Power Calculations for Test of Two Beta Means

Description

Compute the power for a test of two sample means with beta distributions, or determine the minimum sample size to obtain a target power.

Usage

power_Beta(n1 = NULL, n2 = NULL, power = NULL, sig.level = 0.05, 
mu1 = NULL, sd1 = NULL, mu2 = NULL, equal.sample = TRUE,
trials = 100, equal.precision = TRUE, sd2 = NULL, 
link.type = c("logit", "probit", "cloglog", "cauchit", "log", "loglog"))

Arguments

Details

Exactly one of the parameters n1, n2 and power must be passed as NULL, and that parameter is determined from the others.

This function allows you to set the number of trials in the simulation to control the result accuracy, and type of link used in the beta regression. You can choose one of the following: "logit", "probit", "cloglog", "cauchit", "log", "loglog".

Value

Object of class "power.htest", a list of the arguments (including the computed one) augmented with method and note elements.

Examples

# calculate power
power_Beta(mu1 = 0.5, mu2 = 0.80, sd1 = 0.25, n1 = 60)
# calculate sample size for both groups
power_Beta(mu1 = 0.5, mu2 = 0.80, sd1 = 0.25, power=0.8)

Power Calculations for Two-Sample Test for Proportions

Description

Compute power of test, or determine parameters to obtain target power for equal and unequal sample sizes.

Usage

power_Binomial(n1 = NULL, n2 = NULL, power = NULL, sig.level = 0.05,
p1 = 0.5, p2 = 0.5, equal.sample = TRUE, alternative = c("two.sided", "one.sided"))

Arguments

Details

Exactly one of the parameters n1, n2, p1, p2, power, and sig.level must be passed as NULL, and that parameter is determined from the others. Notice that p1, p2, sig.level have non-NULL defaults, so NULL must be explicitly expressed if you want to compute them.

If equal.sample = TRUE is used, N in output will denote the number in each group.

Value

Object of class "power.htest", a list of the arguments (including the computed one) augmented with note and method elements.

Examples

# calculate power, equal sizes
power_Binomial(n1 = 100, p1 = 0.5, p2 = 0.7)
# calculate power, unequal sizes
power_Binomial(n1 = 150, n2 = 100, p1 = 0.5, p2 = 0.7)
# calculate n2
power_Binomial(n1 = 100, p1 = 0.5, p2 = 0.7, power = 0.9, equal.sample = FALSE)

Power Calculations for Test of Two Gamma Means

Description

Compute the power for a test of two sample means with Gamma distributions, or determine parameters to obtain a target power.

Usage

power_Gamma(n1 = NULL, n2 = NULL, power = NULL, sig.level = 0.05, 
mu1 = NULL, mu2 = NULL, gmu1 = NULL, gmu2 = NULL, trials = 100, 
M = 10000, equal.sample = TRUE, equal.shape = NULL, trace = FALSE)

Arguments

Details

Exactly one of the parameters n1, n2, and power must be passed as NULL, and that parameter is determined from the others. Notice that sig.level has non-NULL defaults, so NULL must be explicitly passed if you want to compute it.

If equal.sample = TRUE is used, N in output will denote the number in each group.

The equal shape parameter assumption will be tested automatically; otherwise it could be set manually with equal.shape.

Value

Object of class "power.htest", a list of the arguments (including the computed one) augmented with method element.

References

Chang et al. (2011). Testing the equality of several gamma means: a parametric bootstrap method with applications. Computational Statistics, 26:55-76.

Examples

# Calculate power, equal sizes
power_Gamma(n1 = 50, mu1 = 1, mu2 = 1.5, gmu1 = 0.6, gmu2 = 0.6, M = 100)

Power Calculation for Comparing Two Geometric Rates.

Description

Compute sample size or power for comparing two geometric rates.

Usage

power_Geometric(n1 = NULL, n2 = NULL, power = NULL, sig.level = 0.05,
mu1 = NULL, mu2 = NULL, duration = 1, equal.sample = TRUE, 
alternative = c("two.sided", "one.sided"), approach = 3)

Arguments

Details

Exactly one of the parameters n1, n2, and power must be passed as NULL, and that parameter is determined from the others.

If equal.sample = TRUE is used, N in output will denote the number in each group.

Since the geometric distribution is a special case of negative binomial distribution, we used the algorithm for negative binomial distribution with setting theta = 1. See power.nb.test for more details.

Value

Object of class "power.htest", a list of the arguments (including the computed one) augmented with note and method elements.

