This function searches a numeric value **d** in dataset **vd** and returns the value in **vref** whose index number is the same as **d** in **vd**. Interpolation can be employed during this process. This function is usually used to find out the fitted X or Y values after performing the linear or nonlinear fitting, depending on the order of the dataset arguments (See the two examples below).

//If vref contains numeric values, double Table(vector vd, vector vref, double d[, int option]) //If vref contains strings, string Table(vector vd, vector<string> vref, double d[, int option])

**vd**

- is the dataset in which value
**d**is looked up. The values in**vd**must be placed in ascending order. Otherwise, the function might not return the correct value.

**vref**

- is the reference vector whose
*n*th value will be return.*n*is the index number of**d**within**vd**.

**d**

- is the numeric value to find in
**vd**.

**option**

- [optional] integer that specifies how to find out value
**d**. The default value -1 means the function does linear interpolation on**vd**vs**vref**and returns the interpolated value. When option = 0, the function searches in**vd**and finds a value that is equal to**d**or closest from left. If return value is string, 0 will be used as default since linear interpolation is meaningless to sting. When option = 1, the function finds a value that is equal to**d**or closest from right. When option = 2, the function finds a value that is equal to**d**or closest from both sides.

Returns the value in **vref** whose index number is the same as **d** in **vd**.

*Example1*

The following script returns new Y values from a fit curve:

linearfit_Ynew = Table(linearfit_a, linearfit_data1b, linearfit_b)

where linearfit_a is the X fit dataset, linearfit_data1b is the Y fit dataset, and linearfit_b holds the X values for predicting Y values.

*Example2*

The following script returns new X values from a fit curve:

linearfit_Xnew = Table(linearfit_data1b, linearfit_a, linearfit_b)

where linearfit_data1b is the Y fit dataset, linearfit_a is the X fit dataset, and linearfit_b holds the Y values for predicting X values.

*Example3*

The following example shows you how the return value is determined by parameter **option**

col(a) = data(1,20); col(b) = data(2,20); a1 = table(col(a),col(b),4.3,1); //search 4.3 from right a1 = ; //should return a1=6 a2 = table(col(a),col(b),4.3,0); //search 4.3 from left a2 = ; //should return a2=5 a3 = table(col(a),col(b),4.3,2); //search 4.3 from both sides a3 = ; //should return a3=5 a4 = table(col(a),col(b),4.3,-1); //perform linear interpolation on column A and B a4 = ; //should return a4=5.3