The function performs the Dunnett test

dunnett(
  trat,
  resp,
  control,
  model = "DIC",
  block = NA,
  column = NA,
  line = NA,
  alpha.t = 0.05,
  pointsize = 5,
  pointshape = 21,
  linesize = 1,
  labelsize = 4,
  textsize = 12,
  errorsize = 1,
  widthsize = 0.2,
  label = "Response",
  fontfamily = "sans"
)

Arguments

trat

Numerical or complex vector with treatments

resp

Numerical vector containing the response of the experiment.

control

Treatment considered control (write identical to the name in the vector)

model

Experimental design (DIC, DBC or DQL)

block

Numerical or complex vector with blocks

column

Numerical or complex vector with columns

line

Numerical or complex vector with lines

alpha.t

Significance level (default is 0.05)

pointsize

Point size

pointshape

Shape

linesize

Line size

labelsize

Label size

textsize

Font size

errorsize

Errorbar size

widthsize

Width errorbar

label

Variable label

fontfamily

font family

Value

I return the Dunnett test for experiments in a completely randomized design, randomized blocks or Latin square.

Note

Do not use the "-" symbol or space in treatment names

Examples


#====================================================
# complete randomized design
#====================================================
data("pomegranate")
with(pomegranate,dunnett(trat=trat,resp=WL,control="T1"))
#>           Estimate     IC-lwr    IC-upr t value p-value sig
#> T1  -  T2  -0.1650 -0.6427072 0.3127072 -0.9528  0.8034  ns
#> T1  -  T3   0.7750  0.2972928 1.2527072  4.4753  0.0014   *
#> T1  -  T4   0.7775  0.2997928 1.2552072  4.4898  0.0012   *
#> T1  -  T5   0.7950  0.3172928 1.2727072  4.5908  0.0010   *
#> T1  -  T6   0.3200 -0.1577072 0.7977072  1.8479  0.2668  ns


