我进行了Logistic回归和多重比较
library(lme4)
model < glmer(y ~ Genger + Age + subject +
(1 | ParticipantID), data = data, family = binomial(link = "logit"))
library(multcomp)
multicomp<-glht(model,mcp(Behavior="Tukey"))
summary(multicomp)
输出:
Estimate Std. Error z value Pr(>|z|)
subject2 - subject1 == 0 -2.19503726 0.66179761 -2.89596 < 0.001 ***
我想要显示准确的p值，而不是< 0.001***.我该怎么办？
我试过了
options(digits=10)
但yields 并没有改变.
您可以将其更改为:
library(lme4)
data(Orthodont,package="nlme")
model <-lmer(distance ~ Sex + age + (age|Subject), data=Orthodont)
library(multcomp)
multicomp<-glht(model, mcp(Sex = "Tukey"))
res <- summary(multicomp)
print(res)
# Estimate Std. Error z value Pr(>|z|)
#Female - Male == 0 -2.1454 0.7575 -2.832 0.00462 **
coeftable <- do.call(cbind, res$test[c("coefficients", "sigma", "tstat", "pvalues")])
colnames(coeftable) <- c("Estimate", "Std. Error", "z value", "Pr(>|z|)")
printCoefmat(coeftable)
#more digits (but still some protection against showing more digits than meaningful)
printCoefmat(coeftable, digits = 10)
#no protection
print(coeftable)
#even more digits
options(digits = 20)
print(coeftable)
但是，没有打印出确切的p值是有原因的.它很容易受到precision of floating-point numbers的影响.multcomp个开发人员正在试图保护您，使其不会被解释为无意义的数字.