估算数据的多项回归

Question

我需要估算缺失数据，然后使用生成的数据集进行多项回归。我尝试使用鼠标进行插补，然后使用 nnet 的多项函数进行多项回归。但这给了我不可读的输出。下面是一个使用mice 包提供的nhanes2 数据集的示例：

library(mice)
library(nnet)

test <- mice(nhanes2, meth=c('sample','pmm','logreg','norm'))
#age is categorical, bmi is continuous
m <- with(test, multinom(age ~ bmi, model = T))
summary(pool(m))

m1 <- with(test, lm(bmi ~ age, model = T))
summary(pool(m1))

使用上面的代码，'m' 的输出将系数列为 1、2、3 和 4，而不是它们的变量名。使用 lm 命令时 m1 的输出是正确的。这似乎是在 mira 对象上运行多项式命令的问题。有没有人对如何解决这个问题或其他可以更好地工作的插补和多项回归方法有任何建议？

Answer 1

编辑

这已经在mice 的开发分支上更新，并按预期执行：见https://github.com/stefvanbuuren/mice/issues/85

---

使用mulitnom 时有几个问题。 pool 方法混合了系数和标准误差的顺序 - 所以（asaik）使用 pool 返回的对象是 not correct （实际上混合有点强 - 没有特定的 pool 方法multinom 模型，因此它使用默认值，在这种情况下不太适用）。

另外，正如您所提到的，名称被删除 - 这是由于 pool 调用 names(coef(modelobject))，但 multinom 模型返回一个系数矩阵，因此没有 names（他们有 @ 987654333@ & colnames)。

因此，您可以更改 pool 函数以适应 multinom 模型 - 请参阅下面的 pooly（实际上您可以编写一个小得多的函数来处理这个模型类，但我选择快速编写一个，更通用的方法，希望不会破坏其他模型类，但需要注意的是，我还没有完全测试它没有。）

用你的例子测试

library(mice)
library(nnet)

test <- mice(nhanes2, meth=c('polyreg','pmm','logreg','norm'), print=0)
m <- with(test, multinom(age ~ bmi, model = T))
summary(pooly(m))
#                          est        se         t       df   Pr(>|t|)      lo 95       hi 95 nmis       fmi    lambda
# 40-59:(Intercept)  5.8960594 4.5352921  1.300040 12.82882 0.21646037 -3.9151498 15.70726867   NA 0.2951384 0.1931975
# 40-59:bmi         -0.2356516 0.1669807 -1.411250 12.81702 0.18198189 -0.5969156  0.12561248   NA 0.2955593 0.1935923
# 60-99:(Intercept)  8.2723321 4.7656701  1.735817 15.55876 0.10235284 -1.8537729 18.39843700   NA 0.1989371 0.1021831
# 60-99:bmi         -0.3364014 0.1832718 -1.835533 15.03938 0.08627846 -0.7269469  0.05414413   NA 0.2174394 0.1198595
# 

与（我认为）混合了 coefs 和 se's 的原始版本比较

summary(pool(m))

