describe.augmentedRCBD {augmentedRCBD}

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

D'Agostino RB (1970). “Transformation to normality of the null distribution of g₁.” Biometrika, 57(3), 679–681.

Anscombe FJ, Glynn WJ (1983). “Distribution of the kurtosis statistic b₂ for normal samples.” Biometrika, 70(1), 227–234.

See Also

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)