slope_sen | R Documentation |
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
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, ...)
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, |
slope_FUN |
one of |
times |
The number of bootstrap replicates. |
alpha |
significant level, defalt 0.1 |
seed |
a single value, interpreted as an integer, or |
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.
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)
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