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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_logisticregression.wasp
Title produced by softwareBias-Reduced Logistic Regression
Date of computationTue, 15 Nov 2011 13:34:59 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/15/t1321382109nlemd1ruzie3rpb.htm/, Retrieved Thu, 28 Mar 2024 12:43:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=143332, Retrieved Thu, 28 Mar 2024 12:43:01 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact57
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bias-Reduced Logistic Regression] [] [2011-11-15 18:34:59] [c80accbb627afb8a1e74b91ef6a0d2c4] [Current]
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Dataseries X:
1	99.2	96.7	101
1	99	98.1	100.1
1	100	100	100
1	111.6	104.9	90.6
1	122.2	104.9	86.5
1	117.6	109.5	89.7
1	121.1	110.8	90.6
1	136	112.3	82.8
1	154.2	109.3	70.1
1	153.6	105.3	65.4
1	158.5	101.7	61.3
0	140.6	95.4	62.5
0	136.2	96.4	63.6
0	168	97.6	52.6
0	154.3	102.4	59.7
0	149	101.6	59.5
0	165.5	103.8	61.3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\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 & 1 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143332&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143332&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143332&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Coefficients of Bias-Reduced Logistic Regression
VariableParameterS.E.t-stat2-sided p-value
(Intercept)-34.098694084868123.6681280275493-1.440700931023270.17331828309421
X10.04522288176420020.1312258956405050.3446185796139580.735892431342738
X20.1680063434751580.2455432379221170.6842230512918760.505849335612275
X30.1522761910316210.1870420752467130.8141280021127070.430232615868456

\begin{tabular}{lllllllll}
\hline
Coefficients of Bias-Reduced Logistic Regression \tabularnewline
Variable & Parameter & S.E. & t-stat & 2-sided p-value \tabularnewline
(Intercept) & -34.0986940848681 & 23.6681280275493 & -1.44070093102327 & 0.17331828309421 \tabularnewline
X1 & 0.0452228817642002 & 0.131225895640505 & 0.344618579613958 & 0.735892431342738 \tabularnewline
X2 & 0.168006343475158 & 0.245543237922117 & 0.684223051291876 & 0.505849335612275 \tabularnewline
X3 & 0.152276191031621 & 0.187042075246713 & 0.814128002112707 & 0.430232615868456 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143332&T=1

[TABLE]
[ROW][C]Coefficients of Bias-Reduced Logistic Regression[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.E.[/C][C]t-stat[/C][C]2-sided p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-34.0986940848681[/C][C]23.6681280275493[/C][C]-1.44070093102327[/C][C]0.17331828309421[/C][/ROW]
[ROW][C]X1[/C][C]0.0452228817642002[/C][C]0.131225895640505[/C][C]0.344618579613958[/C][C]0.735892431342738[/C][/ROW]
[ROW][C]X2[/C][C]0.168006343475158[/C][C]0.245543237922117[/C][C]0.684223051291876[/C][C]0.505849335612275[/C][/ROW]
[ROW][C]X3[/C][C]0.152276191031621[/C][C]0.187042075246713[/C][C]0.814128002112707[/C][C]0.430232615868456[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143332&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143332&T=1

As an alternative you can also use a QR Code:  

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

Coefficients of Bias-Reduced Logistic Regression
VariableParameterS.E.t-stat2-sided p-value
(Intercept)-34.098694084868123.6681280275493-1.440700931023270.17331828309421
X10.04522288176420020.1312258956405050.3446185796139580.735892431342738
X20.1680063434751580.2455432379221170.6842230512918760.505849335612275
X30.1522761910316210.1870420752467130.8141280021127070.430232615868456