#====================================================
# randomized block design in factorial double
#====================================================
library(AgroR)
data(cloro)
attach(cloro)
#> The following object is masked from simulate3:
#> 
#>     resp
#> The following object is masked from simulate1:
#> 
#>     resp
#> The following object is masked from aristolochia (pos = 5):
#> 
#>     resp
#> The following objects are masked from simulate2:
#> 
#>     bloco, resp
#> The following objects are masked from laranja:
#> 
#>     bloco, resp
#> The following object is masked from aristolochia (pos = 8):
#> 
#>     resp
#> The following objects are masked from cloro (pos = 9):
#> 
#>     bloco, f1, f2, resp
#> The following object is masked from passiflora:
#> 
#>     bloco
respAd=c(268, 322, 275, 350, 320)
a=FAT2DBC.ad(f1, f2, bloco, resp, respAd,
             ylab="Number of nodules",
             legend = "Stages",mcomp="sk")
#> 
#> -----------------------------------------------------------------
#> Normality of errors
#> -----------------------------------------------------------------
#>                          Method Statistic   p.value
#>  Shapiro-Wilk normality test(W) 0.9548911 0.1117923
#> 
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, errors can be considered normal
#> 
#> -----------------------------------------------------------------
#> Homogeneity of Variances
#> -----------------------------------------------------------------
#>                               Method Statistic    p.value
#>  Bartlett test(Bartlett's K-squared)  16.11086 0.02412261
#> 
#> As the calculated p-value is less than the 5% significance level, H0 is rejected. Therefore, the variances are not homogeneous
#> 
#> -----------------------------------------------------------------
#> Independence from errors
#> -----------------------------------------------------------------
#>                  Method Statistic   p.value
#>  Durbin-Watson test(DW)  2.047899 0.1769663
#> 
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, errors can be considered independent
#> 
#> -----------------------------------------------------------------
#> Additional Information
#> -----------------------------------------------------------------
#> 
#> CV (%) =  27.38
#> Mean Factorial =  218.35
#> Median Factorial =  185
#> Mean Aditional =  307
#> Median Aditional =  320
#> Possible outliers =  No discrepant point
#> 
#> -----------------------------------------------------------------
#> Analysis of Variance
#> -----------------------------------------------------------------
#>                Df   Sum Sq    Mean.Sq     F value        Pr(F)
#> Fator1          1  16160.4  16160.400   4.1407431 4.927778e-02
#> Fator2          3 116554.5  38851.500   9.9548327 6.428916e-05
#> block           4  11613.6   2903.400   0.7439317 5.684322e-01
#> Fator1:Fator2   3 452096.2 150698.733  38.6131986 2.411216e-11
#> Ad x Factorial  1 475410.7 475410.700 121.8134178 4.174439e-13
#> Residuals      36 140500.0   3902.778                         
#> 
#> 
#> Your analysis is not valid, suggests using a non-parametric test and try to transform the data
#> 
#> -----------------------------------------------------------------
#> Significant interaction: analyzing the interaction
#> -----------------------------------------------------------------
#>                          Df Sum Sq Mean Sq F value    Pr(>F)    
#> Fator2                    3 116554   38851  9.9548 6.429e-05 ***
#> block                     4  11614    2903  0.7439 0.5684322    
#> Fator2:Fator1             4 468257  117064 29.9951 5.126e-11 ***
#>   Fator2:Fator1: Plantio  1  26112   26112  6.6906 0.0138814 *  
#>   Fator2:Fator1: R1+15    1  70896   70896 18.1656 0.0001393 ***
#>   Fator2:Fator1: V1+15    1 258888  258888 66.3343 1.101e-09 ***
#>   Fator2:Fator1: V3+15    1 112360  112360 28.7898 4.897e-06 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> -----------------------------------------------------------------
#> Analyzing  F2  inside of the level of  F1
#> -----------------------------------------------------------------
#> 
#>                     Df Sum Sq Mean Sq F value    Pr(>F)    
#> Fator1               1  16160   16160  4.1407  0.049278 *  
#> block                4  11614    2903  0.7439  0.568432    
#> Fator1:Fator2        6 568651   94775 24.2840 2.774e-11 ***
#>   Fator1:Fator2: IN  3  75470   25157  6.4458  0.001317 ** 
#>   Fator1:Fator2: NI  3 493181  164394 42.1222 7.282e-12 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> -----------------------------------------------------------------
#> Final table
#> -----------------------------------------------------------------
#>     Plantio    R1+15    V1+15    V3+15
#> IN 272.8 aA 236.6 aA 140.4 bB 304.0 aA
#> NI 170.6 bB  68.2 bC 462.2 aA  92.0 bC
#> 
#> 
#> Averages followed by the same lowercase letter in the column and 
#> uppercase in the row do not differ by the sk (p< 0.05 )

data=rbind(data.frame(trat=paste(f1,f2,sep = ""),bloco=bloco,resp=resp),
           data.frame(trat=c("Test","Test","Test","Test","Test"),
                      bloco=unique(bloco),resp=respAd))
with(data,dunnett(trat = trat,
                  resp = resp,
                  control = "Test",
                  block=bloco,model = "DBC"))
#>                    Estimate     IC-lwr     IC-upr t value p-value sig
#> Test  -  INPlantio    -34.2 -148.76741   80.36741 -0.8376  0.9479  ns
#> Test  -  INV1+15     -166.6 -281.16741  -52.03259 -4.0802  0.0020   *
#> Test  -  INV3+15       -3.0 -117.56741  111.56741 -0.0735  1.0000  ns
#> Test  -  INR1+15      -70.4 -184.96741   44.16741 -1.7242  0.4040  ns
#> Test  -  NIPlantio   -136.4 -250.96741  -21.83259 -3.3405  0.0137   *
#> Test  -  NIV1+15      155.2   40.63259  269.76741  3.8010  0.0042   *
#> Test  -  NIV3+15     -215.0 -329.56741 -100.43259 -5.2655  0.0001   *
#> Test  -  NIR1+15     -238.8 -353.36741 -124.23259 -5.8484  0.0000   *