R：如何“提取” Dunnetts 检验的 p 值（方差分析后的事后）

Question

我是一名生物化学家，与 R 作为非专业人士一起工作，现在遇到了问题。 我有一个 dataframe，我想比较我的不同治疗组和阳性对照与中等对照。 我想使用的统计检验是方差分析，然后是 Dunnetts 检验。 我为此使用了multcomp - 和DescTools - 包，然后我用这段代码到达了那里

Particle <- factor(c("Medium", "PosCon", "Trt1", "Trt2", "Trt3", "Medium", "PosCon", "Trt1", "Trt2", "Trt3", "Medium", "PosCon", "Trt1", "Trt2", "Trt3"))
Values <- c(1.0, 263.0, 3.1, 1.2, 0.9, 1.0, 244.0, 2.4, 1.6, 1.1, 1.0, 255.0, 3.8, 2.0, 0.8)

myDataframe <- data.frame(Particle, Values)

str(myDataframe)

a1 <- aov(Values ~ Particle, data= myDataframe) 
summary(a1)

#Output
#            Df Sum Sq Mean Sq F value   Pr(>F)    
#Particle     4 152832   38208    2084 1.48e-14 ***
#Residuals   10    183      18                     
#---
#Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

myDataframe.dunnett <- glht(a1, linfct = mcp(Particle= "Dunnett"))
myDataframe.dunnett


summary(myDataframe.dunnett)
# Output:
#    Simultaneous Tests for General Linear Hypotheses
#
#Multiple Comparisons of Means: Dunnett Contrasts
#
#
#Fit: aov(formula = Values ~ Particle, data = myDataframe)
#
#Linear Hypotheses:
#                      Estimate Std. Error t value Pr(>|t|)    
#PosCon - Medium == 0 253.00000    3.49616  72.365   <0.001 ***
#Trt1 - Medium == 0     2.10000    3.49616   0.601    0.930    
#Trt2 - Medium == 0     0.60000    3.49616   0.172    0.999    
#Trt3 - Medium == 0    -0.06667    3.49616  -0.019    1.000    
#---
#Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#(Adjusted p values reported -- single-step method)

现在我想获取提取的 p 值（或 Pr(>|t|)），并且我想将它们作为四位数字（三个也可以）。 我使用str(summary(myDataframe.dunnett))和names(summary(myDataframe.dunnett))来了解要提取什么，但是当我提取它时，它是一个没有数字的数字，因为：

str(summary(myDataframe.dunnett))
names(summary(myDataframe.dunnett)) #just to get to know the names

x <- summary(myDataframe.dunnett)$test$pvalues
x
[1] 0 1 1 1
attr(,"error")
[1] 0.0001612462

