From 8a8e723c32b6d9a87f2da993284c97159ef6061d Mon Sep 17 00:00:00 2001 From: pr0m1th3as Date: Mon, 27 Feb 2023 19:55:46 +0200 Subject: [PATCH] update HTML documentation --- docs/ConfusionMatrixChart.html | 184 ++++++++++++++++++++++ docs/anova1.html | 18 +-- docs/assets/ConfusionMatrixChart_101.png | Bin 0 -> 8990 bytes docs/assets/anova1_201.png | Bin 9360 -> 9327 bytes docs/assets/anova1_301.png | Bin 8416 -> 8427 bytes docs/assets/boxplot_101.png | Bin 15055 -> 15006 bytes docs/assets/boxplot_201.png | Bin 19520 -> 19272 bytes docs/assets/boxplot_301.png | Bin 16531 -> 15577 bytes docs/assets/boxplot_401.png | Bin 17356 -> 17789 bytes docs/assets/cdfplot_101.png | Bin 14211 -> 14244 bytes docs/assets/clusterdata_101.png | Bin 10642 -> 10548 bytes docs/assets/dcov_101.png | Bin 14705 -> 14615 bytes docs/assets/dendrogram_201.png | Bin 16350 -> 17645 bytes docs/assets/dendrogram_301.png | Bin 33237 -> 33400 bytes docs/assets/dendrogram_401.png | Bin 27874 -> 25415 bytes docs/assets/dendrogram_501.png | Bin 20731 -> 21287 bytes docs/assets/ecdf_101.png | Bin 13587 -> 13415 bytes docs/assets/ecdf_201.png | Bin 34524 -> 34735 bytes docs/assets/fitgmdist_101.png | Bin 20579 -> 20753 bytes docs/assets/gscatter_101.png | Bin 15601 -> 15301 bytes docs/assets/histfit_101.png | Bin 11873 -> 10675 bytes docs/assets/jackknife_101.png | Bin 11776 -> 11744 bytes docs/assets/jackknife_201.png | Bin 16680 -> 16720 bytes docs/assets/kmeans_101.png | Bin 13734 -> 13195 bytes docs/assets/kmeans_301.png | Bin 22392 -> 22613 bytes docs/assets/kmeans_302.png | Bin 14410 -> 14869 bytes docs/assets/kmeans_401.png | Bin 44021 -> 43071 bytes docs/assets/kruskalwallis_201.png | Bin 9054 -> 9517 bytes docs/assets/kruskalwallis_301.png | Bin 8414 -> 8440 bytes docs/assets/mhsample_101.png | Bin 29625 -> 19474 bytes docs/assets/multcompare_101.png | Bin 22972 -> 23095 bytes docs/assets/procrustes_101.png | Bin 8904 -> 8677 bytes docs/assets/regress_gp_101.png | Bin 9961 -> 10097 bytes docs/assets/regress_gp_201.png | Bin 24190 -> 30665 bytes docs/assets/regress_gp_301.png | Bin 15745 -> 15352 bytes docs/assets/silhouette_101.png | Bin 19719 -> 19670 bytes docs/assets/slicesample_101.png | Bin 27862 -> 17947 bytes docs/assets/violin_101.png | Bin 53396 -> 52095 bytes docs/assets/violin_201.png | Bin 24705 -> 24787 bytes docs/assets/violin_301.png | Bin 11610 -> 10671 bytes docs/assets/violin_401.png | Bin 11499 -> 11191 bytes docs/assets/violin_501.png | Bin 11580 -> 12525 bytes docs/assets/violin_601.png | Bin 11626 -> 10607 bytes docs/chi2gof.html | 46 +++--- docs/cl_multinom.html | 191 +++++++++++++++++++++++ docs/cophenet.html | 2 +- docs/correlation_test.html | 144 +++++++++++++++++ docs/dendrogram.html | 2 +- docs/fitgmdist.html | 14 +- docs/index.html | 25 +++ docs/kmeans.html | 12 +- docs/kruskalwallis.html | 12 +- docs/mhsample.html | 12 +- docs/multcompare.html | 30 ++-- docs/optimalleaforder.html | 2 +- docs/regress_gp.html | 10 +- docs/regression_ttest.html | 10 +- docs/slicesample.html | 6 +- docs/wblplot.html | 12 +- 59 files changed, 638 insertions(+), 94 deletions(-) create mode 100644 docs/ConfusionMatrixChart.html create mode 100644 docs/assets/ConfusionMatrixChart_101.png create mode 100644 docs/cl_multinom.html create mode 100644 docs/correlation_test.html diff --git a/docs/ConfusionMatrixChart.html b/docs/ConfusionMatrixChart.html new file mode 100644 index 00000000..4d7c6ca4 --- /dev/null +++ b/docs/ConfusionMatrixChart.html @@ -0,0 +1,184 @@ + + + + Statistics: ConfusionMatrixChart + + + + + + + + + +
+ +
+
+
+
+
+
+