Summary of Bias-Reduced Logistic Regression
Deviance7.86851242762289
Penalized deviance-6.46292909339894
Residual Degrees of Freedom13
ROC Area0.984848484848485
Hosmer–Lemeshow test
Chi-square2.40652705692957
Degrees of Freedom8
P(>Chi)0.965947247529661

\begin{tabular}{lllllllll}
\hline
Summary of Bias-Reduced Logistic Regression \tabularnewline
Deviance & 7.86851242762289 \tabularnewline
Penalized deviance & -6.46292909339894 \tabularnewline
Residual Degrees of Freedom & 13 \tabularnewline
ROC Area & 0.984848484848485 \tabularnewline
Hosmer–Lemeshow test \tabularnewline
Chi-square & 2.40652705692957 \tabularnewline
Degrees of Freedom & 8 \tabularnewline
P(>Chi) & 0.965947247529661 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143332&T=2

[TABLE]
[ROW][C]Summary of Bias-Reduced Logistic Regression[/C][/ROW]
[ROW][C]Deviance[/C][C]7.86851242762289[/C][/ROW]
[ROW][C]Penalized deviance[/C][C]-6.46292909339894[/C][/ROW]
[ROW][C]Residual Degrees of Freedom[/C][C]13[/C][/ROW]
[ROW][C]ROC Area[/C][C]0.984848484848485[/C][/ROW]
[ROW][C]Hosmer–Lemeshow test[/C][/ROW]
[ROW][C]Chi-square[/C][C]2.40652705692957[/C][/ROW]
[ROW][C]Degrees of Freedom[/C][C]8[/C][/ROW]
[ROW][C]P(>Chi)[/C][C]0.965947247529661[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143332&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143332&T=2

As an alternative you can also use a QR Code:  

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

Summary of Bias-Reduced Logistic Regression
Deviance7.86851242762289
Penalized deviance-6.46292909339894
Residual Degrees of Freedom13
ROC Area0.984848484848485
Hosmer–Lemeshow test
Chi-square2.40652705692957
Degrees of Freedom8
P(>Chi)0.965947247529661







Fit of Logistic Regression
IndexActualFittedError
110.8822097661325320.117790233867468
210.8911595301626940.108840469837306
310.920696453070470.0793035469295298
410.9143753431461420.0856246568538582
510.9023222587021790.0976777412978215
610.9635746764885170.0364253235114829
710.9778847122954990.0221152877045013
810.9714518293412360.0285481706587638
910.8712805298280370.128719470171963
1010.6218649207195470.378135079280453
1110.3751720330060090.62482796699399
1200.100174264607692-0.100174264607692
1300.113170187248927-0.113170187248927
1400.109671283683399-0.109671283683399
1500.304477694122989-0.304477694122989
1600.226075232440962-0.226075232440962
1700.539736476184367-0.539736476184367

\begin{tabular}{lllllllll}
\hline
Fit of Logistic Regression \tabularnewline
Index & Actual & Fitted & Error \tabularnewline
1 & 1 & 0.882209766132532 & 0.117790233867468 \tabularnewline
2 & 1 & 0.891159530162694 & 0.108840469837306 \tabularnewline
3 & 1 & 0.92069645307047 & 0.0793035469295298 \tabularnewline
4 & 1 & 0.914375343146142 & 0.0856246568538582 \tabularnewline
5 & 1 & 0.902322258702179 & 0.0976777412978215 \tabularnewline
6 & 1 & 0.963574676488517 & 0.0364253235114829 \tabularnewline
7 & 1 & 0.977884712295499 & 0.0221152877045013 \tabularnewline
8 & 1 & 0.971451829341236 & 0.0285481706587638 \tabularnewline
9 & 1 & 0.871280529828037 & 0.128719470171963 \tabularnewline
10 & 1 & 0.621864920719547 & 0.378135079280453 \tabularnewline
11 & 1 & 0.375172033006009 & 0.62482796699399 \tabularnewline
12 & 0 & 0.100174264607692 & -0.100174264607692 \tabularnewline
13 & 0 & 0.113170187248927 & -0.113170187248927 \tabularnewline
14 & 0 & 0.109671283683399 & -0.109671283683399 \tabularnewline
15 & 0 & 0.304477694122989 & -0.304477694122989 \tabularnewline
16 & 0 & 0.226075232440962 & -0.226075232440962 \tabularnewline
17 & 0 & 0.539736476184367 & -0.539736476184367 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143332&T=3

