Hayman: Diallel Analysis using Hayman Approach

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/Hayman.R

Description

Hayman is used for performing Diallel Analysis using Hayman's Approach.

Usage

1
Hayman(y, Rep, Cross1, Cross2, data)

Arguments

y

Numeric Response Vector

Rep

Replicate as factor

Cross1

Cross 1 as factor

Cross2

Cross 2 as factor

data

A data.frame

Details

Diallel Analysis using Haymans's approach.

Value

Means Means

ANOVA Analysis of Variance (ANOVA) table

Genetic.Components Genetic Components

Effects Effects of Crosses

StdErr Standard Errors of Crosses

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)

References

  1. Hayman, B. I. (1954 a) The Theory and Analysis of Diallel Crosses. Genetics, 39, 789–809.

  2. Hayman, B. I. (1954 b) The Analysis of Variance of Diallel Tables. Biometrics, 10, 235–244.

  3. Hayman, B. I. (1957) Interaction, Heterosis and Diallel Crosses. Genetics, 42, 336–355.

  4. Singh, R. K. and Chaudhary, B. D. (2004) Biometrical Methods in Quantitative Genetic Analysis. New Delhi: Kalyani.

See Also

Griffing , HaymanData

Examples

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#------------------------------------------
## Diallel Analysis with Haymans's Aproach
#------------------------------------------

Hayman1Data <-
 Hayman(
     y      = Yield
   , Rep    = Rep
   , Cross1 = Cross1
   , Cross2 = Cross2
   , data   = HaymanData
   )

Hayman1Data
names(Hayman1Data)

Hayman1DataMeans <- Hayman1Data$Means
Hayman1DataANOVA <- Hayman1Data$ANOVA
Hayman1DataWr.Vr.Table <- Hayman1Data$Wr.Vr.Table

Hayman1DataComponents.of.Variation <- Hayman1Data$Components.of.Variation
Hayman1DataOther.Parameters <- Hayman1Data$Other.Parameters
Hayman1DataFr <- Hayman1Data$Fr

#----------------
# Wr-Vr Graph
#----------------
VOLO     <- Hayman1Data$VOLO
In.Value <- Hayman1Data$In.Value
a        <- Hayman1Data$a
b        <- Hayman1Data$b
Wr.Vr    <- Hayman1Data$Wr.Vr.Table


library(ggplot2)
ggplot(data=data.frame(x=c(0, max(In.Value, Wr.Vr$Vr, Wr.Vr$Wr, Wr.Vr$Wrei))), aes(x)) +
  stat_function(fun=function(x) {sqrt(x*VOLO)}, color="blue") +
  geom_hline(yintercept = 0) +
  geom_vline(xintercept = 0) +
  geom_abline(intercept = a, slope = b) +
  geom_abline(intercept = mean(Wr.Vr$Wr)-mean(Wr.Vr$Vr), slope = 1) +
  geom_segment(aes(
      x     = mean(Wr.Vr$Vr)
    , y     = min(0, mean(Wr.Vr$Wr))
    , xend  = mean(Wr.Vr$Vr)
    , yend  = max(0, mean(Wr.Vr$Wr))
  )
  , color = "green"
  ) +
  geom_segment(aes(
      x     = min(0, mean(Wr.Vr$Vr))
    , y     = mean(Wr.Vr$Wr)
    , xend  = max(0, mean(Wr.Vr$Vr))
    , yend  = mean(Wr.Vr$Wr)
  )
  , color = "green"
  )  +
  lims(x=c(min(0, Wr.Vr$Vr, Wr.Vr$Wrei), max(Wr.Vr$Vr, Wr.Vr$Wrei)),
       y=c(min(0, Wr.Vr$Wr, Wr.Vr$Wrei), max(Wr.Vr$Wr, Wr.Vr$Wri))
  ) +
  labs(
         x = expression(V[r])
       , y = expression(W[r])
       , title = expression(paste(W[r]-V[r] , " Graph"))
       ) +
  theme_bw()

