Description Usage Arguments Details Value Author(s) References See Also Examples
Compute permutation marginal test for linear model. This function produces t statistics with univariate and bivariate pvalues. It gives the choice between multiple methods to handle nuisance variables.
1 
formula 
A formula object. 
data 
A data frame or matrix. 
np 
The number of permutations. Default value is 
method 
A character string indicating the method use to handle nuisance variables. Default is 
... 
Futher arguments, see details. 
The following methods are available for the fixed effects model defined as y = Dη + Xβ + ε. If we want to test β = 0 and take into account the effects of the nuisance variables D, we transform the data :
method argument  y  D  X 
"draper_stoneman"  y  D  PX 
"freedman_lane"  (H_D+PR_D)y  D  X 
"manly"  Py  D  X 
"terBraak"  (H_{X,D}+PR_{X,D})y  D  X 
"kennedy"  PR_D y  R_D X  
"huh_jhun"  PV'R_Dy  V'R_D X  
"dekker"  y  D  PR_D X 
Other arguments could be pass in ...
:
P
: a matrix containing the permutations of class matrix
or Pmat
for the reproductibility of the results. The first column must be the identity. P
overwrites np
argument.
rnd_rotation
: a random matrix of size n \times n to compute the rotation used for the "huh_jhun"
method.
A lmperm
object. see aovperm.
KheradPajouh, S., & Renaud, O. (2010). An exact permutation method for testing any effect in balanced and unbalanced fixed effect ANOVA. Computational Statistics & Data Analysis, 54(7), 18811893.
KheradPajouh, S., & Renaud, O. (2015). A general permutation approach for analyzing repeated measures ANOVA and mixedmodel designs. Statistical Papers, 56(4), 947967.
Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014). Permutation inference for the general linear model. Neuroimage, 92, 381397.
1 2 3 4 5 6 7 8 9 10 11 12 13  ## data
data("emergencycost")
## Testing at 14 days
emergencycost$LOS14 < emergencycost$LOS  14
## Univariate t test
contrasts(emergencycost$insurance) < contr.sum
contrasts(emergencycost$sex) < contr.sum
## Warning : np argument must be greater (recommendation: np>=5000)
modlm_cost_14 < lmperm(cost ~ LOS14*sex*insurance, data = emergencycost, np = 2000)
modlm_cost_14

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