[TABLE]
[ROW][C]Fit of Logistic Regression[/C][/ROW]
[ROW][C]Index[/C][C]Actual[/C][C]Fitted[/C][C]Error[/C][/ROW]
[ROW][C]1[/C][C]1[/C][C]0.882209766132532[/C][C]0.117790233867468[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]0.891159530162694[/C][C]0.108840469837306[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]0.92069645307047[/C][C]0.0793035469295298[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]0.914375343146142[/C][C]0.0856246568538582[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.902322258702179[/C][C]0.0976777412978215[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.963574676488517[/C][C]0.0364253235114829[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]0.977884712295499[/C][C]0.0221152877045013[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]0.971451829341236[/C][C]0.0285481706587638[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.871280529828037[/C][C]0.128719470171963[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]0.621864920719547[/C][C]0.378135079280453[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.375172033006009[/C][C]0.62482796699399[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.100174264607692[/C][C]-0.100174264607692[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.113170187248927[/C][C]-0.113170187248927[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.109671283683399[/C][C]-0.109671283683399[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.304477694122989[/C][C]-0.304477694122989[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.226075232440962[/C][C]-0.226075232440962[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.539736476184367[/C][C]-0.539736476184367[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143332&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143332&T=3

As an alternative you can also use a QR Code:  

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

Fit of Logistic Regression
IndexActualFittedError
110.8822097661325320.117790233867468
210.8911595301626940.108840469837306
310.920696453070470.0793035469295298
410.9143753431461420.0856246568538582
510.9023222587021790.0976777412978215
610.9635746764885170.0364253235114829
710.9778847122954990.0221152877045013
810.9714518293412360.0285481706587638
910.8712805298280370.128719470171963
1010.6218649207195470.378135079280453
1110.3751720330060090.62482796699399
1200.100174264607692-0.100174264607692
1300.113170187248927-0.113170187248927
1400.109671283683399-0.109671283683399
1500.304477694122989-0.304477694122989
1600.226075232440962-0.226075232440962
1700.539736476184367-0.539736476184367







Type I & II errors for various threshold values
ThresholdType IType II
0.0101
0.0201
0.0301
0.0401
0.0501
0.0601
0.0701
0.0801
0.0901
0.101
0.1100.666666666666667
0.1200.5
0.1300.5
0.1400.5
0.1500.5
0.1600.5
0.1700.5
0.1800.5
0.1900.5
0.200.5
0.2100.5
0.2200.5
0.2300.333333333333333
0.2400.333333333333333
0.2500.333333333333333
0.2600.333333333333333
0.2700.333333333333333
0.2800.333333333333333
0.2900.333333333333333
0.300.333333333333333
0.3100.166666666666667
0.3200.166666666666667
0.3300.166666666666667
0.3400.166666666666667
0.3500.166666666666667
0.3600.166666666666667
0.3700.166666666666667
0.380.09090909090909090.166666666666667
0.390.09090909090909090.166666666666667
0.40.09090909090909090.166666666666667
0.410.09090909090909090.166666666666667
0.420.09090909090909090.166666666666667
0.430.09090909090909090.166666666666667
0.440.09090909090909090.166666666666667
0.450.09090909090909090.166666666666667
0.460.09090909090909090.166666666666667
0.470.09090909090909090.166666666666667
0.480.09090909090909090.166666666666667
0.490.09090909090909090.166666666666667
0.50.09090909090909090.166666666666667
0.510.09090909090909090.166666666666667
0.520.09090909090909090.166666666666667
0.530.09090909090909090.166666666666667
0.540.09090909090909090
0.550.09090909090909090
0.560.09090909090909090
0.570.09090909090909090
0.580.09090909090909090
0.590.09090909090909090
0.60.09090909090909090
0.610.09090909090909090
0.620.09090909090909090
0.630.1818181818181820
0.640.1818181818181820
0.650.1818181818181820
0.660.1818181818181820
0.670.1818181818181820
0.680.1818181818181820
0.690.1818181818181820
0.70.1818181818181820
0.710.1818181818181820
0.720.1818181818181820
0.730.1818181818181820
0.740.1818181818181820
0.750.1818181818181820
0.760.1818181818181820
0.770.1818181818181820
0.780.1818181818181820
0.790.1818181818181820
0.80.1818181818181820
0.810.1818181818181820
0.820.1818181818181820
0.830.1818181818181820
0.840.1818181818181820
0.850.1818181818181820
0.860.1818181818181820
0.870.1818181818181820
0.880.2727272727272730
0.890.3636363636363640
0.90.4545454545454550
0.910.5454545454545450
0.920.6363636363636360
0.930.7272727272727270
0.940.7272727272727270
0.950.7272727272727270
0.960.7272727272727270
0.970.8181818181818180
0.9810
0.9910

