Output files

Default output files

General output files

lmt.log

General log file always generated. Provides information of the current state of operation.

Operation dependent output files

Solving the mixed model equation system

results.csv

The file contains the solutions for the mixed model equation system. The file has four columns:

• factor name,
• sub-factor name,
• factor level id, and
• solution.

Note that factor name s are derived according to the lmt factor naming convention, and sub-factor name s are user-defined and are extracted from the equation system. Further, the factor level id is the original as provided by the data, pedigree, etc. For variables undergoing polynomial expansions, e.g. sub-factor c in

 y=x*b+age(t(co(p(1,2))))*c+id*u(v(my_var(1)))

the sub-factor name will be expanded as well to sub-factor name_id , where "id" is the polynomial id. For the above example c would be expanded to c_1 and c_2 .

The file has as many records the system has equations. Fixed factor levels which have been omitted due to rank deficiencies are not printed.

For a random factor with user-defined name a with several correlated sub-factors one can recover the factor level effect matrices with the R code

results<-as.matrix(fread("results.csv")))
data<-matrix(results$V4[results$V1=="a"],ncol=length(unique(results$V2[results$V1=="a")),byrow=F)

PCG solver output files

so_conv.csv

Contains the iteration statistics with columns

• iteration number
• alpha
• beta
• $$||r_i M r_i||$$, where $$M$$ is the preconditioner matrix and $$r_i=Cx_i-b$$ for a system $$Cx=b$$
• convergence criterion CD
• convergence criterion CR
• second per iteration

Variance component estimation using Gibbs sampling

<u.d. variance name>_sigma_SA.csv

The file contains the column-wise upper-triangular elements of the respective $$\Sigma$$ matrix estimated in each round. The file contains as many rows as MC-EM-REML iterations. Estimates for a $$\Sigma$$ matrix being part of variance structure named g , assuming 10,000 samples, a burn-in of 1,000 samples and a thinning of 50 samples, can be obtained by

d<-as.matrix(fread("g_sigma_SA.csv")))
n<-floor(sqrt(ncol(d)*2))
gMean<-matrix(0,n,n);gSd<-gMean
gMean[upper.tri(g,diag=TRUE)]<-colMeans(d[seq(1000,nrow(d),20),]);
gSd[upper.tri(g,diag=TRUE)]<-apply(d[seq(1000,nrow(d),20),],2,sd);

Variance component estimation using MC-EM-REML

mcemreml_conv.csv

Contains the iteration statistic with one parameter vector per iteration. The parameter vector contains the following elements:

• seconds for the last MC-Em iteration
• seconds for solving the equation system
• seconds for sampling the traces
• convergence criterion cd

<u.d. variance name>_sigma_SA.csv

The file contains the column-wise upper-triangular elements of the respective $$\Sigma$$ matrix estimated in each round. The file contains as many rows as MC-EM-REML iterations. Estimates at convergence for a $$\Sigma$$ matrix being part of variance structure named g maybe read into R by

d<-fread("g_sigma_SA.csv"))
n<-floor(sqrt(ncol(d)*2))
g<-matrix(0,n,n);g[upper.tri(g,diag=TRUE)]<-d[nrow(d),];

Variance component estimation using AI-REML-C

aic_conv.csv

Contains the iteration statistic with one parameter vector per iteration. The parameter vector contains the following elements:

• iteration number
• convergence criterion ng
• convergence criterion cd
• convergence criterion ll
• log-likelihood
• $$\beta$$
• number of Levenber-Marquardt iterations
• seconds for the last AI-REML iteration
• $$\alpha$$

<u.d. variance name>_sigma_UPDATE.csv

The file contains the column-wise upper-triangular elements of the respective $$\Sigma$$ matrix estimated in each round. The file contains as many rows as AI-REML-C iterations plus one. The row before the last contains the co-variance estimates at convergence. The last row contains the approximate standard errors of the parameter estimates. Estimates at convergence for a $$\Sigma$$ matrix being part of variance structure named g maybe read into R by

d<-fread("g_sigma_UPDATE.csv"))
n<-floor(sqrt(ncol(d)*2))
g<-matrix(0,n,n);g[upper.tri(g,diag=TRUE)]<-d[nrow(d)-1,];

Note that when restarting lmt will not overwrite this file. Instead it will append records.

Intermediate output files

Files from processing pedigrees

"u.d. pedigree name"_sorted.csv

File contains a 3(ordinary pedigree) or 4(probabilistic pedigree) column matrix containing the sorted and renumbered pedigree generated from the original pedigree.

"u.d. pedigree name"_crossref.csv

File contains a vector of original ids of individuals in "u.d. pedigree name"_sorted.csv. That is, the original id of individual #1 in "u.d. pedigree name"_sorted.csv is located in record 1 of this file, etc.

"u.d. pedigree name".bin

Block file in binary format containing for blocks:

• a: real vector of diagonal elements of $$A$$
• ai: real vector of diagonal elements of $$A^{-1}$$
• m: real vector of mendelian sampling terms
• pe: 3 column integer matrix of the sorted and renumbered pedigree underlying $$A$$

Requested output files

Files from processing pedigrees

Genetic group regression matrix

lmt can write the genetic group regression matrix(aka $$Q$$) of a pedigree containing phantom parents to a user-defined file. For the necessary key string see here.

A $$Q$$ matrix written to Q.coocsv format maybe read into R

dim<-scan("Q.coocsv",n=2,sep=",")
Q<-matrix(0,d[1],d[2])
dat<-fread("Q.coocsv",skip=1)
Q[cbind(d$V1,d$V2)]<-d$V3

Files from building GRMs

Genomic relationship matrix

lmt can write a genomic relationship matrix to a user-nominated file after it has been constructed. Supported output file formats are csc and bin where the latter nominates a block file in binary format. In both cases only the upper triangular in column major order is written out. The matrix maybe reconstructed in R

d<-scan("mygrm.csv")
n<-floor(sqrt(length(d)*2))
G<-matrix(0,n,n)
G[upper.tri(G,diag=T)]<-d

Files from running AI-REML jobs

AI matrix, gradient vector and parameter vector

Upon provision of the respective switch in the instruction file lmt writes the AI matrix, gradient vector and parameter vector to files ai_ai.csv , ai_ja.csv and ai_pa.csv , respectively. Files ai_ai.csv and ai_ja.csv contain as many records as AI-REML iterations. File ai_pa.csv contains contains as many records as AI-REML iterations + 1, where the first record is the parameter vector at the start.

Each row of file ai_ai.csv contains the upper-triangular of the AI in column-major order. The AI matrix of say iteration 2 can be reconstructed by

d<-fread("ai_ai.csv"))
n<-floor(sqrt(ncol(d)*2))
ai<-matrix(0,n,n);ai[upper.tri(ai,diag=TRUE)]<-d[2,];