freqdist.augmentedRCBD plots frequency distribution from an object of class augmentedRCBD along with the corresponding normal curve and check means with standard errors (if specified by argument highlight.check).

freqdist.augmentedRCBD(aug, xlab, highlight.check = TRUE, check.col = "red")

Arguments

aug

An object of class augmentedRCBD.

xlab

The text for x axis label as a character string.

highlight.check

If TRUE, the check means and standard errors are also plotted. Default is TRUE.

check.col

The colour(s) to be used to highlight check values in the plot as a character vector. Must be valid colour values in R (named colours, hexadecimal representation, index of colours [1:8] in default R palette() etc.).

Value

The frequency distribution plot as a ggplot2 plot grob.

See also

Examples

# Example data
blk <- c(rep(1,7),rep(2,6),rep(3,7))
trt <- c(1, 2, 3, 4, 7, 11, 12, 1, 2, 3, 4, 5, 9, 1, 2, 3, 4, 8, 6, 10)
y1 <- c(92, 79, 87, 81, 96, 89, 82, 79, 81, 81, 91, 79, 78, 83, 77, 78, 78,
        70, 75, 74)
y2 <- c(258, 224, 238, 278, 347, 300, 289, 260, 220, 237, 227, 281, 311, 250,
        240, 268, 287, 226, 395, 450)
data <- data.frame(blk, trt, y1, y2)
# Convert block and treatment to factors
data$blk <- as.factor(data$blk)
data$trt <- as.factor(data$trt)
# Results for variable y1
out1 <- augmentedRCBD(data$blk, data$trt, data$y1, method.comp = "lsd",
                      alpha = 0.05, group = TRUE, console = TRUE)
#> 
#> Augmented Design Details
#> ========================
#>                                        
#> Number of blocks           "3"         
#> Number of treatments       "12"        
#> Number of check treatments "4"         
#> Number of test treatments  "8"         
#> Check treatments           "1, 2, 3, 4"
#> 
#> 
#> ANOVA, Treatment Adjusted
#> =========================
#>                                      Df Sum Sq Mean Sq F value Pr(>F)  
#> Block (ignoring Treatments)           2  360.1  180.04   6.675 0.0298 *
#> Treatment (eliminating Blocks)       11  285.1   25.92   0.961 0.5499  
#>   Treatment: Check                    3   52.9   17.64   0.654 0.6092  
#>   Treatment: Test and Test vs. Check  8  232.2   29.02   1.076 0.4779  
#> Residuals                             6  161.8   26.97                 
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> ANOVA, Block Adjusted
#> =====================
#>                                Df Sum Sq Mean Sq F value Pr(>F)
#> Treatment (ignoring Blocks)    11  575.7   52.33   1.940  0.215
#>   Treatment: Check              3   52.9   17.64   0.654  0.609
#>   Treatment: Test               7  505.9   72.27   2.679  0.125
#>   Treatment: Test vs. Check     1   16.9   16.87   0.626  0.459
#> Block (eliminating Treatments)  2   69.5   34.75   1.288  0.342
#> Residuals                       6  161.8   26.97               
#> 
#> Coefficient of Variation
#> ========================
#> 6.372367
#> 
#> Overall Adjusted Mean
#> =====================
#> 81.0625
#> 
#> Standard Errors
#> ===============
#>                                          Std. Error of Diff.  CD (5%)
#> Control Treatment Means                             4.240458 10.37603
#> Two Test Treatments (Same Block)                    7.344688 17.97180
#> Two Test Treatments (Different Blocks)              8.211611 20.09309
#> A Test Treatment and a Control Treatment            6.704752 16.40594
#> 
#> Treatment Means
#> ===============
#>  Treatment Block Means   SE r   Min   Max Adjusted Means
#>          1       84.67 3.84 3 79.00 92.00          84.67
#>         10     3 74.00 <NA> 1 74.00 74.00          77.25
#>         11     1 89.00 <NA> 1 89.00 89.00          86.50
#>         12     1 82.00 <NA> 1 82.00 82.00          79.50
#>          2       79.00 1.15 3 77.00 81.00          79.00
#>          3       82.00 2.65 3 78.00 87.00          82.00
#>          4       83.33 3.93 3 78.00 91.00          83.33
#>          5     2 79.00 <NA> 1 79.00 79.00          78.25
#>          6     3 75.00 <NA> 1 75.00 75.00          78.25
#>          7     1 96.00 <NA> 1 96.00 96.00          93.50
#>          8     3 70.00 <NA> 1 70.00 70.00          73.25
#>          9     2 78.00 <NA> 1 78.00 78.00          77.25
#> 
#> 
#> Comparisons
#> ===========
#> 
#> Method : lsd
#> 
#>                   contrast estimate   SE df t.ratio p.value sig
#>    treatment1 - treatment2     5.67 4.24  6   1.336   0.230    
#>    treatment1 - treatment3     2.67 4.24  6   0.629   0.553    
#>    treatment1 - treatment4     1.33 4.24  6   0.314   0.764    
