Central Probability in a Normal or T Distribution
These versions of the quantile functions take a vector of central probabilities as its first argument.
cnorm(p, mean = 0, sd = 1, log.p = FALSE, side = c("both", "upper", "lower"))
ct(p, df, ncp, log.p = FALSE, side = c("upper", "lower", "both"))p |
vector of probabilities. |
mean |
vector of means. |
sd |
vector of standard deviations. |
log.p |
logical; if TRUE, probabilities p are given as log(p). |
side |
One of "upper", "lower", or "both" indicating whether a vector of upper or lower quantiles or a matrix of both should be returned. |
df |
degrees of freedom (> 0, maybe non-integer). |
ncp |
non-centrality parameter delta;
currently except for |
qnorm(.975)
cnorm(.95)
xcnorm(.95)
xcnorm(.95, verbose = FALSE, return = "plot") %>%
gf_refine(
scale_fill_manual( values = c("navy", "limegreen")),
scale_color_manual(values = c("black", "black")))
cnorm(.95, mean = 100, sd = 10)
xcnorm(.95, mean = 100, sd = 10)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.