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depower provides a convenient framework to simulate, test, power, and visualize data for differential expression studies with lognormal or negative binomial outcomes. Supported designs are two-sample comparisons of both independent and dependent outcomes. Power may be summarized in the context of controlling the per-family error rate or family-wise error rate.
# Install from CRAN
install.packages("depower")
# Or the development version from bitbucket
remotes::install_bitbucket("bklamer/depower")library(depower)Estimate power to detect the ratio of means for independent two-sample negative binomial data.
set.seed(1234)
power_nb <- sim_nb(
n1 = 30,
n2 = 30,
mean1 = 15,
ratio = c(2, 2.5),
dispersion1 = 1,
dispersion2 = 2,
nsims = 200
) |>
power()
power_nb
#> # A tibble: 2 × 12
#> n1 n2 mean1 mean2 ratio dispersion1 dispersion2 distribution nsims test alpha
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl>
#> 1 30 30 15 30 2 1 2 Independent … 200 NB W… 0.05
#> 2 30 30 15 37.5 2.5 1 2 Independent … 200 NB W… 0.05
#> # ℹ 1 more variable: power <dbl>
plot(power_nb)
Estimate power to detect the ratio of means for bivariate negative binomial data.
set.seed(1234)
power_bnb <- sim_bnb(
n = 30,
mean1 = 15,
ratio = c(1.2, 1.3),
dispersion = 1,
nsims = 300
) |>
power()
power_bnb
#> # A tibble: 2 × 11
#> n1 n2 mean1 mean2 ratio dispersion1 distribution nsims test alpha power
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 30 30 15 18 1.2 1 Dependent two-samp… 300 BNB … 0.05 0.833
#> 2 30 30 15 19.5 1.3 1 Dependent two-samp… 300 BNB … 0.05 0.987
plot(power_bnb)
Estimate power to detect the geometric ratio of means for independent two-sample lognormal data.
set.seed(1234)
power_ind_lognormal <- sim_log_lognormal(
n1 = 30,
n2 = 30,
ratio = c(1.3, 1.5),
cv1 = 0.4,
cv2 = 0.4,
nsims = 500
) |>
power()
power_ind_lognormal
#> # A tibble: 2 × 11
#> n1 n2 ratio cv1 cv2 cor distribution nsims test alpha power
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 30 30 1.3 0.4 0.4 0 Independent two-sample l… 500 Welc… 0.05 0.718
#> 2 30 30 1.5 0.4 0.4 0 Independent two-sample l… 500 Welc… 0.05 0.978
plot(power_ind_lognormal)
Estimate power to detect the geometric mean ratio for dependent two-sample lognormal data.
set.seed(1234)
power_dep_lognormal <- sim_log_lognormal(
n1 = 30,
n2 = 30,
ratio = c(1.3, 1.5),
cv1 = 0.4,
cv2 = 0.4,
cor = 0.3,
nsims = 500
) |>
power()
power_dep_lognormal
#> # A tibble: 2 × 11
#> n1 n2 ratio cv1 cv2 cor distribution nsims test alpha power
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 30 30 1.3 0.4 0.4 0.3 Dependent two-sample log… 500 Pair… 0.05 0.848
#> 2 30 30 1.5 0.4 0.4 0.3 Dependent two-sample log… 500 Pair… 0.05 0.998
plot(power_dep_lognormal)
Estimate power to detect the geometric mean for one-sample lognormal data.
set.seed(1234)
power_one_lognormal <- sim_log_lognormal(
n1 = 30,
ratio = c(1.3, 1.5),
cv1 = 0.4,
nsims = 500
) |>
power()
power_one_lognormal
#> # A tibble: 2 × 8
#> n1 ratio cv1 distribution nsims test alpha power
#> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <dbl>
#> 1 30 1.3 0.4 One-sample log(lognormal) 500 One-sample t-Test 0.05 0.96
#> 2 30 1.5 0.4 One-sample log(lognormal) 500 One-sample t-Test 0.05 1
plot(power_one_lognormal)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.