Linear quadratic family that assumes the following relation for the variance
of the normal distribution Var = mu*(1+s*mu).
regression on mu and on the sigma (log and identity links)
dLQNO(x, mu = 1, sigma = 1, log = FALSE) pLQNO(q, mu = 1, sigma = 1, lower.tail = TRUE, log.p = FALSE) qLQNO(p, mu = 1, sigma = 1, lower.tail = TRUE, log.p = FALSE) rLQNO(n, mu = 1, sigma = 1) LQNO(mu.link="log", sigma.link="log") dLQNO(x, mu = 1, sigma = 1, log = FALSE) pLQNO(q, mu = 1, sigma = 1, lower.tail = TRUE, log.p = FALSE) qLQNO(p, mu = 1, sigma = 1, lower.tail = TRUE, log.p = FALSE) rLQNO(n, mu = 1, sigma = 1)
| mu.link | Type of transformation |
|---|---|
| sigma.link | Type of transformation |
| x | Vector of quantiles. |
| mu | Vector of means. |
| sigma | Vector of standard deviations. |
| log | Logical; if TRUE, probabilities p are given as |
| q | Vector of quantiles. |
| lower.tail | Logical; if TRUE (default), probabilities are |
| log.p | Logical; if TRUE, probabilities p are given as |
| p | Vector of probabilities. |
| n | Number of observations. If length(n) > 1, the length is taken to be the number required. |
LQNO function
Methods adapted from:
Argyropoulos, Christos, et al. "Modeling bias and variation in the stochastic processes of small RNA sequencing." Nucleic Acids Research (2017).