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Review of Sleep Analysis

*The author of this computation has been verified*

R Software Module: Patrick.Wessa/rwasp_partial_least_squares.wasp (opens new window with default values)

Title produced by software: Partial Least Squares - Path Modeling

Date of computation: Sat, 01 May 2010 17:45:35 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/May/01/t1272737487k5nllcexhl8tkfh.htm/, Retrieved Sat, 01 May 2010 20:11:27 +0200

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/May/01/t1272737487k5nllcexhl8tkfh.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

0.65321251377534 0 0.81954393554187 1.6232492903979 0.47712125471966 0 0.47712125471966 0.79934054945358 0.30102999566398 1.83884909073726 3.40602894496362 3.66304097489397 2.79518458968242 0.47712125471966 0.69897000433602 0.60205999132796 0.32221929473392 0.25527250510331 1.43136376415899 1.02325245963371 2.25406445291434 2.25527250510331 0.60205999132796 0.60205999132796 0.60205999132796 0.95904139232109 -0.15490195998574 1.27875360095283 -1.69897000433602 -0.52287874528034 1.54406804435028 0 0 0 1.19865708695442 0.5910646070265 1.48287358360875 2.20411998265592 2.22788670461367 2.59328606702046 0.60205999132796 0.69897000433602 0.60205999132796 0.7160033436348 0 1.44715803134222 0.51851393987789 1.40823996531185 1.79934054945358 0 0.30102999566398 0 1.03742649794062 0.55630250076729 1.69897000433602 1.71733758272386 2.64345267648619 2.36172783601759 0 0 0 0.91907809237607 0.14612803567824 0.84509804001426 -0.36653154442041 0.80617997398389 2.04921802267018 0.69897000433602 0.60205999132796 0.602059 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

PARTIAL LEAST SQUARES PATH MODELING (PLS-PM)
MODEL SPECIFICATION
Number of Cases	39
Latent Variables	3
Manifest Variables	9
Scaled?	FALSE
Weighting Scheme	centroid
Bootstrapping?	TRUE
Bootstrap samples	100

BLOCKS DEFINITION
Block	Type	NMVs	Mode
ecol	Exogenous	3	Reflective
const	Endogenous	4	Reflective
sleep	Endogenous	2	Reflective

BLOCKS UNIDIMENSIONALITY
Block	Type.measure	MVs	eig.1st	eig.2nd	C.alpha	DG.rho
ecol	Reflective	3	2.43	0.44	0.8963	0.9364
const	Reflective	4	3.2	0.35	0.9266	0.9483
sleep	Reflective	2	1.52	0.43	0.7163	0.8758

OUTER MODEL
Block	weights	std.loads	communal	redundan
ecol
logP	0.8451	0.8528	0.7273	0
logS	1.898	0.9149	0.8371	0
logD	1.3263	0.9291	0.8633	0
const
logL	0.0936	0.7569	0.5729	0.1417
logWb	0.4372	0.9851	0.9704	0.24
logWbr	0.3477	0.984	0.9682	0.2395
logtg	0.1684	0.7761	0.6024	0.149
sleep
logSWS	2.0101	0.837	0.7005	0.4169
logPS	2.1906	0.9212	0.8485	0.505

CORRELATIONS BETWEEN MVs AND LVs
Block	ecol	const	sleep
ecol
logP	0.8528	0.1842	-0.4817
logS	0.9149	0.6338	-0.6696
logD	0.9291	0.3272	-0.676
const
logL	0.2922	0.7569	-0.4352
logWb	0.5007	0.9851	-0.5941
logWbr	0.4653	0.984	-0.6128
logtg	0.4632	0.7761	-0.7243
sleep
logSWS	-0.5607	-0.7352	0.837
logPS	-0.6633	-0.431	0.9212

INNER MODEL
Block	Concept	Value
S2
	R2	0.247
(Intercept)	Intercept	0.403
Y.lvs[, c]	path_S1	0.497
S3
	R2	0.595
(Intercept)	Intercept	3.307
Y.lvs[, c]S1	path_S1	-0.515
Y.lvs[, c]S2	path_S2	-0.373

CORRELATIONS BETWEEN LVs
	ecol	const	sleep
ecol	1	0.4973	-0.7004
const	0.4973	1	-0.629
sleep	-0.7004	-0.629	1

