time-to-botec/squiggle/node_modules/jstat/doc/md/test.md

Statistical Tests

The test module includes methods that enact popular statistical tests. The tests that are implemented are Z tests, T tests, and F tests. Also included are methods for developing confidence intervals. Currently regression is not included but it should be included soon (once matrix inversion is fixed).

Statistics Instance Functionality

zscore( value[, flag] )

Returns the z-score of value taking the jStat object as the observed values. flag===true denotes use of sample standard deviation.

ztest( value, sides[, flag] )

Returns the p-value of value taking the jStat object as the observed values. sides is an integer value 1 or 2 denoting a 1 or 2 sided z-test. The test defaults to a 2 sided z-test if sides is not specified. flag===true denotes use of sample standard deviation.

tscore( value )

Returns the t-score of value taking the jStat object as the observed values.

ttest( value, sides )

Returns the p-value of value taking the jStat object as the observed values. sides is an integer value 1 or 2 denoting a 1 or 2 sided t-test. The test defaults to a 2 sided t-test if sides is not specified.

anovafscore()

Returns the f-score of the ANOVA test on the arrays of the jStat object.

anovaftest()

Returns the p-value of an ANOVA test on the arrays of the jStat object.

Static Methods

Z Statistics

jStat.zscore( value, mean, sd )

Returns the z-score of value given the mean mean and the sd standard deviation of the test.

jStat.zscore( value, array[, flag] )

Returns the z-score of value given the data from array. flag===true denotes use of the sample standard deviation.

jStat.ztest( value, mean, sd, sides )

Returns the p-value of a the z-test of value given the mean mean and sd standard deviation of the test. sides is an integer value 1 or 2 denoting a one or two sided z-test. If sides is not specified the test defaults to a two sided z-test.

jStat.ztest( zscore, sides )

Returns the p-value of the zscore z-score. sides is an integer value 1 or 2 denoting a one or two sided z-test. If sides is not specified the test defaults to a two sided z-test

jStat.ztest( value, array, sides[, flag] )

Returns the p-value of value given the data from array. sides is an integer value 1 or 2 denoting a one or two sided z-test. If sides is not specified the test defaults to a two sided z-test. flag===true denotes the use of the sample standard deviation.

T Statistics

jStat.tscore( value, mean, sd, n )

Returns the t-score of value given the mean mean, sd standard deviation, and the sample size n.

jStat.tscore( value, array )

Returns the t-score of value given the data from array.

jStat.ttest( value, mean, sd, n, sides )

Returns the p-value of value given the mean mean, sd standard deviation, and the sample size n. sides is an integer value 1 or 2 denoting a one or two sided t-test. If sides is not specified the test defaults to a two sided t-test.

jStat.ttest( tscore, n, sides )

Returns the p-value of the tscore t-score given the sample size n. sides is an integer value 1 or 2 denoting a one or two sided t-test. If sides is not specified the test defaults to a two sided t-test.

jStat.ttest( value, array, sides )

Returns the p-value of value given the data in array. sides is an integer value 1 or 2 denoting a one or two sided t-test. If sides is not specified the test defaults to a two sided t-test.

F Statistics

jStat.anovafscore( array1, array2, ..., arrayn )

Returns the f-score of an ANOVA on the arrays.

jStat.anovafscore( [array1,array2, ...,arrayn] )

Returns the f-score of an ANOVA on the arrays.

jStat.anovaftest( array1, array2, ...., arrayn )

Returns the p-value of the f-statistic from the ANOVA test on the arrays.

jStat.ftest( fscore, df1, df2)

Returns the p-value for the fscore f-score with a df1 numerator degrees of freedom and a df2 denominator degrees of freedom.

Tukey's Range Test

jStat.qscore( mean1, mean2, n1, n2, sd )

Returns the q-score of a single pairwise comparison between arrays of mean mean1 and mean2, size n1 and n2, and standard deviation (of all vectors) sd.

jStat.qscore( array1, array2, sd )

Same as above, but the means and sizes are calculated automatically from the arrays.

jStat.qtest( qscore, n, k )

Returns the p-value of the q-score given the total sample size n and k number of populations.

jStat.qtest( mean1, mean2, n1, n2, sd, n, k )

Returns the p-value of a single pairwise comparison between arrays of mean mean1 and mean2, size n1 and n2, and standard deviation (of all vectors) sd, where the total sample size is n and the number of populations is k.

jStat.qtest( array1, array2, sd, n, k )

Same as above, but the means and sizes are calculated automatically from the arrays.

jStat.tukeyhsd( arrays )

Performs the full Tukey's range test returning p-values for every pairwise combination of the arrays in the format of [[[index1, index2], pvalue], ...]

For example:

> jStat.tukeyhsd([[1, 2], [3, 4, 5], [6], [7, 8]])
[ [ [ 0, 1 ], 0.10745283896120883 ],
  [ [ 0, 2 ], 0.04374051946838586 ],
  [ [ 0, 3 ], 0.007850804224287633 ],
  [ [ 1, 2 ], 0.32191548545694226 ],
  [ [ 1, 3 ], 0.03802747415485819 ],
  [ [ 2, 3 ], 0.5528665999257486 ] ]

Confidence Intervals

jStat.normalci( value, alpha, sd, n )

Returns a 1-alpha confidence interval for value given a normal distribution with a standard deviation sd and a sample size n

jStat.normalci( value, alpha, array )

Returns a 1-alpha confidence interval for value given a normal distribution in the data from array.

jStat.tci( value, alpha, sd, n )

Returns a 1-alpha confidence interval for value given the standard deviation sd and the sample size n.

jStat.tci( value, alpha, array )

Returns a 1-alpha confidence interval for value given the data from array.

jStat.fn.oneSidedDifferenceOfProportions( p1, n1, p2, n2 )

Returns the p-value for a 1-sided test for the difference between two proportions. p1 is the sample proportion for the first sample, whereas p2 is the sample proportion for the second sample. Similiarly, n1 is the sample size of the first sample and n2 is the sample size for the second sample.

jStat.fn.twoSidedDifferenceOfProportions( p1, n1, p2, n2 )

Returns the p-value for a 2-sided test for the difference between two proportions. p1 is the sample proportion for the first sample, whereas p2 is the sample proportion for the second sample. Similiarly, n1 is the sample size of the first sample and n2 is the sample size for the second sample.