Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_partial_least_squares.wasp
Title produced by softwarePartial Least Squares - Path Modeling
Date of computationWed, 03 Dec 2025 16:35:06 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2025/Dec/03/t1764776244n0fa3nctqep8o1a.htm/, Retrieved Mon, 07 Sep 2026 17:24:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=320566, Retrieved Mon, 07 Sep 2026 17:24:01 +0000
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Original text written by user:privado
IsPrivate?No (this computation is public)
User-defined keywordsdesempeño autodireccion
Estimated Impact121
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataseries X:
ID.VD1.VD2.VD3.VD4.VD5.VD6.VD7.VD8.VD9.VD10.VD11.VD12.VD13.VD14.VD15.VD16.VD17.VD18.VD19.VD20.VD21.VD22.VD23.VD24.VD25.VD26.VD27.VD28.VD29.VD30.VD31.VD32.VD33.VD34.VD35.VD36.VD37.VD38.VD39.VD40.VD41.VD42.VD43.VD44.VD45.VD46.VD47.VD48.VD49.VD50.VD51.VD52.VD53.VD54.VD55.VD56.VD57.VD58.VD59.VD60.VD61.VD62.VD63.VD64.VD65.C1.C2.C3.C4.C5.C6.C7.C8.C9.C10.C11.C12.C13.C14.C15.C16.C17.C18.C19.C20.C21.C22.C23.C24.C25.C26.C27.C28.C29.C30.C31.C32.C33.C34.C35.C36.C37.C38.C39.C40.C41.C42.C43.C44.C45.C46.C47.C48.C49.C50
ID.1.0.-0.0154642540902304.-0.0240497183288247.0.09237623780392609.0.02911438950538364.-0.01836401484837719.0.07859304909539936.0.017041480928622537.0.005510362536381919.0.025375402795159504.0.04106853618756515.-0.008502756813923427.0.07288312563224667.0.00337925622701748.0.03096887183190514.0.0615010151184024.0.041661858871751536.0.015198607507131504.-0.003533812604952324.0.03786235305596374.0.0313214328186526.0.03727644937998022.0.03088024544392284.0.057229401387511846.0.05799908729735048.0.04010343601879634.0.04427356220299385.0.0424532629092185.0.020276242239178553.0.00815955815077486.0.026392030087548843.0.04456016938886275.0.040209541039321376.0.013871846184448738.0.027563579288038286.0.007692369155006034.0.06098337923081154.0.02137113249866363.0.03195134321417648.0.038082284234978234.0.031184814191550695.0.015577212742668782.0.02453247145416033.0.020017972868002846.0.012719012213786008.0.015244900936744773.0.03178633329162128.0.04512975782404033.0.017154592500322586.0.030984340005587496.0.0214981959754267.0.023790775215770286.0.027230375995542248.0.040256619082507254.0.015029973959034272.0.03017610664781405.0.03303144128115382.0.04780652384652196.0.05048227831922003.0.04583257917898623.0.0361757873107053.0.1352699768819337.0.2296576692940248.0.21412331322562838.0.17966113681940496.0.10065009032838046.0.01755302388765214.0.025475072120365894.0.1355261372069207.0.07656520494813285.0.08653092364423128.0.1469456057301547.0.10381691113492005.0.04959197765902861.0.043457390706198865.0.04541682103059068.0.11164123192426382.0.09542373634641843.0.06975689025949291.0.07488910468034131.0.1101432004428126.0.12325150295155143.0.10216330166733276.0.1180484947750637.0.09206026849325821.0.06275869326044017.0.14141178194367805.0.1308260583569171.0.15294028457073748.0.13310843556446436.0.0901575578683254.0.09793394591182015.0.09810757407694413.0.1043757832561163
