*The author of this computation has been verified*

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

Title produced by software: Partial Least Squares - Path Modeling

Date of computation: Thu, 16 Dec 2010 09:56:46 +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/Dec/16/t1292493293pxta8z23zmtdvgv.htm/, Retrieved Thu, 16 Dec 2010 10:54:54 +0100

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/Dec/16/t1292493293pxta8z23zmtdvgv.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:

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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 8 seconds R Server 193.190.124.10:1001 @ 193.190.124.10:1001 PARTIAL LEAST SQUARES PATH MODELING (PLS-PM) MODEL SPECIFICATION Number of Cases 39 Latent Variables 3 Manifest Variables 9 Scaled? TRUE 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 2.49392667785567 0.44918051829769 0.896300070317852 0.936385726725812 const Reflective 4 3.28523164521364 0.354740490703899 0.926598168727977 0.948298634274641 sleep Reflective 2 1.55800616818027 0.441993831819727 0.716308034687713 0.875774855093694 OUTER MODEL Block weights std.loads communal redundan ecol logP 0.2266 0.8676 0.7528 0 logS 0.5046 0.9015 0.8127 0 logD 0.3709 0.9397 0.8831 0 const logL 0.1927 0.8382 0.7026 0.1564 logWb 0.301 0.9266 0.8587 0.1912 logWbr 0.2924 0.97 0.9409 0.2095 logtg 0.3134 0.8808 0.7757 0.1727 sleep logSWS 0.5879 0.8919 0.7956 0.5167 logPS 0.5449 0.8729 0.762 0.4949 CORRELATIONS BETWEEN MVs AND LVs Block ecol const sleep ecol logP 0.8676 0.1287 -0.4639 logS 0.9015 0.6488 -0.6703 logD 0.9397 0.3109 -0.6589 const logL 0.274 0.8382 -0.4481 logWb 0.4876 0.9266 -0.64 logWbr 0.4507 0.97 -0.6448 logtg 0.4484 0.8808 -0.7261 sleep logSWS -0.5548 -0.7105 0.8919 logPS -0.6635 -0.5089 0.8729 INNER MODEL Block Concept Value S2 1 R2 0.2226 2 Intercept 0 3 path_S1 0.4718 S3 1 R2 0.6495 2 Intercept 0 3 path_S1 -0.4628 4 path_S2 -0.4767 CORRELATIONS BETWEEN LVs ecol const sleep ecol 1 0.4718 -0.6877 const 0.4718 1 -0.695 sleep -0.6877 -0.695 1 SUMMARY INNER MODEL LV.Type Measure MVs R.square Av.Commu Av.Redun AVE ecol Exogen Rflct 3 0 0.8162 0 0.816 const Endogen Rflct 4 0.2226 0.8195 0.1824 0.819 sleep Endogen Rflct 2 0.6495 0.7788 0.5058 0.779 GOODNESS-OF-FIT GoF Value Absolute 0.594072491071591 Relative 0.769531486764 Outer.mod 0.996540232339925 Inner.mod 0.772203130180808 TOTAL EFFECTS relationships dir.effect ind.effect tot.effect S1->S2 0.47183687754145 0 0.47183687754145 S1->S3 -0.462761043658846 -0.224904184530796 -0.687665228189642 S2->S3 -0.476656648167647 0 -0.476656648167647 BOOTSTRAP VALIDATION - WEIGHTS Original Mean.Boot Std.Error perc.05 perc.95 logP 0.226611731207995 0.21993939256954 0.0666469632019318 0.0877299783497136 