+ Function Reference: ConfusionMatrixChart +

+
+
+
+
+
+
statistics: p = ConfusionMatrixChart ()
+
+

Create object p, a Confusion Matrix Chart object. +

+
+
+
"DiagonalColor"
+
+

The color of the patches on the diagonal, default is [0.0, 0.4471, 0.7412]. +

+ +
"OffDiagonalColor"
+ +

The color of the patches off the diagonal, default is [0.851, 0.3255, 0.098]. +

+ +
"GridVisible"
+ +

Available values: on (default), off. +

+ +
"Normalization"
+ +

Available values: absolute (default), column-normalized, + row-normalized, total-normalized. +

+ +
"ColumnSummary"
+ +

Available values: off (default), absolute, + column-normalized,total-normalized. +

+ +
"RowSummary"
+ +

Available values: off (default), absolute, + row-normalized, total-normalized. +

+ + +

MATLAB compatibility – the not implemented properties are: FontColor, + PositionConstraint, InnerPosition, Layout. +

+

See also: + confusionchart +

+

Source Code: + ConfusionMatrixChart +

+
+
+
+
+
+

+ Example: 1 +

+
+
+
+
+
+
+ + + +
 
+
+ ## Create a simple ConfusionMatrixChart Object
+
+ cm = ConfusionMatrixChart (gca, [1 2; 1 2], {"A","B"},{"XLabel","LABEL A"})
+ NormalizedValues = cm.NormalizedValues
+ ClassLabels = cm.ClassLabels
+
+cm =
+
+ConfusionMatrixChart with properties:
+
+	NormalizedValues: [ 2x2 double ]
+	ClassLabels: { 1x2 cell }
+
+
+NormalizedValues =
+
+   1   2
+   1   2
+
+ClassLabels =
+{
+  [1,1] = A
+  [1,2] = B
+}
+
+                    
+
+ plotted figure +

+ +
+
+
+
+
+ + +
+
+
+ + + diff --git a/docs/anova1.html b/docs/anova1.html index 6432d065..7862e749 100644 --- a/docs/anova1.html +++ b/docs/anova1.html @@ -186,9 +186,9 @@

Source SS df MS F Prob>F ------------------------------------------------------ -Groups 112.7223 5 22.5445 39.66 0.0000 -Error 17.0514 30 0.5684 -Total 129.7736 35 +Groups 128.9120 5 25.7824 34.13 0.0000 +Error 22.6632 30 0.7554 +Total 151.5752 35 @@ -226,9 +226,9 @@

Source SS df MS F Prob>F ------------------------------------------------------ -Groups 130.4467 5 26.0893 24.18 0.0000 -Error 32.3655 30 1.0788 -Total 162.8122 35 +Groups 102.5418 5 20.5084 14.39 0.0000 +Error 42.7504 30 1.4250 +Total 145.2923 35 @@ -270,9 +270,9 @@

