# describe.augmentedRCBD: Compute Descriptive Statistics from 'augmentedRCBD' Output In augmentedRCBD: Analysis of Augmented Randomised Complete Block Designs

## Description

`describe.augmentedRCBD` computes descriptive statistics from the adjusted means in an object of class `augmentedRCBD`.

## Usage

 `1` ```describe.augmentedRCBD(aug) ```

## Arguments

 `aug` An object of class `augmentedRCBD`.

## Details

`describe.augmentedRCBD` computes the following descriptive statistics from the adjusted means in an object of class `augmentedRCBD`.

• Count

• Mean

• Standard deviation

• Standard error

• Minimum

• Maximum

• Skewness statistic along with p-value from D'Agostino test of skewness (D'Agostino, 1970).

• Kurtosis statistic along with p-value from Anscombe-Glynn test of kurtosis (Anscombe and Glynn, 1983).

## Value

A list with the following descriptive statistics:

 `Count` The number of treatments/genotypes. `Mean` The mean value. `Std.Error` The standard error. `Std.Deviation` The standard deviation. `Min` The minimum value `Max` The maximum value `Skewness(statistic)` The skewness estimator. `Skewness(p.value)` The p-value from D'Agostino test of skewness. `Kurtosis(statistic)` The kurtosis estimator. `Kurtosis(p.value)` The p-value from Anscombe-Glynn test of kurtosis.

## References

\insertRef

dagostino_transformation_1970augmentedRCBD

\insertRef

anscombe_distribution_1983augmentedRCBD

`augmentedRCBD`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```# 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) # Results for variable y2 out2 <- augmentedRCBD(data\$blk, data\$trt, data\$y2, method.comp = "lsd", alpha = 0.05, group = TRUE, console = TRUE) # Descriptive statistics describe.augmentedRCBD(out1) describe.augmentedRCBD(out2) ```