| Type: | Package |
| Title: | Joint Analysis of Experiments with Mixtures and Random Effects |
| Version: | 1.0.5 |
| Date: | 2024-06-21 |
| Imports: | MASS, lattice |
| Description: | Performs a joint analysis of experiments with mixtures and random effects, taking on a process variable represented by a covariable. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| NeedsCompilation: | no |
| Author: | Paulo Cesar Ossani
 [aut, cre],
Marcelo Angelo Cirillo
[aut] |
| Maintainer: | Paulo Cesar Ossani <ossanipc@hotmail.com> |
| Repository: | CRAN |
| Packaged: | 2024-06-21 14:17:19 UTC; Ossan |
| Date/Publication: | 2024-06-21 14:30:02 UTC |
Joint Analysis of Experiments with Mixtures and Random Effects.
Description
Joint analysis of experiments with mixtures and random effects, taking on a process variable represented by a covariable.
Details
| Package: | Blendstat |
| Type: | Package |
| Version: | 1.0.5 |
| Date: | 2024-06-21 |
| License: | GPL(>= 2) |
| LazyLoad: | yes |
Author(s)
Marcelo Angelo Cirillo and Paulo Cesar Ossani.
Maintainer: Paulo Cesar Ossani <ossanipc@hotmail.com>
References
Kalirajan, K. P. On the estimation of a regression model with fixed and random coefficients. Journal of Applied Statistics, 17(2): 237-244, 1990. doi:10.1080/757582835
Swany, P. A. V. B. Statistical Inference in Random Coefficient Regression Models. Amsterdam: Springer Science & Business Media, 1971. 209 p.
Joint analysis of experiments with mixtures and random effects.
Description
Joint analysis of experiments with mixtures and random effects, taking on a process variable represented by a covariable.
Usage
Blend(exp, X, Y, conc = NULL, effects = NULL)Arguments
exp |
Vector with the names of the experiments. |
X |
Mixture variables (components), without the vector of the concentrations (covariable). |
Y |
Response variable. |
conc |
Vector with the concentrations (covariable) of the experiments. |
effects |
Vector of the effects of the mixtures in a reference mixture (example: centroid). |
Value
MPred |
Matrix with the predicted and observed values. |
MCPred |
Matrix with the values predicted by components. |
Mexp |
Matrix with the design of the experiments. |
theta |
Vector with the theta estimates. |
Author(s)
Marcelo Angelo Cirillo
Paulo Cesar Ossani
References
Kalirajan, K. P. On the estimation of a regression model with fixed and random coefficients. Journal of Applied Statistics, 17(2): 237-244, 1990. doi:10.1080/757582835
Swany, P. A. V. B. Statistical Inference in Random Coefficient Regression Models. Amsterdam: Springer Science & Business Media, 1971. 209 p.
See Also
Examples
data(DataNAT) # dataset
Exp <- DataNAT[,2] # identification of experiments
X <- DataNAT[,3:6] # independent variable
Y <- DataNAT[,11] # dependent variable
# effects of the blends in a reference mixture
Effects <- rep(c(-0.1,0,0.1,0.2,0.3,0.4,0.5,0.6,0.7),4)
Conc <- as.matrix(DataNAT[,7]) # covariate (process variable)
Res <- Blend(exp = Exp, X = X, Y = Y, conc = Conc, effects = Effects)
print("Predicted and observed values"); Res$MPred
print("Values predicted by components:"); Res$MCPred
print("Design of the experiments:"); Res$Mexp
print("Estimates of the linear model parameters:"); Res$theta
Tit <- c("Covariate (process variable)","Variable")
Xlab = "effects" # label of the X axis
Ylab = "Predicted values" # label of the Y axis
Plot.Blend(Res, titles = Tit, posleg = 2, xlabel = Xlab,
ylabel = Ylab, boxleg = TRUE, color = TRUE,
expcolor = c("goldenrod3","gray53","red2", "blue2"),
casc = TRUE)
Dataset, peeled cherry coffee.
Description
Database of coffee blends of different varieties processed via wet (peeled cherry).
Usage
data(DataCD)Format
Database of coffee blends of different varieties processed via wet (peeled cherry). Formed by the variables: Exp (code of the experiments); CEB (specialty Bourbon Yellow coffee produced at an altitude above 1,200m); CT (roasted commercial coffee); CC (Conillon coffee); CEA (Acaia specialty coffee produced at altitude below 1,100m); Conc (concentrations at 7% and 10% (m/v) of roasted and ground coffee beans in 100 ml of water). Response variables defined by the sensorial attributes: Body, Taste, Acidity, Bitterness, Score.
