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Author

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R Software Module

Patrick.Wessarwasp_partial_least_squares.wasp

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 https://freestatistics.org/blog/index.php?v=date/2010/May/01/t1272737487k5nllcexhl8tkfh.htm/, Retrieved Tue, 23 Apr 2024 10:31:53 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=75156, Retrieved Tue, 23 Apr 2024 10:31:53 +0000

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IsPrivate?

No (this computation is public)

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Estimated Impact

191

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Kendall tau Correlation Matrix] [Sleep in Mammals ...] [2010-03-20 10:44:59] [b98453cac15ba1066b407e146608df68]
- RMPD    [Partial Least Squares - Path Modeling] [Review of Sleep A...] [2010-05-01 17:45:35] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
- RMP       [Partial Least Squares - Path Modeling] [] [2010-12-16 09:56:46] [b98453cac15ba1066b407e146608df68] 

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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	0.60205999132796	1.04139268515823	0.17609125905568
1.47712125471966	2.66745295288995	2.62634036737504	2.44870631990508	0.69897000433602	0.69897000433602	0.69897000433602	0.50514997831991	-0.15490195998574
0.54406804435028	-1.09691001300806	0.079181246047625	1.6232492903979	0	0	0	0.79934054945358	0.32221929473392
0.77815125038364	-0.10237290870956	0.54406804435028	1.6232492903979	0.30102999566398	0.30102999566398	0.30102999566398	0.81954393554187	0.61278385671974
1.01703333929878	-0.69897000433602	0.69897000433602	2.07918124604762	0.30102999566398	0.30102999566398	0.30102999566398	0.97772360528885	0.079181246047625
1.30102999566398	1.44185217577329	2.06069784035361	2.17026171539496	0.69897000433602	0.69897000433602	0.69897000433602	0.51851393987789	-0.30102999566398
0.5910646070265	-0.92081875395238	0	1.20411998265592	0.47712125471966	0	0.30102999566398	1.04139268515823	0.53147891704226
1.61278385671974	1.92941892571429	2.51188336097887	2.49136169383427	0	0.47712125471966	0	0.67209785793572	0.17609125905568
0.95424250943932	-1	0.60205999132796	1.44715803134222	0.69897000433602	0	0.47712125471966	1.01703333929878	0.53147891704226
0.88081359228079	0.01703333929878	0.74036268949424	1.83250891270624	0.69897000433602	0.47712125471966	0.60205999132796	0.86923171973098	-0.096910013008056
1.66275783168157	2.71683772329952	2.81624129999178	2.52633927738984	0.69897000433602	0.69897000433602	0.69897000433602	0.32221929473392	-0.096910013008056
1.38021124171161	-2	-0.60205999132796	1.69897000433602	0	0	0	1.25285303097989	0.30102999566398
2	1.79239168949825	3.12057393120585	2.42651126136458	0	0	0	0.78532983501077	0.27875360095283
0.50514997831991	-1.69897000433602	-0.39794000867204	1.27875360095283	0.60205999132796	0	0.47712125471966	1.07554696139253	0.11394335230684
0.69897000433602	0.23044892137827	0.79934054945358	1.07918124604762	0.30102999566398	0	0	1.13987908640124	0.7481880270062
0.81291335664286	0.54406804435028	1.03342375548695	2.07918124604762	0.30102999566398	0	0	1.15533603746506	0.49136169383427
1.07918124604762	-0.31875876262441	1.19033169817029	2.14612803567824	0.30102999566398	0.30102999566398	0.30102999566398	1.18184358794477	0.25527250510331
1.30535136944662	1	2.06069784035361	2.23044892137827	0.60205999132796	0.60205999132796	0.60205999132796	1	-0.045757490560675
1.11394335230684	0.20951501454263	1.05690485133647	1.23044892137827	0.30102999566398	0	0.30102999566398	1.07554696139253	0.25527250510331
1.43136376415899	2.28330122870355	2.25527250510331	2.06069784035361	0.60205999132796	0.60205999132796	0.60205999132796	0.81291335664286	0.27875360095283
1.25527250510331	0.39794000867204	1.08278537031645	1.49136169383427	0.69897000433602	0.69897000433602	0.69897000433602	0.8750612633917	-0.045757490560675
0.67209785793572	-0.55284196865778	0.27875360095283	1.32221929473392	0.47712125471966	0	0.47712125471966	1.02530586526477	0.41497334797082
0.99122607569249	0.62736585659273	1.70243053644553	1.7160033436348	0	0	0	0.86923171973098	0.38021124171161
1.46239799789896	0.83250891270624	2.25285303097989	2.2148438480477	0.30102999566398	0.47712125471966	0.30102999566398	0.92427928606188	0.079181246047625
0.84509804001426	-0.1249387366083	1.0899051114394	2.35218251811136	0.30102999566398	0.30102999566398	0.30102999566398	0.75587485567249	-0.045757490560675
0.77815125038364	0.55630250076729	1.32221929473392	2.35218251811136	0.47712125471966	0.30102999566398	0.47712125471966	0.69019608002851	-0.30102999566398
1.30102999566398	1.74429298312268	2.24303804868629	2.17897694729317	0.69897000433602	0.69897000433602	0.69897000433602	0.50514997831991	-0.22184874961636
0.65321251377534	-0.045757490560675	0.41497334797082	1.77815125038364	0.30102999566398	0	0.30102999566398	1.04139268515823	0.36172783601759
0.8750612633917	0.30102999566398	1.0899051114394	2.30102999566398	0.47712125471966	0	0.47712125471966	0.69019608002851	-0.30102999566398
0.36172783601759	-1	0.39794000867204	1.66275783168157	0.47712125471966	0.30102999566398	0.30102999566398	1.12057393120585	0.41497334797082
1.38021124171161	0.6222140229663	1.76342799356294	2.32221929473392	0.60205999132796	0.47712125471966	0.60205999132796	0.98677173426624	-0.22184874961636
0.47712125471966	0.54406804435028	0.5910646070265	1.14612803567824	0.30102999566398	0	0	1.10720996964787	0.81954393554187

