geosptdb (version 1.0-1)

cp.xnews: Generate the principal coordinates of a new individual from Gower's distance.

Description

Function for generates a numeric matrix with principal coordinates of a new individual then you could obtain distances from this matrix and you can do a prediction using a Gower's result (1971) and Cuadras & Arenas (1990) which relates the squared distances vector with the principal coordinates vector associated to the new individual.

Usage

cp.xnews(newdata,eigenvalues, data,trend, ...)

Arguments

newdata

data frame values of new individual.

eigenvalues

the \(n\) eigenvalues computed during the scaling process (see cmdscale)

data

matrix or data frame containing the explanatory variables. These variables can be numeric, ordered, or factor, the symmetric or asymmetric binary variables should be numeric and only contain 0 and 1 character variables will be converted to factor. NAs are tolerated. With these variables the principal coordinates are built which become the regressors in the linear model.

trend

matrix \(nxk\) of the \(k\) most statistically significant principal coordinates (5%) with the response variable, obtained from the matrix or data frame containing explanatory variables.

...

further parameters to be passed to the gower.dist function (see gower.dist).

Value

Returns a numeric matrix with principal coordinates of the new individual.

References

Cuadras, CM. and Arenas, C. (1990).A distance-based regression model for prediction with mixed data. Communications in Statistics A - Theory and Methods 19, 2261-2279

Gower, J. C. (1971). A general coefficient of similarity and some of its properties. Biometrics 27:857-871.

Melo, C. E. (2012). Analisis geoestadistico espacio tiempo basado en distancias y splines con aplicaciones. PhD. Thesis. Universitat de Barcelona. 276 p. [link]

See Also

dblm, rbfST

Examples

# NOT RUN {
data(croatia.temp)
data(croatiadb)
# prediction case: one point
point <- data.frame(670863,5043464,5,170,200,15.7,3)
names(point) <- c("x","y","t","dem","dsea","twi","est")

croatia.temp[,7] <- as.factor(croatia.temp[,7])
dblm1 <- dblm(data=croatia.temp,y=croatiadb$MTEMP)                                 
newdata1 <- t(cp.xnews(newdata=point,eigenvalues=dblm1$ev, data=croatia.temp,
                       trend=dblm1$cp))
colnames(newdata1) <- c("X1","X2","X3","X4","X5","X6","X7","X8","X9","X10")
# }