线没有出现在交互图中 - y 轴太短?

Line not showing up in interactionplot - y-axis too short?

我正在尝试根据因子设计的数据创建交互图,但线条没有显示出来。我的数据框是

dr
   Nr. Bruch Keimf Einw Temp Zeit
1   h1  2.63    54    0   30    4
2   h2  1.71    51    4   30    4
3   h3  2.37    56    0   50    4
4   h4  4.00    51    4   50    4
5   h5  1.63    55    0   30   10
6   h6  1.47    55    4   30   10
7   h7  3.11    43    0   50   10
8   h8  2.42    60    4   50   10
9   c1  2.07    51    2   40    7
10  c2  2.37    46    2   40    7
11  c3  2.48    39    2   40    7

我的剧情代码是

dr$temp=factor(dr$Temp)
interaction.plot(dr$Zeit,dr$Temp,dr$Keimf, 
                 main="Interactionplot Zeit*Temp",
                 xlab="Zeit (h)", ylab="Keimf (%)", col="olivedrab3", lwd=3, trace.label=deparse(substitute(Temperatur)))

我得到了预期的下图,但没有显示线条

[][1]


  [1]: https://i.stack.imgur.com/u18KA.png

我检查了 https://rdrr.io/r/stats/interaction.plot.html 并认为问题可能在于 yaxis 没有涵盖所有值,但是添加 ylim=c(30,65) 导致出现错误消息并且无法正常工作。我在论坛 中找到了另一个 interactionplot 的例子,但是整个代码太嵌套太复杂而无法通过它,因为它是 r 的新手。您认为 yaxis 是问题所在还是我监督了其他事情?

根据您的评论,是的,这是缺少数据。这是一个模拟数据示例,显示您的代码很好,还有您可能喜欢的自定义交互绘图功能。

伪造一些数据。使用您的代码。


mock_dr <- data.frame(
   Temp = sample(x = c(30, 40, 50), size = 45, replace = TRUE),
   Zeit = sample(x = c(4, 7, 10), size = 45, replace = TRUE),
   Keimf = sample(x = 39:60, size = 45, replace = TRUE)
)

interaction.plot(mock_dr$Zeit, mock_dr$Temp, mock_dr$Keimf,
                 main="Interactionplot Zeit*Temp",
                 xlab="Zeit (h)", ylab="Keimf (%)", col="olivedrab3", lwd=3, trace.label=deparse(substitute(Temperatur)))

您认为将来有用的自定义函数

CGPfunctions::Plot2WayANOVA(Keimf ~ Zeit * Temp, mock_dr)


#> Converting Zeit to a factor --- check your results
#> 
#> Converting Temp to a factor --- check your results
#> Warning in qt(confidence/2 + 0.5, n() - 1): NaNs produced
#> 
#>              --- WARNING! ---
#>      You have an unbalanced design. Using Type II sum of 
#>             squares, to calculate factor effect sizes eta and omega.
#>             Your two factors account for 0.204 of the type II sum of 
#>             squares.
#>                term    sumsq  meansq df statistic p.value etasq partial.etasq
#> Zeit           Zeit  203.060 101.530  2     2.818   0.073 0.125         0.135
#> Temp           Temp  102.773  51.386  2     1.426   0.253 0.063         0.073
#> Zeit:Temp Zeit:Temp   27.253   6.813  4     0.189   0.943 0.017         0.021
#> ...4      Residuals 1297.086  36.030 36        NA      NA    NA            NA
#>           omegasq partial.omegasq epsilonsq cohens.f power
#> Zeit        0.079           0.075     0.080    0.396 0.554
#> Temp        0.018           0.019     0.019    0.281 0.307
#> Zeit:Temp  -0.070          -0.078    -0.072    0.145 0.091
#> ...4           NA              NA        NA       NA    NA
#> 
#> Measures of overall model fit
#> # A tibble: 1 x 5
#>   logLik   AIC   BIC deviance  nobs
#>    <dbl> <dbl> <dbl>    <dbl> <int>
#> 1  -139.  299.  317.    1297.    45
#> 
#> Table of group means
#> # A tibble: 9 x 15
#> # Groups:   Zeit [3]
#>   Zeit  Temp  TheMean TheSD TheSEM CIMuliplier LowerBoundCI UpperBoundCI
#>   <fct> <fct>   <dbl> <dbl>  <dbl>       <dbl>        <dbl>        <dbl>
#> 1 4     30       53.2  3.27   1.46        2.78         49.1         57.3
#> 2 4     40       49   NA     NA         NaN            NA           NA  
#> 3 4     50       54.3  3.21   1.86        4.30         46.3         62.3
#> 4 7     30       48.7  5.05   2.06        2.57         43.4         54.0
#> 5 7     40       48    6.32   2.58        2.57         41.4         54.6
#> 6 7     50       52.6  4.83   2.16        2.78         46.6         58.6
#> 7 10    30       47.9  7.54   2.85        2.45         40.9         54.8
#> 8 10    40       44.7  7.59   2.87        2.45         37.7         51.7
#> 9 10    50       47.8  5.63   2.52        2.78         40.8         54.8
#> # … with 7 more variables: LowerBoundSEM <dbl>, UpperBoundSEM <dbl>,
#> #   LowerBoundSD <dbl>, UpperBoundSD <dbl>, N <int>, LowerBound <dbl>,
#> #   UpperBound <dbl>
#> 
#> Post hoc tests for all effects that were significant
#> [1] "No signfiicant effects"
#> 
#> Testing Homogeneity of Variance with Brown-Forsythe
#> Levene's Test for Homogeneity of Variance (center = median)
#>       Df F value Pr(>F)
#> group  8  0.5877 0.7812
#>       36
#> 
#> Testing Normality Assumption with Shapiro-Wilk
#> 
#>  Shapiro-Wilk normality test
#> 
#> data:  MyAOV_residuals
#> W = 0.95464, p-value = 0.07623
#> 
#> Interaction graph plotted...