,F,F,F,F),mmalg="mmmk_ml", tolparconv=1e-10,
tolparinv=1e-10,maxiter=1000,geterrors=F)
str(samout36)
outmat<-c()
for (lambda in seq(0,.999999, length=30)){
samout37<-SAMM(Y=matrix(y,ncol=1),X=X,
Zlist=list(Z,Z,Z,Z),lambda=lambda,Klist=list(K1,K2,K3,K4),
W=Z,R=list(diag(n)),Siglist=list("","","","",""),
corfunc=c(F,F,F,F,F), corfuncfixed=c(T,T,T,T,T),
sigfunc=c(F,F,F,F,F),mmalg="mmmk_ml",
tolparconv=1e-10, tolparinv=1e-10,
maxiter=1000,geterrors=F)
outmat<-cbind(outmat,c(samout37$Vu*samout37$weights, samout37$Ve))
}
str(samout37)
colnames(outmat)<-seq(0,.999999, length=30)
maxmat<-max(c(outmat))
minmat<-min(c(outmat))
plot(seq(0,.999999, length=30),outmat[1,],
ylim=c(minmat-1, maxmat+1), col=2, type="l")
for (i in 2:5){
par(new=T)
plot(seq(0,.999999, length=30), outmat[i,],
axes=F, ylim=c(minmat-1, maxmat+1), col=i+1, type="l", xlab="", ylab="")
}
## End(Not run)
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