SUMMARY INNER MODEL
	LV.Type	Measure	MVs	R.square	Av.Commu	Av.Redun	AVE
ecol	Exogen	Rflct	3	0	0.8092	0	0.809
const	Endogen	Rflct	4	0.2474	0.7785	0.1926	0.778
sleep	Endogen	Rflct	2	0.5952	0.7745	0.461	0.775

GOODNESS-OF-FIT
GoF	Value
Absolute	0.5759
Relative	0.6626
Outer.mod	0.9719
Inner.mod	0.4518

TOTAL EFFECTS
relationships	dir.effect	ind.effect	tot.effect
S1->S2	0.497346784812073	0	0.497346784812073
S1->S3	-0.514891739709143	-0.1855	-0.700391739709143
S2->S3	-0.372926779555084	0	-0.372926779555084

BOOTSTRAP VALIDATION - WEIGHTS
	Original	Mean.Boot	Std.Err	t.statis	p.value	perc.025	perc.975
logP	0.8451	0.8233	0.2119	-1.026	0.3074	0.3947	1.2519
logS	1.898	1.9282	0.3082	0.9802	0.3294	1.3048	2.5516
logD	1.3263	1.3294	0.1299	0.2397	0.8111	1.0667	1.5921
logL	0.0936	0.1042	0.0397	2.671	0.0088	0.0238	0.1845
logWb	0.4372	0.4472	0.04	2.5038	0.0139	0.3663	0.5282
logWbr	0.3477	0.3553	0.0499	1.5279	0.1297	0.2544	0.4562
logtg	0.1684	0.1816	0.0524	2.5179	0.0134	0.0755	0.2877
logSWS	2.0101	2.0033	0.3904	-0.1722	0.8637	1.2136	2.793
logPS	2.1906	2.2184	0.32	0.87	0.3864	1.5711	2.8658

BOOTSTRAP VALIDATION - LOADINGS
	Original	Mean.Boot	Std.Err	t.statis	p.value	perc.025	perc.975
logP	0.8528	0.8479	0.0732	-0.6759	0.5007	0.6998	0.9959
logS	0.9149	0.9231	0.0264	3.0947	0.0026	0.8696	0.9766
logD	0.9291	0.9243	0.0453	-1.0631	0.2903	0.8326	1.016
logL	0.7569	0.7813	0.0947	2.5786	0.0114	0.5899	0.9728
logWb	0.9851	0.9832	0.01	-1.905	0.0597	0.9629	1.0035
logWbr	0.984	0.9836	0.0047	-0.7932	0.4296	0.9742	0.993
logtg	0.7761	0.7781	0.0516	0.3689	0.713	0.6736	0.8825
logSWS	0.837	0.8363	0.0632	-0.104	0.9174	0.7084	0.9642
logPS	0.9212	0.9164	0.0509	-0.9348	0.3522	0.8135	1.0193

BOOTSTRAP VALIDATION - PATHS
	Original	Mean.Boot	Std.Err	t.statis	p.value	perc.025	perc.975
S1->S2	0.4973	0.5093	0.112	1.0679	0.2882	0.2827	0.7359
S1->S3	-0.5149	-0.5123	0.0842	0.3134	0.7546	-0.6825	-0.342
S2->S3	-0.3729	-0.3693	0.088	0.4094	0.6831	-0.5473	-0.1914

BOOTSTRAP VALIDATION - RSQ
	Original	Mean.Boot	Std.Err	t.statis	p.value	perc.025	perc.975
S2	0.2474	0.272	0.116	2.1115	0.0372	0.037	0.506
S3	0.5952	0.599	0.09	0.3782	0.7061	0.417	0.78

BOOTSTRAP VALIDATION - TOTAL EFFECTS
	Original	Mean.Boot	Std.Err	t.statis	p.value	perc.025	perc.975
S1->S2	0.4973	0.5093	0.112	1.0679	0.2882	0.2827	0.7359
S1->S3	-0.7004	-0.698	0.0693	0.3475	0.7289	-0.8382	-0.5578
S2->S3	-0.3729	-0.3693	0.088	0.4094	0.6831	-0.5473	-0.1914

Charts produced by software:

Parameters (Session):

Parameters (R input):

par1 = ecol const sleep ; par2 = A A A ; par3 = 5 6 7 ; par4 = 1 2 3 4 ; par5 = 8 9 ; par11 = 0 0 0 ; par12 = 1 0 0 ; par13 = 1 1 0 ;