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&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 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

\begin{tabular}{lllllllll}
\hline
PARTIAL LEAST SQUARES PATH MODELING (PLS-PM) \tabularnewline
MODEL SPECIFICATION \tabularnewline
Number of Cases & 39 \tabularnewline
Latent Variables & 3 \tabularnewline
Manifest Variables & 9 \tabularnewline
Scaled? & FALSE \tabularnewline
Weighting Scheme & centroid \tabularnewline
Bootstrapping? & TRUE \tabularnewline
Bootstrap samples & 100 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=1

[TABLE]
[ROW][C]PARTIAL LEAST SQUARES PATH MODELING (PLS-PM)[/C][/ROW]
[ROW][C]MODEL SPECIFICATION[/C][/ROW]
[ROW][C]Number of Cases[/C][C]39[/C][/ROW]
[ROW][C]Latent Variables[/C][C]3[/C][/ROW]
[ROW][C]Manifest Variables[/C][C]9[/C][/ROW]
[ROW][C]Scaled?[/C][C]FALSE[/C][/ROW]
[ROW][C]Weighting Scheme[/C][C]centroid[/C][/ROW]
[ROW][C]Bootstrapping?[/C][C]TRUE[/C][/ROW]
[ROW][C]Bootstrap samples[/C][C]100[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&T=1

As an alternative you can also use a QR Code:

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

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

\begin{tabular}{lllllllll}
\hline
BLOCKS DEFINITION \tabularnewline
Block & Type & NMVs & Mode \tabularnewline
ecol & Exogenous & 3 & Reflective \tabularnewline
const & Endogenous & 4 & Reflective \tabularnewline
sleep & Endogenous & 2 & Reflective \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=2

[TABLE]
[ROW][C]BLOCKS DEFINITION[/C][/ROW]
[ROW][C]Block[/C][C]Type[/C][C]NMVs[/C][C]Mode[/C][/ROW]
[ROW][C]ecol[/C][C]Exogenous[/C][C]3[/C][C]Reflective[/C][/ROW]
[ROW][C]const[/C][C]Endogenous[/C][C]4[/C][C]Reflective[/C][/ROW]
[ROW][C]sleep[/C][C]Endogenous[/C][C]2[/C][C]Reflective[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&T=2

As an alternative you can also use a QR Code:

