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Description of Statistical Computation

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, 30 Nov 2010 20:56:05 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/30/t1291150448d2z5pu5k8ktpv2v.htm/, Retrieved Mon, 29 Apr 2024 09:27:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103822, Retrieved Mon, 29 Apr 2024 09:27:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Quasi Random-Walk Identification] [] [2010-11-30 13:40:02] [7d64bf19f34ddcdf2626356c9d5bd60d]
-    D  [Quasi Random-Walk Identification] [] [2010-11-30 13:44:04] [7d64bf19f34ddcdf2626356c9d5bd60d]
-    D    [Quasi Random-Walk Identification] [] [2010-11-30 13:45:23] [7d64bf19f34ddcdf2626356c9d5bd60d]
-    D      [Quasi Random-Walk Identification] [] [2010-11-30 13:46:48] [7d64bf19f34ddcdf2626356c9d5bd60d]
-    D        [Quasi Random-Walk Identification] [] [2010-11-30 14:01:52] [7d64bf19f34ddcdf2626356c9d5bd60d]
-    D          [Quasi Random-Walk Identification] [] [2010-11-30 14:03:28] [7d64bf19f34ddcdf2626356c9d5bd60d]
-    D            [Quasi Random-Walk Identification] [] [2010-11-30 14:04:50] [7d64bf19f34ddcdf2626356c9d5bd60d]
- RM D              [Bias-Reduced Logistic Regression] [] [2010-11-30 14:56:19] [7d64bf19f34ddcdf2626356c9d5bd60d]
-    D                  [Bias-Reduced Logistic Regression] [] [2010-11-30 20:56:05] [5842cf9dd57f9603e676e11284d3404a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0 0
0 0
0 0
0 0
0 0
0 0
0 0.123600
0 0
0 0
0 0
1 0
1 0
1 0
1 0
1 0
1 0
1 0
1 0
1 0
1 0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103822&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103822&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103822&T=0

As an alternative you can also use a QR Code:  
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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Coefficients of Bias-Reduced Logistic Regression
VariableParameterS.E.t-stat2-sided p-value
(Intercept)0.1000834585569830.4594060847069270.2178540117091170.829993459583843
P-9.6981856571609419.0505846595736-0.5090754866826240.616883014777494

\begin{tabular}{lllllllll}
\hline
Coefficients of Bias-Reduced Logistic Regression \tabularnewline
Variable & Parameter & S.E. & t-stat & 2-sided p-value \tabularnewline
(Intercept) & 0.100083458556983 & 0.459406084706927 & 0.217854011709117 & 0.829993459583843 \tabularnewline
P & -9.69818565716094 & 19.0505846595736 & -0.509075486682624 & 0.616883014777494 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103822&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]0.100083458556983[/C][C]0.459406084706927[/C][C]0.217854011709117[/C][C]0.829993459583843[/C][/ROW]
[ROW][C]P[/C][C]-9.69818565716094[/C][C]19.0505846595736[/C][C]-0.509075486682624[/C][C]0.616883014777494[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103822&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103822&T=1

As an alternative you can also use a QR Code:  
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)0.1000834585569830.4594060847069270.2178540117091170.829993459583843
P-9.6981856571609419.0505846595736-0.5090754866826240.616883014777494






Summary of Bias-Reduced Logistic Regression
Deviance26.8624330217688
Penalized deviance31.1621774354272
Residual Degrees of Freedom18
ROC Area0.55
Hosmer–Lemeshow test
Chi-squareNA
Degrees of FreedomNA
P(>Chi)NA

\begin{tabular}{lllllllll}
\hline
Summary of Bias-Reduced Logistic Regression \tabularnewline
Deviance & 26.8624330217688 \tabularnewline
Penalized deviance & 31.1621774354272 \tabularnewline
Residual Degrees of Freedom & 18 \tabularnewline
ROC Area & 0.55 \tabularnewline
Hosmer–Lemeshow test \tabularnewline
Chi-square & NA \tabularnewline
Degrees of Freedom & NA \tabularnewline
P(>Chi) & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103822&T=2

