ggpredict是否会基于单个数据点绘制分位数回归预测？

我目前使用ggeffects::ggpredict()绘制包含双向交互项的回归模型，覆盖三类交互场景：分类变量与连续变量、连续变量与连续变量、连续变量与连续变量（二次多项式）。我习惯通过设置terms = c("var.cont1[all]", "var.cont2[quart]")，利用分位数（包含最小值和最大值）来呈现连续变量间的交互关系。

但我的数据里，目标变量的最小值和最大值都只有单个观测值，此时ggpredict仍能正常输出预测值，只是最大值对应的置信区间特别宽。我想明确：ggpredict()是否会基于单个数据点生成预测值？

可复现示例 #

以下示例强制将数据的最小值和最大值设为仅含单个观测值，数据基于ggeffects官方示例数据集：

library(ggeffects)
#> Warning: package 'ggeffects' was built under R version 4.2.3
library(tidyverse)
#> Warning: package 'ggplot2' was built under R version 4.2.2
#> Warning: package 'tibble' was built under R version 4.2.3
#> Warning: package 'tidyr' was built under R version 4.2.3
#> Warning: package 'purrr' was built under R version 4.2.3
#> Warning: package 'dplyr' was built under R version 4.2.3
#> Warning: package 'stringr' was built under R version 4.2.3

data(efc)

summary(efc$c12hour)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
#>    4.00   10.00   20.00   42.40   42.75  168.00       6

c(sum(!is.na(efc$c12hour) & efc$c12hour == 4), sum(!is.na(efc$c12hour) & efc$c12hour == 168))
#> [1] 24 86

efc2 <- efc[!is.na(efc$c12hour), ]
efc2 <- arrange(efc2, c12hour)
efc2$c12hour[1] <- 3
efc2$c12hour[nrow(efc2)] <- 169
summary(efc2$c12hour)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    3.00   10.00   20.00   42.40   42.75  169.00
table(efc2$c12hour)
#> 
#>   3   4   5   6   7   8   9  10  11  12  14  15  16  17  18  20  21  22  24  25 
#>   1  23  29  60  32  42  11  70   6  23  34  43  12   4  16  58  12  11  20  28 
#>  26  27  28  30  35  36  39  40  42  43  45  48  49  50  55  56  59  60  62  65 
#>   1   2  28  40  24   1   1  35   9   1   4   4   2  22   1   6   1   7   1   3 
#>  70  77  80  84  85  89  90  91 100 105 110 118 119 120 125 126 128 130 140 148 
#>  13   3   5   6   2   1   2   3  13   1   4   1   1   8   1   1   1   1   6   1 
#> 150 154 160 161 162 168 169 
#>   4   1   6   2   1  85   1

fit <- lm(barthtot ~ c12hour * c161sex + neg_c_7, data = efc2)
plot(ggpredict(fit, terms = c("c161sex", "c12hour[quart]")))

会话信息 #

sessionInfo()
#> R version 4.2.0 (2022-04-22 ucrt)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 22000)
#> 
#> Matrix products: default
#> 
#> locale:
#> [1] LC_COLLATE=Spanish_Spain.utf8  LC_CTYPE=Spanish_Spain.utf8   
#> [3] LC_MONETARY=Spanish_Spain.utf8 LC_NUMERIC=C                  
#> [5] LC_TIME=Spanish_Spain.utf8    
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] forcats_0.5.1     stringr_1.5.0     dplyr_1.1.2       purrr_1.0.1      
#>  [5] readr_2.1.2       tidyr_1.3.0       tibble_3.2.1      ggplot2_3.4.0    
#>  [9] tidyverse_1.3.1   ggeffects_1.3.1.5
#> 
#> loaded via a namespace (and not attached):
#>  [1] lubridate_1.8.0  assertthat_0.2.1 digest_0.6.29    utf8_1.2.2      
#>  [5] R6_2.5.1         cellranger_1.1.0 backports_1.4.1  reprex_2.0.2    
#>  [9] evaluate_0.15    httr_1.4.3       highr_0.9        pillar_1.9.0    
#> [13] rlang_1.1.1      readxl_1.4.0     rstudioapi_0.13  rmarkdown_2.14  
#> [17] labeling_0.4.2   munsell_0.5.0    broom_0.8.0      compiler_4.2.0  
#> [21] modelr_0.1.8     xfun_0.40        pkgconfig_2.0.3  htmltools_0.5.6 
#> [25] insight_0.19.5   tidyselect_1.2.0 fansi_1.0.3      crayon_1.5.1    
#> [29] tzdb_0.3.0       dbplyr_2.1.1     withr_2.5.0      grid_4.2.0      
#> [33] jsonlite_1.8.0   gtable_0.3.0     lifecycle_1.0.3  DBI_1.1.2       
#> [37] magrittr_2.0.3   scales_1.2.1     datawizard_0.7.0 cli_3.6.1       
#> [41] stringi_1.7.6    farver_2.1.0     fs_1.5.2         snakecase_0.11.0
#> [45] xml2_1.3.3       ellipsis_0.3.2   generics_0.1.2   vctrs_0.6.3     
#> [49] sjlabelled_1.2.0 tools_4.2.0      glue_1.6.2       hms_1.1.1       
#> [53] fastmap_1.1.0    yaml_2.3.5       colorspace_2.0-3 rvest_1.0.2     
#> [57] knitr_1.39       haven_2.5.0

Created on 2023-10-05 with reprex v2.0.2

内容的提问来源于stack exchange，提问作者M. Riera