\begin{tabular}{lllllllll}
\hline
Type I & II errors for various threshold values \tabularnewline
Threshold & Type I & Type II \tabularnewline
0.01 & 0 & 1 \tabularnewline
0.02 & 0 & 1 \tabularnewline
0.03 & 0 & 1 \tabularnewline
0.04 & 0 & 1 \tabularnewline
0.05 & 0 & 1 \tabularnewline
0.06 & 0 & 1 \tabularnewline
0.07 & 0 & 1 \tabularnewline
0.08 & 0 & 1 \tabularnewline
0.09 & 0 & 1 \tabularnewline
0.1 & 0 & 1 \tabularnewline
0.11 & 0 & 0.666666666666667 \tabularnewline
0.12 & 0 & 0.5 \tabularnewline
0.13 & 0 & 0.5 \tabularnewline
0.14 & 0 & 0.5 \tabularnewline
0.15 & 0 & 0.5 \tabularnewline
0.16 & 0 & 0.5 \tabularnewline
0.17 & 0 & 0.5 \tabularnewline
0.18 & 0 & 0.5 \tabularnewline
0.19 & 0 & 0.5 \tabularnewline
0.2 & 0 & 0.5 \tabularnewline
0.21 & 0 & 0.5 \tabularnewline
0.22 & 0 & 0.5 \tabularnewline
0.23 & 0 & 0.333333333333333 \tabularnewline
0.24 & 0 & 0.333333333333333 \tabularnewline
0.25 & 0 & 0.333333333333333 \tabularnewline
0.26 & 0 & 0.333333333333333 \tabularnewline
0.27 & 0 & 0.333333333333333 \tabularnewline
0.28 & 0 & 0.333333333333333 \tabularnewline
0.29 & 0 & 0.333333333333333 \tabularnewline
0.3 & 0 & 0.333333333333333 \tabularnewline
0.31 & 0 & 0.166666666666667 \tabularnewline
0.32 & 0 & 0.166666666666667 \tabularnewline
0.33 & 0 & 0.166666666666667 \tabularnewline
0.34 & 0 & 0.166666666666667 \tabularnewline
0.35 & 0 & 0.166666666666667 \tabularnewline
0.36 & 0 & 0.166666666666667 \tabularnewline
0.37 & 0 & 0.166666666666667 \tabularnewline
0.38 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.39 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.4 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.41 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.42 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.43 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.44 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.45 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.46 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.47 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.48 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.49 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.5 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.51 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.52 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.53 & 0.0909090909090909 & 0.166666666666667 \tabularnewline
0.54 & 0.0909090909090909 & 0 \tabularnewline
0.55 & 0.0909090909090909 & 0 \tabularnewline
0.56 & 0.0909090909090909 & 0 \tabularnewline
0.57 & 0.0909090909090909 & 0 \tabularnewline
0.58 & 0.0909090909090909 & 0 \tabularnewline
0.59 & 0.0909090909090909 & 0 \tabularnewline
0.6 & 0.0909090909090909 & 0 \tabularnewline
0.61 & 0.0909090909090909 & 0 \tabularnewline
0.62 & 0.0909090909090909 & 0 \tabularnewline
0.63 & 0.181818181818182 & 0 \tabularnewline
0.64 & 0.181818181818182 & 0 \tabularnewline
0.65 & 0.181818181818182 & 0 \tabularnewline
0.66 & 0.181818181818182 & 0 \tabularnewline
0.67 & 0.181818181818182 & 0 \tabularnewline
0.68 & 0.181818181818182 & 0 \tabularnewline
0.69 & 0.181818181818182 & 0 \tabularnewline
0.7 & 0.181818181818182 & 0 \tabularnewline
0.71 & 0.181818181818182 & 0 \tabularnewline
0.72 & 0.181818181818182 & 0 \tabularnewline
0.73 & 0.181818181818182 & 0 \tabularnewline
0.74 & 0.181818181818182 & 0 \tabularnewline
0.75 & 0.181818181818182 & 0 \tabularnewline
0.76 & 0.181818181818182 & 0 \tabularnewline
0.77 & 0.181818181818182 & 0 \tabularnewline
0.78 & 0.181818181818182 & 0 \tabularnewline
0.79 & 0.181818181818182 & 0 \tabularnewline
0.8 & 0.181818181818182 & 0 \tabularnewline
0.81 & 0.181818181818182 & 0 \tabularnewline
0.82 & 0.181818181818182 & 0 \tabularnewline
0.83 & 0.181818181818182 & 0 \tabularnewline
0.84 & 0.181818181818182 & 0 \tabularnewline
0.85 & 0.181818181818182 & 0 \tabularnewline
0.86 & 0.181818181818182 & 0 \tabularnewline
0.87 & 0.181818181818182 & 0 \tabularnewline
0.88 & 0.272727272727273 & 0 \tabularnewline
0.89 & 0.363636363636364 & 0 \tabularnewline
0.9 & 0.454545454545455 & 0 \tabularnewline
0.91 & 0.545454545454545 & 0 \tabularnewline
0.92 & 0.636363636363636 & 0 \tabularnewline
0.93 & 0.727272727272727 & 0 \tabularnewline
0.94 & 0.727272727272727 & 0 \tabularnewline
0.95 & 0.727272727272727 & 0 \tabularnewline
0.96 & 0.727272727272727 & 0 \tabularnewline
0.97 & 0.818181818181818 & 0 \tabularnewline
0.98 & 1 & 0 \tabularnewline
0.99 & 1 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143332&T=4

