我认为这很棘手。通过一些测试和阅读后电流GH代码的StatSmooth，我总结我的调查结果如下：

观察结果

1. geom_smooth()如果任何数据组对AND的观察值太少，则无法在绘图面板中绘制所有平滑线；method = "gam" formula = y ~ s(x, k = 3)
2. 如果该图面分成多个面板，则仅会影响具有不良数据组的面板；
3. 不会发生这种情况formula = y ~ x（即默认公式）；
4. 这不会发生一些其他的方法（例如"lm"，"glm"）使用默认的公式，但也有发生method = "loess";
5. 如果数据组只有1个观察值，则不会发生这种情况。

我们可以用一些简化的代码重现上面的内容：

# create sample data
n <- 30
set.seed(567)
df.1 <- data.frame( # there is only 1 observation for group == B
  x = rnorm(n), y = rnorm(n),
  group = c(rep("A", n - 1), rep("B", 1)),
  facet = sample(c("X", "Y"), size = n, replace = TRUE))    
set.seed(567)
df.2 <- data.frame( # there are 2 observations for group == B
  x = rnorm(n), y = rnorm(n),
  group = c(rep("A", n - 2), rep("B", 2)),
  facet = sample(c("X", "Y"), size = n, replace = TRUE))

# create base plot
p <- ggplot(df.2, aes(x = x, y = y, color = group)) + 
  geom_point() + theme_bw()

# problem: no smoothed line at all in the entire plot
p + geom_smooth(method = "gam", formula = y ~ s(x, k = 3))

# problem: no smoothed line in the affected panel
p + facet_wrap(~ facet) + 
  geom_smooth(method = "gam", formula = y ~ s(x, k = 3))

# no problem with default formula: smoothed lines in both facet panels
p + facet_wrap(~ facet) + geom_smooth(method = "gam")

# no problem with lm / glm, but problem with loess
p + facet_wrap(~ facet) + geom_smooth(method = "lm")
p + facet_wrap(~ facet) + geom_smooth(method = "glm")
p + facet_wrap(~ facet) + geom_smooth(method = "loess")

# no problem if there's only one observation (instead of two)
p %+% df.1 + geom_smooth(method = "gam", formula = y ~ s(x, k = 3))
p %+% df.1 + facet_wrap(~ facet) + 
  geom_smooth(method = "gam", formula = y ~ s(x, k = 3))

意见1和2的解释：

我认为问题的关键在于中的最后两行StatSmooth的compute_group功能。第一行调用模型的功能（例如stats::glm，stats::loess，mgcv::gam），用于通过指定的每一组数据帧上aes(group = ...)的映射，而第二行调用包装器之一的周围stats::predict()，以获得平滑的值（以及置信区间，如果适用的话），用于模型。

model <- do.call(method, c(base.args, method.args))
predictdf(model, xseq, se, level)

当参数method = "gam", formula = y ~ s(x, k = 3)仅用于带有两个观察值的数据帧时，将发生以下情况：

model <- do.call(mgcv::gam,
                 args = list(formula = y ~ s(x, k = 3),
                             data = df.2 %>% filter(group == "B" & facet == "X")))

smooth.construct.tp.smooth.spec（object，dk $ data，dk $ knots）中的错误：一个术语的唯一协变量组合少于指定的最大自由度

model，定义为采用的结果的对象do.call(...)，甚至尚未创建。代码的最后一行predictdf(...)将抛出错误，因为model不存在。无刻面，这会影响所有所做的计算StatSmooth，并且geom_smooth()没有接收到可用的数据来创建在其层中的任何的geom。使用构面时，将针对每个构面分别进行上述计算，因此仅影响有问题数据的构面会受到影响。

意见3和4的解释：

加上上面的内容，如果我们没有指定公式来替换默认值y ~ x，我们将从gam/ lm/获得一个有效的模型对象glm，可以将其传递给ggplot2的未导出predictdf函数以获取预测值的数据帧：

model <- do.call(mgcv::gam, # or stats::lm, stats::glm
                 args = list(formula = y ~ x,
                             data = df.2 %>% filter(group == "B" & facet == "X")))

result <- ggplot2:::predictdf(
  model, 
  xseq = seq(-2, 1.5, length.out = 80), # pseudo range of x-axis values
  se = FALSE, level = 0.95) # default SE / level parameters

loess也将返回一个有效的对象，尽管带有大量警告。但是，将其传递给predictdf将导致错误：

model <- do.call(stats::loess,
                 args = list(formula = y ~ x,
                             data = df.2 %>% filter(group == "B" & facet == "X")))

result <- ggplot2:::predictdf(
  model, 
  xseq = seq(-2, 1.5, length.out = 80), # pseudo range of x-axis values
  se = FALSE, level = 0.95) # default SE / level parameters

predLoess（object $ y，object $ x，newx = if（is.null（newdata））object $ x else if（is.data.frame（newdata））as.matrix（model.frame（delete.response（ terms（object）），：外部函数调用中的NA / NaN / Inf（arg 5）

