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

 describe.augmentedRCBD R Documentation

## Compute Descriptive Statistics from `augmentedRCBD` Output

### Description

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

### Usage

``````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`

### 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)
# 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)
``````

augmentedRCBD documentation built on Aug. 19, 2023, 1:06 a.m.