[TABLE]
[ROW][C]Type I & II errors for various threshold values[/C][/ROW]
[ROW][C]Threshold[/C][C]Type I[/C][C]Type II[/C][/ROW]
[ROW][C]0.01[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.02[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.03[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.04[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.05[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.06[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.07[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.08[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.09[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.1[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.11[/C][C]0[/C][C]0.666666666666667[/C][/ROW]
[ROW][C]0.12[/C][C]0[/C][C]0.5[/C][/ROW]
[ROW][C]0.13[/C][C]0[/C][C]0.5[/C][/ROW]
[ROW][C]0.14[/C][C]0[/C][C]0.5[/C][/ROW]
[ROW][C]0.15[/C][C]0[/C][C]0.5[/C][/ROW]
[ROW][C]0.16[/C][C]0[/C][C]0.5[/C][/ROW]
[ROW][C]0.17[/C][C]0[/C][C]0.5[/C][/ROW]
[ROW][C]0.18[/C][C]0[/C][C]0.5[/C][/ROW]
[ROW][C]0.19[/C][C]0[/C][C]0.5[/C][/ROW]
[ROW][C]0.2[/C][C]0[/C][C]0.5[/C][/ROW]
[ROW][C]0.21[/C][C]0[/C][C]0.5[/C][/ROW]
[ROW][C]0.22[/C][C]0[/C][C]0.5[/C][/ROW]
[ROW][C]0.23[/C][C]0[/C][C]0.333333333333333[/C][/ROW]
[ROW][C]0.24[/C][C]0[/C][C]0.333333333333333[/C][/ROW]
[ROW][C]0.25[/C][C]0[/C][C]0.333333333333333[/C][/ROW]
[ROW][C]0.26[/C][C]0[/C][C]0.333333333333333[/C][/ROW]
[ROW][C]0.27[/C][C]0[/C][C]0.333333333333333[/C][/ROW]
[ROW][C]0.28[/C][C]0[/C][C]0.333333333333333[/C][/ROW]
[ROW][C]0.29[/C][C]0[/C][C]0.333333333333333[/C][/ROW]
[ROW][C]0.3[/C][C]0[/C][C]0.333333333333333[/C][/ROW]
[ROW][C]0.31[/C][C]0[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.32[/C][C]0[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.33[/C][C]0[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.34[/C][C]0[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.35[/C][C]0[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.36[/C][C]0[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.37[/C][C]0[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.38[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.39[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.4[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.41[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.42[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.43[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.44[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.45[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.46[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.47[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.48[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.49[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.5[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.51[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.52[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.53[/C][C]0.0909090909090909[/C][C]0.166666666666667[/C][/ROW]
[ROW][C]0.54[/C][C]0.0909090909090909[/C][C]0[/C][/ROW]
[ROW][C]0.55[/C][C]0.0909090909090909[/C][C]0[/C][/ROW]
[ROW][C]0.56[/C][C]0.0909090909090909[/C][C]0[/C][/ROW]
[ROW][C]0.57[/C][C]0.0909090909090909[/C][C]0[/C][/ROW]
[ROW][C]0.58[/C][C]0.0909090909090909[/C][C]0[/C][/ROW]
[ROW][C]0.59[/C][C]0.0909090909090909[/C][C]0[/C][/ROW]
[ROW][C]0.6[/C][C]0.0909090909090909[/C][C]0[/C][/ROW]
[ROW][C]0.61[/C][C]0.0909090909090909[/C][C]0[/C][/ROW]
[ROW][C]0.62[/C][C]0.0909090909090909[/C][C]0[/C][/ROW]
[ROW][C]0.63[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.64[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.65[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.66[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.67[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.68[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.69[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.7[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.71[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.72[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.73[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.74[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.75[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.76[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.77[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.78[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.79[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.8[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.81[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.82[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.83[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.84[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.85[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.86[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.87[/C][C]0.181818181818182[/C][C]0[/C][/ROW]
[ROW][C]0.88[/C][C]0.272727272727273[/C][C]0[/C][/ROW]
[ROW][C]0.89[/C][C]0.363636363636364[/C][C]0[/C][/ROW]
[ROW][C]0.9[/C][C]0.454545454545455[/C][C]0[/C][/ROW]
[ROW][C]0.91[/C][C]0.545454545454545[/C][C]0[/C][/ROW]
[ROW][C]0.92[/C][C]0.636363636363636[/C][C]0[/C][/ROW]
[ROW][C]0.93[/C][C]0.727272727272727[/C][C]0[/C][/ROW]
[ROW][C]0.94[/C][C]0.727272727272727[/C][C]0[/C][/ROW]
[ROW][C]0.95[/C][C]0.727272727272727[/C][C]0[/C][/ROW]
[ROW][C]0.96[/C][C]0.727272727272727[/C][C]0[/C][/ROW]
[ROW][C]0.97[/C][C]0.818181818181818[/C][C]0[/C][/ROW]
[ROW][C]0.98[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.99[/C][C]1[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143332&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143332&T=4