References
Project yield and research entitled by "Quality of blends of specialty and non-specialty coffees of the region of the Mantiqueira Mountains - treatment of discrepant scores in tests with consumers". CNPq for their aid via grant number 304974/2015-3.
Examples
data(DataCD) # dataset
Exp <- DataCD[,2] # identification of the experiments
X <- DataCD[,3:6] # independent variables (components)
Y <- DataCD[,11] # dependent variable (response Bitterness)
# effects o the mixtures in the reference mixture
Effects <- rep(c(-0.1,0,0.1,0.2,0.3,0.4,0.5,0.6,0.7),4)
Conc <- as.matrix(DataCD[,7]) # covariable (process variable)
Res <- Blend(exp = Exp, X = X, Y = Y, conc = Conc, effects = Effects)
print("Predicted and observed values"); Res$MPred
print("Values predicted by components:"); Res$MCPred
print("Design of the experiments:"); Res$MExp
print("Estimates of the linear model parameters:"); Res$theta
Dataset, natural cherry coffee.
Description
Database of coffee blends of different varieties processed by dry via.
Usage
data(DataNAT)Format
Database of coffee blends of different varieties processed by dry via. Formed by the variables: Exp (code of the experiments); CEB (specialty Bourbon Yellow coffee produced at an altitude above 1,200m); CT (roasted commercial coffee); CC (Conillon coffee); CEA (Acaia specialty coffee produced at altitude below 1,100m); Conc (concentrations at 7% and 10% (w/v) of roasted and ground coffee beans in 100 ml of water). Variable responses defined by sensory attributes: Body, Taste, Acidity, Bitterness, Score.
References
Project yield and research entitled by "Quality of blends of specialty and non-specialty coffees of the region of the Mantiqueira Mountains - treatment of discrepant scores in tests with consumers". CNPq for their aid via grant number 304974/2015-3.
Examples
data(DataNAT) # dataset
Exp <- DataNAT[,2] # identification of the experiments
X <- DataNAT[,3:6] # independent variables (components)
Y <- DataNAT[,11] # dependent variable (response Bitterness)
# effects o the mixtures in the reference mixture
Effects <- rep(c(-0.1,0,0.1,0.2,0.3,0.4,0.5,0.6,0.7),4)
Conc <- as.matrix(DataNAT[,7]) # covariable (process variable)
Res <- Blend(exp = Exp, X = X, Y = Y, conc = Conc, effects = Effects)
print("Predicted and observed values"); Res$MPred
print("Values predicted by components:"); Res$MCPred
print("Design of the experiments:"); Res$MExp
print("Estimates of the linear model parameters:"); Res$Theta
Plots of the results.
Description
Plots of the results of the joint analysis of the experiments.
Usage
Plot.Blend(BL, titles = c(NA,NA), posleg = 2, xlabel = NA,
ylabel = NA, boxleg = FALSE, color = TRUE, expcolor = NA,
casc = TRUE)Arguments
BL |
Data of the Blend function. |
titles |
Titles for the plot of the effects of the concentrations and components. If it is not defined, it assumes the default text. |
posleg |
1 for caption in the left upper corner, |
xlabel |
Names the X axis, if not set, assumes the default text. |
ylabel |
Names the Y axis, if not set, assumes the default text. |
boxleg |
Puts frame on the caption (default = TRUE). |
color |
Colorful plots (default = TRUE). |
expcolor |
Vector with the colors of the experiments. |
casc |
Cascade effect in the presentation of the plots (default = TRUE). |
Value
Return several plots.
Author(s)
Marcelo Angelo Cirillo
Paulo Cesar Ossani
See Also
Examples
data(DataCD) # dataset
Exp <- DataCD[,2] # identification of the experiments
X <- DataCD[,3:6] # independent variables (components)
Y <- DataCD[,11] # dependent variable (response Bitterness)
# effects o the mixtures in the reference mixture
Effects <- rep(c(-0.1,0,0.1,0.2,0.3,0.4,0.5,0.6,0.7),4)
Conc <- as.matrix(DataCD[,7]) # covariable (process variable)
Res <- Blend(exp = Exp, X = X, Y = Y, conc = Conc, effects = Effects)
print("Predicted and observed values"); Res$MPred
print("Values predicted by components:"); Res$MCPred
print("Design of the experiments:"); Res$MExp
print("Estimates of the linear model parameters:"); Res$Theta
Tit <- c("Covariable (process variable)","Variable")
Xlab = "Effects" # label of the X axis
Ylab = "Predicted values" # label of the Y axis
Plot.Blend(Res, titles = Tit, posleg = 2, xlabel = Xlab,
ylabel = Ylab, boxleg = TRUE, color = TRUE,
expcolor = c("goldenrod3","gray53","red2", "blue2"),
casc = TRUE)