Generally–Altered, –Inflated and –Truncated Logarithmic Distribution
Density, distribution function, quantile function and random generation for the generally–altered, –inflated and –truncated logarithmic distribution. Both parametric and nonparametric variants are supported; these are based on finite mixtures of the parent with itself and the multinomial logit model (MLM) respectively. Altogether it can be abbreviated as GAAIIT–Log(shape.p)–Log(shape.a)–MLM–Log(shape.i)–MLM, and it is also known as the GAIT-Log PNP combo.
dgaitlog(x, shape.p, alt.mix = NULL, alt.mlm = NULL, inf.mix = NULL, inf.mlm = NULL, truncate = NULL, max.support = Inf, pobs.mix = 0, pobs.mlm = 0, pstr.mix = 0, pstr.mlm = 0, byrow.ai = FALSE, shape.a = shape.p, shape.i = shape.p, deflation = FALSE, log = FALSE) pgaitlog(q, shape.p, alt.mix = NULL, alt.mlm = NULL, inf.mix = NULL, inf.mlm = NULL, truncate = NULL, max.support = Inf, pobs.mix = 0, pobs.mlm = 0, pstr.mix = 0, pstr.mlm = 0, byrow.ai = FALSE, shape.a = shape.p, shape.i = shape.p, lower.tail = TRUE) qgaitlog(p, shape.p, alt.mix = NULL, alt.mlm = NULL, inf.mix = NULL, inf.mlm = NULL, truncate = NULL, max.support = Inf, pobs.mix = 0, pobs.mlm = 0, pstr.mix = 0, pstr.mlm = 0, byrow.ai = FALSE, shape.a = shape.p, shape.i = shape.p) rgaitlog(n, shape.p, alt.mix = NULL, alt.mlm = NULL, inf.mix = NULL, inf.mlm = NULL, truncate = NULL, max.support = Inf, pobs.mix = 0, pobs.mlm = 0, pstr.mix = 0, pstr.mlm = 0, byrow.ai = FALSE, shape.a = shape.p, shape.i = shape.p)
x, q, p, n, log, lower.tail |
Same meaning as in |
shape.p, shape.a, shape.i |
Same meaning as |
truncate, max.support |
See |
alt.mix, inf.mix |
See |
alt.mlm, inf.mlm |
See |
pobs.mlm, pstr.mlm, byrow.ai |
See |
pobs.mix, pstr.mix |
See |
deflation |
See |
See Gaitpois
for general information also relevant
to this parent distribution.
T. W. Yee.
ivec <- c(2, 10); avec <- ivec + 4; shape <- 0.95; xgrid <- 0:29 tvec <- 15; max.support <- 25; pobs.a <- 0.10; pstr.i <- 0.15 (ddd <- dgaitlog(xgrid, shape, truncate = tvec, max.support = max.support, pobs.mix = pobs.a, alt.mix = avec, pstr.mix = pstr.i, inf.mix = ivec)) ## Not run: plot(xgrid, ddd, type = "n", ylab = "Probability", xlab = "x", main = "GAIT PMF---Logarithmic Parent") mylwd <- 0.5 abline(v = avec, col = 'blue', lwd = mylwd) abline(v = ivec, col = 'purple', lwd = mylwd) abline(v = tvec, col = 'tan', lwd = mylwd) abline(v = max.support, col = 'magenta', lwd = mylwd) abline(h = c(pobs.a, pstr.i, 0:1), col = 'gray', lty = "dashed") lines(xgrid, dlog(xgrid, shape), col = 'gray', lty = "dashed") # f_{\pi} lines(xgrid, ddd, type = "h", col = "pink", lwd = 3) # GAIT PMF points(xgrid[ddd == 0], ddd[ddd == 0], pch = 16, col = 'tan', cex = 2) ## End(Not run)
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