VD1.-0.0154642540902304.1.0.0.3635131785925432.0.2643860811253645.0.23945690515908697.0.2042031119011111.0.2851321833736902.0.2669152087210817.0.23077853655955538.0.22797183166617103.0.2573631333271026.0.2684482069709942.0.3014516431436891.0.22054491437739727.0.2441514756222428.0.24817996221821246.0.24549453285815343.0.24057018746468082.0.2071772015803309.0.22815912156626754.0.22370525817297833.0.2504857175978946.0.23608210637775346.0.2649056680833505.0.23602033183121535.0.24842618939308754.0.22880113591948676.0.21595785878452695.0.1683258993227689.0.1970014794439464.0.2374318857705089.0.2332166689131298.0.23110656107809285.0.21186428202263595.0.21122659401552373.0.22964754441572516.0.2617263150751981.0.20593599653260728.0.2554450933287648.0.17254099398208414.0.2311673381570682.0.19217345363266803.0.2102452191704642.0.23567959170777063.0.2069612011007678.0.21238592148945035.0.207269342411487.0.20748925361543826.0.25750093461917896.0.2378150272669639.0.18804295180729487.0.2246745926882136.0.22039723840242706.0.1852655143513245.0.19167581346836368.0.1917484638024551.0.2242590685058456.0.1989039469199687.0.20109661826061058.0.19797798843978974.0.22637536480207204.0.2234471987840109.0.21499721641912787.0.2122267453891161.0.3122267453891161.0.38873986416583695.0.4349191916820379.0.3
C35.0.0361757873107053.0.1352699768819337.0.2296576692940248.0.21412331322562838.0.17966113681940496.0.10065009032838046.0.01755302388765214.0.025475072120365894.0.1355261372069207.0.07656520494813285.0.08653092364423128.0.1469456057301547.0.10381691113492005.0.04959197765902861.0.043457390706198865.0.04541682103059068.0.11164123192426382.0.09542373634641843.0.06975689025949291.0.07488910468034131.0.1101432004428126.0.12325150295155143.0.10216330166733276.0.1180484947750637.0.09206026849325821.0.06275869326044017.0.14141178194367805.0.1308260583569171.0.15294028457073748.0.13310843556446436.0.0901575578683254.0.09793394591182015.0.09810757407694413.0.1043757832561163.0.0897723245106373.0.07891553427837914.0.1125261712465951.0.08289194435536228.0.08436353140737366.0.08631898641456799.0.06275666325716414.0.08970112354408343.0.07518917602662475.0.07502560104952508.0.0771735814765123.0.07997552061269286.0.08332776431792792.0.06255748601970119.0.07571870089292633.0.08094694399575663.0.07386798188235224.0.0783766099269481.0.0669683876744337.0.06823931064533489.0.07149779068386388.0.0649188579016342.0.07086694220025387.0.06778779251502198.0.07113954166602074
C36.0.1352699768819337.0.2617263150751981.0.19529203470449218.0.1773248329400011.0.17328306688036348.0.1340814812835583.0.2002534891772861.0.18819228309140447.0.1554814550872461.0.15340873362175622.0.1804834108370623.0.1727097288416269.0.18200734405531585.0.14739846442761797.0.15829516252923303.0.15179803547360217.0.1499603621521748.0.14023990462229592.0.1264544393406768.0.14306003096451493.0.15043927563410422.0.1463860793241744.0.1471121819887149.0.1573795092749336.0.1375514839872093.0.14862999562936737.0.1307528064369446.0.12003842805824983.0.11223663743024629.0.14273698278574732.0.14072846777453086.0.1270289429688571.0.1296019915439729.0.12454733593182879.0.12167342497889702.0.13977066432415304.0.15353320645210838.0.12378305358094614.0.14160108743430563.0.11982329918762273.0.12363677046875417.0.13384525346207015.0.12622998781679056.0.11907854555287902.0.11806532898248383.0.11941058274091821.0.12761688632636514.0.12086630268220833.0.11170722295218945.0.12867129439851864.0.1186360646478849.0.10646799428810177.0.10971784039162165.0.10789991348679984.0.11634886821885625.0.10128072125009786.0.1134371322770228.0.11036518416081746
