SDF Function

Computes a survival function

Category: Probability

See: 'CDF Function' on page 418

Syntax

SDF ( 'dist',quantile,parm-1,...,parm-k )

Arguments

'dist'

• is a character string that identifies the distribution. Valid distributions are as follows :

  Distribution	Argument
  Bernoulli	' BERNOULLI '
  Beta	' BETA '
  Binomial	' BINOMIAL '
  Cauchy	' CAUCHY '
  Chi-Square	' CHISQUARE '
  Exponential	' EXPONENTIAL '
  F	' F '
  Gamma	' GAMMA '
  Geometric	' GEOMETRIC '
  Hypergeometric	' HYPERGEOMETRIC '
  Laplace	' LAPLACE '
  Logistic	' LOGISTIC '
  Lognormal	' LOGNORMAL '
  Negative binomial	' NEGBINOMIAL '
  Normal	' NORMAL ' ' GAUSS '
  Normal mixture	' NORMALMIX '
  Pareto	' PARETO '
  Poisson	' POISSON '
  T	' T '
  Uniform	' UNIFORM '
  Wald (inverse Gaussian)	' WALD ' ' IGAUSS '
  Weibull	' WEIBULL '

• Note: Except for T, F, and NORMALMIX, you can minimally identify any distribution by its first four characters .

quantile

• is a numeric random variable.

parm-1,...,parm-k

• are optional shape , location , or scale parameters appropriate for the specific distribution.

The SDF function computes the survival function (upper tail) from various continuous and discrete distributions. For more information, see the on page 419.

Examples

SAS Statements	Results
y=sdf( ' BERN ' ,0,.25);	0.25
y=sdf( ' BETA ' ,0.2,3,4);	0.09011
y=sdf( ' BINOM ' ,4,.5,10);	0.62305
y=sdf( ' CAUCHY ' ,2);	0.14758
y=sdf( ' CHISQ ' ,11.264,11);	0.42142
y=sdf( ' EXPO ' ,1);	0.36788
y=sdf( ' F ' ,3.32,2,3);	0.17361
y=sdf( ' GAMMA ' ,1,3);	0.91970
y=sdf( ' HYPER ' ,2,200,50,10);	0.47633
y=sdf( ' LAPLACE ' ,1);	0.18394
y=sdf( ' LOGISTIC ' ,1);	0.26894
y=sdf( ' LOGNORMAL ' ,1);	0.5
y=sdf( ' NEGB ' ,1,.5,2);	0.5
y=sdf( ' NORMAL ' ,1.96);	0.025
y=pdf( ' NORMALMIX ' ,2.3,3,.33,.33,.34, .5,1.5,2.5,.79,1.6,4.3);	0.2819
y=sdf( ' PARETO ' ,1,1);	1
y=sdf( ' POISSON ' ,2,1);	0.08030
y=sdf( ' T ' ,.9,5);	0.20469
y=sdf( ' UNIFORM ' ,0.25);	0.75
y=sdf( ' WALD ' ,1,2);	0.37230
y=sdf( ' WEIBULL ' ,1,2);	0.36788