Example output

$Means
        Cross1  Cross2  Cross3  Cross4   Cross5  Cross6   Cross7  Cross8
Cross1  85.645  87.010  90.455 114.945 120.2900  68.550 107.6425  52.640
Cross2  80.690  98.260 111.575  88.170  99.9300  73.265  97.6400  85.650
Cross3 102.230 104.555  74.070 100.645  94.2850 100.885 111.5400 117.735
Cross4 119.115  89.310 102.675  91.640  85.2850 105.795  64.4500  46.855
Cross5 111.290 102.890  88.265  83.390  54.1025  84.150  81.9350  94.820
Cross6  68.835  71.295  99.575 108.665  87.9650 100.390 121.6100  53.740
Cross7 109.265  87.820 108.445  57.650  78.7500 115.670  90.9600 125.270
Cross8  48.720  83.145 115.400  46.740  93.3200  60.240 118.1700  82.000

$ANOVA
                Df Sum Sq Mean Sq F value    Pr(>F)    
Total          255 127716                              
Rep              3   1036   345.4  3.0006  0.031825 *  
Treatment       63 104924  1665.5 14.4683 < 2.2e-16 ***
  Additive       7  15219  2174.1 18.8868 < 2.2e-16 ***
  Non-Additive  28  88243  3151.5 27.3783 < 2.2e-16 ***
      b1         1   1368  1367.6 11.8807  0.000699 ***
      b2         7  13530  1932.9 16.7915 < 2.2e-16 ***
      b3        20  73345  3667.3 31.8586 < 2.2e-16 ***
  Maternal       7    311    44.4  0.3856  0.910004    
  Reciprocal    21   1151    54.8  0.4763  0.975768    
Error          189  21756   115.1                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

$VOLO
[1] 224.9646

$In.Value
[1] 315.1641

$a
[1] 92.20385

$b
[1] -0.2184088

$Wr.Vr.Table
               Wr       Vr Wr.Minus.Vr Wr.Plus.Vr       Yr      Wri
Cross1 -132.56217 532.6487   -665.2108  400.08649  85.6450 346.1605
Cross2  -92.09404 134.8819   -226.9759   42.78784  98.2600 174.1943
Cross3   79.64105 196.9768   -117.3358  276.61788  74.0700 210.5061
Cross4   34.63611 495.1323   -460.4962  529.76840  91.6400 333.7473
Cross5  158.46976 352.0611   -193.5914  510.53087  54.1025 281.4273
Cross6   39.23575 506.7741   -467.5384  546.00985 100.3900 337.6481
Cross7   32.77330 424.9207   -392.1474  457.69404  90.9600 309.1798
Cross8 -153.93912 888.8355  -1042.7746  734.89634  82.0000 447.1650
               Wrei      Wreip
Cross1  -24.1312782   86.88985
Cross2   62.7444691 -310.87692
Cross3   49.1823885 -248.78198
Cross4  -15.9373750   49.37348
Cross5   15.3106225  -93.69769
Cross6  -18.4800492   61.01530
Cross7   -0.6025568  -20.83807
Cross8 -101.9255955  443.07665

$Components.of.Variation
     Estimate    StdErr   t.value
E    29.67728  74.84918 0.3964944
D   195.28734 224.54754 0.8696926
F   422.33301 530.58515 0.7959759
H1 1926.38733 516.20165 3.7318504
H2 1859.08553 449.09509 4.1396256
h2  136.59673 317.04090 0.4308489

$Other.Parameters
  Other.Parameters
1       3.14076006
2       0.24126580
3       2.05009853
4      -0.19614373
5       0.03847236
6       0.07347523
7      -0.19265899

$Fr
           Fr
Fr1  496.7580
Fr2 1211.3553
Fr3  743.6952
Fr4  237.3941
Fr5  275.8692
Fr6  204.9112
Fr7  381.5429
Fr8 -172.8617

 [1] "Means"                   "ANOVA"                  
 [3] "VOLO"                    "In.Value"               
 [5] "a"                       "b"                      
 [7] "Wr.Vr.Table"             "Components.of.Variation"
 [9] "Other.Parameters"        "Fr"                     
Warning messages:
1: In sqrt(x * VOLO) : NaNs produced
2: Removed 11 rows containing missing values (geom_path). 

DiallelAnalysisR documentation built on Feb. 25, 2021, 5:06 p.m.