stat_qq(mapping = NULL, data = NULL, geom = point, position = identity, distribution = stats::qnorm, dparams = list(), na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, …) geom_qq(mapping = NULL, data = NULL, geom = point, position = identity, distribution = stats::qnorm, dparams = list(), na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, …), geom_qq () and stat_qq () produce quantile-quantile plots. geom_qq_line () and stat_qq_line () compute the slope and intercept of the line connecting the points at specified quartiles of the theoretical and sample distributions.
stat_qq_line(mapping = NULL, data = NULL, geom = path, position = identity, na.rm = TRUE, show.legend = NA, inherit.aes = TRUE, distribution = norm, dparams = list(), detrend = FALSE, identity = FALSE, qtype = 7, qprobs = c(0.25, 0.75), …), stat_qq_line( mapping = NULL, data = NULL, geom = path, position = identity, …
distribution = stats::qnorm, dparams = list(), line.p = c(0.25, 0.75), fullrange = FALSE, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE ), stat_qq_line Draws a reference line based on the data quantiles, as in stats::qqline. stat_qq_band Draws confidence bands based on three methods: pointwise, boot, ks, and ts: pointwise constructs simultaneous confidence bands based on the normal distribution boot creates pointwise confidence bands based on a parametric boostrap, This R tutorial describes how to create a qq plot (or quantile-quantile plot) using R software and ggplot2 package. QQ plots is used to check whether a given data follows normal distribution . The function stat_qq () or qplot () can be used.
You need to pass the distribution parameters as a list of named values to dparams in stat_qq(). For your data, that would be (if I understood your qbeta() call) params = list(shape1 = 1, shape2 = 3) ggplot(samples1, aes(sample = p0))+ stat_ qq(distribution = qbeta, dparams = params), StatQq2 StatQq , Stat, default_aes = aes(y = after_stat(sample), x = after_stat(theoretical)), required_aes = c(sample), compute_group = function(data, scales, quantiles = NULL, distribution = stats::qnorm, dparams = list(), na.rm = FALSE) { sample <- sort(data$sample) n <- length(sample) # Compute theoretical quantiles if (is.null(quantiles)) { quantiles <- stats::ppoints(n) } else if.So to generate the theoretical QQ plot, use the geom_qq function (or stat_qq) to generate the point plot, then call geom_qq_line to generate the IQR fit. library (ggplot2) ggplot () + aes ( sample = alto) + geom_qq ( distribution = qnorm) + geom_qq_line ( line.p = c ( …
