PLS-PM Paper

*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: Sun, 05 Sep 2010 15:14:51 +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/Sep/05/t128369985538nplzee4d983vr.htm/, Retrieved Sun, 05 Sep 2010 17:17:35 +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/Sep/05/t128369985538nplzee4d983vr.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 «

56 34 5 155 124.9 65.55 44.07 4 -2 -2 -2 5 8 11 3 70 40 16 137 550.95 191.95 113.6 4 4 4 -2 8 2 11 3 47 28 15 72 431.64 165.86 112.46 4 4 -2 4 4 4 7 6 12 10 0 0 0 0 0 -2 -2 -2 -2 8 0 12 8 31 20 3 75 111.97 62.78 72.72 0 -2 4 4 6 10 7 6 46 41 2 160 222.41 94.36 70.55 4 0 -2 4 10 8 12 12 38 37 15 165 192.3 94.45 68.92 4 4 4 4 6 7 4 7 33 30 9 211 238.98 106.73 71.66 4 4 4 4 9 7 13 13 63 36 9 83 306.95 95.4 72.51 4 4 -2 -2 10 6 10 5 28 15 17 105 377.49 133.03 93.05 4 4 4 4 3 0 8 8 45 31 13 138 433.17 139.95 145.32 4 -2 -2 4 10 8 11 9 24 17 8 63 142.63 75.71 74.55 4 4 4 4 2 6 6 5 36 25 6 139 158.74 73.83 53.7 4 0 4 4 6 8 8 10 30 25 38 98 199.55 79.16 65.84 4 0 -2 4 5 1 6 2 58 52 8 142 224.65 106.73 80.73 4 0 4 4 2 6 13 11 51 34 12 143 270.93 120.9 87.7 4 4 4 4 7 8 14 11 45 42 21 167 241.92 92.99 61.24 0 4 4 4 6 6 5 10 65 40 7 134 224.99 93.1 94.19 4 -2 4 4 4 4 10 10 64 49 8 135 172.84 76.53 84.26 4 4 4 0 4 7 6 7 24 24 3 83 99.61 68.4 49.84 4 0 -2 4 4 10 7 4 51 33 32 180 395.78 167.04 124.37 4 4 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 17 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 125 Latent Variables 6 Manifest Variables 15 Scaled? TRUE Weighting Scheme centroid Bootstrapping? TRUE Bootstrap samples 100 BLOCKS DEFINITION Block Type NMVs Mode COMPUTE Exogenous 2 Reflective REVIEW Endogenous 5 Reflective EXAM0 Endogenous 2 Formative EXAM1 Endogenous 2 Formative EXAM2 Endogenous 2 Formative EXAM3 Endogenous 2 Formative BLOCKS UNIDIMENSIONALITY Block Type.measure MVs eig.1st eig.2nd C.alpha DG.rho COMPUTE Reflective 2 1.88830220384234 0.111697796157659 0.940847499976236 0.97127337580724 REVIEW Reflective 5 3.32779751719876 0.859763518973694 0.861754569307642 0.90494255461169 EXAM0 Formative 2 1.18466617023922 0.815333829760777 0 0 EXAM1 Formative 2 1.18719641588731 0.812803584112692 0 0 EXAM2 Formative 2 1.2974678574724 0.7025321425276 0 0 EXAM3 Formative 2 1.457006962441 0.542993037558999 0 0 OUTER MODEL Block weights std.loads communal redundan COMPUTE x$numcomp 0.5538 0.9759 0.9524 0 x$numreceived 0.4752 0.9671 0.9353 0 REVIEW x$mrt 0.1331 0.6444 0.4153 0.059 x$nfm 0.3325 0.6501 0.4227 0.0601 x$afl 0.2485 0.8944 0.8 0.1137 x$lpm 0.2621 0.937 0.8781 0.1248 x$lpc 0.2673 0.861 0.7413 0.1053 EXAM0 x$Q5 0.5603 0.6953 0.4834 0.0185 x$Q9 0.7313 0.8348 0.6968 0.0267 EXAM1 x$Q2 0.6987 0.8104 0.6567 0.0343 x$Q4 0.5965 0.7273 0.5289 0.0276 EXAM2 x$Q1_1 0.8423 0.9446 0.8922 0.0759 x$Q2_2 0.3439 0.5944 0.3534 0.0301 EXAM3 x$Q1_3 0.5877 0.8546 0.7303 0.2053 x$Q2_3 0.5839 0.8525 0.7267 0.2043 CORRELATIONS BETWEEN MVs AND LVs Block COMPUTE REVIEW EXAM0 EXAM1 EXAM2 EXAM3 COMPUTE x$numcomp 0.9759 0.3921 0.1435 0.0195 0.3231 0.3105 x$numreceived 0.9671 0.3364 0.1227 -0.0257 0.2924 0.3123 REVIEW x$mrt 0.2134 0.6444 0.1178 0.081 0.0604 0.0976 x$nfm 0.3929 0.6501 0.2333 0.1937 0.2164 0.3864 x$afl 0.2592 0.8944 0.1118 0.1002 0.2623 0.3292 x$lpm 0.2831 0.937 0.1341 0.1151 0.2452 0.3434 x$lpc 0.2967 0.861 0.1484 0.1726 0.2324 0.2929 EXAM0 