As an alternative you can also use a QR Code:  

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

Type I & II errors for various threshold values
ThresholdType IType II
0.0101
0.0201
0.0301
0.0401
0.0501
0.0601
0.0701
0.0801
0.0901
0.101
0.1100.666666666666667
0.1200.5
0.1300.5
0.1400.5
0.1500.5
0.1600.5
0.1700.5
0.1800.5
0.1900.5
0.200.5
0.2100.5
0.2200.5
0.2300.333333333333333
0.2400.333333333333333
0.2500.333333333333333
0.2600.333333333333333
0.2700.333333333333333
0.2800.333333333333333
0.2900.333333333333333
0.300.333333333333333
0.3100.166666666666667
0.3200.166666666666667
0.3300.166666666666667
0.3400.166666666666667
0.3500.166666666666667
0.3600.166666666666667
0.3700.166666666666667
0.380.09090909090909090.166666666666667
0.390.09090909090909090.166666666666667
0.40.09090909090909090.166666666666667
0.410.09090909090909090.166666666666667
0.420.09090909090909090.166666666666667
0.430.09090909090909090.166666666666667
0.440.09090909090909090.166666666666667
0.450.09090909090909090.166666666666667
0.460.09090909090909090.166666666666667
0.470.09090909090909090.166666666666667
0.480.09090909090909090.166666666666667
0.490.09090909090909090.166666666666667
0.50.09090909090909090.166666666666667
0.510.09090909090909090.166666666666667
0.520.09090909090909090.166666666666667
0.530.09090909090909090.166666666666667
0.540.09090909090909090
0.550.09090909090909090
0.560.09090909090909090
0.570.09090909090909090
0.580.09090909090909090
0.590.09090909090909090
0.60.09090909090909090
0.610.09090909090909090
0.620.09090909090909090
0.630.1818181818181820
0.640.1818181818181820
0.650.1818181818181820
0.660.1818181818181820
0.670.1818181818181820
0.680.1818181818181820
0.690.1818181818181820
0.70.1818181818181820
0.710.1818181818181820
0.720.1818181818181820
0.730.1818181818181820
0.740.1818181818181820
0.750.1818181818181820
0.760.1818181818181820
0.770.1818181818181820
0.780.1818181818181820
0.790.1818181818181820
0.80.1818181818181820
0.810.1818181818181820
0.820.1818181818181820
0.830.1818181818181820
0.840.1818181818181820
0.850.1818181818181820
0.860.1818181818181820
0.870.1818181818181820
0.880.2727272727272730
0.890.3636363636363640
0.90.4545454545454550
0.910.5454545454545450
0.920.6363636363636360
0.930.7272727272727270
0.940.7272727272727270
0.950.7272727272727270
0.960.7272727272727270
0.970.8181818181818180
0.9810
0.9910