有谁知道那是什么，或者知道在 Anova 和 Dunnetts 测试到向量后“提取”显着性水平的更好方法？ 我需要那些将它们转换为 plot 上方的重要星。

我觉得这可能会有所帮助，但我不知道如何为我的数据修改它：

谢谢你的帮助！

Answer 1

如果您查看摘要 object 的结构，您可以看到您希望提取的值位于名为test的列表元素中

str( summary(myDataframe.dunnett) )
List of 10
 $ model      :List of 13
  ..$ coefficients : Named num [1:5] 1 253 2.1 0.6 -0.0667
  .. ..- attr(*, "names")= chr [1:5] "(Intercept)" "ParticlePosCon" "ParticleTrt1" "ParticleTrt2" ...
  ..$ residuals    : Named num [1:15] -1.48e-15 9.00 1.92e-15 -4.00e-01 -3.33e-02 ...
  .. ..- attr(*, "names")= chr [1:15] "1" "2" "3" "4" ...
  ..$ effects      : Named num [1:15] -201.8857 -390.926 -2.8833 -0.8957 0.0816 ...
  .. ..- attr(*, "names")= chr [1:15] "(Intercept)" "ParticlePosCon" "ParticleTrt1" "ParticleTrt2" ...
  ..$ rank         : int 5
  ..$ fitted.values: Named num [1:15] 1 254 3.1 1.6 0.933 ...
  .. ..- attr(*, "names")= chr [1:15] "1" "2" "3" "4" ...
  ..$ assign       : int [1:5] 0 1 1 1 1
  ..$ qr           :List of 5
  .. ..$ qr   : num [1:15, 1:5] -3.873 0.258 0.258 0.258 0.258 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:15] "1" "2" "3" "4" ...
  .. .. .. ..$ : chr [1:5] "(Intercept)" "ParticlePosCon" "ParticleTrt1" "ParticleTrt2" ...
  .. .. ..- attr(*, "assign")= int [1:5] 0 1 1 1 1
  .. .. ..- attr(*, "contrasts")=List of 1
  .. .. .. ..$ Particle: chr "contr.treatment"
  .. ..$ qraux: num [1:5] 1.26 1.54 1.54 1.53 1.52
  .. ..$ pivot: int [1:5] 1 2 3 4 5
  .. ..$ tol  : num 1e-07
  .. ..$ rank : int 5
  .. ..- attr(*, "class")= chr "qr"
  ..$ df.residual  : int 10
  ..$ contrasts    :List of 1
  .. ..$ Particle: chr "contr.treatment"
  ..$ xlevels      :List of 1
  .. ..$ Particle: chr [1:5] "Medium" "PosCon" "Trt1" "Trt2" ...
  ..$ call         : language aov(formula = Values ~ Particle, data = myDataframe)
  ..$ terms        :Classes 'terms', 'formula'  language Values ~ Particle
  .. .. ..- attr(*, "variables")= language list(Values, Particle)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "Values" "Particle"
  .. .. .. .. ..$ : chr "Particle"
  .. .. ..- attr(*, "term.labels")= chr "Particle"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
  .. .. ..- attr(*, "predvars")= language list(Values, Particle)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "factor"
  .. .. .. ..- attr(*, "names")= chr [1:2] "Values" "Particle"
  ..$ model        :'data.frame':   15 obs. of  2 variables:
  .. ..$ Values  : num [1:15] 1 263 3.1 1.2 0.9 1 244 2.4 1.6 1.1 ...
  .. ..$ Particle: Factor w/ 5 levels "Medium","PosCon",..: 1 2 3 4 5 1 2 3 4 5 ...
  .. ..- attr(*, "terms")=Classes 'terms', 'formula'  language Values ~ Particle
  .. .. .. ..- attr(*, "variables")= language list(Values, Particle)
  .. .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. .. ..$ : chr [1:2] "Values" "Particle"
  .. .. .. .. .. ..$ : chr "Particle"
  .. .. .. ..- attr(*, "term.labels")= chr "Particle"
  .. .. .. ..- attr(*, "order")= int 1
  .. .. .. ..- attr(*, "intercept")= int 1
  .. .. .. ..- attr(*, "response")= int 1
  .. .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
  .. .. .. ..- attr(*, "predvars")= language list(Values, Particle)
  .. .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "factor"
  .. .. .. .. ..- attr(*, "names")= chr [1:2] "Values" "Particle"
  ..- attr(*, "class")= chr [1:2] "aov" "lm"
 $ linfct     : num [1:4, 1:5] 0 0 0 0 1 0 0 0 0 1 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : Named chr [1:4] "PosCon - Medium" "Trt1 - Medium" "Trt2 - Medium" "Trt3 - Medium"
  .. .. ..- attr(*, "names")= chr [1:4] "Particle1" "Particle2" "Particle3" "Particle4"
  .. ..$ : chr [1:5] "(Intercept)" "ParticlePosCon" "ParticleTrt1" "ParticleTrt2" ...
  ..- attr(*, "type")= chr "Dunnett"
 $ rhs        : num [1:4] 0 0 0 0
 $ coef       : Named num [1:5] 1 253 2.1 0.6 -0.0667
  ..- attr(*, "names")= chr [1:5] "(Intercept)" "ParticlePosCon" "ParticleTrt1" "ParticleTrt2" ...
 $ vcov       : num [1:5, 1:5] 6.11 -6.11 -6.11 -6.11 -6.11 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:5] "(Intercept)" "ParticlePosCon" "ParticleTrt1" "ParticleTrt2" ...
  .. ..$ : chr [1:5] "(Intercept)" "ParticlePosCon" "ParticleTrt1" "ParticleTrt2" ...
 $ df         : int 10
 $ alternative: chr "two.sided"
 $ type       : chr "Dunnett"
 $ focus      : chr "Particle"
 $ test       :List of 7
  ..$ pfunction   :function (type = c("univariate", "adjusted", p.adjust.methods), ...)  
  ..$ qfunction   :function (conf.level, adjusted = TRUE, ...)  
  ..$ coefficients: Named num [1:4] 253 2.1 0.6 -0.0667
  .. ..- attr(*, "names")= chr [1:4] "PosCon - Medium" "Trt1 - Medium" "Trt2 - Medium" "Trt3 - Medium"
  ..$ sigma       : Named num [1:4] 3.5 3.5 3.5 3.5
  .. ..- attr(*, "names")= chr [1:4] "PosCon - Medium" "Trt1 - Medium" "Trt2 - Medium" "Trt3 - Medium"
  ..$ tstat       : Named num [1:4] 72.3652 0.6007 0.1716 -0.0191
  .. ..- attr(*, "names")= chr [1:4] "PosCon - Medium" "Trt1 - Medium" "Trt2 - Medium" "Trt3 - Medium"
  ..$ pvalues     : num [1:4] 0 0.93 0.999 1
  .. ..- attr(*, "error")= num 0.000305
  ..$ type        : chr "single-step"
  ..- attr(*, "class")= chr "mtest"
 - attr(*, "class")= chr [1:2] "summary.glht" "glht"

...并且test列表本身就是一个相当复杂的列表，...