C37.0.2296576692940248.0.20593599653260728.0.3622450860719348.0.3106946763185093.0.24306807084139188.0.213511854625063.0.2948938206009519.0.28604708765515784.0.23227127328671134.0.2420662836684823.0.24801845030896928.0.2748470362507198.0.2716408149256029.0.1997845382030979.0.23426830666877402.0.22797476359249583.0.21689860852347608.0.21219910863206476.0.19104032762523813.0.21415878692316373.0.21410311833746085.0.23532487739670685.0.22668371506988986.0.24516887497130882.0.22190330822467188.0.2379711874694171.0.21648787655073897.0.20567257605204676.0.19023979508585705.0.2000369797517552.0.21709243965004513.0.21116751651574695.0.2042914773134749.0.19772649883549002.0.1962057091499631.0.20737493810906406.0.22393150605980502.0.19191219946828602.0.21083360323191213.0.18480413577493774.0.19981979823696434.0.19748147025340902.0.19392987179966804.0.20529388934327092.0.1888646310750731.0.18948218249233526.0.189224360926375.0.214382736933193.0.2022054957641871.0.17794421514618368.0.19903701358722983.0.19869951900772164.0.1753740486505596.0.19030919440096286.0.16965717022980344.0.19245847594387948
C38.0.21412331322562838.0.2554450933287648.0.3106946763185093.0.3979624029409098.0.25863871153003996.0.236946698350095.0.2966504463585858.0.2916139090358189.0.24580389803314144.0.2547362752080916.0.26098158071384464.0.2846882389825011.0.27039365596047834.0.2341633853516177.0.2464694277855129.0.2601457122045016.0.24930387358672682.0.22594610149120304.0.22699985874914116.0.2479475316644093.0.2452463243468656.0.27763130597516046.0.25028280290235696.0.27366244723800786.0.2562863776103974.0.27365540024138686.0.242545398091246.0.2324269530679384.0.2000274643370713.0.23512671367124056.0.24725082188060985.0.24427934480942945.0.23015287625071253.0.23095800772048165.0.20616591738864494.0.2429986412714995.0.25507937606231265.0.22688279786103964.0.2502636236965122.0.21922206991166992.0.24521175605172434.0.22647654258513074.0.23092343023650396.0.2507039760858371.0.2309183365469904.0.23756503668348202.0.22709599294828616.0.2468289583958576.0.2394850619286201.0.20894682228654746.0.23582874256602074.0.2331673935543059.0.20532906420625843.0.22019609313207948.0.2137770219023194.0.21449196857887023
C39.0.17966113681940496.0.17254099398208414.0.24306807084139188.0.25863871153003996.0.3447772229146294.0.22606201111968282.0.19880937184702347.0.22435525562983899.0.20787517918719793.0.2006029622628138.0.21584735587745692.0.2142220309624821.0.23227813039561372.0.1782899027550659.0.21553506505242643.0.21486881649454554.0.1935299837641533.0.1960989013711167.0.17338889256208096.0.18587325526723338.0.19087954337888886.0.20017455369677764.0.20344685827847253.0.2132355987088849.0.1979826004287058.0.20761723861795795.0.2001845126633128.0.1899456747635017.0.16036559512572603.0.1807059619517135.0.2017081103700926.0.20900777989853916.0.18935077662082253.0.18875460156688764.0.1729141209084975.0.18825739468284476.0.17705987779044583.0.18909920610375638.0.1690735084351968.0.19060196087241673.0.18781511624532533.0.1781292713907282.0.18518341380776605.0.19211162051526794.0.16395551925733006.0.1744279532787391.0.1725729899515761.0.18739477184631194.0.18791465556097058.0.167946827025348.0.1848377852404343.0.18517048392507568.0.15779182797797507.0.1699952446836684.0.15343706479737692.0.1738577287669221
C40.0.10065009032838046.0.2311673381570682.0.213511854625063.0.236946698350095.0.22606201111968282.0.2509308922481677.0.23016442786132908.0.22901832061308078.0.21500333119706827.0.2266482626391965.0.23683322821585665.0.23987392920923339.0.2381339589751121.0.20638924160178628.0.2200192523173982.0.22434455627014268.0.2162233661249726.0.20811318472377007.0.18375001708937186.0.2042713801736407.0.2040569963725855.0.22728629704547658.0.22354922559063636.0.23610753971871272.0.22752888471652106.0.2329929167916245.0.21324738357616965.0.20697280210148746.0.17537566055142557.0.19154316946616435.0.21688180755823585.0.2144639617778917.0.21100061363312778.0.1978006955136942.0.18904705816131952.0.21940063249497566.0.20598176977047642.0.19848647119612017.0.20423654988446164.0.19062163773679218.0.2035542795880305.0.21210783927609923.0.1969532557923471.0.1957172571552643.0.20019895312873817.0.19754835142685788.0.2005879440939837.0.1795918712010335.0.20991861554823402.0.18854010148941946.0.17377514110504444.0.18847273408261664.0.16602659688059662.0.20084559262064086.0.1821927346930592.0.18169794674680346