[TABLE]
[ROW][C]Summary of Bias-Reduced Logistic Regression[/C][/ROW]
[ROW][C]Deviance[/C][C]26.8624330217688[/C][/ROW]
[ROW][C]Penalized deviance[/C][C]31.1621774354272[/C][/ROW]
[ROW][C]Residual Degrees of Freedom[/C][C]18[/C][/ROW]
[ROW][C]ROC Area[/C][C]0.55[/C][/ROW]
[ROW][C]Hosmer–Lemeshow test[/C][/ROW]
[ROW][C]Chi-square[/C][C]NA[/C][/ROW]
[ROW][C]Degrees of Freedom[/C][C]NA[/C][/ROW]
[ROW][C]P(>Chi)[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103822&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103822&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Summary of Bias-Reduced Logistic Regression
Deviance26.8624330217688
Penalized deviance31.1621774354272
Residual Degrees of Freedom18
ROC Area0.55
Hosmer–Lemeshow test
Chi-squareNA
Degrees of FreedomNA
P(>Chi)NA






Fit of Logistic Regression
IndexActualFittedError
100.525-0.525
200.525-0.525
300.525-0.525
400.525-0.525
500.525-0.525
600.525-0.525
700.25-0.25
800.525-0.525
900.525-0.525
1000.525-0.525
1110.5250.475
1210.5250.475
1310.5250.475
1410.5250.475
1510.5250.475
1610.5250.475
1710.5250.475
1810.5250.475
1910.5250.475
2010.5250.475

\begin{tabular}{lllllllll}
\hline
Fit of Logistic Regression \tabularnewline
Index & Actual & Fitted & Error \tabularnewline
1 & 0 & 0.525 & -0.525 \tabularnewline
2 & 0 & 0.525 & -0.525 \tabularnewline
3 & 0 & 0.525 & -0.525 \tabularnewline
4 & 0 & 0.525 & -0.525 \tabularnewline
5 & 0 & 0.525 & -0.525 \tabularnewline
6 & 0 & 0.525 & -0.525 \tabularnewline
7 & 0 & 0.25 & -0.25 \tabularnewline
8 & 0 & 0.525 & -0.525 \tabularnewline
9 & 0 & 0.525 & -0.525 \tabularnewline
10 & 0 & 0.525 & -0.525 \tabularnewline
11 & 1 & 0.525 & 0.475 \tabularnewline
12 & 1 & 0.525 & 0.475 \tabularnewline
13 & 1 & 0.525 & 0.475 \tabularnewline
14 & 1 & 0.525 & 0.475 \tabularnewline
15 & 1 & 0.525 & 0.475 \tabularnewline
16 & 1 & 0.525 & 0.475 \tabularnewline
17 & 1 & 0.525 & 0.475 \tabularnewline
18 & 1 & 0.525 & 0.475 \tabularnewline
19 & 1 & 0.525 & 0.475 \tabularnewline
20 & 1 & 0.525 & 0.475 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103822&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]0[/C][C]0.525[/C][C]-0.525[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.525[/C][C]-0.525[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.525[/C][C]-0.525[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.525[/C][C]-0.525[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.525[/C][C]-0.525[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.525[/C][C]-0.525[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.25[/C][C]-0.25[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.525[/C][C]-0.525[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.525[/C][C]-0.525[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.525[/C][C]-0.525[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.525[/C][C]0.475[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]0.525[/C][C]0.475[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]0.525[/C][C]0.475[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]0.525[/C][C]0.475[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.525[/C][C]0.475[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.525[/C][C]0.475[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.525[/C][C]0.475[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]0.525[/C][C]0.475[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]0.525[/C][C]0.475[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.525[/C][C]0.475[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103822&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103822&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Fit of Logistic Regression
IndexActualFittedError
100.525-0.525
200.525-0.525
300.525-0.525
400.525-0.525
500.525-0.525
600.525-0.525
700.25-0.25
800.525-0.525
900.525-0.525
1000.525-0.525
1110.5250.475
1210.5250.475
1310.5250.475
1410.5250.475
1510.5250.475
1610.5250.475
1710.5250.475
1810.5250.475
1910.5250.475
2010.5250.475






