View source: R/augmentedRCBD.R

augmentedRCBD | R Documentation |

`augmentedRCBD`

is a function for analysis of variance of an augmented
randomised block design (Federer, 1956; Federer, 1961; Searle, 1965) and the
generation as well as comparison of the adjusted means of the
treatments/genotypes.

```
augmentedRCBD(
block,
treatment,
y,
checks = NULL,
method.comp = c("lsd", "tukey", "none"),
alpha = 0.05,
group = TRUE,
console = TRUE,
simplify = FALSE,
truncate.means = TRUE
)
```

`block` |
Vector of blocks (as a factor). |

`treatment` |
Vector of treatments/genotypes (as a factor). |

`y` |
Numeric vector of response variable (Trait). |

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

`method.comp` |
Method for comparison of treatments ( |

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

`group` |
If |

`console` |
If |

`simplify` |
If |

`truncate.means` |
If |

This function borrows code from `DAU.test`

function of `agricolae`

package (de Mendiburu et al., 2016) as well as from Appendix VIII of Mathur et
al., (2008).

A list of class `augmentedRCBD`

containing the following
components:

`Details` |
Details of the augmented design used. |

`Means` |
A data frame with the "Means", "Block", "SE", "Mix", "Max" and "Adjusted Means" for each "Treatment". |

```
ANOVA,
Treatment Adjusted
``` |
An object of class |

`ANOVA, Block Adjusted` |
An object of
class |

`Block effects` |
A vector of block effects. |

`Treatment effects` |
A vector of treatment effects. |

`Std. Errors` |
A data frame of standard error of difference
between various combinations along with critical difference and tukey's
honest significant difference (when |

`Overall adjusted mean` |
Overall adjusted mean. |

`CV` |
Coefficient of variation. |

`Comparisons` |
A data
frame of pairwise comparisons of treatments. This is computed only if
argument |

`Groups` |
A data frame with
compact letter display of pairwise comparisons of treatments. Means with at
least one letter common are not significantly different statistically. This
is computed only if argument |

`warning` |
A vector of warning messages (if any) captured during model fitting. |

Data should preferably be balanced i.e. all the check genotypes should be present in all the blocks. If not, a warning is issued.

There should not be any missing values.

The number of test genotypes can vary within a block.

In case the large number of treatments or genotypes, it is advisable to
avoid comparisons with the `group = FALSE`

argument as it will be
memory and processor intensive. Further it is advised to simplify output
with `simplify = TRUE`

in order to reduce output object size.

federer_augmented_1956augmentedRCBD

\insertReffederer_augmented_1956-1augmentedRCBD

\insertReffederer_augmented_1961augmentedRCBD

\insertRefsearle_computing_1965augmentedRCBD

\insertRefmathur_data_2008augmentedRCBD

\insertRefde_mendiburu_agricolae_2015augmentedRCBD

`DAU.test`

,
`ea1`

,
`emmeans`

,
`cld.emmGrid`

,
`aug.rcb`

```
# 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 (checks inferred)
out1 <- augmentedRCBD(data$blk, data$trt, data$y1, method.comp = "lsd",
alpha = 0.05, group = TRUE, console = TRUE)
# Results for variable y2 (checks inferred)
out2 <- augmentedRCBD(data$blk, data$trt, data$y1, method.comp = "lsd",
alpha = 0.05, group = TRUE, console = TRUE)
# Results for variable y1 (checks specified)
out1 <- augmentedRCBD(data$blk, data$trt, data$y1, method.comp = "lsd",
alpha = 0.05, group = TRUE, console = TRUE,
checks = c("1", "2", "3", "4"))
# Results for variable y2 (checks specified)
out2 <- augmentedRCBD(data$blk, data$trt, data$y1, method.comp = "lsd",
alpha = 0.05, group = TRUE, console = TRUE,
checks = c("1", "2", "3", "4"))
## Not run:
# Error in case checks not replicated across all blocks
# Check 1 and 4 not replicated in all 3 blocks
trt <- c(1, 2, 3, 14, 7, 11, 12, 1, 2, 3, 4, 5, 9, 13, 2, 3, 4, 8, 6, 10)
data$trt <- as.factor(trt)
table(data$trt, data$blk)
# Results for variable y1 (checks specified)
out1 <- augmentedRCBD(data$blk, data$trt, data$y1, method.comp = "lsd",
alpha = 0.05, group = TRUE, console = TRUE,
checks = c("1", "2", "3", "4"))
## End(Not run)
# Warning in case test treatments are replicated
out1 <- augmentedRCBD(data$blk, data$trt, data$y1, method.comp = "lsd",
alpha = 0.05, group = TRUE, console = TRUE)
out1 <- augmentedRCBD(data$blk, data$trt, data$y1, method.comp = "lsd",
alpha = 0.05, group = TRUE, console = TRUE,
checks = c("2", "3"))
```

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