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C43.0.1355261372069207.0.031184814191550695.0.23227127328671134.0.24580389803314144.0.20787517918719793.0.21500333119706827.0.23663617768529225.0.2478763584875437.0.22430749205268336.0.2386318937928343.0.20826635584080347.0.2482445710912955.0.2467079636928432.0.16445146305816552.0.20356324335079495.0.199212666052542.0.20783748377385795.0.19189206297394856.0.17079660459134115.0.19865279958392423.0.19017826653066638.0.2153679912409572.0.2058913823450464.0.22774759338703503.0.209693492878277.0.23007911140961773.0.21559458154483793.0.19473646140627112.0.15878105382269728.0.18021815981486884.0.20000834216456098.0.19865569746543355.0.1899122862942667.0.20152372395735963.0.1887114855830759.0.2015510308347843.0.2158002905982398.0.19690353671900743.0.19839497960027363.0.18468556474093887.0.1974323267275526.0.1935661330351253.0.19804945091111812.0.1839058687342329.0.19163705358466646.0.20348500005057465.0.17619163211989432.0.20664397891861103.0.1806239385940609.0.17036836419923422.0.18522585305782337.0.17676250302182854.0.18405133332696553.0.16965652582828366
C44.0.08653092364423128.0.02453247145416033.0.2420662836684823.0.2547362752080916.0.2006029622628138.0.2266482626391965.0.24595199679631884.0.261028927826071.0.2386318937928343.0.2657641771726243.0.24965394801468124.0.26819640006221266.0.2624731356528034.0.19636183416015023.0.2345605133757883.0.24660849367521627.0.24261589385771427.0.21467633498775934.0.2013386308197705.0.23367153003696863.0.2352507688918307.0.2594213841423981.0.2405137130802234.0.26511605710114156.0.24390799364048328.0.2503378387947719.0.2424680936653758.0.22406206567393478.0.1753827231240211.0.19982809363927807.0.22952567838485815.0.2242045742297953.0.22049994859277573.0.2168646084212028.0.20597918659819646.0.2385062123326751.0.23170670048536757.0.21479969554173793.0.236445958711818.0.21042563691308315.0.22610417458819907.0.2267293873465392.0.2266332076912979.0.234439663836687.0.23200200376556747.0.24415881787628447.0.21903053620775234.0.24649420504220424.0.21442234644316812.0.2034396484685149.0.2120031977778836.0.20981860322794278.0.21443701141013207.0.19368490999282978.0.2143563309452469.0.20438373312577884
C45.0.1469456057301547.0.020017972868002846.0.24801845030896928.0.26098158071384464.0.21584735587745692.0.23683322821585665.0.24193835329682398.0.24338141704411678.0.20826635584080347.0.24965394801468124.0.28042670665148935.0.2502639471032146.0.24728360743006356.0.15241358939433768.0.19655391225248702.0.21008839194277102.0.20673833810929107.0.1952315168190188.0.16004550537410346.0.21197665804432103.0.19427652080611777.0.22480102019230816.0.2266654569337406.0.24186881073280864.0.2257940298701669.0.24680239623892906.0.22702647264281972.0.20973183031303905.0.17079729215948927.0.19896094526196067.0.2376848809267449.0.2218837291978174.0.21907498551332972.0.21717552535290342.0.1888536175196258.0.2217662986111914.0.214732354543101.0.20336476785499238.0.23543011647104855.0.19847908325454902.0.21936879632076934.0.215846877648119.0.21043932118334202.0.21858211234485595.0.2065309841232518.0.2063653544651926.0.19994028801605864.0.22375183085636163.0.20591762043068772.0.1956552895970441.0.2143482089590984.0.21321676849793264.0.21023057245251436.0.20100025823196216.0.2113627998567432.0.1990204783450638