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z9}VMoxxY!ymMi`CyQNFn4bW+zi|4QfB_MFT6F~&Mz-`^-HN*;4%+0#qA4PT25!gxI1rp*0D=d{ksL9AxzaqIGBa&~I<_R=TUvhnzg-q5uO zA+^n2R|dZQ^@M-g@;ao)`U?F!dBwAcjwW(t*tJV9zqnBNV4X=%1Qn=9?AlooJ?K6es!OZB_T(aS68 zZD*(RGt6J}B?C*7Qzfq!c}@4MO!pc-J2SgF;A-eQ`_g-NT%}=u3h*m_a5xT`TYaV& z%KcO}GI|U3cF$ZDm;}CJu3XwA%YPw6*dNqNz}lyVvb$8wo|*UFTh4*il$hy%dklm6 zyOQzf((eWuJIhk3{!2F0aT_G){M+?b0%jk2BLTg!(T)a9f9jC+AwA)*dmt!);->mP hs*~#fq^oNK4Yy~o$Yayf2VKjM?gfMM1&H5%|6f7gWfA}Y diff --git a/docs/chi2gof.html b/docs/chi2gof.html index 30721c14..7e5b09f1 100644 --- a/docs/chi2gof.html +++ b/docs/chi2gof.html @@ -187,66 +187,66 @@

[h, p, stats] = chi2gof (x, "cdf", {@normcdf, mean(x), std(x)}) h = 0 -p = 0.6088 +p = 0.2901 stats = scalar structure containing the fields: - chi2stat = 3.5966 - df = 5 + chi2stat = 4.9724 + df = 4 edges = - 38.745 43.644 46.093 48.542 50.991 53.440 55.890 58.339 63.237 + 38.717 43.567 45.992 48.417 50.842 53.266 55.691 62.966 O = - 5 10 17 15 16 20 9 8 + 11 15 13 15 22 11 13 E = - 6.2207 8.6230 14.2787 18.6262 19.1416 15.4972 9.8841 7.7284 + 9.3406 11.4470 17.1333 19.9541 18.0830 12.7512 11.2908 h = 0 -p = 0.6088 +p = 0.2901 stats = scalar structure containing the fields: - chi2stat = 3.5966 - df = 5 + chi2stat = 4.9724 + df = 4 edges = - 38.745 43.644 46.093 48.542 50.991 53.440 55.890 58.339 63.237 + 38.717 43.567 45.992 48.417 50.842 53.266 55.691 62.966 O = - 5 10 17 15 16 20 9 8 + 11 15 13 15 22 11 13 E = - 6.2207 8.6230 14.2787 18.6262 19.1416 15.4972 9.8841 7.7284 + 9.3406 11.4470 17.1333 19.9541 18.0830 12.7512 11.2908 h = 0 -p = 0.6088 +p = 0.2901 stats = scalar structure containing the fields: - chi2stat = 3.5966 - df = 5 + chi2stat = 4.9724 + df = 4 edges = - 38.745 43.644 46.093 48.542 50.991 53.440 55.890 58.339 63.237 + 38.717 43.567 45.992 48.417 50.842 53.266 55.691 62.966 O = - 5 10 17 15 16 20 9 8 + 11 15 13 15 22 11 13 E = - 6.2207 8.6230 14.2787 18.6262 19.1416 15.4972 9.8841 7.7284 + 9.3406 11.4470 17.1333 19.9541 18.0830 12.7512 11.2908 @@ -283,26 +283,26 @@

[h, p, stats] = chi2gof (x, "binedges", binedges, "expected", expectedCounts) h = 0 -p = 0.4559 +p = 0.7399 stats = scalar structure containing the fields: - chi2stat = 8.8000 + chi2stat = 6.0000 df = 9 edges = Columns 1 through 7: - 8.0469e-03 1.0609e-01 2.0414e-01 3.0218e-01 4.0022e-01 4.9827e-01 5.9631e-01 + 1.9129e-03 9.8803e-02 1.9569e-01 2.9258e-01 3.8947e-01 4.8636e-01 5.8325e-01 Columns 8 through 11: - 6.9436e-01 7.9240e-01 8.9044e-01 9.8849e-01 + 6.8014e-01 7.7703e-01 8.7392e-01 9.7081e-01 O = - 14 6 13 6 6 11 13 10 9 12 + 10 9 7 14 14 6 10 10 9 11 E = diff --git a/docs/cl_multinom.html b/docs/cl_multinom.html new file mode 100644 index 00000000..28edcbb1 --- /dev/null +++ b/docs/cl_multinom.html @@ -0,0 +1,191 @@ + + + + Statistics: cl_multinom + + + + + + + + + + +
+
+
+
+
+