0.289896903312728 logS 0.50455510986497 0.512302217814878 0.0888174060094197 0.401911069903199 0.686050847699033 logD 0.370882038453552 0.366430085612776 0.0234730305972253 0.327472204322232 0.403365952329214 logL 0.192654172241961 0.180815076440143 0.0463588536646802 0.0914044961024064 0.238062667205845 logWb 0.300965281469565 0.305147698644337 0.0303783822450857 0.256590290062959 0.353726198814615 logWbr 0.292358061358382 0.291803159035632 0.0259341683346686 0.247658309134176 0.334920251701308 logtg 0.313422150013469 0.31964302086788 0.036270798702301 0.27491386109854 0.382820403966177 logSWS 0.587916401400275 0.58404996511541 0.0586606260589326 0.500224428806398 0.681061322472302 logPS 0.544852711813177 0.549981699606979 0.0501176618082805 0.473591470674297 0.633974071998176 BOOTSTRAP VALIDATION - LOADINGS Original Mean.Boot Std.Error perc.05 perc.95 logP 0.867637144235964 0.859818792721442 0.0686421329199473 0.757151341090742 0.93396984348992 logS 0.90148243160283 0.908250845896857 0.0275775829472729 0.866873235324142 0.955396507197371 logD 0.939748063343516 0.930432999415067 0.0421729871938867 0.850186126043246 0.975845241164157 logL 0.838196412672888 0.821442344494607 0.0909881270674692 0.644424208595763 0.929451769448314 logWb 0.926642148966324 0.928637838888554 0.0268411017900529 0.87712941777375 0.961680675047155 logWbr 0.969982738285346 0.96974957074225 0.00959795870638334 0.955880946339725 0.983451402259195 logtg 0.880756436094418 0.8810885944584 0.0277777317291189 0.837741446817982 0.918791075124928 logSWS 0.891947575341777 0.889541362503736 0.0364344739797181 0.826267554832962 0.932670599868892 logPS 0.87291369016888 0.872070364167138 0.0508496457296118 0.775724271361718 0.927285650635181 BOOTSTRAP VALIDATION - PATHS Original Mean.Boot Std.Error perc.05 perc.95 S1->S2 0.47183687754145 0.483562230062537 0.107995248448403 0.273084624150484 0.647936628327493 S1->S3 -0.462761043658846 -0.45777883072231 0.0859675850984513 -0.596118087159433 -0.304830600724995 S2->S3 -0.476656648167647 -0.47865412360967 0.0737036525665237 -0.583769221183514 -0.326713250725817 BOOTSTRAP VALIDATION - RSQ Original Mean.Boot Std.Error perc.05 perc.95 S2 0.222630039008065 0.245378774293612 0.101153358283062 0.0745758610019033 0.419822385220305 S3 0.649503134084065 0.65639977255227 0.0827425014824753 0.516256605091289 0.77969843522738 BOOTSTRAP VALIDATION - TOTAL EFFECTS Original Mean.Boot Std.Error perc.05 perc.95 S1->S2 0.47183687754145 0.483562230062537 0.107995248448403 0.273084624150484 0.647936628327493 S1->S3 -0.687665228189642 -0.687086921716286 0.0760383948821709 -0.800942444492284 -0.555220497556662 S2->S3 -0.476656648167647 -0.47865412360967 0.0737036525665237 -0.583769221183514 -0.326713250725817

