Description Usage Arguments Details Value Author(s) References Examples
The qkernel Metric Multi-Dimensional Scaling is a nonlinear form of Metric Multi-Dimensional Scaling
1 2 3 4 5 6 7 8 9 10 11 | ## S4 method for signature 'matrix'
qkMDS(x, kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9),
dims = 2, plotResiduals = FALSE, verbose = TRUE, na.action = na.omit, ...)
## S4 method for signature 'cndkernmatrix'
qkMDS(x, dims = 2,plotResiduals = FALSE,
verbose = TRUE, na.action = na.omit, ...)
## S4 method for signature 'qkernmatrix'
qkMDS(x, dims = 2,plotResiduals = FALSE,
verbose = TRUE, na.action = na.omit, ...)
|
x |
N x D matrix (N samples, D features) or a kernel matrix of |
kernel |
the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings:
The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument. |
qpar |
the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are :
Hyper-parameters for user defined kernels can be passed through the qpar parameter as well. |
dims |
vector containing the target space dimension(s) |
plotResiduals |
show a plot with the residuals between the high and the low dimensional data |
verbose |
show a summary of the embedding procedure at the end |
na.action |
A function to specify the action to be taken if |
... |
additional parameters |
There are several versions of non-metric multidimensional scaling in R, but qkerntool offers the following unique combination of using qKernel methods
qkMDS
gives out an S4 object which is a LIST with components
prj |
a N x dim matrix (N samples, dim features) with the reduced input data (list of several matrices if more than one dimension was specified). |
dims |
the dimension of the target space. |
Residuals |
the residual variances for all dimensions. |
eVal |
the corresponding eigenvalues. |
eVec |
the corresponding eigenvectors. |
cndkernf |
the kernel function used. |
kcall |
The formula of the function called |
all the slots of the object can be accessed by accessor functions.
Yusen Zhang
yusenzhang@126.com
Kruskal, J.B. 1964a. Multidimensional scaling by optimizing goodness-of-fit to a nonmetric hypothesis. Psychometrika 29, 1–28.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | # another example using the iris
data(iris)
testset <- sample(1:150,20)
train <- as.matrix(iris[-testset,-5])
labeltrain<- as.integer(iris[-testset,5])
test <- as.matrix(iris[testset,-5])
# ratibase(c=1,q=0.8)
d_low = qkMDS(train, kernel = "ratibase", qpar = list(c=1,q=0.9),dims = 2,
plotResiduals = TRUE)
#plot the data projection on the components
plot(prj(d_low),col=labeltrain, xlab="1st Principal Component",ylab="2nd Principal Component")
prj(d_low)
dims(d_low)
Residuals(d_low)
eVal(d_low)
eVec(d_low)
kcall(d_low)
cndkernf(d_low)
|
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