slope: slope

In rtrend: Trend Estimating Tools

slope

Description

• slope : linear regression slope

• slope_p : linear regression slope and p-value

• slope_mk : mann kendall Sen's slope and p-value

• slope_sen : same as slope_mk, but with no p-value

• slope_boot: bootstrap slope and p-value

Usage

slope_sen(y, x = NULL)

slope(y, x, ...)

slope_p(y, x, fast = TRUE)

slope_sen_r(y, x = seq_along(y), ...)

slope_mk(y, x = NULL, ...)

slope_boot(y, x = NULL, slope_FUN = slope, times = 100, alpha = 0.1, seed, ...)

Arguments

y	vector of observations of length n, or a matrix with n rows.
x	vector of predictor of length n, or a matrix with n rows.
...	ignored.
fast	Boolean. If true, stats::.lm.fit() will be used, which is 10x faster than stats::lm().
slope_FUN	one of slope(), slope_p(), slope_mk()
times	The number of bootstrap replicates.
alpha	significant level, defalt 0.1
seed	a single value, interpreted as an integer, or NULL (see ‘Details’).

Value

• slope : linear regression coefficient

• pvalue : p-value <= 0.05`` means that corresponding slope' is significant.

• sd : Std. Error

For slope_boot, slope is estimated in many times. The lower, mean, upper and standard deviation (sd) are returned.

Examples

y <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
r    <- slope(y)
r_p  <- slope_p(y)
r_mk <- slope_mk(y)
r_boot <- slope_boot(y)

rtrend documentation built on Nov. 10, 2022, 6:14 p.m.