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Resolve matrix commute error in model using random effect and GRM at the same time. #372

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Merged
MikeDMorgan merged 1 commit into from
Aug 28, 2025
Merged

Resolve matrix commute error in model using random effect and GRM at the same time. #372

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1 change: 0 additions & 1 deletion R/glmm.R
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Expand Up @@ -179,7 +179,6 @@ fitGLMM <- function(X, Z, y, offsets, init.theta=NULL, Kin=NULL,
geno.I <- diag(nrow(full.Z))
colnames(geno.I) <- paste0("CovarMat", seq_len(ncol(geno.I)))
full.Z <- do.call(cbind, list(full.Z, geno.I))

# add a genetic variance component
sigma_g <- Matrix(runif(1, 0, 1), ncol=1, nrow=1, sparse=TRUE)
rownames(sigma_g) <- "CovarMat"
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19 changes: 16 additions & 3 deletions R/testNhoods.R
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Expand Up @@ -572,12 +572,25 @@ testNhoods <- function(x, design, design.df, kinship=NULL,
"SE"= unlist(lapply(lapply(fit, `[[`, "SE"), function(x) x[ret.beta])),
"tvalue" = unlist(lapply(lapply(fit, `[[`, "t"), function(x) x[ret.beta])),
"PValue" = unlist(lapply(lapply(fit, `[[`, "PVALS"), function(x) x[ret.beta])),
matrix(unlist(lapply(fit, `[[`, "Sigma")), ncol=length(rand.levels), byrow=TRUE),
"Converged"=unlist(lapply(fit, `[[`, "converged")), "Dispersion" = unlist(lapply(fit, `[[`, "Dispersion")),
"Logliklihood"=unlist(lapply(fit, `[[`, "LOGLIHOOD")))

rownames(res) <- 1:length(fit)
colnames(res)[6:(6+length(rand.levels)-1)] <- paste(names(rand.levels), "variance", sep="_")
# need to know how many variance components there are to get proper data frame dimensions
n.sigmas <- unique(unlist(lapply(lapply(fit, `[[`, "Sigma"), length)))
varcomps <- as.data.frame(matrix(unlist(lapply(fit, `[[`, "Sigma")), ncol=n.sigmas, byrow=TRUE))

if(n.sigmas == length(rand.levels)){
colnames(varcomps) <- paste(names(rand.levels), "variance", sep="_")
} else{
colnames(varcomps) <- paste(c(names(rand.levels), "CovarMat"), "variance", sep="_")
}

res <- do.call(cbind.data.frame, list(res[, c("logFC", "logCPM", "SE", "tvalue", "PValue")],
varcomps,
res[, c("Converged", "Logliklihood")]))

rownames(res) <- c(1:n.nhoods)
# colnames(res)[6:(6+length(rand.levels)-1)] <- paste(names(rand.levels), "variance", sep="_")
} else {
# need to use legacy=TRUE to maintain original edgeR behaviour
fit <- glmQLFit(dge, x.model, robust=robust, legacy=TRUE)
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2 changes: 2 additions & 0 deletions src/fitGeneticPLGlmm.cpp
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Expand Up @@ -220,6 +220,7 @@ List fitGeneticPLGlmm(const arma::mat& Z, const arma::mat& X, const arma::mat& K
Vmu = computeVmu(muvec, curr_disp, vardist);
W = computeW(curr_disp, Dinv, vardist);
Winv = W.i();

// pre-compute matrics: X^T * W^-1, Z^T * W^-1
arma::mat xTwinv = X.t() * Winv;
arma::mat zTwin = Z.t() * Winv;
Expand Down Expand Up @@ -272,6 +273,7 @@ List fitGeneticPLGlmm(const arma::mat& Z, const arma::mat& X, const arma::mat& K
if(REML){
arma::mat VstarZ = V_star_inv * Z;
VP_partial = precomp_list["PZZt"];

VS_partial = pseudovarPartial_VG(u_indices, Z, VstarZ, K);

score_sigma = sigmaScoreREML_arma(VP_partial, y_star, P,
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1 change: 1 addition & 0 deletions src/paramEst.cpp
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Expand Up @@ -30,6 +30,7 @@ arma::vec sigmaScoreREML_arma (const Rcpp::List& pvstar_i, const arma::vec& ysta
for(int i=0; i < c; i++){
const arma::mat& P_pvi = pvstar_i(i); // this is Vstar_inv * partial derivative
const arma::mat& Pdifi = remldiffV(i);

double lhs = -0.5 * arma::trace(Pdifi);
arma::mat mid1(1, 1);
mid1 = arma::trans(ystarminx) * P_pvi * Vstarinv * ystarminx;
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9 changes: 5 additions & 4 deletions src/pseudovarPartial.cpp
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Expand Up @@ -156,14 +156,15 @@ List pseudovarPartial_VG(const List& u_indices, const arma::mat& Z, const arma::
arma::uvec u_idx = u_indices[i];
arma::mat omat(n , n);

arma::mat VsZcols = VstarZ.cols(u_idx-1);
arma::mat Zcols = Z.cols(u_idx-1).t();

if(i == c - 1){
omat = VstarZ.cols(u_idx-1) * K * Z.cols(u_idx-1);
omat = VstarZ.cols(u_idx-1) * K * Zcols;
} else{
omat = VstarZ.cols(u_idx-1) * Z.cols(u_idx-1);
omat = VstarZ.cols(u_idx-1) * Zcols;
}



outlist[i] = omat;
}

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