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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 23 Jan 2017 09:55:35 +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/2017/Jan/23/t1485161741sjnoa8tjkz5ia3o.htm/, Retrieved Thu, 31 Oct 2024 23:48:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=304142, Retrieved Thu, 31 Oct 2024 23:48:21 +0000
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Original text written by user:
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User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2017-01-23 08:55:35] [1e2703d0f11438bcd65480dae45a3781] [Current]
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Dataseries X:
1 1 0 0 0 3.2 3.2 10.24
0 0 1 0 1 3.3 0 10.89
1 0 1 1 1 3 3 9
0 0 1 0 1 3.5 0 12.25
1 0 1 0 0 3.7 3.7 13.69
0 1 0 0 0 2.7 0 7.29
1 0 1 1 1 3.6 3.6 12.96
0 0 1 0 1 3.5 0 12.25
1 1 0 0 0 3.8 3.8 14.44
0 0 1 0 0 3.4 0 11.56
1 0 0 0 1 3.7 3.7 13.69
0 0 1 0 0 3.5 0 12.25
1 0 0 1 0 2.8 2.8 7.84
0 1 0 1 0 3.8 0 14.44
1 0 1 0 0 4.3 4.3 18.49
0 0 0 0 1 3.3 0 10.89
1 0 0 0 0 3.6 3.6 12.96
0 1 0 1 0 3.6 0 12.96
1 1 1 0 0 3.3 3.3 10.89
0 0 0 0 0 2.8 0 7.84




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304142&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]7 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=304142&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304142&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 time7 seconds
R ServerBig Analytics Cloud Computing Center







Multiple Linear Regression - Estimated Regression Equation
Geslacht[t] = -1.2279 -0.00287601X1[t] + 0.00996816X2[t] + 0.0730274X3[t] -0.0180896X4[t] + 0.894381X5[t] + 0.293065Inter[t] -0.157064X6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Geslacht[t] =  -1.2279 -0.00287601X1[t] +  0.00996816X2[t] +  0.0730274X3[t] -0.0180896X4[t] +  0.894381X5[t] +  0.293065Inter[t] -0.157064X6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304142&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Geslacht[t] =  -1.2279 -0.00287601X1[t] +  0.00996816X2[t] +  0.0730274X3[t] -0.0180896X4[t] +  0.894381X5[t] +  0.293065Inter[t] -0.157064X6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304142&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304142&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Geslacht[t] = -1.2279 -0.00287601X1[t] + 0.00996816X2[t] + 0.0730274X3[t] -0.0180896X4[t] + 0.894381X5[t] + 0.293065Inter[t] -0.157064X6[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.228 0.5665-2.1680e+00 0.05101 0.02551
X1-0.002876 0.02492-1.1540e-01 0.91 0.455
X2+0.009968 0.0212+4.7020e-01 0.6466 0.3233
X3+0.07303 0.02156+3.3870e+00 0.005395 0.002697
X4-0.01809 0.02401-7.5350e-01 0.4657 0.2328
X5+0.8944 0.3373+2.6520e+00 0.02111 0.01056
Inter+0.2931 0.005596+5.2370e+01 1.543e-15 7.716e-16
X6-0.1571 0.04954-3.1710e+00 0.00806 0.00403

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -1.228 &  0.5665 & -2.1680e+00 &  0.05101 &  0.02551 \tabularnewline
X1 & -0.002876 &  0.02492 & -1.1540e-01 &  0.91 &  0.455 \tabularnewline
X2 & +0.009968 &  0.0212 & +4.7020e-01 &  0.6466 &  0.3233 \tabularnewline
X3 & +0.07303 &  0.02156 & +3.3870e+00 &  0.005395 &  0.002697 \tabularnewline
X4 & -0.01809 &  0.02401 & -7.5350e-01 &  0.4657 &  0.2328 \tabularnewline
X5 & +0.8944 &  0.3373 & +2.6520e+00 &  0.02111 &  0.01056 \tabularnewline
Inter & +0.2931 &  0.005596 & +5.2370e+01 &  1.543e-15 &  7.716e-16 \tabularnewline
X6 & -0.1571 &  0.04954 & -3.1710e+00 &  0.00806 &  0.00403 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304142&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-1.228[/C][C] 0.5665[/C][C]-2.1680e+00[/C][C] 0.05101[/C][C] 0.02551[/C][/ROW]
[ROW][C]X1[/C][C]-0.002876[/C][C] 0.02492[/C][C]-1.1540e-01[/C][C] 0.91[/C][C] 0.455[/C][/ROW]
[ROW][C]X2[/C][C]+0.009968[/C][C] 0.0212[/C][C]+4.7020e-01[/C][C] 0.6466[/C][C] 0.3233[/C][/ROW]
[ROW][C]X3[/C][C]+0.07303[/C][C] 0.02156[/C][C]+3.3870e+00[/C][C] 0.005395[/C][C] 0.002697[/C][/ROW]
[ROW][C]X4[/C][C]-0.01809[/C][C] 0.02401[/C][C]-7.5350e-01[/C][C] 0.4657[/C][C] 0.2328[/C][/ROW]
[ROW][C]X5[/C][C]+0.8944[/C][C] 0.3373[/C][C]+2.6520e+00[/C][C] 0.02111[/C][C] 0.01056[/C][/ROW]
[ROW][C]Inter[/C][C]+0.2931[/C][C] 0.005596[/C][C]+5.2370e+01[/C][C] 1.543e-15[/C][C] 7.716e-16[/C][/ROW]
[ROW][C]X6[/C][C]-0.1571[/C][C] 0.04954[/C][C]-3.1710e+00[/C][C] 0.00806[/C][C] 0.00403[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304142&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304142&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.228 0.5665-2.1680e+00 0.05101 0.02551
X1-0.002876 0.02492-1.1540e-01 0.91 0.455
X2+0.009968 0.0212+4.7020e-01 0.6466 0.3233
X3+0.07303 0.02156+3.3870e+00 0.005395 0.002697
X4-0.01809 0.02401-7.5350e-01 0.4657 0.2328
X5+0.8944 0.3373+2.6520e+00 0.02111 0.01056
Inter+0.2931 0.005596+5.2370e+01 1.543e-15 7.716e-16
X6-0.1571 0.04954-3.1710e+00 0.00806 0.00403