#>    treatment1 - treatment5     6.42 6.36  6   1.009   0.352    
#>    treatment1 - treatment6     6.42 6.36  6   1.009   0.352    
#>    treatment1 - treatment7    -8.83 6.36  6  -1.389   0.214    
#>    treatment1 - treatment8    11.42 6.36  6   1.795   0.123    
#>    treatment1 - treatment9     7.42 6.36  6   1.166   0.288    
#>   treatment1 - treatment10     7.42 6.36  6   1.166   0.288    
#>   treatment1 - treatment11    -1.83 6.36  6  -0.288   0.783    
#>   treatment1 - treatment12     5.17 6.36  6   0.812   0.448    
#>    treatment2 - treatment3    -3.00 4.24  6  -0.707   0.506    
#>    treatment2 - treatment4    -4.33 4.24  6  -1.022   0.346    
#>    treatment2 - treatment5     0.75 6.36  6   0.118   0.910    
#>    treatment2 - treatment6     0.75 6.36  6   0.118   0.910    
#>    treatment2 - treatment7   -14.50 6.36  6  -2.280   0.063    
#>    treatment2 - treatment8     5.75 6.36  6   0.904   0.401    
#>    treatment2 - treatment9     1.75 6.36  6   0.275   0.792    
#>   treatment2 - treatment10     1.75 6.36  6   0.275   0.792    
#>   treatment2 - treatment11    -7.50 6.36  6  -1.179   0.283    
#>   treatment2 - treatment12    -0.50 6.36  6  -0.079   0.940    
#>    treatment3 - treatment4    -1.33 4.24  6  -0.314   0.764    
#>    treatment3 - treatment5     3.75 6.36  6   0.590   0.577    
#>    treatment3 - treatment6     3.75 6.36  6   0.590   0.577    
#>    treatment3 - treatment7   -11.50 6.36  6  -1.808   0.121    
#>    treatment3 - treatment8     8.75 6.36  6   1.376   0.218    
#>    treatment3 - treatment9     4.75 6.36  6   0.747   0.483    
#>   treatment3 - treatment10     4.75 6.36  6   0.747   0.483    
#>   treatment3 - treatment11    -4.50 6.36  6  -0.707   0.506    
#>   treatment3 - treatment12     2.50 6.36  6   0.393   0.708    
#>    treatment4 - treatment5     5.08 6.36  6   0.799   0.455    
#>    treatment4 - treatment6     5.08 6.36  6   0.799   0.455    
#>    treatment4 - treatment7   -10.17 6.36  6  -1.598   0.161    
#>    treatment4 - treatment8    10.08 6.36  6   1.585   0.164    
#>    treatment4 - treatment9     6.08 6.36  6   0.956   0.376    
#>   treatment4 - treatment10     6.08 6.36  6   0.956   0.376    
#>   treatment4 - treatment11    -3.17 6.36  6  -0.498   0.636    
#>   treatment4 - treatment12     3.83 6.36  6   0.603   0.569    
#>    treatment5 - treatment6     0.00 8.21  6   0.000   1.000    
#>    treatment5 - treatment7   -15.25 8.21  6  -1.857   0.113    
#>    treatment5 - treatment8     5.00 8.21  6   0.609   0.565    
#>    treatment5 - treatment9     1.00 7.34  6   0.136   0.896    
#>   treatment5 - treatment10     1.00 8.21  6   0.122   0.907    
#>   treatment5 - treatment11    -8.25 8.21  6  -1.005   0.354    
#>   treatment5 - treatment12    -1.25 8.21  6  -0.152   0.884    
#>    treatment6 - treatment7   -15.25 8.21  6  -1.857   0.113    
#>    treatment6 - treatment8     5.00 7.34  6   0.681   0.521    
#>    treatment6 - treatment9     1.00 8.21  6   0.122   0.907    
#>   treatment6 - treatment10     1.00 7.34  6   0.136   0.896    
#>   treatment6 - treatment11    -8.25 8.21  6  -1.005   0.354    
#>   treatment6 - treatment12    -1.25 8.21  6  -0.152   0.884    
#>    treatment7 - treatment8    20.25 8.21  6   2.466   0.049   *
#>    treatment7 - treatment9    16.25 8.21  6   1.979   0.095    
#>   treatment7 - treatment10    16.25 8.21  6   1.979   0.095    
#>   treatment7 - treatment11     7.00 7.34  6   0.953   0.377    
#>   treatment7 - treatment12    14.00 7.34  6   1.906   0.105    
#>    treatment8 - treatment9    -4.00 8.21  6  -0.487   0.643    
#>   treatment8 - treatment10    -4.00 7.34  6  -0.545   0.606    
#>   treatment8 - treatment11   -13.25 8.21  6  -1.614   0.158    
#>   treatment8 - treatment12    -6.25 8.21  6  -0.761   0.475    
#>   treatment9 - treatment10     0.00 8.21  6   0.000   1.000    
#>   treatment9 - treatment11    -9.25 8.21  6  -1.126   0.303    
#>   treatment9 - treatment12    -2.25 8.21  6  -0.274   0.793    
#>  treatment10 - treatment11    -9.25 8.21  6  -1.126   0.303    
#>  treatment10 - treatment12    -2.25 8.21  6  -0.274   0.793    
#>  treatment11 - treatment12     7.00 7.34  6   0.953   0.377    
#> 
#> Treatment Groups
#> ================
#> 
#> Method : lsd
#> 
#>  Treatment Adjusted Means   SE df lower.CL upper.CL Group
#>          8          73.25 5.61  6    59.52    86.98    1 
#>          9          77.25 5.61  6    63.52    90.98    12
#>         10          77.25 5.61  6    63.52    90.98    12
#>          5          78.25 5.61  6    64.52    91.98    12