Parameters (Session):
Parameters (R input):
R code (references can be found in the software module):
library(brglm)
roc.plot <- function (sd, sdc, newplot = TRUE, ...)
{
sall <- sort(c(sd, sdc))
sens <- 0
specc <- 0
for (i in length(sall):1) {
sens <- c(sens, mean(sd >= sall[i], na.rm = T))
specc <- c(specc, mean(sdc >= sall[i], na.rm = T))
}
if (newplot) {
plot(specc, sens, xlim = c(0, 1), ylim = c(0, 1), type = 'l',
xlab = '1-specificity', ylab = 'sensitivity', main = 'ROC plot', ...)
abline(0, 1)
}
else lines(specc, sens, ...)
npoints <- length(sens)
area <- sum(0.5 * (sens[-1] + sens[-npoints]) * (specc[-1] -
specc[-npoints]))
lift <- (sens - specc)[-1]
cutoff <- sall[lift == max(lift)][1]
sensopt <- sens[-1][lift == max(lift)][1]
specopt <- 1 - specc[-1][lift == max(lift)][1]
list(area = area, cutoff = cutoff, sensopt = sensopt, specopt = specopt)
}
roc.analysis <- function (object, newdata = NULL, newplot = TRUE, ...)
{
if (is.null(newdata)) {
sd <- object$fitted[object$y == 1]
sdc <- object$fitted[object$y == 0]
}
else {
sd <- predict(object, newdata, type = 'response')[newdata$y ==
1]
sdc <- predict(object, newdata, type = 'response')[newdata$y ==
0]
}
roc.plot(sd, sdc, newplot, ...)
}
hosmerlem <- function (y, yhat, g = 10)
{
cutyhat <- cut(yhat, breaks = quantile(yhat, probs = seq(0,
1, 1/g)), include.lowest = T)
obs <- xtabs(cbind(1 - y, y) ~ cutyhat)
expect <- xtabs(cbind(1 - yhat, yhat) ~ cutyhat)
chisq <- sum((obs - expect)^2/expect)
P <- 1 - pchisq(chisq, g - 2)
c('X^2' = chisq, Df = g - 2, 'P(>Chi)' = P)
}
x <- as.data.frame(t(y))
r <- brglm(x)
summary(r)
rc <- summary(r)$coeff
try(hm <- hosmerlem(y[1,],r$fitted.values),silent=T)
try(hm,silent=T)
bitmap(file='test0.png')
ra <- roc.analysis(r)
dev.off()
te <- array(0,dim=c(2,99))
for (i in 1:99) {
threshold <- i / 100
numcorr1 <- 0
numfaul1 <- 0
numcorr0 <- 0
numfaul0 <- 0
for (j in 1:length(r$fitted.values)) {
if (y[1,j] > 0.99) {
if (r$fitted.values[j] >= threshold) numcorr1 = numcorr1 + 1 else numfaul1 = numfaul1 + 1
} else {
if (r$fitted.values[j] < threshold) numcorr0 = numcorr0 + 1 else numfaul0 = numfaul0 + 1
}
}
te[1,i] <- numfaul1 / (numfaul1 + numcorr1)
te[2,i] <- numfaul0 / (numfaul0 + numcorr0)
}
bitmap(file='test1.png')
op <- par(mfrow=c(2,2))
plot((1:99)/100,te[1,],xlab='Threshold',ylab='Type I error', main='1 - Specificity')
plot((1:99)/100,te[2,],xlab='Threshold',ylab='Type II error', main='1 - Sensitivity')
plot(te[1,],te[2,],xlab='Type I error',ylab='Type II error', main='(1-Sens.) vs (1-Spec.)')