R code (references can be found in the software module):

par18 <- ''

par17 <- ''

par16 <- ''

par15 <- ''

par14 <- ''

par13 <- '1 1 0'

par12 <- '0 0 0'

par11 <- '0 0 0'

par10 <- ''

par9 <- ''

par8 <- ''

par7 <- ''

par6 <- ''

par5 <- '8 9'

par4 <- '1 2 3 4'

par3 <- '5 6 7'

par2 <- 'A A A'

par1 <- 'ecol const sleep'

library(plspm)

y <- as.data.frame(t(y))

is.data.frame(y)

head(y)

trim <-	function(char) {

return(sub('s+$', '', sub('^s+', '', char)))

}

(latnames <- strsplit(par1,' ')[[1]])

(n <- length(latnames))

(L1 <- as.numeric(strsplit(par3,' ')[[1]]))

(L2 <- as.numeric(strsplit(par4,' ')[[1]]))

(L3 <- as.numeric(strsplit(par5,' ')[[1]]))

(L4 <- as.numeric(strsplit(par6,' ')[[1]]))

(L5 <- as.numeric(strsplit(par7,' ')[[1]]))

(L6 <- as.numeric(strsplit(par8,' ')[[1]]))

(L7 <- as.numeric(strsplit(par9,' ')[[1]]))

(L8 <- as.numeric(strsplit(par10,' ')[[1]]))

(S1 <- as.numeric(strsplit(par11,' ')[[1]]))

(S2 <- as.numeric(strsplit(par12,' ')[[1]]))

(S3 <- as.numeric(strsplit(par13,' ')[[1]]))

(S4 <- as.numeric(strsplit(par14,' ')[[1]]))

(S5 <- as.numeric(strsplit(par15,' ')[[1]]))

(S6 <- as.numeric(strsplit(par16,' ')[[1]]))

(S7 <- as.numeric(strsplit(par17,' ')[[1]]))

(S8 <- as.numeric(strsplit(par18,' ')[[1]]))

if (n==1) sat.mat <- rbind(S1)

if (n==2) sat.mat <- rbind(S1,S2)

if (n==3) sat.mat <- rbind(S1,S2,S3)

if (n==4) sat.mat <- rbind(S1,S2,S3,S4)

if (n==5) sat.mat <- rbind(S1,S2,S3,S4,S5)

if (n==6) sat.mat <- rbind(S1,S2,S3,S4,S5,S6)

if (n==7) sat.mat <- rbind(S1,S2,S3,S4,S5,S6,S7)

if (n==8) sat.mat <- rbind(S1,S2,S3,S4,S5,S6,S7,S8)

sat.mat

if (n==1) sat.sets <- list(L1)

if (n==2) sat.sets <- list(L1,L2)

if (n==3) sat.sets <- list(L1,L2,L3)

if (n==4) sat.sets <- list(L1,L2,L3,L4)

if (n==5) sat.sets <- list(L1,L2,L3,L4,L5)

if (n==6) sat.sets <- list(L1,L2,L3,L4,L5,L6)

if (n==7) sat.sets <- list(L1,L2,L3,L4,L5,L6,L7)

if (n==8) sat.sets <- list(L1,L2,L3,L4,L5,L6,L7,L8)

sat.sets

(sat.mod <- strsplit(par2,' ')[[1]])

res <- plspm(y, sat.mat, sat.sets, sat.mod, scheme='centroid', scaled=FALSE,boot.val=TRUE)

(r <- summary(res))

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'PARTIAL LEAST SQUARES PATH MODELING (PLS-PM)',2,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'MODEL SPECIFICATION',2,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Number of Cases',header=TRUE)

a<-table.element(a,r$xxx$obs)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Latent Variables',header=TRUE)

a<-table.element(a,n)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Manifest Variables',header=TRUE)

a<-table.element(a,length(y[1,]))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Scaled?',header=TRUE)

a<-table.element(a,r$xxx$scaled)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Weighting Scheme',header=TRUE)

a<-table.element(a,r$xx$scheme)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Bootstrapping?',header=TRUE)

a<-table.element(a,r$xx$boot.val)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Bootstrap samples',header=TRUE)