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

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

\begin{tabular}{lllllllll}
\hline
BLOCKS UNIDIMENSIONALITY \tabularnewline
Block & Type.measure & MVs & eig.1st & eig.2nd & C.alpha & DG.rho \tabularnewline
ecol & Reflective & 3 & 2.43 & 0.44 & 0.8963 & 0.9364 \tabularnewline
const & Reflective & 4 & 3.2 & 0.35 & 0.9266 & 0.9483 \tabularnewline
sleep & Reflective & 2 & 1.52 & 0.43 & 0.7163 & 0.8758 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=3

[TABLE]
[ROW][C]BLOCKS UNIDIMENSIONALITY[/C][/ROW]
[ROW][C]Block[/C][C]Type.measure[/C][C]MVs[/C][C]eig.1st[/C][C]eig.2nd[/C][C]C.alpha[/C][C]DG.rho[/C][/ROW]
[ROW][C]ecol[/C][C]Reflective[/C][C]3[/C][C]2.43[/C][C]0.44[/C][C]0.8963[/C][C]0.9364[/C][/ROW]
[ROW][C]const[/C][C]Reflective[/C][C]4[/C][C]3.2[/C][C]0.35[/C][C]0.9266[/C][C]0.9483[/C][/ROW]
[ROW][C]sleep[/C][C]Reflective[/C][C]2[/C][C]1.52[/C][C]0.43[/C][C]0.7163[/C][C]0.8758[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&T=3

As an alternative you can also use a QR Code:

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

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

\begin{tabular}{lllllllll}
\hline
OUTER MODEL \tabularnewline
Block & weights & std.loads & communal & redundan \tabularnewline
ecol \tabularnewline
logP & 0.8451 & 0.8528 & 0.7273 & 0 \tabularnewline
logS & 1.898 & 0.9149 & 0.8371 & 0 \tabularnewline
logD & 1.3263 & 0.9291 & 0.8633 & 0 \tabularnewline
const \tabularnewline
logL & 0.0936 & 0.7569 & 0.5729 & 0.1417 \tabularnewline
logWb & 0.4372 & 0.9851 & 0.9704 & 0.24 \tabularnewline
logWbr & 0.3477 & 0.984 & 0.9682 & 0.2395 \tabularnewline
logtg & 0.1684 & 0.7761 & 0.6024 & 0.149 \tabularnewline
sleep \tabularnewline
logSWS & 2.0101 & 0.837 & 0.7005 & 0.4169 \tabularnewline
logPS & 2.1906 & 0.9212 & 0.8485 & 0.505 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=4

[TABLE]
[ROW][C]OUTER MODEL[/C][/ROW]
[ROW][C]Block[/C][C]weights[/C][C]std.loads[/C][C]communal[/C][C]redundan[/C][/ROW]
[ROW][C]ecol[/C][/ROW]
[ROW][C]logP[/C][C]0.8451[/C][C]0.8528[/C][C]0.7273[/C][C]0[/C][/ROW]
[ROW][C]logS[/C][C]1.898[/C][C]0.9149[/C][C]0.8371[/C][C]0[/C][/ROW]
[ROW][C]logD[/C][C]1.3263[/C][C]0.9291[/C][C]0.8633[/C][C]0[/C][/ROW]
[ROW][C]const[/C][/ROW]
[ROW][C]logL[/C][C]0.0936[/C][C]0.7569[/C][C]0.5729[/C][C]0.1417[/C][/ROW]
[ROW][C]logWb[/C][C]0.4372[/C][C]0.9851[/C][C]0.9704[/C][C]0.24[/C][/ROW]
[ROW][C]logWbr[/C][C]0.3477[/C][C]0.984[/C][C]0.9682[/C][C]0.2395[/C][/ROW]
[ROW][C]logtg[/C][C]0.1684[/C][C]0.7761[/C][C]0.6024[/C][C]0.149[/C][/ROW]
[ROW][C]sleep[/C][/ROW]
[ROW][C]logSWS[/C][C]2.0101[/C][C]0.837[/C][C]0.7005[/C][C]0.4169[/C][/ROW]
[ROW][C]logPS[/C][C]2.1906[/C][C]0.9212[/C][C]0.8485[/C][C]0.505[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&T=4

As an alternative you can also use a QR Code:

