SDF Function

Returns a survival function.

Category: Probability
See: CDF Function

Syntax

Required 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
Generalized Poisson
GENPOISSON
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
Tweedie
TWEEDIE
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 constant, variable or expression that specifies the value of a random variable.

parm-1,...,parm-k

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

Details

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

Example

The following SAS statements produce these results.
SAS Statement
Result
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('GENPOISSON',.9,1,.7);
0.6321205588
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('TWEEDIE',.8,5);
0.4082370836
y=sdf('UNIFORM',0.25);
0.75
y=sdf('WALD',1,2);
0.37230
y=sdf('WEIBULL',1,2);
0.36788