pdfsmd | R Documentation |
This function computes the probability density of the Singh–Maddala (Burr Type XII) distribution given parameters (a
, b
, and q
) computed by parsmd
. The probability density function is
f(x) = \frac{b \cdot q \cdot x^{b-1}}{a^b \biggl(1 + \bigl[(x-\xi)/a\bigr]^b \biggr)^{q+1}}\mbox{,}
where f(x)
is the probability density for quantile x
with 0 \le x \le \infty
, \xi
is a location parameter, a
is a scale parameter (a > 0
), b
is a shape parameter (b > 0
), and q
is another shape parameter (q > 0
).
pdfsmd(x, para)
x |
A real value vector. |
para |
The parameters from |
Probability density (f
) for x
.
W.H. Asquith
Kumar, D., 2017, The Singh–Maddala distribution—Properties and estimation: International Journal of System Assurance Engineering and Management, v. 8, no. S2, 15 p., \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s13198-017-0600-1")}.
Shahzad, M.N., and Zahid, A., 2013, Parameter estimation of Singh Maddala distribution by moments: International Journal of Advanced Statistics and Probability, v. 1, no. 3, pp. 121–131, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.14419/ijasp.v1i3.1206")}.
cdfsmd
, quasmd
, lmomsmd
, parsmd
# The SMD approximating the normal and use x=0
tau4_of_normal <- 30 * pi^-1 * atan(sqrt(2)) - 9 # from theory
pdfsmd(0, parsmd( vec2lmom( c( -pi, pi, 0, tau4_of_normal ) ) ) ) # 0.061953
dnorm( 0, mean=-pi, sd=pi*sqrt(pi)) # 0.06110337
## Not run:
LMlo <- vec2lmom(c(10000, 1500, 0.3, 0.1))
LMhi <- vec2lmom(c(10000, 1500, 0.3, 0.6))
SMDlo <- parsmd(LMlo, snap.tau4=TRUE) # Tau4 snapped to 0.15077
SMDhi <- parsmd(LMhi, snap.tau4=TRUE) # Tau4 snapped to 0.25360
FF <- pnorm(seq(-6, 3, by=.01))
x <- sort(c(quasmd(FF, SMDlo), quasmd(FF, SMDhi)))
plot( x, pdfsmd(x, SMDlo), col="red", xlim=range(x), type="l")
lines(x, pdfsmd(x, SMDhi), col="blue") #
## End(Not run)
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