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

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

\begin{tabular}{lllllllll}
\hline
CORRELATIONS BETWEEN MVs AND LVs \tabularnewline
Block & ecol & const & sleep \tabularnewline
ecol \tabularnewline
logP & 0.8528 & 0.1842 & -0.4817 \tabularnewline
logS & 0.9149 & 0.6338 & -0.6696 \tabularnewline
logD & 0.9291 & 0.3272 & -0.676 \tabularnewline
const \tabularnewline
logL & 0.2922 & 0.7569 & -0.4352 \tabularnewline
logWb & 0.5007 & 0.9851 & -0.5941 \tabularnewline
logWbr & 0.4653 & 0.984 & -0.6128 \tabularnewline
logtg & 0.4632 & 0.7761 & -0.7243 \tabularnewline
sleep \tabularnewline
logSWS & -0.5607 & -0.7352 & 0.837 \tabularnewline
logPS & -0.6633 & -0.431 & 0.9212 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=5

[TABLE]
[ROW][C]CORRELATIONS BETWEEN MVs AND LVs[/C][/ROW]
[ROW][C]Block[/C][C]ecol[/C][C]const[/C][C]sleep[/C][/ROW]
[ROW][C]ecol[/C][/ROW]
[ROW][C]logP[/C][C]0.8528[/C][C]0.1842[/C][C]-0.4817[/C][/ROW]
[ROW][C]logS[/C][C]0.9149[/C][C]0.6338[/C][C]-0.6696[/C][/ROW]
[ROW][C]logD[/C][C]0.9291[/C][C]0.3272[/C][C]-0.676[/C][/ROW]
[ROW][C]const[/C][/ROW]
[ROW][C]logL[/C][C]0.2922[/C][C]0.7569[/C][C]-0.4352[/C][/ROW]
[ROW][C]logWb[/C][C]0.5007[/C][C]0.9851[/C][C]-0.5941[/C][/ROW]
[ROW][C]logWbr[/C][C]0.4653[/C][C]0.984[/C][C]-0.6128[/C][/ROW]
[ROW][C]logtg[/C][C]0.4632[/C][C]0.7761[/C][C]-0.7243[/C][/ROW]
[ROW][C]sleep[/C][/ROW]
[ROW][C]logSWS[/C][C]-0.5607[/C][C]-0.7352[/C][C]0.837[/C][/ROW]
[ROW][C]logPS[/C][C]-0.6633[/C][C]-0.431[/C][C]0.9212[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&T=5

As an alternative you can also use a QR Code:

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

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

\begin{tabular}{lllllllll}
\hline
INNER MODEL \tabularnewline
Block & Concept & Value \tabularnewline
S2 \tabularnewline
 & R2 & 0.247 \tabularnewline
(Intercept) & Intercept & 0.403 \tabularnewline
Y.lvs[, c] & path_S1 & 0.497 \tabularnewline
S3 \tabularnewline
 & R2 & 0.595 \tabularnewline
(Intercept) & Intercept & 3.307 \tabularnewline
Y.lvs[, c]S1 & path_S1 & -0.515 \tabularnewline
Y.lvs[, c]S2 & path_S2 & -0.373 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=6

[TABLE]
[ROW][C]INNER MODEL[/C][/ROW]
[ROW][C]Block[/C][C]Concept[/C][C]Value[/C][/ROW]
[ROW][C]S2[/C][/ROW]
[ROW][C][/C][C]R2[/C][C]0.247[/C][/ROW]
[ROW][C](Intercept)[/C][C]Intercept[/C][C]0.403[/C][/ROW]
[ROW][C]Y.lvs[, c][/C][C]path_S1[/C][C]0.497[/C][/ROW]
[ROW][C]S3[/C][/ROW]
[ROW][C][/C][C]R2[/C][C]0.595[/C][/ROW]
[ROW][C](Intercept)[/C][C]Intercept[/C][C]3.307[/C][/ROW]
[ROW][C]Y.lvs[, c]S1[/C][C]path_S1[/C][C]-0.515[/C][/ROW]
[ROW][C]Y.lvs[, c]S2[/C][C]path_S2[/C][C]-0.373[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&T=6

As an alternative you can also use a QR Code:

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

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

\begin{tabular}{lllllllll}
\hline
CORRELATIONS BETWEEN LVs \tabularnewline
 & ecol & const & sleep \tabularnewline
ecol & 1 & 0.4973 & -0.7004 \tabularnewline
const & 0.4973 & 1 & -0.629 \tabularnewline
sleep & -0.7004 & -0.629 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=7