+ Function Reference: cl_multinom +

+
+
+
+
+
+
statistics: CL = cl_multinom (X, N, b)
+
statistics: CL = cl_multinom (X, N, b, method)
+
+ +

Confidence level of multinomial portions. +

+
+

cl_multinom returns confidence level of multinomial parameters + estimated as p = X / sum(X) with predefined confidence interval + b. Finite population is also considered. +

+

This function calculates the level of confidence at which the samples + represent the true distribution given that there is a predefined tolerance + (confidence interval). This is the upside down case of the typical excercises + at which we want to get the confidence interval given the confidence level + (and the estimated parameters of the underlying distribution). + But once we accept (lets say at elections) that we have a standard predefined + maximal acceptable error rate (e.g. b=0.02 ) in the estimation and we + just want to know that how sure we can be that the measured proportions are + the same as in the entire population (ie. the expected value and mean of the + samples are roughly the same) we need to use this function. +

+ +

Arguments

+ + + + + + +
VariableTypeDescription
Xint vectorsample frequencies bins.
Nint scalarPopulation size that was sampled + by X. If N < sum (X), infinite number assumed.
breal vectorconfidence interval. If vector, + it should be the size of X containing confence interval for each cells. + If scalar, each cell will have the same value of b unless it is zero or -1. + If value is 0, b = 0.02 is assumed which is standard choice at + elections otherwise it is calculated in a way that one sample in a cell + alteration defines the confidence interval.
methodstringAn optional argument + for defining the calculation method. Available choices are + "bromaghin" (default), "cochran", and agresti_cull.
+ +

Note! The agresti_cull method is not exactly the solution at + reference given below but an adjustment of the solutions above. +

+ +

Returns

+

Confidence level. +

+ +

Example

+

CL = cl_multinom ([27; 43; 19; 11], 10000, 0.05) + returns 0.69 confidence level. +

+ +

References

+
    +
  1. + "bromaghin" calculation type (default) is based on the article: + +

    Jeffrey F. Bromaghin, "Sample Size Determination for Interval Estimation + of Multinomial Probabilities", The American Statistician vol 47, 1993, + pp 203-206. +

    +
  2. + "cochran" calculation type is based on article: + +

    Robert T. Tortora, "A Note on Sample Size Estimation for Multinomial + Populations", The American Statistician, , Vol 32. 1978, pp 100-102. +

    +
  3. + "agresti_cull" calculation type is based on article: + +

    A. Agresti and B.A. Coull, "Approximate is better than ’exact’ for + interval estimation of binomial portions", The American Statistician, + Vol. 52, 1998, pp 119-126 +

+ + +

Source Code: + cl_multinom +

+ +
+
+
+
+
+

+ Example: 1 +

+
+
+
+
+
+
+ + + +
 
+
+ CL = cl_multinom ([27; 43; 19; 11], 10000, 0.05)
+
+CL = 0.6923
+                    
+ +
+
+
+
+
+ + +
+
+
+ + + diff --git a/docs/cophenet.html b/docs/cophenet.html index 2ad3c044..5cd01663 100644 --- a/docs/cophenet.html +++ b/docs/cophenet.html @@ -132,7 +132,7 @@

Z = linkage (y, "average"); cophenet (Z, y) -ans = 0.7905 +ans = 0.7377 diff --git a/docs/correlation_test.html b/docs/correlation_test.html new file mode 100644 index 00000000..8860cf82 --- /dev/null +++ b/docs/correlation_test.html @@ -0,0 +1,144 @@ + + + + Statistics: correlation_test + + + + + + + + + + +
+
+
+
+
+

+ Function Reference: correlation_test +

+
+
+
+
+
+
statistics: h = correlation_test (x, y)
+
statistics: [h, pval] = correlation_test (y, x)
+
statistics: [h, pval, stats] = correlation_test (y, x)
+
statistics: […] = correlation_test (y, x, Name, Value)
+
+ +

Perform a correlation coefficient test whether two samples x and + y come from uncorrelated populations. +