Examples

# calculate power, equal sizes
power_Geometric(n1 = 100, mu1 = 0.3, mu2 = 0.6)
# calculate power, unequal sizes
power_Geometric(n1 = 180, n2 = 140, mu1 = 0.3, mu2 = 0.5)
# calculate n
power_Geometric( mu1 = 0.3, mu2 = 0.4, power = 0.8)

Power Calculation for Comparing Two Negative Binomial Rates

Description

Compute sample size or power for comparing two negative binomial rates.

Usage

power_NegativeBinomial(n1 = NULL, n2 = NULL, power = NULL, sig.level = 0.05,
mu1 = NULL, mu2 = NULL, duration = 1, theta = NULL, equal.sample = TRUE, 
alternative = c("two.sided", "one.sided"), approach = 3)

Arguments

Details

Exactly one of the parameters n1, n2, and power must be passed as NULL, and that parameter is determined from the others.

If equal.sample = TRUE is used, N in output will denote the number in each group.

The computations are based on the formulas given in Zhu and Lakkis (2014). See power.nb.test for more details.

Value

Object of class "power.htest", a list of the arguments (including the computed one) augmented with note and method elements.

References

H. Zhu and H. Lakkis (2014). Sample size calculation for comparing two negative binomial rates. Statistics in Medicine, 33:376-387.

Examples

# calculate power, equal sizes
power_NegativeBinomial(n1 = 20, mu1 = 1, mu2 = 2, theta = 0.8)
# calculate power, unequal sizes
power_NegativeBinomial(n1 = 80, n2 = 40, mu1 = 1, mu2 = 2, theta = 0.8)
# calculate n
power_NegativeBinomial( mu1 = 1, mu2 = 2, theta = 0.8, power = 0.8)

Power Calculations for One and Two Sample T-tests

Description

Compute power of t test, or determine parameters to obtain target power.

Usage

power_Normal(n1 = NULL, n2 = NULL, power = NULL, sig.level = 0.05,
delta = NULL, sd1 = 1, sd2 = 1, equal.sample = TRUE,
alternative = c("two.sided", "one.sided"),
type = c("two.sample", "one.sample", "paired"),
df.method = c("welch", "classical"), strict = FALSE)

Arguments

Details

Exactly one of the parameters n1, n2, delta, sd1, sd2, power, and sig.level must be passed as NULL, and that parameter is determined from the others. Notice that sd1, sd2, sig.level have non-NULL defaults, so NULL must be explicitly expressed if you want to compute them.

If equal.sample = TRUE is used, N in output will denote the number in each group.

Value

Object of class "power.htest", a list of the arguments (including the computed one) augmented with note and method elements.

Note

'uniroot' is used to solve power equation for unknowns, so you may see errors from it, notably about inability to bracket the root when invalid arguments are given.

Examples

# Calculate power, equal sizes
power_Normal(n1 = 150, delta = 5, sd1 = 20, sd2 = 10)
# Calculate power, unequal sizes
power_Normal(n1 = 150, delta = 5, n2 = 120, sd1 = 10)
# Calculate n1, equal sizes 
power_Normal(delta = 5,  power = 0.9, sd1 = 10, sd2 = 12)

Power Calculations for Test of Two Poisson Ratios

Description

Compute the power for a test of two sample means with Poisson distributions, or determine parameters to obtain a target power.

Usage

power_Poisson(n1 = NULL, n2 = NULL, power = NULL, sig.level = 0.05,
lambda1 = NULL, lambda2 = NULL, t1 = 1, t2 = 1, RR0 = 1,
equal.sample = TRUE, alternative = c("two.sided", "one.sided"))

Arguments

Details

Exactly one of the parameters n1, n2, lambda1, lambda2, power, and sig.level must be passed as NULL, and that parameter is determined from the others. Notice that sig.level has non-NULL defaults, so NULL must be explicitly passed if you want to compute them.

If equal.sample = TRUE is used, n2 would be ignored and N in output denotes the number in each group.

Value

Object of class "power.htest", a list of the arguments (including the computed one) augmented with method element.

Note

'uniroot' is used to solve power equation for unknowns, so you may see errors from it, notably about inability to bracket the root when invalid arguments are given.

References

Gu et al. (2008). Testing the ratio of two poisson rates. Biometrical Journal: Journal of Mathematical Methods in Biosciences. 50:283-298.

Examples

# Calculate power, equal sizes
power_Poisson(lambda1 = 0.0005, lambda2 = 0.003, n1 = 2000, t1 = 2, t2 = 2)
# Calculate sample size, equal sizes
power_Poisson(lambda1 = 0.0005, lambda2 = 0.003, power = 0.8, t1 = 2, t2 = 2)
# Calculate sample size for group 2, unequal sizes
power_Poisson(n1 = 2000, lambda1 = 0.0005, lambda2 = 0.003, power = 0.8, 
t1 = 2, t2 = 2, equal.sample = FALSE)