a<-table.element(a,r$xx$br)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable1.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'BLOCKS DEFINITION',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Block',header=TRUE)

a<-table.element(a,'Type',header=TRUE)

a<-table.element(a,'NMVs',header=TRUE)

a<-table.element(a,'Mode',header=TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,latnames[i],header=TRUE)

a<-table.element(a,r$input$Type[i])

a<-table.element(a,r$unidim$MVs[i])

a<-table.element(a,r$unidim$Type.measure[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable2.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'BLOCKS UNIDIMENSIONALITY',7,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Block',header=TRUE)

a<-table.element(a,'Type.measure',header=TRUE)

a<-table.element(a,'MVs',header=TRUE)

a<-table.element(a,'eig.1st',header=TRUE)

a<-table.element(a,'eig.2nd',header=TRUE)

a<-table.element(a,'C.alpha',header=TRUE)

a<-table.element(a,'DG.rho',header=TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,latnames[i],header=TRUE)

a<-table.element(a,r$unidim$Type.measure[i])

a<-table.element(a,r$unidim$MVs[i])

a<-table.element(a,r$unidim$eig.1st[i])

a<-table.element(a,r$unidim$eig.2nd[i])

a<-table.element(a,r$unidim$C.alpha[i])

a<-table.element(a,r$unidim$DG.rho[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable3.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'OUTER MODEL',5,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Block',header=TRUE)

a<-table.element(a,'weights',header=TRUE)

a<-table.element(a,'std.loads',header=TRUE)

a<-table.element(a,'communal',header=TRUE)

a<-table.element(a,'redundan',header=TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,latnames[i],5,header=TRUE)

a<-table.row.end(a)

for (j in 1:length(r$outer.mod[[i]][,1])) {

a<-table.row.start(a)

a<-table.element(a,rownames(r$outer.mod[[i]])[j],header=T)

a<-table.element(a,r$outer.mod[[i]][j,1])

a<-table.element(a,r$outer.mod[[i]][j,2])

a<-table.element(a,r$outer.mod[[i]][j,3])

a<-table.element(a,r$outer.mod[[i]][j,4])

a<-table.row.end(a)

}

}

a<-table.end(a)

table.save(a,file='mytable4.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'CORRELATIONS BETWEEN MVs AND LVs',n+1,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Block',header=TRUE)

for (iii in 1:n) {

a<-table.element(a,latnames[iii],header=TRUE)

}

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,latnames[i],n+1,header=TRUE)

a<-table.row.end(a)

for (j in 1:length(r$outer.cor[[i]][,1])) {

a<-table.row.start(a)

a<-table.element(a,rownames(r$outer.cor[[i]])[j],header=T)

for (iii in 1:n) {

a<-table.element(a,r$outer.cor[[i]][j,iii])

}

a<-table.row.end(a)

}

}

a<-table.end(a)

table.save(a,file='mytable5.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'INNER MODEL',3,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Block',header=TRUE)

a<-table.element(a,'Concept',header=TRUE)

a<-table.element(a,'Value',header=TRUE)

a<-table.row.end(a)

for (i in 1:(length(labels(r$inner.mod)))) {

a<-table.row.start(a)

print (paste('i=',i,sep=''))

a<-table.element(a,labels(r$inner.mod)[i],3,header=TRUE)

a<-table.row.end(a)

for (j in 1:length(r$inner.mod[[i]][,1])) {

print (paste('j=',j,sep=''))

a<-table.row.start(a)

a<-table.element(a,rownames(r$inner.mod[[i]])[j],header=T)

a<-table.element(a,r$inner.mod[[i]][j,1],header=T)

a<-table.element(a,r$inner.mod[[i]][j,2])

a<-table.row.end(a)

}

}

a<-table.end(a)

table.save(a,file='mytable6.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'CORRELATIONS BETWEEN LVs',n+1,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'',header=TRUE)

for (iii in 1:n) {

a<-table.element(a,latnames[iii],header=TRUE)

}

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,latnames[i],header=T)

for (j in 1:n) {

a<-table.element(a,r$latent.cor[i,j])