> ( summary(myDataframe.dunnett)$test )
$pfunction
function (type = c("univariate", "adjusted", p.adjust.methods), 
    ...) 
{
    type <- match.arg(type)
    pfct <- function(q) {
        switch(object$alternative, two.sided = {
            low <- rep(-abs(q), dim)
            upp <- rep(abs(q), dim)
        }, less = {
            low <- rep(q, dim)
            upp <- rep(Inf, dim)
        }, greater = {
            low <- rep(-Inf, dim)
            upp <- rep(q, dim)
        })
        pmvt(lower = low, upper = upp, df = df, corr = cr, ...)
    }
    switch(object$alternative, two.sided = {
        if (df > 0) pvals <- 2 * (1 - pt(abs(tstat), df)) else pvals <- 2 * 
            (1 - pnorm(abs(tstat)))
    }, less = {
        if (df > 0) pvals <- pt(tstat, df) else pvals <- pnorm(tstat)
    }, greater = {
        if (df > 0) pvals <- 1 - pt(tstat, df) else pvals <- 1 - 
            pnorm(tstat)
    })
    if (type == "univariate") 
        return(pvals)
    if (type == "adjusted") {
        ret <- numeric(length(tstat))
        error <- 0
        for (i in 1:length(tstat)) {
            tmp <- pfct(tstat[i])
            if (attr(tmp, "msg") != "Normal Completion" && length(grep("^univariate", 
                attr(tmp, "msg"))) == 0) 
                warning(attr(tmp, "msg"))
            if (error < attr(tmp, "error")) 
                error <- attr(tmp, "error")
            ret[i] <- tmp
        }
        ret <- 1 - ret
        attr(ret, "error") <- error
        return(ret)
    }
    return(p.adjust(pvals, method = type))
}
<bytecode: 0x55c5419b3f18>
<environment: 0x55c540a7c100>

$qfunction
function (conf.level, adjusted = TRUE, ...) 
{
    tail <- switch(object$alternative, two.sided = "both.tails", 
        less = "lower.tail", greater = "upper.tail")
    if (adjusted) {
        calpha <- qmvt(conf.level, df = df, corr = cr, tail = tail, 
            ...)
    }
    else {
        calpha <- qmvt(conf.level, df = df, corr = matrix(1), 
            tail = tail, ...)
    }
    ret <- calpha$quantile
    attr(ret, "error") <- calpha$estim.prec
    return(ret)
}
<bytecode: 0x55c5419b9d20>
<environment: 0x55c540a7c100>

$coefficients
PosCon - Medium   Trt1 - Medium   Trt2 - Medium   Trt3 - Medium 
   253.00000000      2.10000000      0.60000000     -0.06666667 

$sigma
PosCon - Medium   Trt1 - Medium   Trt2 - Medium   Trt3 - Medium 
       3.496157        3.496157        3.496157        3.496157 

$tstat
PosCon - Medium   Trt1 - Medium   Trt2 - Medium   Trt3 - Medium 
    72.36517911      0.60065959      0.17161703     -0.01906856 

$pvalues
[1] 0.0000000 0.9298994 0.9992779 0.9999999
attr(,"error")
[1] 0.0001864546

$type
[1] "single-step"

attr(,"class")
[1] "mtest"

...并且 p 值位于名为pvalues的元素中该列表的子列表中：

> ( summary(myDataframe.dunnett)$test$pvalues )
[1] 0.0000000 0.9299389 0.9992772 0.9999999
attr(,"error")
[1] 0.0001414288

Answer 2

... 或使用 DescTools function DunnetTest

(z <- DunnettTest(formula(a1$call)))

##   Dunnett's test for comparing several treatments with a control :  
##     95% family-wise confidence level
## 
## $Medium
##                       diff     lwr.ci    upr.ci   pval    
## PosCon-Medium 253.00000000 242.883587 263.11641 <2e-16 ***
## Trt1-Medium     2.10000000  -8.016413  12.21641 0.9299    
## Trt2-Medium     0.60000000  -9.516413  10.71641 0.9993    
## Trt3-Medium    -0.06666667 -10.183079  10.04975 1.0000    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

z$Medium[,"pval"]

## PosCon-Medium   Trt1-Medium   Trt2-Medium   Trt3-Medium 
##     0.0000000     0.9298875     0.9992763     0.9999999