Description Arguments Details Value Author(s) Examples
The "kernel" functions end with "cov_cppforR" and sigma functions end with "Sig_cppforR". Check table below and the examples for details and usage. Documentation for some of these functions is missing. Let K be a given covariance matrix, D be a given Euclidean distance matrix, let q be the dimension of desired sigma or covariance matrices.
params |
a numeric vector for the parameters (a mapping of the original parameters) |
data |
a numeric matrix (see examples and details) |
function name | function ref | params | data | formula |
IdentSig_cppforR | Ident | 1 | matrix(q,1,1) | A=σ I_q |
ar1hetcov_cppforR | ar1het | q | matrix(q,1,1) | A_{ij}=σ_iσ_jρ^{|i-j|}, |
σ_1=1 | ||||
arma11cov_cppforR | arma11 | 2 | matrix(q,1,1) | A_{ij}=(λρ^{|i-j|-1}* |
1(i\neq j)+1(i=j)) | ||||
FA1hetSig_cppforR | FA1het | 2q-2 | matrix(q,1,1) | f_if_j+σ_{ii}1(i=j) |
f_1=1, σ_1=1 | ||||
FA1homSig_cppforR | FA1hom | q | matrix(q,1,1) | f_if_j+σ 1(i=j) |
f_1=1 | ||||
compsymmcov_cppforR | compsymm | 1 | matrix(q,1,1) | A_{ij}=ρ, i\neq j; |
A_{ij}=1, i=j | ||||
compsymmhetSig_cppforR | compsymmhet | q+1 | matrix(q,1,1) | A_{ij}=σ_iσ_jρ, i\neq j |
A_{ij}=σ_iσ_j, i= j | ||||
compsymmhetcov_cppforR | compsymmhet | q | matrix(q,1,1) | A_{ij}=σ_iσ_jρ, i\neq j |
A_{ij}=σ_iσ_j, i= j | ||||
σ_1=1 | ||||
compsymmhomSig_cppforR | compsymmhom | 2 | matrix(q,1,1) | A_{ij}=σρ, i\neq j; |
A_{ij}=σ, i=j | ||||
diagSig_cppforR | diag | q | matrix(q,1,1) | diag(σ_1,..,σ_q) |
diagcov_cppforR | Diag | q-1 | matrix(q,1,1) | diag(1,σ_2,..,σ_q) |
expcov_cppforR | exp | 1 | M | exp(-σ*d_{ij}) |
M defines D | ||||
lincombcov_cppforR | lincomb | k | (K_1;…; K_k) | ∑^k_{j=1}w_jK_j |
rbfcov_cppforR | rbf | 1 | M | exp(-σ*d^2_{ij}) |
M defines D | ||||
ar1cov_cppforR | ar1 | 1 | matrix(nrow, 1,1) | A_{ij}=ρ^{|i-j|} |
relmatcov_cppforR | RelMat | 1 | M | Genetic Similarity+ |
M is coded as -1, 0, 1 | σ I | |||
unstrcov_cppforR | unstr | \frac{q(q+1)}{2}-1 | matrix(q,1,1) | A_{ij}=σ_iσ_jρ_{ij} |
σ_1=1, | ||||
ρ_{ii}=1 | ||||
ConstMatcov_cppforR | Const | 0 | K | A=K |
expdistcov_cppforR | expdist | 1 | D | exp(-σ d_{ij}) |
rbfdistcov_cppforR | rbfdist | 1 | D | exp(-σ d^2_{ij}) |
splincov_cppforR | splin | 1 | D | 1-ρ d_{ij}, ρ d_{ij}≤q 1 |
0, ρ d_{ij}> 1 | ||||
sppowcov_cppforR | sppow | 1 | D | ρ^{d_{ij}} |
A kernel or sigma matrix.
Deniz Akdemir
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 | ## Not run:
library(SAMM)
n=100
nsample=80
rhotrans=5
ar1cov_cppforR(c(rhotrans),matrix(5))
rho=(2/pi)*atan(rhotrans)
rho
tan((pi/2)*(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(0.03), M2)
W=(diag(5)[sample(1:5,n, replace=TRUE),])
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,]
samout<-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)
samout$corfuncparamslist[[3]]
rhohat=(2/pi)*atan(samout$corfuncparamslist[[3]])
rhohat
ar1cov_cppforR(c(samout$corfuncparamslist[[3]]),matrix(5,1,1))
## End(Not run)
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