Output files - Revision history

127.0.0.1: /* results.csv */

2022-11-04T00:23:47Z

results.csv

← Older revision		Revision as of 00:23, 4 November 2022
Line 19:		Line 19:

	The file has as many records the system has equations. Fixed factor levels which have been omitted due to rank deficiencies are not printed.		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 {{cc\|a}} with several correlated sub-factors one can recover the factor level effect matrices with the R code

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

	=====PCG solver output files=====		=====PCG solver output files=====
	======so_conv.csv======		======so_conv.csv======

2022-11-03T23:37:26Z

aic_conv.csv

← Older revision		Revision as of 23:37, 3 November 2022
Line 77:		Line 77:
	*[[Algorithms#REML_Iteration_mechanism\|number of Levenber-Marquardt iterations]]		*[[Algorithms#REML_Iteration_mechanism\|number of Levenber-Marquardt iterations]]
	*seconds for the last AI-REML iteration		*seconds for the last AI-REML iteration
			*[[Algorithms#REML_Iteration_mechanism\|$$\alpha$$]]

	=====<u.d. variance name>_sigma_UPDATE.csv=====		=====<u.d. variance name>_sigma_UPDATE.csv=====

2022-11-03T23:36:29Z

aic_conv.csv

← Older revision		Revision as of 23:36, 3 November 2022
Line 75:		Line 75:
	*log-likelihood		*log-likelihood
	*[[Algorithms#REML_Iteration_mechanism\|$$\beta$$]]		*[[Algorithms#REML_Iteration_mechanism\|$$\beta$$]]
	*number of ~~Newton over~~-~~relaxation~~ iterations		*[[Algorithms#REML_Iteration_mechanism\|number of Levenber-Marquardt iterations]]
	*seconds for the last AI-REML iteration		*seconds for the last AI-REML iteration

2022-11-03T23:35:22Z

aic_conv.csv

← Older revision		Revision as of 23:35, 3 November 2022
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	*convergence criterion '''ll'''		*convergence criterion '''ll'''
	*log-likelihood		*log-likelihood
	*~~Newton over-relaxation parameter~~		*[[Algorithms#REML_Iteration_mechanism\|$$\beta$$]]
	*number of Newton over-relaxation iterations		*number of Newton over-relaxation iterations
	*seconds for the last AI-REML iteration		*seconds for the last AI-REML iteration

2022-09-22T10:24:35Z

_sigma_UPDATE.csv

← Older revision		Revision as of 10:24, 22 September 2022
Line 86:		Line 86:
	g<-matrix(0,n,n);g[upper.tri(g,diag=TRUE)]<-d[nrow(d)-1,];		g<-matrix(0,n,n);g[upper.tri(g,diag=TRUE)]<-d[nrow(d)-1,];
	</syntaxhighlight>		</syntaxhighlight>

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

	===Intermediate output files===		===Intermediate output files===

2022-08-28T11:24:22Z

Requested output files

@@ Line 112: / Line 112: @@
 dat<-fread("Q.coocsv",skip=1)
 Q[cbind(d$V1,d$V2)]<-d$V3
 </syntaxhighlight>

2022-06-07T04:22:03Z

Variance component estimation using MC-EM-REML

@@ Line 29: / Line 29: @@
 *convergence criterion CR
 *second per iteration
 ====Variance component estimation using MC-EM-REML====

2022-06-07T04:17:57Z

_sigma_SA.csv

← Older revision		Revision as of 04:17, 7 June 2022
Line 46:		Line 46:
	d<-fread("g_sigma_SA.csv"))		d<-fread("g_sigma_SA.csv"))
	n<-floor(sqrt(ncol(d)*2))		n<-floor(sqrt(ncol(d)*2))
	g<-matrix(0,n,n);g[upper.tri(g,diag=TRUE)]<-d[nrow(d)-1,];		g<-matrix(0,n,n);g[upper.tri(g,diag=TRUE)]<-d[nrow(d),];
	</syntaxhighlight>		</syntaxhighlight>

2022-06-07T04:16:56Z

_sigma_SA.csv

← Older revision		Revision as of 04:16, 7 June 2022
Line 44:		Line 44:

	<syntaxhighlight lang="R" line>		<syntaxhighlight lang="R" line>
	d<-fread("~~g_sigma_UPDATE~~.csv"))		d<-fread("g_sigma_SA.csv"))
	n<-floor(sqrt(ncol(d)*2))		n<-floor(sqrt(ncol(d)*2))
	g<-matrix(0,n,n);g[upper.tri(g,diag=TRUE)]<-d[nrow(d)-1,];		g<-matrix(0,n,n);g[upper.tri(g,diag=TRUE)]<-d[nrow(d)-1,];
	</syntaxhighlight>		</syntaxhighlight>


	====Variance component estimation using AI-REML-C====		====Variance component estimation using AI-REML-C====

2022-06-07T04:15:15Z

Variance component estimation using AI-REML-C

@@ Line 29: / Line 29: @@
 *convergence criterion CR
 *second per iteration
 ====Variance component estimation using AI-REML-C====