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  2. DPROBABILITY procedure


Plots probability distributions, and estimates their parameters (D.B. Baird).


PRINT = string tokens Controls whether to print estimated parameters of the distribution or test statistics (parameters, tests); default para
DISTRIBUTION = string token Distribution for expected values against which to plot values (normal, stdnormal, lognormal, exponential, gamma, weibull, beta, b2, pareto, chisquare, cauchy, logistic, ev1, ev2, ev3, gev, invnormal, t, f, uniform, stduniform, laplace, gpareto, ubetamix, ugammamix, loggamma, loglogistic, paralogistic, igamma, iweibull, burr, iburr); default norm
METHOD = string token Method used for the plot axes (quantile, probability, stabilizedprobability); default quan
GRAPHICS = string token Type of graphics (highresolution, lineprinter); default high
PLOT = string tokens Whether to plot differences from expectations or the 1-1 reference line (differences, reference); default refe
CONSTANT = string token Whether to estimate the constant for the distribution (estimate, omit) default omit
BANDS = string token What type of confidence bands to plot, if any (simultaneous, pointwise); default simu
NSIMULATIONS = scalar Number of simulations for pointwise bands; default 100
ALPHA = scalar Acceptance limits for confidence bands; default 0.95
DF = scalar Number of degrees of freedom of chi-square or t distribution; default 1
DFNUMERATOR = scalar Numerator degrees of freedom of F distribution; default 1
DFDENOMINATOR = scalar Denominator degrees of freedom of F distribution; default 1
WINDOW = scalar Window to use for the plot; default 3
XMETHOD = string token Scaling of X / Expected Plot axes (quantile, probability, stabilizedprobability); if unset, takes the same setting as METHOD
QMETHOD = string token Whether to standardize plotted score in expected quantiles (standardized, unstandardized); default stan
TMETHOD = string tokens Specifies the method used to perform the goodness-of-fit tests (likelihoodratio, traditional); default like
NTIMES = scalar Number of Monte-Carlo simulations to perform for likelihood-ratio tests; default 999
SEED = scalar Seed for random number generation for the likelihood-ratio tests; default 0 continues an existing sequence or, if none, selects a seed automatically


DATA = variates Values to plot
TITLE = text Title for the graph; default * generates an appropriate title automatically
ESTIMATES = variates Saves the estimated parameters for the distribution
SE = variates Saves standard errors for the estimated parameters
LOWERTRUNCATION = scalars Lower truncation points for Loss distributions
UPPERTRUNCATION = scalars Upper truncation points for Loss distributions
DEVIANCE = scalars Saves the deviance for the fitted distribution
PROBABILITIES = variates Saves the probabilities from the goodness-of-fit tests


To assess the how well empirical data approximates a particular theoretical distribution, DPROBABILITY plots the sorted values (order statistics, Xi) against the expected values of the order statistics Ei from the given distribution. However, usually the particular parameters of the distribution are not known and these have to be estimated first to obtain the expected values.

If the distribution has a cumulative density function of F(x), and the inverse of this function is G(x) (i.e. G(F(x)) = x), then the expected values of the order statistics, are approximately G((i-0.5)/n), where i = 1…n, and n is the number of values in the sample. A plot of Xi versus Ei is known as a Quantile-Quantile (or Q-Q) plot. The data can also be plotted on the probability scale by plotting the cumulative probabilities of the data under the assumed distribution against their expected probabilities, i.e. F(X(i)) versus (i-0.5)/n. This is known as a Probability-Probability (or P-P) plot.

A third plot called the stabilized probability (SP) plot (Michael 1983), was introduced, which rescales the probabilities using the transformation

sp = (2/π) × ARCSIN(SQRT(p))

so that the variance of the plotted points is approximately equal over the range of probability values. In the SP plot the scaled values sp are plotted rather than the unscaled p values. The METHOD option allows the choice of which scale is used in the graph (quantile, probability or stabilizedprobability for the Q-Q, P-P or SP plots respectively).

By default the x-value used in plotting Q, P or SP is the corresponding expected value of these statistics. Alternative x-values can be used by setting the XMETHOD option to quantile, probability, or stabilizedprobability. So for example a Q-P plot can be obtained with the option settings METHOD=quantile and XMETHOD=probability or a P-Q plot with the settings METHOD=probability and XMETHOD=quantile.

The QMETHOD option allows the scaling of the expected quantiles plotted on the x-axis to be set. By default quantiles are standardized to have a mean of zero and variance of one (as in a normal score plot) but, if QMETHOD=unstandardized, the quantiles are scaled to the same mean and variance as the data.

The DATA parameter specifies the data values, in a variate. The TITLE parameter can specify a title for the graph. The ESTIMATES parameter can be used to save the values estimated for the parameters for the distribution, and the SE parameter can save their standard errors.