x$Q5 0.1034 0.108 0.6953 0.151 0.1581 0.0477 x$Q9 0.1091 0.1851 0.8348 0.1264 0.062 0.1411 EXAM1 x$Q2 -0.0032 0.1453 0.1074 0.8104 -0.0793 0.0082 x$Q4 0.0014 0.1255 0.171 0.7273 -0.0023 0.1209 EXAM2 x$Q1_1 0.261 0.2641 0.0866 -0.0972 0.9446 0.374 x$Q2_2 0.2851 0.1429 0.1773 0.073 0.5944 0.3936 EXAM3 x$Q1_3 0.2918 0.3008 0.0766 -0.0083 0.4191 0.8546 x$Q2_3 0.255 0.3679 0.1454 0.1417 0.3494 0.8525 INNER MODEL Block Concept Value S2 1 R2 0.1421 2 Intercept 0 3 path_S1 0.377 S3 1 R2 0.0384 2 Intercept 0 3 path_S2 0.1959 S4 1 R2 0.0522 2 Intercept 0 3 path_S2 0.1474 4 path_S3 0.1482 S5 1 R2 0.0851 2 Intercept 0 3 path_S2 0.2906 4 path_S4 -0.1081 S6 1 R2 0.2811 2 Intercept 0 3 path_S2 0.2907 4 path_S5 0.3714 CORRELATIONS BETWEEN LVs COMPUTE REVIEW EXAM0 EXAM1 EXAM2 EXAM3 COMPUTE 1 0.377 0.1377 -0.0014 0.3179 0.3203 REVIEW 0.377 1 0.1959 0.1764 0.2716 0.3916 EXAM0 0.1377 0.1959 1 0.177 0.1339 0.1299 EXAM1 -0.0014 0.1764 0.177 1 -0.0568 0.0779 EXAM2 0.3179 0.2716 0.1339 -0.0568 1 0.4504 EXAM3 0.3203 0.3916 0.1299 0.0779 0.4504 1 SUMMARY INNER MODEL LV.Type Measure MVs R.square Av.Commu Av.Redun AVE COMPUTE Exogen Rflct 2 0 0.9438 0 0.944 REVIEW Endogen Rflct 5 0.1421 0.6515 0.0926 0.651 EXAM0 Endogen Frmtv 2 0.0384 0.5901 0.0226 0 EXAM1 Endogen Frmtv 2 0.0522 0.5928 0.031 0 EXAM2 Endogen Frmtv 2 0.0851 0.6228 0.053 0 EXAM3 Endogen Frmtv 2 0.2811 0.7285 0.2048 0 GOODNESS-OF-FIT GoF Value Absolute 0.285579477892365 Relative 0.818028650850482 Outer.mod 0.994436145545231 Inner.mod 0.82260550817164 TOTAL EFFECTS relationships dir.effect ind.effect tot.effect S1->S2 0.376975881555302 0 0.376975881555302 S1->S3 0 0.0738322446028806 0.0738322446028806 S1->S4 0 0.0664905421215465 0.0664905421215465 S1->S5 0 0.102378533748008 0.102378533748008 S1->S6 0 0.147624545744898 0.147624545744898 S2->S3 0.195854027314078 0 0.195854027314078 S2->S4 0.147355494275565 0.0290232752287973 0.176378769504363 S2->S5 0.290636626547546 -0.0190581549338686 0.271578471613678 S2->S6 0.290735137442249 0.100866959582735 0.391602097024984 S3->S4 0.148188299351408 0 0.148188299351408 S3->S5 0 -0.0160121060848867 -0.0160121060848867 S3->S6 0 -0.00594705628801173 -0.00594705628801173 S4->S5 -0.108052431635754 0 -0.108052431635754 S4->S6 0 -0.0401317534113076 -0.0401317534113076 S5->S6 0.371409997940556 0 0.371409997940556 BOOTSTRAP VALIDATION - WEIGHTS Original Mean.Boot Std.Error perc.05 perc.95 x$numcomp 0.553753617826643 0.555462030725114 0.0273001964992708 0.520498566970891 0.594266765009948 x$numreceived 0.475220473028747 0.473511090194505 0.0268002199770149 0.436156626676904 0.505196008904125 x$mrt 0.133090737242483 0.129435984007925 0.0540824712887308 0.0221581697913146 0.202673975052174 x$nfm 0.3325261368262 0.318628768899111 0.0462158382222463 0.243863644985372 0.398935948998172 x$afl 0.248513653673905 0.25524602023479 0.0252850812070595 0.219773297197264 0.297083556305511 x$lpm 0.262117893929234 0.267384469467292 0.0180338248314874 0.245268064533582 0.293771981157163 x$lpc 0.267319748632162 0.264237699053353 0.0389969072090675 0.203458477009033 0.324773274644734 x$Q5 0.56025661062034 0.45067010462858 0.430518170214844 -0.680779366076353 0.98006820734314 x$Q9 0.731294966719872 0.670934653337788 0.298909043161908 0.105162028711268 0.98507454527349 x$Q2 0.698692267988511 0.586555328810607 0.316512889058067 -0.0674262422298432 