hue {lcc}

Hue color data

Description

An observational study conducted at the Vegetable Production Department at "Luiz de Queiroz" College of Agriculture/University of São Paulo in 2010/2011 to evaluate the peel color of 20 papaya fruits cv. Sunrise Solo over time. The color hue was measured on the equatorial region of each fruit using four points observed by the colorimeter and 1,000 points observed by the scanner. Thus, the circular mean hue was calculated for each fruit by each device at time t. The aim of the agreement study was to assess how well the colorimeter agreed with the scanner over time.

Usage

data(hue)

Format

A data frame with 554 observations on the mean hue variable. The format is:

H_mean	numeric; mean hue of papaya's peel
Method	a factor with levels Colorimeter, Scanner
Time	integer; time in days
Fruit	a factor with 20 levels; from 1 to 20
	where each level is represented by one fruit.

Source

Oliveira, T.P.; Hinde, J.; Zocchi S.S. Longitudinal Concordance Correlation Function Based on Variance Components: An Application in Fruit Color Analysis. Journal of Agricultural, Biological, and Environmental Statistics, v. 23, n. 2, 233–254, 2018.

Oliveira, T.P.; Zocchi S.S.; Jacomino, A.P. Measuring color hue in 'Sunrise Solo' papaya using a flatbed scanner. Rev. Bras. Frutic., v. 39, n. 2, e-911, 2017.

References

Oliveira, T.P.; Hinde, J.; Zocchi S.S. Longitudinal Concordance Correlation Function Based on Variance Components: An Application in Fruit Color Analysis. Journal of Agricultural, Biological, and Environmental Statistics, v. 23, n. 2, 233–254, 2018.

See Also

lcc.

Examples

data(hue)
summary(hue)
str(hue)
## Second degree polynomial model with random intercept, slope and
## quadratic term including an exponential variance function using
## time as covariate.
model<-lcc(data = hue, subject = "Fruit", resp = "H_mean",
          method = "Method", time = "Time", qf = 2, qr = 2,
          components = TRUE, time_lcc = list(from = min(hue$Time),
          to = max(hue$Time), n=40), var.class=varExp,
          weights.form="time")
summary(model, type="model")
summary(model, type="lcc")
## for discussion on the analysis of complete data set,
## see Oliveira et al. (2018)