Multiple Linear Regression - Regression Statistics
Multiple R 0.998
R-squared 0.9961
Adjusted R-squared 0.9938
F-TEST (value) 433.3
F-TEST (DF numerator)7
F-TEST (DF denominator)12
p-value 1.852e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.04052
Sum Squared Residuals 0.0197

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.998 \tabularnewline
R-squared &  0.9961 \tabularnewline
Adjusted R-squared &  0.9938 \tabularnewline
F-TEST (value) &  433.3 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 12 \tabularnewline
p-value &  1.852e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.04052 \tabularnewline
Sum Squared Residuals &  0.0197 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304142&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.998[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9961[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9938[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 433.3[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]12[/C][/ROW]
[ROW][C]p-value[/C][C] 1.852e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.04052[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 0.0197[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304142&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304142&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R 0.998
R-squared 0.9961
Adjusted R-squared 0.9938
F-TEST (value) 433.3
F-TEST (DF numerator)7
F-TEST (DF denominator)12
p-value 1.852e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.04052
Sum Squared Residuals 0.0197







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 1 0.9607 0.03928
2 0 0.005011-0.005011
3 1 0.9858 0.01423
4 0-0.02972 0.02972
5 1 1.025-0.02541
6 0 0.03906-0.03906
7 1 1.076-0.07626
8 0-0.02972 0.02972
9 1 1.014-0.01352
10 0 0.007305-0.007305
11 1 0.9974 0.002645
12 0-0.01163 0.01163
13 1 0.9386 0.0614
14 0-0.0271 0.0271
15 1 0.984 0.01603
16 0-0.004957 0.004957
17 1 1.011-0.01136
18 0 0.02647-0.02647
19 1 0.9873 0.01266
20 0 0.04499-0.04499