0.9696495470284 x$Q4 0.596487066591881 0.584975164962354 0.354503296307656 0.0287305903755525 1.00365383649942 x$Q1_1 0.842269090285016 0.727907456832702 0.213462874523915 0.285402693013386 1.00057252966934 x$Q2_2 0.343890468169123 0.448992428710073 0.252946438891498 -0.00190316255355388 0.868596827525318 x$Q1_3 0.587705544412529 0.57558507791292 0.182927053057814 0.296677669390845 0.863828645332055 x$Q2_3 0.583905164998168 0.573910361668806 0.188051139316879 0.263321708344395 0.829320250519676 BOOTSTRAP VALIDATION - LOADINGS Original Mean.Boot Std.Error perc.05 perc.95 x$numcomp 0.97589301132908 0.97600086723082 0.00414381541001758 0.970486521322756 0.983262829786502 x$numreceived 0.967121032129824 0.96664717624658 0.00665439472259558 0.95643431438181 0.976466613670537 x$mrt 0.644418928726694 0.652334471954828 0.0839292703589878 0.498841169248474 0.78972315186129 x$nfm 0.65012097002215 0.63992824129666 0.0606669922444902 0.538498763152236 0.734561402546278 x$afl 0.894422045366667 0.895435609473154 0.0226031812683594 0.860599662474177 0.929704612193706 x$lpm 0.937045840238933 0.939950702779052 0.0130710215386941 0.920737771222743 0.959937350552744 x$lpc 0.86098772593548 0.854810869414369 0.0432548513746062 0.774666668616105 0.91197539408801 x$Q5 0.695302051439719 0.571112219526708 0.396791614694739 -0.447057664672051 0.985552275161499 x$Q9 0.834755409354338 0.743335680933893 0.273159576242699 0.177886464153935 0.996250626485454 x$Q2 0.810352508977645 0.700921054327393 0.25623761823037 0.156167251692173 0.990599245844105 x$Q4 0.727279754967505 0.69090099436637 0.313053069310489 0.227196802413121 0.995035237505536 x$Q1_1 0.944565451056465 0.859976201029919 0.143079372936456 0.600034655719405 0.999135608691803 x$Q2_2 0.594438449871434 0.662643419985725 0.199504391198469 0.278309911510852 0.962811846323056 x$Q1_3 0.854554270221953 0.837338267080858 0.105294427452294 0.657213218653616 0.974699320155514 x$Q2_3 0.852490690659873 0.8393117984157 0.103725086682567 0.633894494729432 0.966762104638334 BOOTSTRAP VALIDATION - PATHS Original Mean.Boot Std.Error perc.05 perc.95 S1->S2 0.376975881555302 0.385050750503302 0.071412882124357 0.279026775722166 0.488480292554856 S2->S3 0.195854027314078 0.173549872071601 0.0960990901237967 0.0128015007891916 0.301956012716224 S2->S4 0.147355494275565 0.147826751519835 0.071953044926407 0.0230115977771695 0.237522144614576 S2->S5 0.290636626547546 0.275901175366566 0.0928001983498093 0.134792313760784 0.410405458177398 S2->S6 0.290735137442249 0.306271317294721 0.0681425387701249 0.181587607297289 0.404034039269814 S3->S4 0.148188299351408 0.186510041851263 0.104593256865028 0.0628241461526001 0.361049436574114 S4->S5 -0.108052431635754 -0.0739743458845461 0.148123903013346 -0.266868995043899 0.17213873479644 S5->S6 0.371409997940556 0.387944844588541 0.0834611410100133 0.26417514701225 0.506640226705388 BOOTSTRAP VALIDATION - RSQ Original Mean.Boot Std.Error perc.05 perc.95 S2 0.142110815274397 0.15331288219913 0.0573407003967456 0.0778559648246902 0.238621572109348 S3 0.0383588000151434 0.0392622428674643 0.0292883209751501 0.00292409605448227 0.091177992705348 S4 0.0522268918915289 0.0815562678466298 0.0420849987173447 0.0327142129091161 0.162264317437125 S5 0.0850669809569372 0.103081115506381 0.0444381239753458 0.0482950107064089 0.17238091487901 S6 0.281123445429104 