#>          6          78.25 5.61  6    64.52    91.98    12
#>          2          79.00 3.00  6    71.66    86.34    12
#>         12          79.50 5.61  6    65.77    93.23    12
#>          3          82.00 3.00  6    74.66    89.34    12
#>          4          83.33 3.00  6    76.00    90.67    12
#>          1          84.67 3.00  6    77.33    92.00    12
#>         11          86.50 5.61  6    72.77   100.23    12
#>          7          93.50 5.61  6    79.77   107.23     2
# Results for variable y2
out2 <- augmentedRCBD(data$blk, data$trt, data$y2, method.comp = "lsd",
                     alpha = 0.05, group = TRUE, console = TRUE)
#> 
#> Augmented Design Details
#> ========================
#>                                        
#> Number of blocks           "3"         
#> Number of treatments       "12"        
#> Number of check treatments "4"         
#> Number of test treatments  "8"         
#> Check treatments           "1, 2, 3, 4"
#> 
#> 
#> ANOVA, Treatment Adjusted
#> =========================
#>                                      Df Sum Sq Mean Sq F value   Pr(>F)    
#> Block (ignoring Treatments)           2   7019    3510  12.261 0.007597 ** 
#> Treatment (eliminating Blocks)       11  58965    5360  18.727 0.000920 ***
#>   Treatment: Check                    3   2150     717   2.504 0.156116    
#>   Treatment: Test and Test vs. Check  8  56815    7102  24.810 0.000473 ***
#> Residuals                             6   1718     286                     
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> ANOVA, Block Adjusted
#> =====================
#>                                Df Sum Sq Mean Sq F value   Pr(>F)    
#> Treatment (ignoring Blocks)    11  64708    5883  20.550 0.000707 ***
#>   Treatment: Check              3   2150     717   2.504 0.156116    
#>   Treatment: Test               7  34863    4980  17.399 0.001366 ** 
#>   Treatment: Test vs. Check     1  27694   27694  96.749 6.36e-05 ***
#> Block (eliminating Treatments)  2   1277     639   2.231 0.188645    
#> Residuals                       6   1717     286                     
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Coefficient of Variation
#> ========================
#> 6.057617
#> 
#> Overall Adjusted Mean
#> =====================
#> 298.4792
#> 
#> Standard Errors
#> ===============
#>                                          Std. Error of Diff.  CD (5%)
#> Control Treatment Means                             13.81424 33.80224
#> Two Test Treatments (Same Block)                    23.92697 58.54719
#> Two Test Treatments (Different Blocks)              26.75117 65.45775
#> A Test Treatment and a Control Treatment            21.84224 53.44603
#> 
#> Treatment Means
#> ===============
#>  Treatment Block  Means    SE r    Min    Max Adjusted Means
#>          1       256.00  3.06 3 250.00 260.00         256.00
#>         10     3 450.00  <NA> 1 450.00 450.00         437.67
#>         11     1 300.00  <NA> 1 300.00 300.00         299.42
#>         12     1 289.00  <NA> 1 289.00 289.00         288.42
#>          2       228.00  6.11 3 220.00 240.00         228.00
#>          3       247.67 10.17 3 237.00 268.00         247.67
#>          4       264.00 18.68 3 227.00 287.00         264.00
#>          5     2 281.00  <NA> 1 281.00 281.00         293.92
#>          6     3 395.00  <NA> 1 395.00 395.00         382.67
#>          7     1 347.00  <NA> 1 347.00 347.00         346.42
#>          8     3 226.00  <NA> 1 226.00 226.00         213.67
#>          9     2 311.00  <NA> 1 311.00 311.00         323.92
#> 
#> 
#> Comparisons
#> ===========
#> 
#> Method : lsd
#> 
#>                   contrast estimate    SE df t.ratio p.value sig
#>    treatment1 - treatment2    28.00 13.81  6   2.027   0.089    
#>    treatment1 - treatment3     8.33 13.81  6   0.603   0.568    
#>    treatment1 - treatment4    -8.00 13.81  6  -0.579   0.584    
#>    treatment1 - treatment5   -37.92 20.72  6  -1.830   0.117    
#>    treatment1 - treatment6  -126.67 20.72  6  -6.113   0.001 ***
#>    treatment1 - treatment7   -90.42 20.72  6  -4.363   0.005  **
#>    treatment1 - treatment8    42.33 20.72  6   2.043   0.087    
#>    treatment1 - treatment9   -67.92 20.72  6  -3.278   0.017   *
#>   treatment1 - treatment10  -181.67 20.72  6  -8.767   0.000 ***
#>   treatment1 - treatment11   -43.42 20.72  6  -2.095   0.081    
#>   treatment1 - treatment12   -32.42 20.72  6  -1.564   0.169    
#>    treatment2 - treatment3   -19.67 13.81  6  -1.424   0.204    
