自定义列名称作为稳定性r包函数中的参数

我开发了 stability 可从起重机上安装的R包。

install.packages("stability")

但是,我很难将自定义列名作为函数参数。下面是一个例子 add_anova 功能

library(stability)
data(ge_data)

YieldANOVA <-
  add_anova(
      .data = ge_data
    , .y    = Yield
    , .rep  = Rep
    , .gen  = Gen
    , .env  = Env
  )
YieldANOVA

以上代码工作正常。但是,当我更改data.frame的列名时,它的工作方式不如下:

df1 <- ge_data
names(df1) <- c("G", "Institute", "R", "Block", "E", "Y")

fm1 <-
  add_anova(
      .data = df1
    , .y    = Y
    , .rep  = Rep
    , .gen  = G
    , .env  = E
  )

Error in model.frame.default(formula = terms(.data$Y ~ .data$E + .data$Rep:.data$E +  : 
  invalid type (NULL) for variable '.data$Rep'

类似地,另一个函数 stab_reg

fm1Reg <-
  stab_reg(
      .data = df1
    , .y    = Y
    , .gen  = G
    , .env  = E
  )

Error in eval(predvars, data, env) : object 'Gen' not found

这些功能的代码可以通过

getAnywhere(add_anova.default)

function (.data, .y, .rep, .gen, .env) 
{
    Y <- enquo(.y)
    Rep <- enquo(.rep)
    G <- enquo(.gen)
    E <- enquo(.env)
    fm1 <- lm(formula = terms(.data$Y ~ .data$E + .data$Rep:.data$E + 
        .data$G + .data$G:.data$E, keep.order = TRUE), data = .data)
    fm1ANOVA <- anova(fm1)
    rownames(fm1ANOVA) <- c("Env", "Rep(Env)", "Gen", "Gen:Env", 
        "Residuals")
    fm1ANOVA[1, 4] <- fm1ANOVA[1, 3]/fm1ANOVA[2, 3]
    fm1ANOVA[2, 4] <- NA
    fm1ANOVA[1, 5] <- 1 - pf(as.numeric(fm1ANOVA[1, 4]), fm1ANOVA[1, 
        1], fm1ANOVA[2, 1])
    fm1ANOVA[2, 5] <- 1 - pf(as.numeric(fm1ANOVA[2, 4]), fm1ANOVA[2, 
        1], fm1ANOVA[5, 1])
    class(fm1ANOVA) <- c("anova", "data.frame")
    return(list(anova = fm1ANOVA))
}
<bytecode: 0xc327c28>
<environment: namespace:stability>

和

   getAnywhere(stab_reg.default)

function (.data, .y, .rep, .gen, .env) 
{
    Y <- enquo(.y)
    Rep <- enquo(.rep)
    G <- enquo(.gen)
    E <- enquo(.env)
    g <- length(levels(.data$G))
    e <- length(levels(.data$E))
    r <- length(levels(.data$Rep))
    g_means <- .data %>% dplyr::group_by(!!G) %>% dplyr::summarize(Mean = mean(!!Y))
    names(g_means) <- c("G", "Mean")
    DataNew <- .data %>% dplyr::group_by(!!G, !!E) %>% dplyr::summarize(GEMean = mean(!!Y)) %>% 
        dplyr::group_by(!!E) %>% dplyr::mutate(EnvMean = mean(GEMean))
    IndvReg <- lme4::lmList(GEMean ~ EnvMean | Gen, data = DataNew)
    IndvRegFit <- summary(IndvReg)
    StabIndvReg <- tibble::as_tibble(data.frame(g_means, Slope = coef(IndvRegFit)[, 
        , 2][, 1], LCI = confint(IndvReg)[, , 2][, 1], UCI = confint(IndvReg)[, 
        , 2][, 2], R.Sqr = IndvRegFit$r.squared, RMSE = IndvRegFit$sigma, 
        SSE = IndvRegFit$sigma^2 * IndvRegFit$df[, 2], Delta = IndvRegFit$sigma^2 * 
            IndvRegFit$df[, 2]/r))
    MeanSlopePlot <- ggplot(data = StabIndvReg, mapping = aes(x = Slope, 
        y = Mean)) + geom_point() + geom_text(aes(label = G), 
        size = 2.5, vjust = 1.25, colour = "black") + geom_vline(xintercept = 1, 
        linetype = "dotdash") + geom_hline(yintercept = mean(StabIndvReg$Mean), 
        linetype = "dotdash") + labs(x = "Slope", y = "Mean") + 
        scale_x_continuous(sec.axis = dup_axis(), labels = scales::comma) + 
        scale_y_continuous(sec.axis = dup_axis(), labels = scales::comma) + 
        theme_bw()
    return(list(StabIndvReg = StabIndvReg, MeanSlopePlot = MeanSlopePlot))
}
<bytecode: 0xe431010>
<environment: namespace:stability>