Compute a permutation matrix used as argument in aovperm, lmperm, clusterlm functions. The first column represents the identity permutation. Generally not suited for the "huh_jhun" method, as the dimension of this matrix does not correspond to the number of observations and may vary for different factors.
Pmat(np = 5000, n, type = "permutation", counting = "random")A numeric value for the number of permutations. Default is 5000.
A numeric value for the number of observations.
A character string to specify the type of transformations: "permutation" and "signflip" are available. See details.
A character string to specify the selection of the transformations. "all" and "random" are available. See details.
A matrix n x np containing the permutations/signflips. First permutation is the identity.
couting can set to :"random" : np random with replacement permutations/signflips among the n!/2^n permutations/signflips."all" : all n!/2^n possible permutations/signflips.
Other pmat:
Pmat_product(),
as.Pmat()
## data
data("emergencycost")
## Create a set of 2000 permutations
set.seed(42)
pmat = Pmat(np = 2000, n = nrow(emergencycost))
sfmat = Pmat(np = 2000, n = nrow(emergencycost), type = "signflip")
## centrering the covariate to the mean
emergencycost$LOSc <- scale(emergencycost$LOS, scale = FALSE)
## ANCOVA
mod_cost_0 <- aovperm(cost ~ LOSc*sex*insurance, data = emergencycost, np = 2000)
mod_cost_1 <- aovperm(cost ~ LOSc*sex*insurance, data = emergencycost, P = pmat)
mod_cost_2 <- aovperm(cost ~ LOSc*sex*insurance, data = emergencycost, P = pmat)
mod_cost_3 <- aovperm(cost ~ LOSc*sex*insurance, data = emergencycost, P = sfmat)
mod_cost_4 <- aovperm(cost ~ LOSc*sex*insurance, data = emergencycost,type="signflip")
## Same p-values for both models 1 and 2 but different of model 0
mod_cost_0
#> Anova Table
#> Resampling test using freedman_lane to handle nuisance variables and 2000 permutations.
#> SS df F parametric P(>F) resampled P(>F)
#> LOSc 2.162e+09 1 483.4422 0.00000 0.0005
#> sex 1.463e+07 1 3.2714 0.07229 0.0905
#> insurance 6.184e+05 1 0.1383 0.71048 0.6925
#> LOSc:sex 8.241e+06 1 1.8427 0.17646 0.1645
#> LOSc:insurance 2.911e+07 1 6.5084 0.01163 0.0270
#> sex:insurance 1.239e+05 1 0.0277 0.86801 0.8590
#> LOSc:sex:insurance 1.346e+07 1 3.0091 0.08463 0.0885
#> Residuals 7.514e+08 168
mod_cost_1
#> Anova Table
#> Resampling test using freedman_lane to handle nuisance variables and 2000 permutations.
#> SS df F parametric P(>F) resampled P(>F)
#> LOSc 2.162e+09 1 483.4422 0.00000 0.0005
#> sex 1.463e+07 1 3.2714 0.07229 0.0685
#> insurance 6.184e+05 1 0.1383 0.71048 0.6805
#> LOSc:sex 8.241e+06 1 1.8427 0.17646 0.1530
#> LOSc:insurance 2.911e+07 1 6.5084 0.01163 0.0220
#> sex:insurance 1.239e+05 1 0.0277 0.86801 0.8550
#> LOSc:sex:insurance 1.346e+07 1 3.0091 0.08463 0.0825
#> Residuals 7.514e+08 168
mod_cost_2
#> Anova Table
#> Resampling test using freedman_lane to handle nuisance variables and 2000 permutations.
#> SS df F parametric P(>F) resampled P(>F)
#> LOSc 2.162e+09 1 483.4422 0.00000 0.0005
#> sex 1.463e+07 1 3.2714 0.07229 0.0685
#> insurance 6.184e+05 1 0.1383 0.71048 0.6805
#> LOSc:sex 8.241e+06 1 1.8427 0.17646 0.1530
#> LOSc:insurance 2.911e+07 1 6.5084 0.01163 0.0220
#> sex:insurance 1.239e+05 1 0.0277 0.86801 0.8550
#> LOSc:sex:insurance 1.346e+07 1 3.0091 0.08463 0.0825
#> Residuals 7.514e+08 168
mod_cost_3
#> Anova Table
#> Resampling test using freedman_lane to handle nuisance variables and 2000 signflips.
#> SS df F parametric P(>F) resampled P(>F)
#> LOSc 2.162e+09 1 483.4422 0.00000 0.0005
#> sex 1.463e+07 1 3.2714 0.07229 0.2090
#> insurance 6.184e+05 1 0.1383 0.71048 0.7340
#> LOSc:sex 8.241e+06 1 1.8427 0.17646 0.1575
#> LOSc:insurance 2.911e+07 1 6.5084 0.01163 0.0300
#> sex:insurance 1.239e+05 1 0.0277 0.86801 0.8815
#> LOSc:sex:insurance 1.346e+07 1 3.0091 0.08463 0.1305
#> Residuals 7.514e+08 168
mod_cost_4
#> Anova Table
#> Resampling test using freedman_lane to handle nuisance variables and 5000 signflips.
#> SS df F parametric P(>F) resampled P(>F)
#> LOSc 2.162e+09 1 483.4422 0.00000 0.0002
#> sex 1.463e+07 1 3.2714 0.07229 0.2160
#> insurance 6.184e+05 1 0.1383 0.71048 0.7392
#> LOSc:sex 8.241e+06 1 1.8427 0.17646 0.1602
#> LOSc:insurance 2.911e+07 1 6.5084 0.01163 0.0316
#> sex:insurance 1.239e+05 1 0.0277 0.86801 0.8748
#> LOSc:sex:insurance 1.346e+07 1 3.0091 0.08463 0.1378
#> Residuals 7.514e+08 168