HI, GUYS,

I used the following codes to run SVM and get prediction on new data set hh.

 dim(all_h)
[1] 2034   24
 dim(hh)    # it contains all the variables besides the variables in all_h
data set.
[1] 640 415


require(e1071)

svm.tune<-tune(svm, as.factor(out) ~ ., data=all_h,
ranges=list(gamma=2^(-5:5), cost=2^(-5:5)))# find the best parameters.

bestg<-svm.tune$best.parameters[[1]]
bestc<-svm.tune$best.parameters[[2]]

svm.fit<-svm(as.factor(out) ~ ., data=all_h, method="C-classification",
kernel="radial", probability = TRUE, cost=bestc, gamma=bestg, cross=10) #
model fitting

svm.pred<-predict(svm.fit, hh, decision.values = TRUE, probability = TRUE) #
find the probability.
*
Error in matrix(ret$dec, nrow = nrow(newdata), byrow = TRUE, dimnames =
list(rowns,  :
  invalid 'ncol' value (too large or NA)*


> head(all_h)
       DD    HK HQ      IL      LP          NE          NP
TA          TP            WA      WC
1 0.00543  0  0 0.00815 0.00272 0.00543 0.00000 0.00000 0.00000 0.00000  0
3 0.00000  0  0 0.00890 0.00890 0.00712 0.00534 0.00000 0.00890 0.00178  0
4 0.00448  0  0 0.00448 0.00299 0.00448 0.00149 0.00299 0.00000 0.00149  0
5 0.00312  0  0 0.00467 0.00467 0.00000 0.00156 0.00467 0.00312 0.00467  0
6 0.00587  0  0 0.02053 0.00587 0.00000 0.00293 0.00587 0.00293 0.00000  0
7 0.00000  0  0 0.02422 0.00346 0.00000 0.00346 0.00346 0.00000 0.00346  0
       WD      WG      WN              YW        acid_per
base_per  charge_per
1 0.00000 0.00000 0.00000 0.00000 0.14402174 0.12228261 0.019021739
3 0.00178 0.00178 0.00534 0.00178 0.12277580 0.09252669 0.016014235
4 0.00149 0.00448 0.00448 0.00000 0.16591928 0.11509716 0.022421525
5 0.00000 0.00156 0.00000 0.00156 0.13084112 0.10903427 0.009345794
6 0.00293 0.00000 0.00000 0.00000 0.07038123 0.08797654 0.002932551
7 0.00000 0.00346 0.00000 0.00346 0.05536332 0.08650519 0.010380623
  hydrophob_per polar_per num_cell num_genes position             out
1     0.3804348 0.1929348        1         4        1   0
3     0.3540925 0.2508897        1         4        3   0
4     0.3393124 0.2032885        1         4        4   1
5     0.3753894 0.2305296        2         7        1   0
6     0.4868035 0.1964809        2         7        2   0
7     0.4878893 0.1522491        2         7        3   0

> quantile(hh$HK)
     0%     25%     50%     75%    100%
0.00000 0.00000 0.00000 0.00000 0.02703
> quantile(hh$HQ)
   0%   25%   50%   75%  100%
0.000 0.000 0.000 0.000 0.025
> quantile(hh$WC)
     0%     25%     50%     75%    100%
0.00000 0.00000 0.00000 0.00000 0.01266

Can someone give some suggestions?

Thanks!





-- 
Sincerely,
Changbin
--

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