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
0.1101
0.1201
0.1301
0.1401
0.1501
0.1601
0.1701
0.1801
0.1901
0.201
0.2101
0.2201
0.2301
0.2401
0.2501
0.2600.9
0.2700.9
0.2800.9
0.2900.9
0.300.9
0.3100.9
0.3200.9
0.3300.9
0.3400.9
0.3500.9
0.3600.9
0.3700.9
0.3800.9
0.3900.9
0.400.9
0.4100.9
0.4200.9
0.4300.9
0.4400.9
0.4500.9
0.4600.9
0.4700.9
0.4800.9
0.4900.9
0.500.9
0.5100.9
0.5200.9
0.5310
0.5410
0.5510
0.5610
0.5710
0.5810
0.5910
0.610
0.6110
0.6210
0.6310
0.6410
0.6510
0.6610
0.6710
0.6810
0.6910
0.710
0.7110
0.7210
0.7310
0.7410
0.7510
0.7610
0.7710
0.7810
0.7910
0.810
0.8110
0.8210
0.8310
0.8410
0.8510
0.8610
0.8710
0.8810
0.8910
0.910
0.9110
0.9210
0.9310
0.9410
0.9510
0.9610
0.9710
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 & 1 \tabularnewline
0.12 & 0 & 1 \tabularnewline
0.13 & 0 & 1 \tabularnewline
0.14 & 0 & 1 \tabularnewline
0.15 & 0 & 1 \tabularnewline
0.16 & 0 & 1 \tabularnewline
0.17 & 0 & 1 \tabularnewline
0.18 & 0 & 1 \tabularnewline
0.19 & 0 & 1 \tabularnewline
0.2 & 0 & 1 \tabularnewline
0.21 & 0 & 1 \tabularnewline
0.22 & 0 & 1 \tabularnewline
0.23 & 0 & 1 \tabularnewline
0.24 & 0 & 1 \tabularnewline
0.25 & 0 & 1 \tabularnewline
0.26 & 0 & 0.9 \tabularnewline
0.27 & 0 & 0.9 \tabularnewline
0.28 & 0 & 0.9 \tabularnewline
0.29 & 0 & 0.9 \tabularnewline
0.3 & 0 & 0.9 \tabularnewline
0.31 & 0 & 0.9 \tabularnewline
0.32 & 0 & 0.9 \tabularnewline
0.33 & 0 & 0.9 \tabularnewline
0.34 & 0 & 0.9 \tabularnewline
0.35 & 0 & 0.9 \tabularnewline
0.36 & 0 & 0.9 \tabularnewline
0.37 & 0 & 0.9 \tabularnewline
0.38 & 0 & 0.9 \tabularnewline
0.39 & 0 & 0.9 \tabularnewline
0.4 & 0 & 0.9 \tabularnewline
0.41 & 0 & 0.9 \tabularnewline
0.42 & 0 & 0.9 \tabularnewline
0.43 & 0 & 0.9 \tabularnewline
0.44 & 0 & 0.9 \tabularnewline
0.45 & 0 & 0.9 \tabularnewline
0.46 & 0 & 0.9 \tabularnewline
0.47 & 0 & 0.9 \tabularnewline
0.48 & 0 & 0.9 \tabularnewline
0.49 & 0 & 0.9 \tabularnewline
0.5 & 0 & 0.9 \tabularnewline
0.51 & 0 & 0.9 \tabularnewline
0.52 & 0 & 0.9 \tabularnewline
0.53 & 1 & 0 \tabularnewline
0.54 & 1 & 0 \tabularnewline
0.55 & 1 & 0 \tabularnewline
0.56 & 1 & 0 \tabularnewline
0.57 & 1 & 0 \tabularnewline
0.58 & 1 & 0 \tabularnewline
0.59 & 1 & 0 \tabularnewline
0.6 & 1 & 0 \tabularnewline
0.61 & 1 & 0 \tabularnewline
0.62 & 1 & 0 \tabularnewline
0.63 & 1 & 0 \tabularnewline
0.64 & 1 & 0 \tabularnewline
0.65 & 1 & 0 \tabularnewline
0.66 & 1 & 0 \tabularnewline
0.67 & 1 & 0 \tabularnewline
0.68 & 1 & 0 \tabularnewline
0.69 & 1 & 0 \tabularnewline
0.7 & 1 & 0 \tabularnewline
0.71 & 1 & 0 \tabularnewline
0.72 & 1 & 0 \tabularnewline
0.73 & 1 & 0 \tabularnewline
0.74 & 1 & 0 \tabularnewline
0.75 & 1 & 0 \tabularnewline
0.76 & 1 & 0 \tabularnewline
0.77 & 1 & 0 \tabularnewline
0.78 & 1 & 0 \tabularnewline
0.79 & 1 & 0 \tabularnewline
0.8 & 1 & 0 \tabularnewline
0.81 & 1 & 0 \tabularnewline
0.82 & 1 & 0 \tabularnewline
0.83 & 1 & 0 \tabularnewline
0.84 & 1 & 0 \tabularnewline
0.85 & 1 & 0 \tabularnewline
0.86 & 1 & 0 \tabularnewline
0.87 & 1 & 0 \tabularnewline
0.88 & 1 & 0 \tabularnewline
0.89 & 1 & 0 \tabularnewline
0.9 & 1 & 0 \tabularnewline
0.91 & 1 & 0 \tabularnewline
0.92 & 1 & 0 \tabularnewline
0.93 & 1 & 0 \tabularnewline
0.94 & 1 & 0 \tabularnewline
0.95 & 1 & 0 \tabularnewline
0.96 & 1 & 0 \tabularnewline
0.97 & 1 & 0 \tabularnewline
0.98 & 1 & 0 \tabularnewline
0.99 & 1 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103822&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]1[/C][/ROW]
[ROW][C]0.12[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.13[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.14[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.15[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.16[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.17[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.18[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.19[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.2[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.21[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.22[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.23[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.24[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.25[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]0.26[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.27[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.28[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.29[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.3[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.31[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.32[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.33[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.34[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.35[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.36[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.37[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.38[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.39[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.4[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.41[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.42[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.43[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.44[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.45[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.46[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.47[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.48[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.49[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.5[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.51[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.52[/C][C]0[/C][C]0.9[/C][/ROW]
[ROW][C]0.53[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.54[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.55[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.56[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.57[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.58[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.59[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.6[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.61[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.62[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.63[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.64[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.65[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.66[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.67[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.68[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.69[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.7[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.71[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.72[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.73[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.74[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.75[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.76[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.77[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.78[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.79[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.8[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.81[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.82[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.83[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.84[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.85[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.86[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.87[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.88[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.89[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.9[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.91[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.92[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.93[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.94[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.95[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.96[/C][C]1[/C][C]0[/C][/ROW]
[ROW][C]0.97[/C][C]1[/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=103822&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103822&T=4