plot((1:99)/100,te[1,]+te[2,],xlab='Threshold',ylab='Sum of Type I & II error', main='(1-Sens.) + (1-Spec.)')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Coefficients of Bias-Reduced Logistic Regression',5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.E.',header=TRUE)
a<-table.element(a,'t-stat',header=TRUE)
a<-table.element(a,'2-sided p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(rc[,1])) {
a<-table.row.start(a)
a<-table.element(a,labels(rc)[[1]][i],header=TRUE)
a<-table.element(a,rc[i,1])
a<-table.element(a,rc[i,2])
a<-table.element(a,rc[i,3])
a<-table.element(a,2*(1-pt(abs(rc[i,3]),r$df.residual)))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Summary of Bias-Reduced Logistic Regression',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Deviance',1,TRUE)
a<-table.element(a,r$deviance)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Penalized deviance',1,TRUE)
a<-table.element(a,r$penalized.deviance)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Residual Degrees of Freedom',1,TRUE)
a<-table.element(a,r$df.residual)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'ROC Area',1,TRUE)
a<-table.element(a,ra$area)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Hosmer–Lemeshow test',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Chi-square',1,TRUE)
phm <- array('NA',dim=3)
for (i in 1:3) { try(phm[i] <- hm[i],silent=T) }
a<-table.element(a,phm[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degrees of Freedom',1,TRUE)
a<-table.element(a,phm[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'P(>Chi)',1,TRUE)
a<-table.element(a,phm[3])
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,'Fit of Logistic Regression',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Index',1,TRUE)
a<-table.element(a,'Actual',1,TRUE)
a<-table.element(a,'Fitted',1,TRUE)
a<-table.element(a,'Error',1,TRUE)
a<-table.row.end(a)
for (i in 1:length(r$fitted.values)) {
a<-table.row.start(a)
a<-table.element(a,i,1,TRUE)
a<-table.element(a,y[1,i])
a<-table.element(a,r$fitted.values[i])
a<-table.element(a,y[1,i]-r$fitted.values[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,'Type I & II errors for various threshold values',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Threshold',1,TRUE)
a<-table.element(a,'Type I',1,TRUE)
a<-table.element(a,'Type II',1,TRUE)
a<-table.row.end(a)
for (i in 1:99) {
a<-table.row.start(a)
a<-table.element(a,i/100,1,TRUE)
a<-table.element(a,te[1,i])
a<-table.element(a,te[2,i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable3.tab')