#>    treatment2 - treatment4   -36.00 13.81  6  -2.606   0.040   *
#>    treatment2 - treatment5   -65.92 20.72  6  -3.181   0.019   *
#>    treatment2 - treatment6  -154.67 20.72  6  -7.464   0.000 ***
#>    treatment2 - treatment7  -118.42 20.72  6  -5.715   0.001  **
#>    treatment2 - treatment8    14.33 20.72  6   0.692   0.515    
#>    treatment2 - treatment9   -95.92 20.72  6  -4.629   0.004  **
#>   treatment2 - treatment10  -209.67 20.72  6 -10.118   0.000 ***
#>   treatment2 - treatment11   -71.42 20.72  6  -3.447   0.014   *
#>   treatment2 - treatment12   -60.42 20.72  6  -2.916   0.027   *
#>    treatment3 - treatment4   -16.33 13.81  6  -1.182   0.282    
#>    treatment3 - treatment5   -46.25 20.72  6  -2.232   0.067    
#>    treatment3 - treatment6  -135.00 20.72  6  -6.515   0.001 ***
#>    treatment3 - treatment7   -98.75 20.72  6  -4.766   0.003  **
#>    treatment3 - treatment8    34.00 20.72  6   1.641   0.152    
#>    treatment3 - treatment9   -76.25 20.72  6  -3.680   0.010   *
#>   treatment3 - treatment10  -190.00 20.72  6  -9.169   0.000 ***
#>   treatment3 - treatment11   -51.75 20.72  6  -2.497   0.047   *
#>   treatment3 - treatment12   -40.75 20.72  6  -1.967   0.097    
#>    treatment4 - treatment5   -29.92 20.72  6  -1.444   0.199    
#>    treatment4 - treatment6  -118.67 20.72  6  -5.727   0.001  **
#>    treatment4 - treatment7   -82.42 20.72  6  -3.977   0.007  **
#>    treatment4 - treatment8    50.33 20.72  6   2.429   0.051    
#>    treatment4 - treatment9   -59.92 20.72  6  -2.892   0.028   *
#>   treatment4 - treatment10  -173.67 20.72  6  -8.381   0.000 ***
#>   treatment4 - treatment11   -35.42 20.72  6  -1.709   0.138    
#>   treatment4 - treatment12   -24.42 20.72  6  -1.178   0.283    
#>    treatment5 - treatment6   -88.75 26.75  6  -3.318   0.016   *
#>    treatment5 - treatment7   -52.50 26.75  6  -1.963   0.097    
#>    treatment5 - treatment8    80.25 26.75  6   3.000   0.024   *
#>    treatment5 - treatment9   -30.00 23.93  6  -1.254   0.257    
#>   treatment5 - treatment10  -143.75 26.75  6  -5.374   0.002  **
#>   treatment5 - treatment11    -5.50 26.75  6  -0.206   0.844    
#>   treatment5 - treatment12     5.50 26.75  6   0.206   0.844    
#>    treatment6 - treatment7    36.25 26.75  6   1.355   0.224    
#>    treatment6 - treatment8   169.00 23.93  6   7.063   0.000 ***
#>    treatment6 - treatment9    58.75 26.75  6   2.196   0.070    
#>   treatment6 - treatment10   -55.00 23.93  6  -2.299   0.061    
#>   treatment6 - treatment11    83.25 26.75  6   3.112   0.021   *
#>   treatment6 - treatment12    94.25 26.75  6   3.523   0.012   *
#>    treatment7 - treatment8   132.75 26.75  6   4.962   0.003  **
#>    treatment7 - treatment9    22.50 26.75  6   0.841   0.433    
#>   treatment7 - treatment10   -91.25 26.75  6  -3.411   0.014   *
#>   treatment7 - treatment11    47.00 23.93  6   1.964   0.097    
#>   treatment7 - treatment12    58.00 23.93  6   2.424   0.052    
#>    treatment8 - treatment9  -110.25 26.75  6  -4.121   0.006  **
#>   treatment8 - treatment10  -224.00 23.93  6  -9.362   0.000 ***
#>   treatment8 - treatment11   -85.75 26.75  6  -3.205   0.018   *
#>   treatment8 - treatment12   -74.75 26.75  6  -2.794   0.031   *
#>   treatment9 - treatment10  -113.75 26.75  6  -4.252   0.005  **
#>   treatment9 - treatment11    24.50 26.75  6   0.916   0.395    
#>   treatment9 - treatment12    35.50 26.75  6   1.327   0.233    
#>  treatment10 - treatment11   138.25 26.75  6   5.168   0.002  **
#>  treatment10 - treatment12   149.25 26.75  6   5.579   0.001  **
#>  treatment11 - treatment12    11.00 23.93  6   0.460   0.662    
#> 
#> Treatment Groups
#> ================
#> 
#> Method : lsd
#> 
#>  Treatment Adjusted Means    SE df lower.CL upper.CL    Group
#>          8         213.67 18.27  6   168.95   258.38  12     
#>          2         228.00  9.77  6   204.10   251.90  1      
#>          3         247.67  9.77  6   223.76   271.57  123    
#>          1         256.00  9.77  6   232.10   279.90  1234   
#>          4         264.00  9.77  6   240.10   287.90   234   
#>         12         288.42 18.27  6   243.70   333.13    345  
#>          5         293.92 18.27  6   249.20   338.63    345  
#>         11         299.42 18.27  6   254.70   344.13     45  
#>          9         323.92 18.27  6   279.20   368.63      56 
#>          7         346.42 18.27  6   301.70   391.13      56 
#>          6         382.67 18.27  6   337.95   427.38       67
#>         10         437.67 18.27  6   392.95   482.38        7