[TABLE]
[ROW][C]CORRELATIONS BETWEEN LVs[/C][/ROW]
[ROW][C][/C][C]ecol[/C][C]const[/C][C]sleep[/C][/ROW]
[ROW][C]ecol[/C][C]1[/C][C]0.4973[/C][C]-0.7004[/C][/ROW]
[ROW][C]const[/C][C]0.4973[/C][C]1[/C][C]-0.629[/C][/ROW]
[ROW][C]sleep[/C][C]-0.7004[/C][C]-0.629[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&T=7

As an alternative you can also use a QR Code:

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

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

\begin{tabular}{lllllllll}
\hline
SUMMARY INNER MODEL \tabularnewline
 & LV.Type & Measure & MVs & R.square & Av.Commu & Av.Redun & AVE \tabularnewline
ecol & Exogen & Rflct & 3 & 0 & 0.8092 & 0 & 0.809 \tabularnewline
const & Endogen & Rflct & 4 & 0.2474 & 0.7785 & 0.1926 & 0.778 \tabularnewline
sleep & Endogen & Rflct & 2 & 0.5952 & 0.7745 & 0.461 & 0.775 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=8

[TABLE]
[ROW][C]SUMMARY INNER MODEL[/C][/ROW]
[ROW][C][/C][C]LV.Type[/C][C]Measure[/C][C]MVs[/C][C]R.square[/C][C]Av.Commu[/C][C]Av.Redun[/C][C]AVE[/C][/ROW]
[ROW][C]ecol[/C][C]Exogen[/C][C]Rflct[/C][C]3[/C][C]0[/C][C]0.8092[/C][C]0[/C][C]0.809[/C][/ROW]
[ROW][C]const[/C][C]Endogen[/C][C]Rflct[/C][C]4[/C][C]0.2474[/C][C]0.7785[/C][C]0.1926[/C][C]0.778[/C][/ROW]
[ROW][C]sleep[/C][C]Endogen[/C][C]Rflct[/C][C]2[/C][C]0.5952[/C][C]0.7745[/C][C]0.461[/C][C]0.775[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&T=8

As an alternative you can also use a QR Code:

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

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

\begin{tabular}{lllllllll}
\hline
GOODNESS-OF-FIT \tabularnewline
GoF & Value \tabularnewline
Absolute & 0.5759 \tabularnewline
Relative & 0.6626 \tabularnewline
Outer.mod & 0.9719 \tabularnewline
Inner.mod & 0.4518 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=9

[TABLE]
[ROW][C]GOODNESS-OF-FIT[/C][/ROW]
[ROW][C]GoF[/C][C]Value[/C][/ROW]
[ROW][C]Absolute[/C][C]0.5759[/C][/ROW]
[ROW][C]Relative[/C][C]0.6626[/C][/ROW]
[ROW][C]Outer.mod[/C][C]0.9719[/C][/ROW]
[ROW][C]Inner.mod[/C][C]0.4518[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&T=9

As an alternative you can also use a QR Code:

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

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

\begin{tabular}{lllllllll}
\hline
TOTAL EFFECTS \tabularnewline
relationships & dir.effect & ind.effect & tot.effect \tabularnewline
S1->S2 & 0.497346784812073 & 0 & 0.497346784812073 \tabularnewline
S1->S3 & -0.514891739709143 & -0.1855 & -0.700391739709143 \tabularnewline
S2->S3 & -0.372926779555084 & 0 & -0.372926779555084 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=10

[TABLE]
[ROW][C]TOTAL EFFECTS[/C][/ROW]
[ROW][C]relationships[/C][C]dir.effect[/C][C]ind.effect[/C][C]tot.effect[/C][/ROW]
[ROW][C]S1->S2[/C][C]0.497346784812073[/C][C]0[/C][C]0.497346784812073[/C][/ROW]
[ROW][C]S1->S3[/C][C]-0.514891739709143[/C][C]-0.1855[/C][C]-0.700391739709143[/C][/ROW]
[ROW][C]S2->S3[/C][C]-0.372926779555084[/C][C]0[/C][C]-0.372926779555084[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=10

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&T=10

As an alternative you can also use a QR Code:

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

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

\begin{tabular}{lllllllll}
\hline
BOOTSTRAP VALIDATION - WEIGHTS \tabularnewline
 & Original & Mean.Boot & Std.Err & t.statis & p.value & perc.025 & perc.975 \tabularnewline
logP & 0.8451 & 0.8233 & 0.2119 & -1.026 & 0.3074 & 0.3947 & 1.2519 \tabularnewline
logS & 1.898 & 1.9282 & 0.3082 & 0.9802 & 0.3294 & 1.3048 & 2.5516 \tabularnewline
logD & 1.3263 & 1.3294 & 0.1299 & 0.2397 & 0.8111 & 1.0667 & 1.5921 \tabularnewline
logL & 0.0936 & 0.1042 & 0.0397 & 2.671 & 0.0088 & 0.0238 & 0.1845 \tabularnewline
logWb & 0.4372 & 0.4472 & 0.04 & 2.5038 & 0.0139 & 0.3663 & 0.5282 \tabularnewline
logWbr & 0.3477 & 0.3553 & 0.0499 & 1.5279 & 0.1297 & 0.2544 & 0.4562 \tabularnewline
logtg & 0.1684 & 0.1816 & 0.0524 & 2.5179 & 0.0134 & 0.0755 & 0.2877 \tabularnewline
logSWS & 2.0101 & 2.0033 & 0.3904 & -0.1722 & 0.8637 & 1.2136 & 2.793 \tabularnewline
logPS & 2.1906 & 2.2184 & 0.32 & 0.87 & 0.3864 & 1.5711 & 2.8658 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=11

[TABLE]
[ROW][C]BOOTSTRAP VALIDATION - WEIGHTS[/C][/ROW]
[ROW][C][/C][C]Original[/C][C]Mean.Boot[/C][C]Std.Err[/C][C]t.statis[/C][C]p.value[/C][C]perc.025[/C][C]perc.975[/C][/ROW]
[ROW][C]logP[/C][C]0.8451[/C][C]0.8233[/C][C]0.2119[/C][C]-1.026[/C][C]0.3074[/C][C]0.3947[/C][C]1.2519[/C][/ROW]
[ROW][C]logS[/C][C]1.898[/C][C]1.9282[/C][C]0.3082[/C][C]0.9802[/C][C]0.3294[/C][C]1.3048[/C][C]2.5516[/C][/ROW]
[ROW][C]logD[/C][C]1.3263[/C][C]1.3294[/C][C]0.1299[/C][C]0.2397[/C][C]0.8111[/C][C]1.0667[/C][C]1.5921[/C][/ROW]
[ROW][C]logL[/C][C]0.0936[/C][C]0.1042[/C][C]0.0397[/C][C]2.671[/C][C]0.0088[/C][C]0.0238[/C][C]0.1845[/C][/ROW]
[ROW][C]logWb[/C][C]0.4372[/C][C]0.4472[/C][C]0.04[/C][C]2.5038[/C][C]0.0139[/C][C]0.3663[/C][C]0.5282[/C][/ROW]
[ROW][C]logWbr[/C][C]0.3477[/C][C]0.3553[/C][C]0.0499[/C][C]1.5279[/C][C]0.1297[/C][C]0.2544[/C][C]0.4562[/C][/ROW]
[ROW][C]logtg[/C][C]0.1684[/C][C]0.1816[/C][C]0.0524[/C][C]2.5179[/C][C]0.0134[/C][C]0.0755[/C][C]0.2877[/C][/ROW]
[ROW][C]logSWS[/C][C]2.0101[/C][C]2.0033[/C][C]0.3904[/C][C]-0.1722[/C][C]0.8637[/C][C]1.2136[/C][C]2.793[/C][/ROW]
[ROW][C]logPS[/C][C]2.1906[/C][C]2.2184[/C][C]0.32[/C][C]0.87[/C][C]0.3864[/C][C]1.5711[/C][C]2.8658[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=11

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&T=11

As an alternative you can also use a QR Code:

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

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

\begin{tabular}{lllllllll}
\hline
BOOTSTRAP VALIDATION - LOADINGS \tabularnewline
 & Original & Mean.Boot & Std.Err & t.statis & p.value & perc.025 & perc.975 \tabularnewline
logP & 0.8528 & 0.8479 & 0.0732 & -0.6759 & 0.5007 & 0.6998 & 0.9959 \tabularnewline
logS & 0.9149 & 0.9231 & 0.0264 & 3.0947 & 0.0026 & 0.8696 & 0.9766 \tabularnewline
logD & 0.9291 & 0.9243 & 0.0453 & -1.0631 & 0.2903 & 0.8326 & 1.016 \tabularnewline
logL & 0.7569 & 0.7813 & 0.0947 & 2.5786 & 0.0114 & 0.5899 & 0.9728 \tabularnewline
logWb & 0.9851 & 0.9832 & 0.01 & -1.905 & 0.0597 & 0.9629 & 1.0035 \tabularnewline
logWbr & 0.984 & 0.9836 & 0.0047 & -0.7932 & 0.4296 & 0.9742 & 0.993 \tabularnewline
logtg & 0.7761 & 0.7781 & 0.0516 & 0.3689 & 0.713 & 0.6736 & 0.8825 \tabularnewline
logSWS & 0.837 & 0.8363 & 0.0632 & -0.104 & 0.9174 & 0.7084 & 0.9642 \tabularnewline
logPS & 0.9212 & 0.9164 & 0.0509 & -0.9348 & 0.3522 & 0.8135 & 1.0193 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=12

[TABLE]
[ROW][C]BOOTSTRAP VALIDATION - LOADINGS[/C][/ROW]
[ROW][C][/C][C]Original[/C][C]Mean.Boot[/C][C]Std.Err[/C][C]t.statis[/C][C]p.value[/C][C]perc.025[/C][C]perc.975[/C][/ROW]
[ROW][C]logP[/C][C]0.8528[/C][C]0.8479[/C][C]0.0732[/C][C]-0.6759[/C][C]0.5007[/C][C]0.6998[/C][C]0.9959[/C][/ROW]
[ROW][C]logS[/C][C]0.9149[/C][C]0.9231[/C][C]0.0264[/C][C]3.0947[/C][C]0.0026[/C][C]0.8696[/C][C]0.9766[/C][/ROW]
[ROW][C]logD[/C][C]0.9291[/C][C]0.9243[/C][C]0.0453[/C][C]-1.0631[/C][C]0.2903[/C][C]0.8326[/C][C]1.016[/C][/ROW]
[ROW][C]logL[/C][C]0.7569[/C][C]0.7813[/C][C]0.0947[/C][C]2.5786[/C][C]0.0114[/C][C]0.5899[/C][C]0.9728[/C][/ROW]
[ROW][C]logWb[/C][C]0.9851[/C][C]0.9832[/C][C]0.01[/C][C]-1.905[/C][C]0.0597[/C][C]0.9629[/C][C]1.0035[/C][/ROW]
[ROW][C]logWbr[/C][C]0.984[/C][C]0.9836[/C][C]0.0047[/C][C]-0.7932[/C][C]0.4296[/C][C]0.9742[/C][C]0.993[/C][/ROW]
[ROW][C]logtg[/C][C]0.7761[/C][C]0.7781[/C][C]0.0516[/C][C]0.3689[/C][C]0.713[/C][C]0.6736[/C][C]0.8825[/C][/ROW]
[ROW][C]logSWS[/C][C]0.837[/C][C]0.8363[/C][C]0.0632[/C][C]-0.104[/C][C]0.9174[/C][C]0.7084[/C][C]0.9642[/C][/ROW]
[ROW][C]logPS[/C][C]0.9212[/C][C]0.9164[/C][C]0.0509[/C][C]-0.9348[/C][C]0.3522[/C][C]0.8135[/C][C]1.0193[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=12

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&T=12

As an alternative you can also use a QR Code:

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

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

\begin{tabular}{lllllllll}
\hline
BOOTSTRAP VALIDATION - PATHS \tabularnewline
 & Original & Mean.Boot & Std.Err & t.statis & p.value & perc.025 & perc.975 \tabularnewline
S1->S2 & 0.4973 & 0.5093 & 0.112 & 1.0679 & 0.2882 & 0.2827 & 0.7359 \tabularnewline
S1->S3 & -0.5149 & -0.5123 & 0.0842 & 0.3134 & 0.7546 & -0.6825 & -0.342 \tabularnewline
S2->S3 & -0.3729 & -0.3693 & 0.088 & 0.4094 & 0.6831 & -0.5473 & -0.1914 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=13