0.317199026565434 0.0746978673744551 0.186807547488693 0.426993071956298 BOOTSTRAP VALIDATION - TOTAL EFFECTS Original Mean.Boot Std.Error perc.05 perc.95 S1->S2 0.376975881555302 0.385050750503302 0.071412882124357 0.279026775722166 0.488480292554856 S1->S3 0.0738322446028806 0.068517707232986 0.0412423142409355 0.0042148195668242 0.134564566479968 S1->S4 0.0664905421215465 0.0685948592817424 0.0325022689339978 0.0234285624526436 0.111832415145125 S1->S5 0.102378533748008 0.102606069877474 0.0417021008391825 0.0422156305801746 0.177279232572301 S1->S6 0.147624545744898 0.156530112327706 0.0361788417235287 0.100952016559953 0.216933279483106 S2->S3 0.195854027314078 0.173549872071601 0.0960990901237967 0.0128015007891916 0.301956012716224 S2->S4 0.176378769504363 0.17810947381534 0.076063229044457 0.0573454004356738 0.293301957489129 S2->S5 0.271578471613678 0.263045152844675 0.0880140089798083 0.123926591926111 0.388667852002544 S2->S6 0.391602097024984 0.408862612605098 0.0702951554730089 0.291693036121398 0.512377287924419 S3->S4 0.148188299351408 0.186510041851263 0.104593256865028 0.0628241461526001 0.361049436574114 S3->S5 -0.0160121060848867 -0.010707809151843 0.0305380859928105 -0.0527337540885374 0.0455885124285204 S3->S6 -0.00594705628801173 -0.00397391901761467 0.0118180909011259 -0.0218388345205717 0.0135577289231678 S4->S5 -0.108052431635754 -0.0739743458845461 0.148123903013346 -0.266868995043899 0.17213873479644 S4->S6 -0.0401317534113076 -0.0284742782255909 0.0593913661867966 -0.105127025955422 0.0776164495998728 S5->S6 0.371409997940556 0.387944844588541 0.0834611410100133 0.26417514701225 0.506640226705388

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Sep/05/t128369985538nplzee4d983vr/1rdac1283699667.png (open in new window) http://www.freestatistics.org/blog/date/2010/Sep/05/t128369985538nplzee4d983vr/1rdac1283699667.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Sep/05/t128369985538nplzee4d983vr/2dd8i1283699667.png (open in new window) http://www.freestatistics.org/blog/date/2010/Sep/05/t128369985538nplzee4d983vr/2dd8i1283699667.ps (open in new window)

Parameters (Session):

Parameters (R input):

par1 = COMPUTE REVIEW EXAM0 EXAM1 EXAM2 EXAM3 ; par2 = A A B B B B ; par3 = 1 2 ; par4 = 3 4 5 6 7 ; par5 = 8 9 ; par6 = 10 11 ; par7 = 12 13 ; par8 = 14 15 ; par11 = 0 0 0 0 0 0 ; par12 = 1 0 0 0 0 0 ; par13 = 0 1 0 0 0 0 ; par14 = 0 1 1 0 0 0 ; par15 = 0 1 0 1 0 0 ; par16 = 0 1 0 0 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) { 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$total.efs[myind,'perc.05'] < 0) { myr[i,j] = 0 } myind = myind + 1 } } } bitmap(file='test2.png') plotmat(round(myr,4), pos = NULL, curve = 0, name = latnames, lwd = 1, box.lwd = 1, cex.txt = 1, box.type = 'circle', box.prop = 0.5, box.cex = 1, arr.type = 'triangle', arr.pos = 0.5, shadow.size = 0.01, prefix = '', arr.lcol = 'blue', arr.col = 'blue', arr.width = 0.2, main = c('Inner Model', 'Total Effects')) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'PARTIAL LEAST SQUARES PATH MODELING (PLS-PM)',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'MODEL SPECIFICATION',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of Cases',header=TRUE) a<-table.element(a,r$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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