memisc パッケージの mtable() 関数に関して、1 つの解決策は、関数 lme4() に対してここで提案されているように、カスタム getSummary メソッドを作成することです: https://stat.ethz.ch/pipermail/r-sig-mixed-models /2009q1/002058.html
library(lme4)
library(memisc)
### create three models
fm1 <- lmer(Reaction ~ 1 + (Days|Subject), sleepstudy)
fm1.1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
fm1.2 <- lmer(Reaction ~ as.factor(Days) + (Days|Subject), sleepstudy)
### note: need to run the code below fro setCoefTemplate and
### getSummary.lmer first
mtable("Model 1"=fm1, "Model 2"=fm1.1, "Model 3"=fm1.2,
coef.style = "est.ci", # using "homegrown" est.ci, specified above
summary.stats=c("AIC","BIC"),
getSummary = "getSummary.lmer")#,
setCoefTemplate(
est.ci=c(
est = "($est:#)($p:*)",
ci = "[($lwr:#),($upr:#)]"))
getSummary.lmer <- function (obj, alpha = 0.05, ...)
{
require(lme4)
smry <- summary(obj)
#N <- if (length(weights(obj))) ### NOTE: how to deal with groups/samp size?
# sum(weights(obj))
#else sum(smry$df[1:2])
coef <- smry at coefs
lower <- qnorm(p = alpha/2, mean = coef[, 1], sd = coef[,2])
upper <- qnorm(p = 1 - alpha/2, mean = coef[, 1], sd = coef[,2])
if (ncol(smry at coefs) == 3) {
p <- (1 - pnorm(smry at coefs[,3]))*2 # NOTE: no p-values for lmer() due to
# unclear dfs; calculate p-values based on z
coef <- cbind(coef, p, lower, upper)
} else {
coef <- cbind(coef, lower, upper) # glmer will have 4 columns with p-values
}
colnames(coef) <- c("est", "se", "stat", "p", "lwr", "upr")
#phi <- smry$dispersion
#LR <- smry$null.deviance - smry$deviance
#df <- smry$df.null - smry$df.residual
ll <- smry at AICtab[3][,1]
deviance <- smry at AICtab[4][,1]
#if (df > 0) {
# p <- pchisq(LR, df, lower.tail = FALSE)
# L0.pwr <- exp(-smry$null.deviance/N)
# McFadden <- 1 - smry$deviance/smry$null.deviance
# Cox.Snell <- 1 - exp(-LR/N)
# Nagelkerke <- Cox.Snell/(1 - L0.pwr)
#}
#else {
# LR <- NA
# df <- NA
# p <- NA
# McFadden <- NA
# Cox.Snell <- NA
# Nagelkerke <- NA
#}
AIC <- smry at AICtab[1][,1] # NOTE: these are both data.frames? not sure why...
BIC <- smry at AICtab[2][,1]
### NOTE: don't see a similar slot for "xlevels" to get levels of
### factor variables used as predictors; for time being, force
### user to specify explicitly; nope that didn't work...
#if (fac != NULL) {
# n <- length(fac)
# xlevels <- vector(n, mode = "list")
# for (i in 1:n) {
# xlevels[i] <- levels(obj at frame[,fac[i]])
# }
# }
#sumstat <- c(phi = phi, LR = LR, df = df, p = p, logLik = ll,
# deviance = deviance, McFadden = McFadden, Cox.Snell = Cox.Snell,
# Nagelkerke = Nagelkerke, AIC = AIC, BIC = BIC, N = N)
sumstat <- c(logLik = ll, deviance = deviance, AIC = AIC, BIC = BIC)
list(coef = coef, sumstat = sumstat,
contrasts = attr(model.matrix(obj), "contrasts"),
xlevels = NULL, call = obj at call)
}