Tuning parameters of SVM in tune function

I would like to fit a radial SVM onto the data. The tuning parameters cost and gamma are chosen by CV with the tune function. However the summary of the optimal model does not show the optimal gamma value.

The summary where gamma is not shown:

> svmult= svmcv$best.model
> svmult

Call:
best.tune(method = svm, train.x = type ~ ., data = wheat[tr, 
    ], ranges = list(cost = seq(0.001, 1, length.out = 50)), 
    kernel = "radial", gamma = c(0.1, 1, 2, 3, 4, 5))


Parameters:
   SVM-Type:  C-classification 
 SVM-Kernel:  radial 
       cost:  0.2864286 

Number of Support Vectors:  171

Meanwhile when I try to plot the data and the SVM hyperplanes, I received this error:
Error in plot.svm(svmult, wheat) : missing formula.. Please help me with the CV and plotting. Thanks in advance!

The data:

> head(wheat)
  class  density  hardness    size  weight moisture    type
1   hrw 1.313662  73.32533 2.19946 26.3070  6.97526 Healthy
2   srw 1.233610  31.06556 1.84809 28.4400 12.51565 Healthy
3   srw 1.203221 -23.43620 3.15953 40.0530 12.36660  Sprout
4   hrw 1.279238  70.97478 3.09405 25.0035  7.53971 Healthy
5   srw 1.341604  60.25526 2.14827 30.0990 12.22773 Healthy
6   hrw 1.384803  53.07190 2.42810 26.5440  7.22400 Healthy
> dim(wheat)
[1] 275   7

rm(list=ls())
wheat= read.csv('Wheat.csv')
wheat= na.omit(wheat)
head(wheat)
wheat$class= factor(wheat$class)
attach(wheat)
library(e1071)
x= model.matrix(type~., data= wheat)[,-1]
y= wheat$type
tr= sample(1:nrow(wheat), floor(nrow(wheat)*2/3))
trainX= x[tr,]
testX= x[-tr,]
trainY= y[tr]
testY= y[-tr]
svmcv= tune(svm, type~., data = wheat[tr,], kernel= 'radial', ranges = list(cost=seq(0.001, 1, length.out = 50)), gamma= c(0.1,1,2,3,4,5))
summary(svmcv)
svmult= svmcv$best.model
plot(svmult, wheat)

plot(svmModel, data=wheat, type~Variable2ChosenAsFree,
    slice=list(
        Variable3ChosenAsConstant=3,
        Variable4ChosenAsConstant=8
    )