To install and load PowerUpR:
PowerUpR functions are not vectorized to avoid possible complications. However, researchers often explore variety of design charactersitics when they determine their sample size. Creating custom plots and tables may assist with their decision. In what follows, example code snippets are provided to demonstrate vectorization of PowerUpR functions over single or multiple design parameters.
NOTE: We would like to thank Dr. Andi Fugard for inspiring this vignette. Dr. Fugard realized that PowerUpR functions does not evaluate arguments when they are embedded within a user-specificed function. This vignette provides example vectorization of PowerUpR functions for creating custom plots and tables.
mdes) against level-3 intra-class correlation coefficient (rho3)custom_fun <- function(x) {
parms <- list(rho3 = x,
power = .80, rho2 = .06,
g3 = 1, r21 = .55, r22 = .50, r23 = .45,
p = .40, n = 10, J = 2, K = 83)
design <- do.call("mdes.cra3", parms)
design$mdes[1]
}
x = seq(.10,.90,.01)
mdes <- mapply(custom_fun, x)
plot(x, mdes, type = "l", xlab = "rho3")
power) against sample size (K) and explanatory power of level-3 covarites (r23)custom_fun <- function(x, y) {
parms <- list(K = x, r23 = y,
es = .23, rho2 = .06, rho3 = .18,
g3 = 1, r21 = .55, r22 = .50,
p = .40, n = 10, J = 2)
design <- do.call("power.cra3", parms)
design$power
}
x = seq(10,100,5)
power.r23.30 <- mapply(custom_fun, x, .30)
power.r23.40 <- mapply(custom_fun, x, .40)
power.r23.50 <- mapply(custom_fun, x, .50)
power.r23.60 <- mapply(custom_fun, x, .60)
# plot
plot(x, power.r23.30, pch = 18, type = "b",
ylim = c(0,1), xlab = "K", ylab = "Power")
lines(x, power.r23.40, col = 2, pch = 19, type = "b")
lines(x, power.r23.50, col = 3, pch = 20, type = "b")
lines(x, power.r23.60, col = 4, pch = 21, type = "b")
legend("bottomright", bty = "n",
legend = c("r23=.30", "r23=.40", "r23=.50", "r23=.60"),
col = c(1, 2, 3, 4), lty = c(1, 1, 1, 1), pch = c(18, 19, 20, 21))
grid(nx = 20, ny = 18)
K) for various effect size (es) valuescustom_fun <- function(x) {
parms <- list(es = x, power = .80, rho2 = .06, rho3 = .18,
g3 = 1, r21 = .55, r22 = .50, r23 = .45,
p = .40, n = 10, J = 2)
design <- do.call("mrss.cra3", parms)
design$K
}
x = seq(.10,.50,.05)
K <- mapply(custom_fun, x)
table <- data.frame(es = x, K = K)## es K
## 1 0.10 431
## 2 0.15 193
## 3 0.20 109
## 4 0.25 71
## 5 0.30 50
## 6 0.35 37
## 7 0.40 29
## 8 0.45 23
## 9 0.50 19K) for various effect size (es) and R-squared values (r23) valuescustom_fun <- function(x1,x2) {
parms <- list(es = x1, r23 = x2,
power = .80, rho2 = .06, rho3 = .18,
g3 = 1, r21 = .55, r22 = .50,
p = .40, n = 10, J = 2)
design <- do.call("mrss.cra3", parms)
design$K
}
vec.custom_fun <- Vectorize(custom_fun, c("x1", "x2"))
x1 = seq(.10,.50,.05)
x2 = seq(.20,.70,.10)
table.K <- outer(x1, x2, vec.custom_fun)
rownames(table.K) <- paste0("es=",x1)
colnames(table.K) <- paste0("r23=",x2)## r23=0.2 r23=0.3 r23=0.4 r23=0.5 r23=0.6 r23=0.7
## es=0.1 578 519 460 401 342 284
## es=0.15 258 232 206 179 153 127
## es=0.2 146 131 117 102 87 72
## es=0.25 94 85 75 66 57 47
## es=0.3 66 59 53 46 40 33
## es=0.35 49 44 39 35 30 25
## es=0.4 38 34 31 27 23 20
## es=0.45 31 28 25 22 19 16
## es=0.5 25 23 21 18 16 14–o–