观察说明5：

StatSmooth的compute_group功能从以下内容开始：

if (length(unique(data$x)) < 2) {
      # Not enough data to perform fit
      return(data.frame())
    }

换句话说，如果指定组中只有1个观察值，则StatSmooth立即返回空白数据帧。因此，它将永远不会到达代码的后续部分以引发任何错误。

解决方法：

查明发生问题的地方后，我们可以对compute_group代码进行调整（请参阅带注释和注释掉的部分）：

new.compute_group <- function(
  data, scales, method = "auto", formula = y~x, se = TRUE, n = 80, span = 0.75, 
  fullrange = FALSE, xseq = NULL, level = 0.95, method.args = list(), na.rm = FALSE) {
  if (length(unique(data$x)) < 2) return(data.frame()) 
  if (is.null(data$weight)) data$weight <- 1
  if (is.null(xseq)) {
    if (is.integer(data$x)) {
      if (fullrange) {
        xseq <- scales$x$dimension()
      } else {
        xseq <- sort(unique(data$x))
      }
    } else {
      if (fullrange) {
        range <- scales$x$dimension()
      } else {
        range <- range(data$x, na.rm = TRUE)
      }
      xseq <- seq(range[1], range[2], length.out = n)
    }
  }
  if (identical(method, "loess")) method.args$span <- span 
  if (is.character(method)) method <- match.fun(method)
  base.args <- list(quote(formula), data = quote(data), weights = quote(weight))

  # if modelling fails, return empty data frame
  # model <- do.call(method, c(base.args, method.args))
  model <- try(do.call(method, c(base.args, method.args)))
  if(inherits(model, "try-error")) return(data.frame())

  # if modelling didn't fail, but prediction returns NA,
  # also return empty data frame
  # predictdf(model, xseq, se, level)
  pred <- try(ggplot2:::predictdf(model, xseq, se, level))
  if(inherits(pred, "try-error")) return(data.frame())
  return(pred)
}

定义一个使用此版本的新统计图层：

# same as stat_smooth() except that it uses stat = StatSmooth2, rather 
# than StatSmooth
stat_smooth_local <- function(
  mapping = NULL, data = NULL, geom = "smooth", position = "identity", ...,
  method = "auto", formula = y ~ x, se = TRUE, n = 80, span = 0.75,
  fullrange = FALSE, level = 0.95, method.args = list(), na.rm = FALSE,
  show.legend = NA, inherit.aes = TRUE) {
  layer(
    data = data, mapping = mapping, stat = StatSmooth2,
    geom = geom, position = position, show.legend = show.legend,
    inherit.aes = inherit.aes,
    params = list(
      method = method, formula = formula, se = se, n = n,
      fullrange = fullrange, level = level, na.rm = na.rm,
      method.args = method.args, span = span, ...
    )
  )
}

# inherit from StatSmooth
StatSmooth2 <- ggproto(
  "StatSmooth2", ggplot2::StatSmooth,
  compute_group = new.compute_group
)

结果：

我们可以像以前一样处理相同的情况，替换geom_smooth()为stat_smooth_local()，并验证在每种情况下都可以看到平滑的geom图层（请注意，有些图层仍会产生错误消息）：

# problem resolved: smoothed line for applicable group in the entire plot
p + stat_smooth_local(method = "gam", formula = y ~ s(x, k = 3))

# problem resolved: smoothed line for applicable group in the affected panel
p + facet_wrap(~ facet) + 
  stat_smooth_local(method = "gam", formula = y ~ s(x, k = 3))

# still no problem with default formula
p + facet_wrap(~ facet) + stat_smooth_local(method = "gam")

# still no problem with lm / glm; problem resolved for loess
p + facet_wrap(~ facet) + stat_smooth_local(method = "lm")
p + facet_wrap(~ facet) + stat_smooth_local(method = "glm")
p + facet_grid(~ facet) + stat_smooth_local(method = "loess")

# still no problem if there's only one observation (instead of two)
p %+% df.1 + stat_smooth_local(method = "gam", formula = y ~ s(x, k = 3))
p %+% df.1 + facet_wrap(~ facet) + 
  stat_smooth_local(method = "gam", formula = y ~ s(x, k = 3))

# showing one pair of contrasts here
cowplot::plot_grid(
  p + facet_wrap(~ facet) + ggtitle("Before") +
    geom_smooth(method = "gam", formula = y ~ s(x, k = 3)),
  p + facet_wrap(~ facet) + ggtitle("After") +
    stat_smooth_local(method = "gam", formula = y ~ s(x, k = 3)),
  nrow = 2
)