As an alternative you can also use a QR Code:  
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
0.1101
0.1201
0.1301
0.1401
0.1501
0.1601
0.1701
0.1801
0.1901
0.201
0.2101
0.2201
0.2301
0.2401
0.2501
0.2600.9
0.2700.9
0.2800.9
0.2900.9
0.300.9
0.3100.9
0.3200.9
0.3300.9
0.3400.9
0.3500.9
0.3600.9
0.3700.9
0.3800.9
0.3900.9
0.400.9
0.4100.9
0.4200.9
0.4300.9
0.4400.9
0.4500.9
0.4600.9
0.4700.9
0.4800.9
0.4900.9
0.500.9
0.5100.9
0.5200.9
0.5310
0.5410
0.5510
0.5610
0.5710
0.5810
0.5910
0.610
0.6110
0.6210
0.6310
0.6410
0.6510
0.6610
0.6710
0.6810
0.6910
0.710
0.7110
0.7210
0.7310
0.7410
0.7510
0.7610
0.7710
0.7810
0.7910
0.810
0.8110
0.8210
0.8310
0.8410
0.8510
0.8610
0.8710
0.8810
0.8910
0.910
0.9110
0.9210
0.9310
0.9410
0.9510
0.9610
0.9710
0.9810
0.9910


Figures (Output of Computation)

Input Parameters & R Code

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')