View source: R/augmentedRCBD.bulk.R

augmentedRCBD.bulk | R Documentation |

`augmentedRCBD.bulk`

is a wrapper around the functions
`augmentedRCBD`

, `describe.augmentedRCBD`

,
`freqdist.augmentedRCBD`

and `gva.augmentedRCBD`

. It will carry out
these analyses for multiple traits/characters from the input data as a data
frame object.

```
augmentedRCBD.bulk(
data,
block,
treatment,
traits,
checks = NULL,
alpha = 0.05,
describe = TRUE,
freqdist = TRUE,
gva = TRUE,
k = 2.063,
check.col = "red",
console = TRUE
)
```

`data` |
The data as a data frame object. The data frame should possess columns specifying the block, treatment and multiple traits/characters. |

`block` |
Name of column specifying the blocks in the design as a character string. |

`treatment` |
Name of column specifying the treatments as a character string. |

`traits` |
Name of columns specifying the multiple traits/characters as a character vector. |

`checks` |
Character vector of the checks present in |

`alpha` |
Type I error probability (Significance level) to be used for multiple comparisons. |

`describe` |
If |

`freqdist` |
If |

`gva` |
If |

`k` |
The standardized selection differential or selection intensity
required for computation of Genetic advance. Default is 2.063 for 5%
selection proportion (see |

`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 [ |

`console` |
If |

A list of class `augmentedRCBD.bulk`

containing the following
components:

`Details` |
Details of the augmented design used and the traits/characters. |

`ANOVA, Treatment Adjusted` |
A data frame of mean sum of squares, p value and stastical significance of the specified traits from treatment adjusted ANOVA. |

```
ANOVA, Block
Adjusted
``` |
A data frame of mean sum of squares, p value and stastical significance of the specified traits from block adjusted ANOVA |

`Means` |
A data frame of the adjusted means of the treatments for the specified traits. |

`Check statistics` |
A list of data frames with check statistics such as number of replications, standard error, minimum and maximum value |

`alpha` |
Type I error probability (Significance level) used. |

`Std. Errors` |
A data frame of standard error of difference between various combinations for the specified traits. |

`CD` |
A data frame of critical difference (at the specified alpha) between various combinations for the specified traits. |

`Overall adjusted mean` |
A data frame of the overall adjusted mean for the specified traits. |

`CV` |
A data frame of the coefficient of variance for the specified traits. |

`Descriptive statistics` |
A data frame of descriptive statistics for the specified traits. |

`Frequency distribution` |
A list of ggplot2 plot grobs of the frequency distribution plots. |

`k` |
The standardized selection differential or selection intensity used for computaton of Genetic advance. |

`Genetic variability analysis` |
A data frame of genetic variability statistics for the specified traits. |

`GVA plots` |
A list of three ggplot2 objects with the plots for (a) Phenotypic and Genotypic CV, (b) Broad sense heritability and (c) Genetic advance over mean |

`warnings` |
A list of warning messages (if any) captured during model fitting, frequency distribution plotting and genetic variability analysis. |

In this case treatment comparisons/grouping by least significant
difference or Tukey's honest significant difference method is not computed.
Also the output object size is reduced using the `simplify = TRUE`

argument in the `augmentedRCBD`

function.

`augmentedRCBD`

,
`describe.augmentedRCBD`

,
`freqdist.augmentedRCBD`

,
`gva.augmentedRCBD`

```
# 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)
dataf <- data.frame(blk, trt, y1, y2)
bout <- augmentedRCBD.bulk(data = dataf, block = "blk",
treatment = "trt", traits = c("y1", "y2"),
checks = NULL, alpha = 0.05, describe = TRUE,
freqdist = TRUE, gva = TRUE,
check.col = c("brown", "darkcyan",
"forestgreen", "purple"),
console = TRUE)
# Frequency distribution plots
lapply(bout$`Frequency distribution`, plot)
# GVA plots
bout$`GVA plots`
```

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