permresults {ecodist} | R Documentation |
These results explore the variability in randomization tests of significance and in bootstrap confidence limits for different sample sizes, strengths of relationship and numbers of permutations.
data(permresults)
A data frame with 15000 observations on the following 17 variables.
n
r
p500
l100
u100
p1000
l500
u500
p10000
l1000
u1000
p100000
l10000
u10000
p1000000
l100000
u100000
These simulation results are included here because the entire simulation takes an extremely long time to run. The tests explored how the p-value and confidence limits varied with:
- number of samples (10 (45 dissimilarities), 25 (300), 50 (1225), 100 (4950)) - number of permutations: 500, 1000, 10000, 100000, 1000000 - number of bootstrap samples: 100, 500, 1000, 10000, 100000 - correlation of the original data: 0.01, 0.05, 0.10, 0.25, 0.50The larger correlation values were omitted for the larger sample sizes because they were always highly signficant, and an additional low value of 0.025 was added for the largest sample size.
The method used to generate the data is shown in the example section.
data(permresults) ## Not run: # Simulation Method # example for sample size 10, correlation of 0.01 permresults <- data.frame(matrix(0, nrow=1000, ncol=17)) colnames(permresults) <- c("n", "r", "p500", "l100", "u100", "p1000", "l500", "u500", "p10000", "l1000", "u1000", "p100000", "l10000", "u10000", "p1000000", "l100000", "u100000") # generate correlated data corxy.len010.r010 <- corgen(len= 10, r= 0.01, epsilon=0.0001) all.nperm <- c(500, 1000, 10000, 100000, 1000000) all.nboot <- c(100, 500, 1000, 10000, 100000) for(i in 1:1000) { permresults[i, 1] <- 10 permresults[i, 2] <- 0.01 for(j in 1:5) { # record p-value, lower and upper confidence limits thismantel <- mantel(corxy.len010.r010$y ~ corxy.len010.r010$x, nperm=all.nperm[j], nboot=all.nboot[j]) permresults[i, (3*j):(3*j + 2)] <- c(thismantel[c(2,5,6)]) } } ## End(Not run)