[TABLE]
[ROW][C]BOOTSTRAP VALIDATION - PATHS[/C][/ROW]
[ROW][C][/C][C]Original[/C][C]Mean.Boot[/C][C]Std.Err[/C][C]t.statis[/C][C]p.value[/C][C]perc.025[/C][C]perc.975[/C][/ROW]
[ROW][C]S1->S2[/C][C]0.4973[/C][C]0.5093[/C][C]0.112[/C][C]1.0679[/C][C]0.2882[/C][C]0.2827[/C][C]0.7359[/C][/ROW]
[ROW][C]S1->S3[/C][C]-0.5149[/C][C]-0.5123[/C][C]0.0842[/C][C]0.3134[/C][C]0.7546[/C][C]-0.6825[/C][C]-0.342[/C][/ROW]
[ROW][C]S2->S3[/C][C]-0.3729[/C][C]-0.3693[/C][C]0.088[/C][C]0.4094[/C][C]0.6831[/C][C]-0.5473[/C][C]-0.1914[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=13

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&T=13

As an alternative you can also use a QR Code:

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

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

\begin{tabular}{lllllllll}
\hline
BOOTSTRAP VALIDATION - RSQ \tabularnewline
 & Original & Mean.Boot & Std.Err & t.statis & p.value & perc.025 & perc.975 \tabularnewline
S2 & 0.2474 & 0.272 & 0.116 & 2.1115 & 0.0372 & 0.037 & 0.506 \tabularnewline
S3 & 0.5952 & 0.599 & 0.09 & 0.3782 & 0.7061 & 0.417 & 0.78 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=14

[TABLE]
[ROW][C]BOOTSTRAP VALIDATION - RSQ[/C][/ROW]
[ROW][C][/C][C]Original[/C][C]Mean.Boot[/C][C]Std.Err[/C][C]t.statis[/C][C]p.value[/C][C]perc.025[/C][C]perc.975[/C][/ROW]
[ROW][C]S2[/C][C]0.2474[/C][C]0.272[/C][C]0.116[/C][C]2.1115[/C][C]0.0372[/C][C]0.037[/C][C]0.506[/C][/ROW]
[ROW][C]S3[/C][C]0.5952[/C][C]0.599[/C][C]0.09[/C][C]0.3782[/C][C]0.7061[/C][C]0.417[/C][C]0.78[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=14

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&T=14

As an alternative you can also use a QR Code:

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

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

\begin{tabular}{lllllllll}
\hline
BOOTSTRAP VALIDATION - TOTAL EFFECTS \tabularnewline
 & Original & Mean.Boot & Std.Err & t.statis & p.value & perc.025 & perc.975 \tabularnewline
S1->S2 & 0.4973 & 0.5093 & 0.112 & 1.0679 & 0.2882 & 0.2827 & 0.7359 \tabularnewline
S1->S3 & -0.7004 & -0.698 & 0.0693 & 0.3475 & 0.7289 & -0.8382 & -0.5578 \tabularnewline
S2->S3 & -0.3729 & -0.3693 & 0.088 & 0.4094 & 0.6831 & -0.5473 & -0.1914 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=75156&T=15

[TABLE]
[ROW][C]BOOTSTRAP VALIDATION - TOTAL EFFECTS[/C][/ROW]
[ROW][C][/C][C]Original[/C][C]Mean.Boot[/C][C]Std.Err[/C][C]t.statis[/C][C]p.value[/C][C]perc.025[/C][C]perc.975[/C][/ROW]
[ROW][C]S1->S2[/C][C]0.4973[/C][C]0.5093[/C][C]0.112[/C][C]1.0679[/C][C]0.2882[/C][C]0.2827[/C][C]0.7359[/C][/ROW]
[ROW][C]S1->S3[/C][C]-0.7004[/C][C]-0.698[/C][C]0.0693[/C][C]0.3475[/C][C]0.7289[/C][C]-0.8382[/C][C]-0.5578[/C][/ROW]
[ROW][C]S2->S3[/C][C]-0.3729[/C][C]-0.3693[/C][C]0.088[/C][C]0.4094[/C][C]0.6831[/C][C]-0.5473[/C][C]-0.1914[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=75156&T=15

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=75156&T=15

As an alternative you can also use a QR Code:

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

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

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 ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = 0 0 0 ; par12 = 1 0 0 ; par13 = 1 1 0 ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ;

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

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code