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=320566&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=320566&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=320566&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center



Parameters (Session):
Parameters (R input):
par1 = Desempeño Autodireccion ; par2 = B B B B A A A A A ; par3 = 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 ; par4 = 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = 0 1 ; par12 = 0 0 ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ;
R code (references can be found in the software module):
par18 <- ''
par17 <- ''
par16 <- ''
par15 <- ''
par14 <- ''
par13 <- ''
par12 <- ''
par11 <- ''
par10 <- ''
par9 <- ''
par8 <- ''
par7 <- ''
par6 <- ''
par5 <- ''
par4 <- ''
par3 <- ''
par2 <- ''
par1 <- ''
library(plspm)
library(diagram)
y <- as.data.frame(t(y))
is.data.frame(y)
head(y)
trim <- function(char) {
return(sub('s+$', '', sub('^s+', '', char)))
}
(latnames <- strsplit(par1,' ')[[1]])
(n <- length(latnames))
(L1 <- as.numeric(strsplit(par3,' ')[[1]]))
(L2 <- as.numeric(strsplit(par4,' ')[[1]]))
(L3 <- as.numeric(strsplit(par5,' ')[[1]]))
(L4 <- as.numeric(strsplit(par6,' ')[[1]]))
(L5 <- as.numeric(strsplit(par7,' ')[[1]]))
(L6 <- as.numeric(strsplit(par8,' ')[[1]]))
(L7 <- as.numeric(strsplit(par9,' ')[[1]]))
(L8 <- as.numeric(strsplit(par10,' ')[[1]]))
(S1 <- as.numeric(strsplit(par11,' ')[[1]]))
(S2 <- as.numeric(strsplit(par12,' ')[[1]]))
(S3 <- as.numeric(strsplit(par13,' ')[[1]]))
(S4 <- as.numeric(strsplit(par14,' ')[[1]]))
(S5 <- as.numeric(strsplit(par15,' ')[[1]]))
(S6 <- as.numeric(strsplit(par16,' ')[[1]]))
(S7 <- as.numeric(strsplit(par17,' ')[[1]]))
(S8 <- as.numeric(strsplit(par18,' ')[[1]]))
if (n==1) sat.mat <- rbind(S1)
if (n==2) sat.mat <- rbind(S1,S2)
if (n==3) sat.mat <- rbind(S1,S2,S3)
if (n==4) sat.mat <- rbind(S1,S2,S3,S4)
if (n==5) sat.mat <- rbind(S1,S2,S3,S4,S5)
if (n==6) sat.mat <- rbind(S1,S2,S3,S4,S5,S6)
if (n==7) sat.mat <- rbind(S1,S2,S3,S4,S5,S6,S7)
if (n==8) sat.mat <- rbind(S1,S2,S3,S4,S5,S6,S7,S8)
sat.mat
if (n==1) sat.sets <- list(L1)
if (n==2) sat.sets <- list(L1,L2)
if (n==3) sat.sets <- list(L1,L2,L3)
if (n==4) sat.sets <- list(L1,L2,L3,L4)
if (n==5) sat.sets <- list(L1,L2,L3,L4,L5)
if (n==6) sat.sets <- list(L1,L2,L3,L4,L5,L6)
if (n==7) sat.sets <- list(L1,L2,L3,L4,L5,L6,L7)
if (n==8) sat.sets <- list(L1,L2,L3,L4,L5,L6,L7,L8)
sat.sets
(sat.mod <- strsplit(par2,' ')[[1]])
res <- plspm(y, sat.mat, sat.sets, sat.mod, scheme='centroid', scaled=TRUE, boot.val=TRUE)
(r <- summary(res))
(myr <- res$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.025'] < 0) && (res$boot$path[myind,'perc.975'] > 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$total.efs[myind,'perc.025'] < 0) && (res$boot$total.efs[myind,'perc.975'] > 0)) {
myr[i,j] = 0
}
myind = myind + 1
}
}
}
bitmap(file='test2.png')
plotmat(round(myr,4), pos = NULL, curve = 0, name = latnames,
lwd = 1, box.lwd = 1, cex.txt = 1, box.type = 'circle',
box.prop = 0.5, box.cex = 1, arr.type = 'triangle',
arr.pos = 0.5, shadow.size = 0.01, prefix = '', arr.lcol = 'blue',
arr.col = 'blue', arr.width = 0.2, main = c('Inner Model',
'Total Effects'))
dev.off()
labels(r)
labels(r$model)
labels(r$gof)
labels(r$inputs)
print(r$model)
print(r$gof)
print(r$inputs)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'PARTIAL LEAST SQUARES PATH MODELING (PLS-PM)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'MODEL SPECIFICATION',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of Cases',header=TRUE)
