Blend {Blendstat} | R Documentation |
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)