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我的设计不平衡，因此当我emmeans在特定级别应用于我的模型时，缺少的嵌套因子（存在于其他级别）被标记为nonEst在我的输出表中。如何更改我的代码，以便下表仅显示三个可估计的行？

emmeans(model, specs = ~ Rot/Crop | Herb, at = list(Rot = "3", Herb="conv"))
Herb = conv:
 Rot Crop    emmean    SE  df lower.CL upper.CL
 3   alfalfa nonEst    NA  NA       NA       NA
 3   corn      3.50 0.283 270     2.94     4.06
 3   oat       3.44 0.283 270     2.88     3.99
 3   soybean   2.65 0.253 270     2.15     3.15

Confidence level used: 0.95
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1 回答 1

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一个选项是tidy它，broom然后删除NA行na.omit

library(emmeans)
library(broom)
library(dplyr)
emmeans(model, specs = ~ Rot/Crop | Herb, at = list(Rot = "3", Herb="conv")) %>% 
            tidy %>%
            na.omit

或与as.data.frame/subset

subset(as.data.frame(  emmeans(model, specs = ~ Rot/Crop | Herb,
          at = list(Rot = "3", Herb="conv"))),  !is.na(emmean))

使用可重现的示例

warp.lm <- lm(breaks ~ wool * tension, data = head(warpbreaks, 30))
emmeans (warp.lm,  ~ wool | tension)
#tension = L:
# wool emmean   SE df lower.CL upper.CL
# A      44.6 4.24 26    35.85     53.3
# B      23.3 7.34 26     8.26     38.4

#tension = M:
# wool emmean   SE df lower.CL upper.CL
# A      24.0 4.24 26    15.29     32.7
# B    nonEst   NA NA       NA       NA

#tension = H:
# wool emmean   SE df lower.CL upper.CL
# A      24.6 4.24 26    15.85     33.3
# B    nonEst   NA NA       NA       NA


emmeans (warp.lm,  ~ wool | tension) %>% 
     tidy %>% 
     na.omit
# A tibble: 4 x 7
#  wool  tension estimate std.error    df statistic  p.value
#  <chr> <chr>      <dbl>     <dbl> <dbl>     <dbl>    <dbl>
#1 A     L           44.6      4.24    26     10.5  7.29e-11
#2 B     L           23.3      7.34    26      3.18 3.78e- 3
#3 A     M           24.0      4.24    26      5.67 5.84e- 6
#4 A     H           24.6      4.24    26      5.80 4.15e- 6

或在 中，将base R其强制为非 NA 行data.framesubset

subset(as.data.frame(emmeans (warp.lm,  ~ wool | tension)), !is.na(emmean))
#  wool tension   emmean       SE df  lower.CL upper.CL
#1    A       L 44.55556 4.235135 26 35.850110 53.26100
#2    B       L 23.33333 7.335470 26  8.255059 38.41161
#3    A       M 24.00000 4.235135 26 15.294554 32.70545
#5    A       H 24.55556 4.235135 26 15.850110 33.26100
于 2020-07-21T22:24:03.123 回答