+
+

h = correlation_test (y, x) tests the null + hypothesis that the two samples x and y come from uncorrelated + populations. The result is h = 0 if the null hypothesis cannot be + rejected at the 5% significance level, or h = 1 if the null hypothesis + can be rejected at the 5% level. y and x must be vectors of + equal length with finite real numbers. +

+

The p-value of the test is returned in pval. stats is a + structure with the following fields: +

+ + + + + + + +
FieldValue
methodthe type of correlation coefficient used + for the test
dfthe degrees of freedom (where applicable)
corrcoefthe correlation coefficient
statthe test’s statistic
distthe respective distribution for the test
altthe alternative hypothesis for the test
+ + +

[…] = correlation_test (…, name, value) + specifies one or more of the following name/value pairs: +

+ + + + +
NameValue
"alpha"the significance level. Default is 0.05.
"tail"a string specifying the alternative hypothesis
+ + + + +
"both"corrcoef is not 0 (two-tailed, default)
"left"corrcoef is less than 0 (left-tailed)
"right"corrcoef is greater than 0 + (right-tailed)
+ + + +
"method"a string specifying the correlation + coefficient used for the test
+ + + + +
"pearson"Pearson’s product moment correlation + (Default)
"kendall"Kendall’s rank correlation tau
"spearman"Spearman’s rank correlation rho
+ +

See also: + regression_ftest, + regression_ttest +

+

Source Code: + correlation_test +

+ +
+
+
+ + + diff --git a/docs/dendrogram.html b/docs/dendrogram.html index 53f26b29..d07d39bb 100644 --- a/docs/dendrogram.html +++ b/docs/dendrogram.html @@ -245,7 +245,7 @@

ans = - 5 12 37 41 + 5 16 36 58 diff --git a/docs/fitgmdist.html b/docs/fitgmdist.html index 3e9f26f2..657b5979 100644 --- a/docs/fitgmdist.html +++ b/docs/fitgmdist.html @@ -222,13 +222,13 @@

"RegularizationValue", 0.0001) Gaussian mixture distribution with 2 components in 1 dimension(s) -Clust 1: weight 0.648459 - Mean: 52.6943 - Variance:131.09 -Clust 2: weight 0.351541 - Mean: 32.3745 - Variance:51.264 -AIC=171.34 BIC=176.319 NLogL=80.6702 Iter=100 Cged=0 Reg=0.0001 +Clust 1: weight 0.542486 + Mean: 62.488 + Variance:136.71 +Clust 2: weight 0.457514 + Mean: 24.8305 + Variance:101.91 +AIC=185.463 BIC=190.442 NLogL=87.7315 Iter=53 Cged=1 Reg=0.0001 diff --git a/docs/index.html b/docs/index.html index 0105497b..a593a728 100644 --- a/docs/index.html +++ b/docs/index.html @@ -126,6 +126,14 @@

Compute a confusion matrix for classification problems + + + + ConfusionMatrixChart + + + Create object P, a Confusion Matrix Chart object. + @@ -398,6 +406,14 @@

+ + + + + + + +
+ + cl_multinom + + Confidence level of multinomial portions.
@@ -2214,6 +2230,15 @@

Perform a chi-squared test (for independence or homogeneity).
+ + correlation_test + + Perform a correlation coefficient test whether two samples X and Y come +from uncorrelated populations.
diff --git a/docs/kmeans.html b/docs/kmeans.html index dec0bd73..d7b9d007 100644 --- a/docs/kmeans.html +++ b/docs/kmeans.html @@ -368,12 +368,12 @@

title ("Cluster Assignments and Centroids"); hold off -Replicate 1, 8 iterations, total sum of distances = 193.571. -Replicate 2, 5 iterations, total sum of distances = 193.571. -Replicate 3, 5 iterations, total sum of distances = 193.571. -Replicate 4, 5 iterations, total sum of distances = 193.571. -Replicate 5, 7 iterations, total sum of distances = 193.571. -Best total sum of distances = 193.571 +Replicate 1, 4 iterations, total sum of distances = 197.268. +Replicate 2, 5 iterations, total sum of distances = 197.268. +Replicate 3, 3 iterations, total sum of distances = 197.268. +Replicate 4, 1 iterations, total sum of distances = 349.143. +Replicate 5, 1 iterations, total sum of distances = 322.676. +Best total sum of distances = 197.268

diff --git a/docs/kruskalwallis.html b/docs/kruskalwallis.html index c645a909..deb2ad72 100644 --- a/docs/kruskalwallis.html +++ b/docs/kruskalwallis.html @@ -166,8 +166,8 @@

