combine-methods {jointest} | R Documentation |
Nonparametric combination of jointest
objects
Description
Methods for combining jointest
objects.
combine
combines the tests derived from multiverse models.
combine_contrasts
combines the tests derived from the contrasts of a factor variable to get a
global test for the factor (i.e. categorical predictor).
It has strong analogies with ANOVA test.
Usage
combine(mods, comb_funct = "maxT", by = NULL, by_list=NULL, tail = 0)
combine_contrasts(mods, comb_funct = "Mahalanobis", tail = 0)
Arguments
mods |
a |
comb_funct |
combining function to be used.
Several functions are implemented: "mean", "median", "Fisher", "Liptak", (equal to) "Stoufer", "Tippet", (equal to) "minp", "maxT", "Mahalanobis".
Alternatively it can be a custom function that has a Tspace matrix as input.
For |
by |
if |
by_list |
NULL (default) or a list of vectors. For each vector of the list it combines test statistics with position given by the element of the vector. If the vectors in the list are characters, these refer to names(mods$Tspace). |
tail |
direction of the alternative hypothesis. It can be "two.sided" (or 0, the default), "less" (or -1) or "greater" (or +1). |
Value
The function returns a jointest
-object.
Examples
#First example
library(jointest)
set.seed(123)
#Simulate data
n=20
D=data.frame(X=rnorm(n),Z1=rnorm(n),Z2=rnorm(n))
D$Y=D$Z1+D$X+rnorm(n)
# Run four glms abd combine it in a list
mod1=glm(Y~X+Z1+Z2,data=D)
mod2=glm(Y~X+poly(Z1,2)+Z2,data=D)
mod3=glm(Y~X+poly(Z1,2)+poly(Z2,2),data=D)
mod4=glm(Y~X+Z1+poly(Z2,2),data=D)
mods=list(mod1=mod1,mod2=mod2,mod3=mod3,mod4=mod4)
# Let us analyze the tests related to coefficient "X" and combine them
res=join_flipscores(mods,n_flips = 5000, seed = 1, tested_coeffs = "X")
summary(combine(res))
# Second (continued) example
# flipscores jointly on all models and all coefficients
res=join_flipscores(mods,n_flips = 2000)
summary(combine(res))
summary(combine(res, by="Model"))
summary(combine(res, by="Coeff"))
res2=combine_contrasts(res)
summary(res2)
#custom combinations:
coeffs=c("(Intercept)","X","Z1","Z2")
coeffs_ids=lapply(coeffs,grep,res2$summary_table$Coeff)
names(coeffs_ids)=coeffs
summary(combine(res2,by_list = coeffs_ids))