a<-table.element(a,r$model$gens$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,as.character(r$model$specs$scaled))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Weighting Scheme',header=TRUE)
a<-table.element(a,r$model$specs$scheme)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Bootstrapping?',header=TRUE)
a<-table.element(a,as.character(r$model$specs$boot.val))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Bootstrap samples',header=TRUE)
a<-table.element(a,r$model$specs$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$inputs$Type[i])
a<-table.element(a,r$inputs$Size[i])
a<-table.element(a,r$inputs$Mode[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$inputs$Type[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',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'MV Number',header=TRUE)
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:length(r$outer_model[,1])) {
a<-table.row.start(a)
a<-table.element(a,i,header=T)
a<-table.element(a,r$outer_model[i,1])
a<-table.element(a,r$outer_model[i,3])
a<-table.element(a,r$outer_model[i,4])
a<-table.element(a,r$outer_model[i,5])
a<-table.element(a,r$outer_model[i,6])
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:length(r$crossloadings[,1])) {
a<-table.row.start(a)
a<-table.element(a,r$crossloadings[i,1],header=TRUE)
for(j in 1:n) {
a<-table.element(a,r$crossloadings[i,2+j])
}
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'INNER MODEL',5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Block',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'S.E.',header=TRUE)
a<-table.element(a,'t value',header=TRUE)
a<-table.element(a,'Pr(>|t|)',header=TRUE)
a<-table.row.end(a)
for (i in 1:(length(labels(r$inner_model)))) {
a<-table.row.start(a)
print (paste('i=',i,sep=''))
a<-table.element(a,labels(r$inner_model)[i],3,header=TRUE)
a<-table.row.end(a)
for (j in 1:length(r$inner_model[[i]][,1])) {
print (paste('j=',j,sep=''))
a<-table.row.start(a)
a<-table.element(a,rownames(r$inner_model[[i]])[j],header=T)
a<-table.element(a,r$inner_model[[i]][j,1])
a<-table.element(a,r$inner_model[[i]][j,2])
a<-table.element(a,r$inner_model[[i]][j,3])
a<-table.element(a,r$inner_model[[i]][j,4])
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable6.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'CORRELATIONS BETWEEN LVs',n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',header=TRUE)
for (iii in 1:n) {
a<-table.element(a,latnames[iii],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,latnames[i],header=T)
for (j in 1:n) {
a<-table.element(a,r$correlations[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',6,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,'R-squared',header=TRUE)
a<-table.element(a,'Block Communality',header=TRUE)
a<-table.element(a,'Mean Redundancy',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$inner_summary[i,1])
a<-table.element(a,r$inner_summary[i,2])
a<-table.element(a,r$inner_summary[i,3])
a<-table.element(a,r$inner_summary[i,4])
a<-table.element(a,r$inner_summary[i,5])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'TOTAL EFFECTS',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'relationships',header=TRUE)
a<-table.element(a,'dir.effect',header=TRUE)
a<-table.element(a,'ind.effect',header=TRUE)
a<-table.element(a,'tot.effect',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(r$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')