linearTrendTestN {EnvStats}  R Documentation 
Compute the sample size necessary to achieve a specified power for a ttest for linear trend, given the scaled slope and significance level.
linearTrendTestN(slope.over.sigma, alpha = 0.05, power = 0.95,
alternative = "two.sided", approx = FALSE, round.up = TRUE,
n.max = 5000, tol = 1e07, maxiter = 1000)
slope.over.sigma 
numeric vector specifying the ratio of the true slope to the standard deviation of
the error terms ( 
alpha 
numeric vector of numbers between 0 and 1 indicating the Type I error level
associated with the hypothesis test. The default value is 
power 
numeric vector of numbers between 0 and 1 indicating the power
associated with the hypothesis test. The default value is 
alternative 
character string indicating the kind of alternative hypothesis. The possible values
are 
approx 
logical scalar indicating whether to compute the power based on an approximation to
the noncentral tdistribution. The default value is 
round.up 
logical scalar indicating whether to round up the values of the computed
sample size(s) to the next smallest integer. The default value is

n.max 
positive integer greater than 2 indicating the maximum sample size.
The default value is 
tol 
numeric scalar indicating the toloerance to use in the

maxiter 
positive integer indicating the maximum number of iterations
argument to pass to the 
If the arguments slope.over.sigma
, alpha
, and power
are not
all the same length, they are replicated to be the same length as the length of
the longest argument.
Formulas for the power of the ttest of linear trend for specified values of
the sample size, scaled slope, and Type I error level are given in
the help file for linearTrendTestPower
. The function
linearTrendTestN
uses the uniroot
search algorithm to
determine the required sample size(s) for specified values of the power,
scaled slope, and Type I error level.
a numeric vector of sample sizes.
See the help file for linearTrendTestPower
.
Steven P. Millard (EnvStats@ProbStatInfo.com)
See the help file for linearTrendTestPower
.
linearTrendTestPower
, linearTrendTestScaledMds
,
plotLinearTrendTestDesign
, lm
,
summary.lm
, kendallTrendTest
,
Power and Sample Size, Normal, t.test
.
# Look at how the required sample size for the ttest for zero slope
# increases with increasing required power:
seq(0.5, 0.9, by = 0.1)
#[1] 0.5 0.6 0.7 0.8 0.9
linearTrendTestN(slope.over.sigma = 0.1, power = seq(0.5, 0.9, by = 0.1))
#[1] 18 19 21 22 25
#
# Repeat the last example, but compute the sample size based on the approximate
# power instead of the exact:
linearTrendTestN(slope.over.sigma = 0.1, power = seq(0.5, 0.9, by = 0.1),
approx = TRUE)
#[1] 18 19 21 22 25
#==========
# Look at how the required sample size for the ttest for zero slope decreases
# with increasing scaled slope:
seq(0.05, 0.2, by = 0.05)
#[1] 0.05 0.10 0.15 0.20
linearTrendTestN(slope.over.sigma = seq(0.05, 0.2, by = 0.05))
#[1] 41 26 20 17
#==========
# Look at how the required sample size for the ttest for zero slope decreases
# with increasing values of Type I error:
linearTrendTestN(slope.over.sigma = 0.1, alpha = c(0.001, 0.01, 0.05, 0.1))
#[1] 33 29 26 25