Kruskal-Wallis ANOVA Table Source SS df MS Chi-sq Prob>Chi-sq --------------------------------------------------------- -Columns 3060.33 5 612.07 27.57 4.41534e-05 -Error 824.67 30 27.49 +Columns 3284.00 5 656.80 29.59 1.77936e-05 +Error 601.00 30 20.03 Total 3885.00 35 @@ -203,8 +203,8 @@

Kruskal-Wallis ANOVA Table Source SS df MS Chi-sq Prob>Chi-sq --------------------------------------------------------- -Columns 2898.00 5 579.60 26.11 8.50293e-05 -Error 987.00 30 32.90 +Columns 2841.00 5 568.20 25.59 1.06929e-04 +Error 1044.00 30 34.80 Total 3885.00 35 @@ -244,8 +244,8 @@

Kruskal-Wallis ANOVA Table Source SS df MS Chi-sq Prob>Chi-sq --------------------------------------------------------- -Columns 82945.67 3 27648.56 68.55 8.77076e-15 -Error 61044.33 116 526.24 +Columns 98001.87 3 32667.29 80.99 0.00000e+00 +Error 45988.13 116 396.45 Total 143990.00 119 diff --git a/docs/mhsample.html b/docs/mhsample.html index ee615ab6..f1488a0a 100644 --- a/docs/mhsample.html +++ b/docs/mhsample.html @@ -235,9 +235,9 @@

printf ("The actual error %f\n", trueerr); mesh (x, y, reshape (f([x(:), y(:)]), size(x)), "facecolor", "None"); -Monte Carlo integral estimate int f(x) dx = 0.279097 -Monte Carlo integral error estimate 0.048295 -The actual error 0.017298 +Monte Carlo integral estimate int f(x) dx = 0.224140 +Monte Carlo integral error estimate 0.030264 +The actual error 0.037659
@@ -286,9 +286,9 @@

printf ("Monte Carlo integral error estimate %f\n", errest); printf ("The actual error %f\n", trueerr); -Monte Carlo integral estimate int f(x) dx = 2.398843 -Monte Carlo integral error estimate 0.016088 -The actual error 0.006267 +Monte Carlo integral estimate int f(x) dx = 2.418896 +Monte Carlo integral error estimate 0.014575 +The actual error 0.026320
diff --git a/docs/multcompare.html b/docs/multcompare.html index 6925abf9..61316ad6 100644 --- a/docs/multcompare.html +++ b/docs/multcompare.html @@ -270,12 +270,12 @@

Group ID Group ID LBoundDiff EstimatedDiff UBoundDiff p-value ---------------------------------------------------------------------- - 1 2 -2.185 -1.374 -0.564 <.001 - 1 3 -3.906 -3.096 -2.285 <.001 - 1 4 -7.410 -6.600 -5.789 <.001 - 2 3 -2.532 -1.722 -0.911 <.001 - 2 4 -6.036 -5.226 -4.415 <.001 - 3 4 -4.314 -3.504 -2.693 <.001 + 1 2 -3.030 -2.237 -1.443 <.001 + 1 3 -4.353 -3.559 -2.766 <.001 + 1 4 -8.472 -7.678 -6.885 <.001 + 2 3 -2.116 -1.323 -0.529 .001 + 2 4 -6.235 -5.442 -4.648 <.001 + 3 4 -4.912 -4.119 -3.325 <.001 @@ -536,21 +536,21 @@

