stats.rdoc

Path: rdoc/stats.rdoc
Last Update: Sun Nov 14 14:53:48 -0800 2010

Statistics

  1. Mean, Standard Deviation and Variance
  2. Absolute deviation
  3. Higher moments (skewness and kurtosis)
  4. Autocorrelation
  5. Covariance
  6. Correlation
  7. Weighted samples
  8. Maximum and minimum values
  9. Median and percentiles
  10. Examples

Mean, Standard Deviation and Variance


  • GSL::Stats::mean(v)
  • GSL::Vector#mean

    Arithmetic mean.

    • Ex:
         >> require("gsl")
         => true
         >> v = Vector[1..7]
         => GSL::Vector:
         [ 1.000e+00 2.000e+00 3.000e+00 4.000e+00 5.000e+00 6.000e+00 7.000e+00 ]
         >> v.mean
         => 4.0
         >> Stats::mean(v)
         => 4.0
      

  • GSL::Vector#tss

    Returns the total sum of squares about self.mean. (Requires GSL 1.11)


  • GSL::Vector#tss_m(mean)

    Returns the total sum of squares about mean. (Requires GSL 1.11)


  • GSL::Stats::variance_m(v[, mean])
  • GSL::Vector#variance_m([mean])

    Variance of v relative to the given value of mean.


  • GSL::Stats::sd(v[, mean])
  • GSL::Vector#sd([mean])

    Standard deviation.


  • GSL::Stats::tss(v[, mean])
  • GSL::Vector#tss([mean])

    (GSL-1.11 or later) These methods return the total sum of squares (TSS) of data about the mean.


  • GSL::Stats::variance_with_fixed_mean(v, mean)
  • GSL::Vector#variance_with_fixed_mean(mean)

    Unbiased estimate of the variance of v when the population mean mean of the underlying distribution is known a priori.


  • GSL::Stats::variance_with_fixed_mean(v, mean)
  • GSL::Vector#variance_with_fixed_mean(mean)
  • GSL::Stats::sd_with_fixed_mean(v, mean)
  • GSL::Vector#sd_with_fixed_mean(mean)

    Unbiased estimate of the variance of v when the population mean mean of the underlying distribution is known a priori.

Absolute deviation


  • GSL::Stats::absdev(v[, mean])
  • GSL::Vector#absdev([mean])

    Compute the absolute deviation (from the mean mean if given).

Higher moments (skewness and kurtosis)


  • GSL::Stats::skew(v[, mean, sd])
  • GSL::Vector#skew([mean, sd])

    Skewness


  • GSL::Stats::kurtosis(v[, mean, sd])
  • GSL::Vector#kurtosis([mean, sd])

    Kurtosis

Autocorrelation


  • GSL::Stats::lag1_autocorrelation(v[, mean])
  • GSL::Vector#lag1_autocorrelation([mean])

    The lag-1 autocorrelation

Covariance


  • GSL::Stats::covariance(v1, v2)
  • GSL::Stats::covariance_m(v1, v2, mean1, mean2)

    Covariance of vectors v1, v2.

Correlation


  • GSL::Stats::correlation(v1, v2)

    This efficiently computes the Pearson correlation coefficient between the vectors v1, v2. (>= GSL-1.10)

Weighted samples


  • GSL::Vector#wmean(w)
  • GSL::Vector#wvariance(w)
  • GSL::Vector#wsd(w)
  • GSL::Vector#wabsdev(w)
  • GSL::Vector#wskew(w)
  • GSL::Vector#wkurtosis(w)

Maximum and Minimum values


  • GSL::Stats::max(data)
  • GSL::Vector#max

    Return the maximum value in data.


  • GSL::Stats::min(data)
  • GSL::Vector#min

    Return the minimum value in data.


  • GSL::Stats::minmax(data)
  • GSL::Vectorminmax

    Find both the minimum and maximum values in data and returns them.


  • GSL::Stats::max_index(data)
  • GSL::Vector#max_index

    Return the index of the maximum value in data. The maximum value is defined as the value of the element x_i which satisfies x_i >= x_j for all j. When there are several equal maximum elements then the first one is chosen.


  • GSL::Stats::min_index(data)
  • GSL::Vector#min_index

    Returns the index of the minimum value in data. The minimum value is defined as the value of the element x_i which satisfies x_i >= x_j for all j. When there are several equal minimum elements then the first one is chosen.


  • GSL::Stats::minmax_index(data)
  • GSL::Vector#minmax_index

    Return the indexes of the minimum and maximum values in data in a single pass.

Median and Percentiles


  • GSL::Stats::median_from_sorted_data(v)
  • GSL::Vector#median_from_sorted_data

    Return the median value. The elements of the data must be in ascending numerical order. There are no checks to see whether the data are sorted, so the method GSL::Vector#sort should always be used first.


  • GSL::Stats::quantile_from_sorted_data(v)
  • GSL::Vector#quantile_from_sorted_data

    Return the quantile value. The elements of the data must be in ascending numerical order. There are no checks to see whether the data are sorted, so the method GSL::Vector#sort should always be used first.

Example

     #!/usr/bin/env ruby
     require 'gsl'

     ary =  [17.2, 18.1, 16.5, 18.3, 12.6]
     data = Vector.alloc(ary)
     mean     = data.mean()
     variance = data.stats_variance()
     largest  = data.stats_max()
     smallest = data.stats_min()

     printf("The dataset is %g, %g, %g, %g, %g\n",
            data[0], data[1], data[2], data[3], data[4]);

     printf("The sample mean is %g\n", mean);
     printf("The estimated variance is %g\n", variance);
     printf("The largest value is %g\n", largest);
     printf("The smallest value is %g\n", smallest);

prev next

Reference index top

[Validate]