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  1 &  0.9607 &  0.03928 \tabularnewline
2 &  0 &  0.005011 & -0.005011 \tabularnewline
3 &  1 &  0.9858 &  0.01423 \tabularnewline
4 &  0 & -0.02972 &  0.02972 \tabularnewline
5 &  1 &  1.025 & -0.02541 \tabularnewline
6 &  0 &  0.03906 & -0.03906 \tabularnewline
7 &  1 &  1.076 & -0.07626 \tabularnewline
8 &  0 & -0.02972 &  0.02972 \tabularnewline
9 &  1 &  1.014 & -0.01352 \tabularnewline
10 &  0 &  0.007305 & -0.007305 \tabularnewline
11 &  1 &  0.9974 &  0.002645 \tabularnewline
12 &  0 & -0.01163 &  0.01163 \tabularnewline
13 &  1 &  0.9386 &  0.0614 \tabularnewline
14 &  0 & -0.0271 &  0.0271 \tabularnewline
15 &  1 &  0.984 &  0.01603 \tabularnewline
16 &  0 & -0.004957 &  0.004957 \tabularnewline
17 &  1 &  1.011 & -0.01136 \tabularnewline
18 &  0 &  0.02647 & -0.02647 \tabularnewline
19 &  1 &  0.9873 &  0.01266 \tabularnewline
20 &  0 &  0.04499 & -0.04499 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=304142&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 1[/C][C] 0.9607[/C][C] 0.03928[/C][/ROW]
[ROW][C]2[/C][C] 0[/C][C] 0.005011[/C][C]-0.005011[/C][/ROW]
[ROW][C]3[/C][C] 1[/C][C] 0.9858[/C][C] 0.01423[/C][/ROW]
[ROW][C]4[/C][C] 0[/C][C]-0.02972[/C][C] 0.02972[/C][/ROW]
[ROW][C]5[/C][C] 1[/C][C] 1.025[/C][C]-0.02541[/C][/ROW]
[ROW][C]6[/C][C] 0[/C][C] 0.03906[/C][C]-0.03906[/C][/ROW]
[ROW][C]7[/C][C] 1[/C][C] 1.076[/C][C]-0.07626[/C][/ROW]
[ROW][C]8[/C][C] 0[/C][C]-0.02972[/C][C] 0.02972[/C][/ROW]
[ROW][C]9[/C][C] 1[/C][C] 1.014[/C][C]-0.01352[/C][/ROW]
[ROW][C]10[/C][C] 0[/C][C] 0.007305[/C][C]-0.007305[/C][/ROW]
[ROW][C]11[/C][C] 1[/C][C] 0.9974[/C][C] 0.002645[/C][/ROW]
[ROW][C]12[/C][C] 0[/C][C]-0.01163[/C][C] 0.01163[/C][/ROW]
[ROW][C]13[/C][C] 1[/C][C] 0.9386[/C][C] 0.0614[/C][/ROW]
[ROW][C]14[/C][C] 0[/C][C]-0.0271[/C][C] 0.0271[/C][/ROW]
[ROW][C]15[/C][C] 1[/C][C] 0.984[/C][C] 0.01603[/C][/ROW]
[ROW][C]16[/C][C] 0[/C][C]-0.004957[/C][C] 0.004957[/C][/ROW]
[ROW][C]17[/C][C] 1[/C][C] 1.011[/C][C]-0.01136[/C][/ROW]
[ROW][C]18[/C][C] 0[/C][C] 0.02647[/C][C]-0.02647[/C][/ROW]
[ROW][C]19[/C][C] 1[/C][C] 0.9873[/C][C] 0.01266[/C][/ROW]
[ROW][C]20[/C][C] 0[/C][C] 0.04499[/C][C]-0.04499[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=304142&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304142&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 1 0.9607 0.03928
2 0 0.005011-0.005011
3 1 0.9858 0.01423
4 0-0.02972 0.02972
5 1 1.025-0.02541
6 0 0.03906-0.03906
7 1 1.076-0.07626
8 0-0.02972 0.02972
9 1 1.014-0.01352
10 0 0.007305-0.007305
11 1 0.9974 0.002645
12 0-0.01163 0.01163
13 1 0.9386 0.0614
14 0-0.0271 0.0271
15 1 0.984 0.01603
16 0-0.004957 0.004957
17 1 1.011-0.01136
18 0 0.02647-0.02647
19 1 0.9873 0.01266
20 0 0.04499-0.04499







Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 499.59, df1 = 2, df2 = 10, p-value = 9.553e-11
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = -2675.1, df1 = 14, df2 = -2, p-value = NA
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1195.7, df1 = 2, df2 = 10, p-value = 1.252e-12

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 499.59, df1 = 2, df2 = 10, p-value = 9.553e-11
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = -2675.1, df1 = 14, df2 = -2, p-value = NA
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1195.7, df1 = 2, df2 = 10, p-value = 1.252e-12
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=304142&T=5

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]
> reset_test_fitted
	RESET test
data:  mylm
RESET = 499.59, df1 = 2, df2 = 10, p-value = 9.553e-11
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW] [ROW][C]
> reset_test_regressors
	RESET test
data:  mylm
RESET = -2675.1, df1 = 14, df2 = -2, p-value = NA
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW] [ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1195.7, df1 = 2, df2 = 10, p-value = 1.252e-12
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=304142&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304142&T=5

As an alternative you can also use a QR Code:  

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 499.59, df1 = 2, df2 = 10, p-value = 9.553e-11
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = -2675.1, df1 = 14, df2 = -2, p-value = NA
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1195.7, df1 = 2, df2 = 10, p-value = 1.252e-12







Variance Inflation Factors (Multicollinearity)
> vif
        X1         X2         X3         X4         X5      Inter         X6 
  1.588847   1.368582   1.061520   1.596918 200.374005   1.200382 201.713687 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
        X1         X2         X3         X4         X5      Inter         X6 
  1.588847   1.368582   1.061520   1.596918 200.374005   1.200382 201.713687 
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=304142&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
        X1         X2         X3         X4         X5      Inter         X6 
  1.588847   1.368582   1.061520   1.596918 200.374005   1.200382 201.713687 
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=304142&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=304142&T=6

As an alternative you can also use a QR Code:  

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

Variance Inflation Factors (Multicollinearity)
> vif
        X1         X2         X3         X4         X5      Inter         X6 
  1.588847   1.368582   1.061520   1.596918 200.374005   1.200382 201.713687 



Parameters (Session):
par1 = 0.99 ; par2 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = No Linear Trend ; par4 = ; par5 = ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
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,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
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,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
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,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')