# Frequency distribution plots
freq1 <- freqdist.augmentedRCBD(out1, xlab = "Trait 1")
#> Warning: Removed 2 rows containing missing values (`geom_bar()`).
class(freq1)
#> [1] "gtable" "gTree"  "grob"   "gDesc" 
plot(freq1)

freq2 <- freqdist.augmentedRCBD(out2, xlab = "Trait 2")
#> Warning: Removed 2 rows containing missing values (`geom_bar()`).
plot(freq2)


# Change check colours
colset <- c("red3", "green4", "purple3", "darkorange3")
freq1 <- freqdist.augmentedRCBD(out1, xlab = "Trait 1", check.col = colset)
#> Warning: Removed 2 rows containing missing values (`geom_bar()`).
plot(freq1)

freq2 <- freqdist.augmentedRCBD(out2, xlab = "Trait 2", check.col = colset)
#> Warning: Removed 2 rows containing missing values (`geom_bar()`).
plot(freq2)


# Without checks highlighted
freq1 <- freqdist.augmentedRCBD(out1, xlab = "Trait 1",
                                highlight.check = FALSE)
#> Warning: Removed 2 rows containing missing values (`geom_bar()`).
plot(freq1)

freq2 <- freqdist.augmentedRCBD(out2, xlab = "Trait 2",
                                highlight.check = FALSE)
#> Warning: Removed 2 rows containing missing values (`geom_bar()`).
plot(freq2)