}

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable7.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'SUMMARY INNER MODEL',8,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'',header=TRUE)

a<-table.element(a,'LV.Type',header=TRUE)

a<-table.element(a,'Measure',header=TRUE)

a<-table.element(a,'MVs',header=TRUE)

a<-table.element(a,'R.square',header=TRUE)

a<-table.element(a,'Av.Commu',header=TRUE)

a<-table.element(a,'Av.Redun',header=TRUE)

a<-table.element(a,'AVE',header=TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,latnames[i],header=T)

a<-table.element(a,r$inner.sum[i,1])

a<-table.element(a,r$inner.sum[i,2])

a<-table.element(a,r$inner.sum[i,3])

a<-table.element(a,r$inner.sum[i,4])

a<-table.element(a,r$inner.sum[i,5])

a<-table.element(a,r$inner.sum[i,6])

a<-table.element(a,r$inner.sum[i,7])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable8.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'GOODNESS-OF-FIT',2,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'GoF',header=TRUE)

a<-table.element(a,'Value',header=TRUE)

a<-table.row.end(a)

for (i in 1:4) {

a<-table.row.start(a)

a<-table.element(a,r$gof[i,1],header=T)

a<-table.element(a,r$gof[i,2])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable9.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'TOTAL EFFECTS',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'relationships',header=TRUE)

a<-table.element(a,'dir.effect',header=TRUE)

a<-table.element(a,'ind.effect',header=TRUE)

a<-table.element(a,'tot.effect',header=TRUE)

a<-table.row.end(a)

for (i in 1:length(r$effects[,1])) {

a<-table.row.start(a)

a<-table.element(a,r$effects[i,1],header=T)

a<-table.element(a,r$effects[i,2])

a<-table.element(a,r$effects[i,3])

a<-table.element(a,r$effects[i,4])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable10.tab')

dum <- r$boot$weights

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'BOOTSTRAP VALIDATION - WEIGHTS',length(colnames(dum))+1,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'',header=TRUE)

for (i in 1:length(colnames(dum))) {

a<-table.element(a,colnames(dum)[i],header=TRUE)

}

a<-table.row.end(a)

for (i in 1:length(rownames(dum))) {

a<-table.row.start(a)

a<-table.element(a,rownames(dum)[i],header=T)

for (j in 1:length(colnames(dum))) {

a<-table.element(a,dum[i,j])

}

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable11.tab')

dum <- r$boot$loadings

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'BOOTSTRAP VALIDATION - LOADINGS',length(colnames(dum))+1,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'',header=TRUE)

for (i in 1:length(colnames(dum))) {

a<-table.element(a,colnames(dum)[i],header=TRUE)

}

a<-table.row.end(a)

for (i in 1:length(rownames(dum))) {

a<-table.row.start(a)

a<-table.element(a,rownames(dum)[i],header=T)

for (j in 1:length(colnames(dum))) {

a<-table.element(a,dum[i,j])

}

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable12.tab')

dum <- r$boot$paths

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'BOOTSTRAP VALIDATION - PATHS',length(colnames(dum))+1,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'',header=TRUE)

for (i in 1:length(colnames(dum))) {

a<-table.element(a,colnames(dum)[i],header=TRUE)

}

a<-table.row.end(a)

for (i in 1:length(rownames(dum))) {

a<-table.row.start(a)

a<-table.element(a,rownames(dum)[i],header=T)

for (j in 1:length(colnames(dum))) {

a<-table.element(a,dum[i,j])

}

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable13.tab')

dum <- r$boot$rsq

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'BOOTSTRAP VALIDATION - RSQ',length(colnames(dum))+1,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'',header=TRUE)

for (i in 1:length(colnames(dum))) {

a<-table.element(a,colnames(dum)[i],header=TRUE)

}

a<-table.row.end(a)

for (i in 1:length(rownames(dum))) {

a<-table.row.start(a)

a<-table.element(a,rownames(dum)[i],header=T)

for (j in 1:length(colnames(dum))) {

a<-table.element(a,dum[i,j])

}

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable14.tab')

dum <- r$boot$total.efs

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'BOOTSTRAP VALIDATION - TOTAL EFFECTS',length(colnames(dum))+1,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'',header=TRUE)

for (i in 1:length(colnames(dum))) {

a<-table.element(a,colnames(dum)[i],header=TRUE)

}

a<-table.row.end(a)

for (i in 1:length(rownames(dum))) {

a<-table.row.start(a)

a<-table.element(a,rownames(dum)[i],header=T)

for (j in 1:length(colnames(dum))) {

a<-table.element(a,dum[i,j])

}

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable15.tab')

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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