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292493293pxta8z23zmtdvgv/1oi101292493392.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292493293pxta8z23zmtdvgv/1oi101292493392.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292493293pxta8z23zmtdvgv/29iz61292493392.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/16/t1292493293pxta8z23zmtdvgv/29iz61292493392.ps (open in new window)

Parameters (Session):

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 ;

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):

library(plspm) library(diagram) y <- as.data.frame(t(y)) is.data.frame(y) head(y) trim <- function(char) { return(sub('s+$', '', sub('^s+', '', char))) } (latnames <- strsplit(par1,' ')[[1]]) (n <- length(latnames)) (L1 <- as.numeric(strsplit(par3,' ')[[1]])) (L2 <- as.numeric(strsplit(par4,' ')[[1]])) (L3 <- as.numeric(strsplit(par5,' ')[[1]])) (L4 <- as.numeric(strsplit(par6,' ')[[1]])) (L5 <- as.numeric(strsplit(par7,' ')[[1]])) (L6 <- as.numeric(strsplit(par8,' ')[[1]])) (L7 <- as.numeric(strsplit(par9,' ')[[1]])) (L8 <- as.numeric(strsplit(par10,' ')[[1]])) (S1 <- as.numeric(strsplit(par11,' ')[[1]])) (S2 <- as.numeric(strsplit(par12,' ')[[1]])) (S3 <- as.numeric(strsplit(par13,' ')[[1]])) (S4 <- as.numeric(strsplit(par14,' ')[[1]])) (S5 <- as.numeric(strsplit(par15,' ')[[1]])) (S6 <- as.numeric(strsplit(par16,' ')[[1]])) (S7 <- as.numeric(strsplit(par17,' ')[[1]])) (S8 <- as.numeric(strsplit(par18,' ')[[1]])) if (n==1) sat.mat <- rbind(S1) if (n==2) sat.mat <- rbind(S1,S2) if (n==3) sat.mat <- rbind(S1,S2,S3) if (n==4) sat.mat <- rbind(S1,S2,S3,S4) if (n==5) sat.mat <- rbind(S1,S2,S3,S4,S5) if (n==6) sat.mat <- rbind(S1,S2,S3,S4,S5,S6) if (n==7) sat.mat <- rbind(S1,S2,S3,S4,S5,S6,S7) if (n==8) sat.mat <- rbind(S1,S2,S3,S4,S5,S6,S7,S8) sat.mat if (n==1) sat.sets <- list(L1) if (n==2) sat.sets <- list(L1,L2) if (n==3) sat.sets <- list(L1,L2,L3) if (n==4) sat.sets <- list(L1,L2,L3,L4) if (n==5) sat.sets <- list(L1,L2,L3,L4,L5) if (n==6) sat.sets <- list(L1,L2,L3,L4,L5,L6) if (n==7) sat.sets <- list(L1,L2,L3,L4,L5,L6,L7) if (n==8) sat.sets <- list(L1,L2,L3,L4,L5,L6,L7,L8) sat.sets (sat.mod <- strsplit(par2,' ')[[1]]) res <- plspm(x=y, sat.mat, sat.sets, sat.mod, scheme='centroid', scaled=TRUE, boot.val=TRUE) (r <- summary(res)) myr <- res$path.coefs myind <- 1 for (j in 1:(length(sat.mat[1,])-1)) { for (i in 1:length(sat.mat[,1])) { if (sat.mat[i,j] == 1) { if ((res$boot$path[myind,'perc.05'] < 0) && (res$boot$path[myind,'perc.95'] > 0)) { myr[i,j] = 0 } myind = myind + 1 } } } bitmap(file='test1.png') plotmat(round(myr,4), pos = NULL, curve = 0, name = latnames, lwd = 1, box.lwd = 1, cex.txt = 1, box.type = 'circle', box.prop = 0.5, box.cex = 1, arr.type = 'triangle', arr.pos = 0.5, shadow.size = 0.01, prefix = '', arr.lcol = 'blue', arr.col = 'blue', arr.width = 0.2, main = c('Inner Model', 'Path Coefficients')) dev.off() myr <- res$path.coefs myind <- 1 myi <- 1 for (j in 1:(length(sat.mat[1,])-1)) { for (i in 1:length(sat.mat[,1])) { if (i > j) { myr[i,j] = res$boot$total.efs[myi,'Original'] myi = myi + 1 if ((res$boot$total.efs[myind,'perc.05'] < 0) && (res$boot$total.efs[myind,'perc.95'] > 0)) { myr[i,j] = 0 } myind = myind + 1 } } } bitmap(file='test2.png') plotmat(round(myr,4), pos = NULL, curve = 0, name = latnames, lwd = 1, box.lwd = 1, cex.txt = 1, box.type = 'circle', box.prop = 0.5, box.cex = 1, arr.type = 'triangle', arr.pos = 0.5, shadow.size = 0.01, prefix = '', arr.lcol = 'blue', arr.col = 'blue', arr.width = 0.2, main = c('Inner Model', 'Total Effects')) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'PARTIAL LEAST SQUARES PATH MODELING (PLS-PM)',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'MODEL SPECIFICATION',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of Cases',header=TRUE) a<-table.element(a,r$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') -SERVER-193.190.124.10:1001

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