Group ID Group ID LBoundDiff EstimatedDiff UBoundDiff p-value ---------------------------------------------------------------------- - 1 2 -42.087 -26.750 -11.413 <.001 - 1 3 -26.797 -14.125 -1.453 .027 - 2 3 -5.227 12.625 30.477 .198 + 1 2 -42.089 -26.750 -11.411 <.001 + 1 3 -26.799 -14.125 -1.451 .027 + 2 3 -5.231 12.625 30.481 .198 C = - 1.0000e+00 2.0000e+00 -4.2087e+01 -2.6750e+01 -1.1413e+01 7.1900e-04 -4.3716e+00 2.1000e+01 - 1.0000e+00 3.0000e+00 -2.6797e+01 -1.4125e+01 -1.4529e+00 2.7224e-02 -2.7937e+00 2.1000e+01 - 2.0000e+00 3.0000e+00 -5.2274e+00 1.2625e+01 3.0477e+01 1.9786e-01 1.7725e+00 2.1000e+01 + 1.0000e+00 2.0000e+00 -4.2089e+01 -2.6750e+01 -1.1411e+01 7.3300e-04 -4.3716e+00 2.1000e+01 + 1.0000e+00 3.0000e+00 -2.6799e+01 -1.4125e+01 -1.4505e+00 2.7259e-02 -2.7937e+00 2.1000e+01 + 2.0000e+00 3.0000e+00 -5.2307e+00 1.2625e+01 3.0481e+01 1.9836e-01 1.7725e+00 2.1000e+01 M = - 9.7500 2.4770 5.3600 14.1400 - 36.5000 5.5953 26.5836 46.4164 - 23.8750 4.4076 16.0635 31.6865 + 9.7500 2.4770 5.3592 14.1408 + 36.5000 5.5953 26.5818 46.4182 + 23.8750 4.4076 16.0621 31.6879 diff --git a/docs/optimalleaforder.html b/docs/optimalleaforder.html index fd3e2ba5..41b0c358 100644 --- a/docs/optimalleaforder.html +++ b/docs/optimalleaforder.html @@ -145,7 +145,7 @@

ans = - 4 9 5 6 3 2 8 1 7 10 + 6 4 1 2 10 7 9 8 3 5 diff --git a/docs/regress_gp.html b/docs/regress_gp.html index a204f1dd..eaac90b4 100644 --- a/docs/regress_gp.html +++ b/docs/regress_gp.html @@ -237,11 +237,11 @@

plot (xi,bsxfun(@plus, yi, [-dy +dy]),'b-'); hold off - 0.982133 - -0.064290 - 0.285578 - 2.317263 - 2.266096 + 0.948102 + 0.010683 + 0.879629 + 2.186001 + 1.435120
diff --git a/docs/regression_ttest.html b/docs/regression_ttest.html index efe2ac22..ccd7f841 100644 --- a/docs/regression_ttest.html +++ b/docs/regression_ttest.html @@ -101,13 +101,13 @@

- - + +
NameValue
"alpha"the significance level. Default is 0.05.
"tail"a string specifying the alternative hypothesis:
"alpha"the significance level. Default is 0.05.
"tail"a string specifying the alternative hypothesis
- - - + + +
"both"beta1 is not 0 (two-tailed, default)
"left"beta1 is less than 0 (left-tailed)
"right"beta1 is greater than 0 (right-tailed)
"both"beta1 is not 0 (two-tailed, default)
"left"beta1 is less than 0 (left-tailed)
"right"beta1 is greater than 0 (right-tailed)

See also: diff --git a/docs/slicesample.html b/docs/slicesample.html index 875bd25e..df821250 100644 --- a/docs/slicesample.html +++ b/docs/slicesample.html @@ -199,9 +199,9 @@

fprintf("The actual error %f\n", trueerr); mesh (x,y,reshape (f([x(:), y(:)]), size(x)), "facecolor", "None"); -Monte Carlo integral estimate int f(x) dx = 0.274908 -Monte Carlo integral error estimate 0.031054 -The actual error 0.013108 +Monte Carlo integral estimate int f(x) dx = 0.243939 +Monte Carlo integral error estimate 0.027897 +The actual error 0.017861
diff --git a/docs/wblplot.html b/docs/wblplot.html index ac21b7f9..d448c4cf 100644 --- a/docs/wblplot.html +++ b/docs/wblplot.html @@ -175,8 +175,8 @@

h = - -180.12 - -179.74 + -176.36 + -175.46 p = @@ -218,10 +218,10 @@

h = - -180.08 - -179.76 - -178.79 - -177.28 + -176.05 + -175.57 + -175.00 + -173.98 p =