---

定义函数以接受multinom 模型。添加的代码旁边有 cmets。

pooly <- function (object, method = "smallsample") {
    call <- match.call()
    if (!is.mira(object)) 
        stop("The object must have class 'mira'")
    m <- length(object$analyses)
    fa <- getfit(object, 1)
    if (m == 1) {
        warning("Number of multiple imputations m=1. No pooling done.")
        return(fa)
    }
    analyses <- getfit(object)
    if (class(fa)[1] == "lme" && !requireNamespace("nlme", quietly = TRUE)) 
        stop("Package 'nlme' needed fo this function to work. Please install it.", 
            call. = FALSE)
    if ((class(fa)[1] == "mer" || class(fa)[1] == "lmerMod" || 
        inherits(fa, "merMod")) && !requireNamespace("lme4", 
        quietly = TRUE)) 
        stop("Package 'lme4' needed fo this function to work. Please install it.", 
            call. = FALSE)
    mess <- try(coef(fa), silent = TRUE)
    if (inherits(mess, "try-error")) 
        stop("Object has no coef() method.")
    mess <- try(vcov(fa), silent = TRUE)
    if (inherits(mess, "try-error")) 
        stop("Object has no vcov() method.")
    if (class(fa)[1] == "mer" || class(fa)[1] == "lmerMod" || 
        inherits(fa, "merMod")) {
        k <- length(lme4::fixef(fa))
        names <- names(lme4::fixef(fa))
    }
    else if (class(fa)[1] == "polr") {
        k <- length(coef(fa)) + length(fa$zeta)
        names <- c(names(coef(fa)), names(fa$zeta))
    }
    # added this ---------------------------------
    else if (class(fa)[1] == "multinom") {
        k <- length(coef(fa)) 
        names <- rownames(vcov(fa))
    }
    # --------------------------------------------
    else {
        k <- length(coef(fa))
        names <- names(coef(fa)) 
    }
    qhat <- matrix(NA, nrow = m, ncol = k, dimnames = list(seq_len(m), 
        names))
    u <- array(NA, dim = c(m, k, k), dimnames = list(seq_len(m), 
        names, names))
    for (i in seq_len(m)) {
        fit <- analyses[[i]]
        if (class(fit)[1] == "mer") {
            qhat[i, ] <- lme4::fixef(fit)
            ui <- as.matrix(vcov(fit))
            if (ncol(ui) != ncol(qhat)) 
                stop("Different number of parameters: class mer, fixef(fit): ", 
                  ncol(qhat), ", as.matrix(vcov(fit)): ", ncol(ui))
            u[i, , ] <- array(ui, dim = c(1, dim(ui)))
        }
        else if (class(fit)[1] == "lmerMod" || inherits(fa, "merMod")) {
            qhat[i, ] <- lme4::fixef(fit)
            ui <- vcov(fit)
            if (ncol(ui) != ncol(qhat)) 
                stop("Different number of parameters: class lmerMod, fixed(fit): ", 
                  ncol(qhat), ", vcov(fit): ", ncol(ui))
            u[i, , ] <- array(ui, dim = c(1, dim(ui)))
        }
        else if (class(fit)[1] == "lme") {
            qhat[i, ] <- fit$coefficients$fixed
            ui <- vcov(fit)
            if (ncol(ui) != ncol(qhat)) 
                stop("Different number of parameters: class lme, fit$coefficients$fixef: ", 
                  ncol(qhat), ", vcov(fit): ", ncol(ui))
            u[i, , ] <- array(ui, dim = c(1, dim(ui)))
        }
        else if (class(fit)[1] == "polr") {
            qhat[i, ] <- c(coef(fit), fit$zeta)
            ui <- vcov(fit)
            if (ncol(ui) != ncol(qhat)) 
                stop("Different number of parameters: class polr, c(coef(fit, fit$zeta): ", 
                  ncol(qhat), ", vcov(fit): ", ncol(ui))
            u[i, , ] <- array(ui, dim = c(1, dim(ui)))
        }
        else if (class(fit)[1] == "survreg") {
            qhat[i, ] <- coef(fit)
            ui <- vcov(fit)
            parnames <- dimnames(ui)[[1]]
            select <- !(parnames %in% "Log(scale)")
            ui <- ui[select, select]
            if (ncol(ui) != ncol(qhat)) 
                stop("Different number of parameters: class survreg, coef(fit): ", 
                  ncol(qhat), ", vcov(fit): ", ncol(ui))
            u[i, , ] <- array(ui, dim = c(1, dim(ui)))
        }
        # added this block -------------------------------------
        else if (class(fit)[1] == "multinom") {
            qhat[i, ] <- c(t(coef(fit))) # transpose to get same order as standard errors
            ui <- vcov(fit)
            if (ncol(ui) != ncol(qhat)) 
                stop("Different number of parameters: class multinom, c(coef(fit)): ", 
                  ncol(qhat), ", vcov(fit): ", ncol(ui))
            u[i, , ] <- array(ui, dim = c(1, dim(ui)))
        }
        # ----------------------------------------------------
        else {
            qhat[i, ] <- coef(fit)
            ui <- vcov(fit)
            ui <- expandvcov(qhat[i, ], ui)
            if (ncol(ui) != ncol(qhat)) 
                stop("Different number of parameters: coef(fit): ", 
                  ncol(qhat), ", vcov(fit): ", ncol(ui))
            u[i, , ] <- array(ui, dim = c(1, dim(ui)))
        }
    }
    qbar <- apply(qhat, 2, mean)
    ubar <- apply(u, c(2, 3), mean)
    e <- qhat - matrix(qbar, nrow = m, ncol = k, byrow = TRUE)
    b <- (t(e) %*% e)/(m - 1)
    t <- ubar + (1 + 1/m) * b
    r <- (1 + 1/m) * diag(b/ubar)
    lambda <- (1 + 1/m) * diag(b/t)
    dfcom <- df.residual(object)
    df <- mice.df(m, lambda, dfcom, method)
    fmi <- (r + 2/(df + 3))/(r + 1)
    names(r) <- names(df) <- names(fmi) <- names(lambda) <- names
    fit <- list(call = call, call1 = object$call, call2 = object$call1, 
        nmis = object$nmis, m = m, qhat = qhat, u = u, qbar = qbar, 
        ubar = ubar, b = b, t = t, r = r, dfcom = dfcom, df = df, 
        fmi = fmi, lambda = lambda)
    oldClass(fit) <- c("mipo", oldClass(object))
    return(fit)
}

environment(pooly) <- environment(mice)