The distribution for the expected values against which to plot the data is specified by the DISTRIBUTION option. Some distributions (Log-Normal, Gamma, Weibull and Pareto) can have an extra parameter (a) estimated, so that Xa follows the specified distribution. Setting option CONSTANT=estimate estimates a value for a. Some of the distributions (Chi Square, T and F) cannot have the parameters estimated by the usual DISTRIBUTION directive, so the procedure provides 3 options (DF, DFNUMERATOR, DFDENOMINATOR) for specifying the parameters of these distributions. However, if for example you set DF=*, the degrees of freedom are estimated along with the other parameters of the distribution.

Some distributions (normal, loggamma, loglogistic, paralogistic, igamma, iweibull, burr, iburr) can be estimated and plotted in a truncated form. The values in the distribution less than LOWERTRUNCATION and greater than UPPERTRUNCATION are removed (if either of these are set), and the distribution between these limits is rescaled to have an area of one. If only LOWERTRUNCATION is set, the distribution is left-truncated, and it is right-truncated if only UPPERTRUNCATION is set.

The BANDS option allows two forms of confidence intervals to be displayed in the graph. BANDS=pointwise simulates NSIMULATIONS distributions of the same size as the data, from the theoretical distribution, and plots the range of values at each value of the order statistics that contain the proportion specified by the option ALPHA of simulated values. Thus a sample drawn from the assumed distribution has approximately a probability ALPHA of lying within the limits at each point. However, overall there will be a probability of less than ALPHA that a sample will completely lie within the confidence bands. The BANDS=simultaneous uses a statistic given by Michael (1983) for which the overall probability of plotted data lying completely within the confidence bands is approximately the specified value of ALPHA, under the null hypothesis that the data is a random iid sample from the specified distribution. This form of confidence limits has the advantage that it is much faster to calculate and that probability of the data points falling outside the limits is approximately constant over the range of the data.

When plotting the data against the expected values, setting option PLOT=reference allows the 1-1 line to be added to the graph, so that departures from this can be more easily observed. The other PLOT setting, difference, plots the difference between the data and the expected values, so that departures can be observed more easily in a horizontal direction rather than on a 45 degree slant. Setting option GRAPHICS=lineprinter produces a character based graph in the output window rather than in the high-resolution graphics window as usual. The WINDOW option can be used to specify which graphics window to use for a high-resolution graph.

The PRINT option control of the output that is printed. The parameters setting prints the fitted parameters of the specified distribution, and some sample statistics of the observed data. The test setting provides output from three empirical distribution tests, namely the Anderson-Darling, Cramer-von Mises and Watson statistics. The method used to perform these tests is specified by the TMETHOD option, with settings likelihoodratio for the Zhang (2002) likelihood-ratio based method, and traditional for the traditional approach. The default is to use the likelihood-ratio based tests, which are generally more powerful. Monte-Carlo simulations are used to calculate the empirical probability values of the test statistics under the likelihood-ratio based method. The NTIMES option defines how many Monte-Carlo simulations are used; default 999. The SEED option specifies the seed for the random-number generator used during the Monte-Carlo simulations. The default of zero continues the sequence of random numbers from a previous generation or, if this is the first use of the generator in this run of Genstat, the seed is initialized automatically. The test probabilities can be saved, in a variate, by the PROBABILITIES parameter.

The distributions fitted in this procedure are described further in the books by Hogg & Klugman (1984) and Johnson, Kotz & Balakrishnan (1994, 1995).




The parameters for the distribution are estimated using the DISTRIBUTION or FITNONLINEAR directives. The cumulative distribution probability values of the observed and expected values are calculated with the CL series of functions. The goodness-of-fit tests are performed by the EDFTEST procedure.

Action with RESTRICT

If the DATA variate is restricted, the plots and tests will be calculated using only the units included by the restriction.


Hogg, R. V. & Klugman, S. A. (1984). Loss Distributions. John Wiley & Sons, New York.

Johnson, N. L., Kotz, S. & Balakrishnan N. (1994). Continuous Univariate Distributions, Volume 1, 2nd edition. John Wiley & Sons, New York.

Johnson, N. L., Kotz, S. & Balakrishnan N. (1995). Continuous Univariate Distributions, Volume 2, 2nd edition. John Wiley & Sons, New York.

Michael, J. R. (1983). The stabilized probability plot. Biometrika, 70, 11-17.

Zhang (2002). Powerful goodness-of-fit tests based on the likelihood ratio. Journal of the Royal Statistical Society, Series B, 64, 281-294.

See also



Commands for: Graphics, Basic and nonparametric statistics.


CAPTION      'DPROBABILITY example'; STYLE=major
CALCULATE    [SEED=287987] N = GRNORMAL(100;1;2)
DPROBABILITY [PRINT=parameters,tests; DISTRIBUTION=normal] N
             BANDS=pointwise; ALPHA=0.99; NSIMULATIONS=400] N
             BANDS=simultaneous; PLOT=difference] N
DPROBABILITY [PRINT=tests; DISTRIBUTION=chiSquare; DF=3; METHOD=probability;\
             BANDS=*] C; TITLE='Chi Square 3 df P-P plot'
Updated on March 8, 2019

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