filling missing values in column with obtained results

clear all; clc; format long;
TBFi = [2993 5036 6150 6919 8862 11488 13545];
Frequence = [6 24 32 41 59 76 94];
p = polyfit(TBFi,Frequence,1);
x_min = min(TBFi);
x_max = max(TBFi);
d_min = polyval(p,x_min);
d_max = polyval(p,x_max);
N=11;
i=[2;6;8;10];
missing_frequences=(i-0.3)/(N+.4)
missing_TBFi=((missing_frequences.*10^2)-p(2))/p(1)

both t and freq are my replaced manually by me

Answer 1

[Star Strider]

Try this —

Order = [1 3 4 5 7 9 11].';
TBFi = [2993 5036 6150 6919 8862 11488 13545].';
Frequence = [6 24 32 41 59 76 94].';
OrderInterp = 1:11;
Filled = interp1(Order, [TBFi Frequence], OrderInterp(:))

Filled = 11×2

1.0e+04 * 0.2993 0.0006 0.4014 0.0015 0.5036 0.0024 0.6150 0.0032 0.6919 0.0041 0.7891 0.0050 0.8862 0.0059 1.0175 0.0067 1.1488 0.0076 1.2516 0.0085

FilledTable = table(OrderInterp(:),Filled(:,1),Filled(:,2), 'VariableNames',{'Order','TBFi','Frequence'})

FilledTable = 11×3 table
    Order     TBFi     Frequence
    _____    ______    _________

      1        2993         6   
      2      4014.5        15   
      3        5036        24   
      4        6150        32   
      5        6919        41   
      6      7890.5        50   
      7        8862        59   
      8       10175      67.5   
      9       11488        76   
     10       12516        85   
     11       13545        94   

It uses the interp1 function (introduced before R2006a) and the default linear interpolation to fill the table.

The table call is useful, however lacking it:

sprintf('\n\tOrder\tTBFi\t  Frequence\n')

ans = 
    '
     	Order	TBFi	Frequence
     '

sprintf('\t%2.0f\t%6.0f\t\t%3.0f\n', [OrderInterp(:) Filled]')

ans = 
    '	 1	  2993		  6
     	 2	  4014		 15
     	 3	  5036		 24
     	 4	  6150		 32
     	 5	  6919		 41
     	 6	  7890		 50
     	 7	  8862		 59
     	 8	 10175		 68
     	 9	 11488		 76
     	10	 12516		 85
     	11	 13545		 94
     '

EDIT — (27 Jun 2021 at 20:02)

Added sprintf calls after noticing R2012a.

.

[firrou bouteflika]

thank you for your answer but i have to use my linear regression equation y=0.0083x-17.776284 if you can find another way please help me

this interpolation methode is giving me close but wrong answers

and also in this interpolation method if i wanted to use the TBF answer in an equation how would i do that

[Star Strider]

That was not in the original post, at least as far as I can determine.

There is one regression equation and two variables. I have no idea what ‘x’ and ‘y’ are.

Try this:

format short g
Order = [1 3 4 5 7 9 11].';
TBFi = [2993 5036 6150 6919 8862 11488 13545].';
Frequence = [6 24 32 41 59 76 94].';

B = [Order ones(size(Order))] \ [TBFi Frequence]

B = 2×2

1055.1 8.7821 1827.2 -2.7549

OrderInterp = 1:11;
Filled = [OrderInterp(:) ones(size(OrderInterp(:)))] * B;
Result = [OrderInterp(:) Filled]

Result = 11×3

1 2882.3 6.0272 2 3937.4 14.809 3 4992.4 23.591 4 6047.5 32.374 5 7102.5 41.156 6 8157.6 49.938 7 9212.6 58.72 8 10268 67.502 9 11323 76.284 10 12378 85.066

ResultTable = array2table(Result, 'VariableNames',{'Order','TBFi','Frequence'})

ResultTable = 11×3 table
    Order     TBFi     Frequence
    _____    ______    _________

      1      2882.3     6.0272  
      2      3937.4     14.809  
      3      4992.4     23.591  
      4      6047.5     32.374  
      5      7102.5     41.156  
      6      8157.6     49.938  
      7      9212.6      58.72  
      8       10268     67.502  
      9       11323     76.284  
     10       12378     85.066  
     11       13433     93.848  

That is the best I can do with respect to a linear regression.

Also, I did not realise that this is homework. The policy here on MATLAB Answers is not to provide complete solutions to homework problems. Too late now.

.

[firrou bouteflika]

thank you very much but i don't think i will use this because as i understand interpolation it a different method (i study mechanical ingeneering and this stuff is not my specialty) so i have to stick to my linear regression, my home work was done this is just something extra i tried to do and learn more, i tried to connect two scripts

and i just i want to learn please tell me if there is a way to use this on a equation

[firrou bouteflika]

just to clarifie my first question maybe u didn't understand me what i wanted is just to to place the results in the missing spots (is that possible)

[Star Strider]

My code performs a linear regression using the mldivide,\ function or operator.

This is the first time the ‘R’ function has appeared, and ‘t’ is new as well:

R = @(t) exp(-((t/9400).^2.2));

What is ‘t’ and what result do you want?

.

[firrou bouteflika]

this is how i imagined it in my head

i give him this

clear all; clc; format long;
TBFi = [2993 5036 6150 6919 8862 11488 13545];
Frequence = [6 24 32 41 59 76 94];
p = polyfit(TBFi,Frequence,1);
x_min = min(TBFi);
x_max = max(TBFi);
d_min = polyval(p,x_min);
d_max = polyval(p,x_max);                     
N=11;
i=[2;6;8;10];
missing_frequences=(i-0.3)/(N+.4)
missing_TBFi=((missing_frequences.*10^2)-p(2))/p(1)

and now i tell him where to fill the empty spots with results (missing_TBFi and missing_frequences)

TBFi = [2993 [1] 5036 6150 6919 [2] 8862 [3] 11488 [4] 13545];
Frequence = [6 [1] 24  32 41 [2] 59 [3] 76 [4] 94];

so that i get a new full 'TBFi' (wich i called 't' because i had to seperate their name because i insert them manually but if i can get them automatically i will call them TBFi or not)

TBFi = [2993;3938.1;5063;6150;6919;8165.8;8862;10279;11488;12392.3;13545];

to use after in some equations (i wont use frequence in this part i just want to see them together)

R = @(TBFi) exp(-((TBFi/9400).^2.2));
F = @(TBFi) 1-exp(-((TBFi/9400).^2.2));
f = @(TBFi) (2.2/9400).*((TBFi/9400).^1.2).*exp(-((TBFi/9400).^2.2));
y = @(TBFi) (2.2/9400).*((TBFi/9400).^1.2);

[Star Strider]

Doing a linear interpolation would appear to be appropriate:

Order = [1 3 4 5 7 9 11].';
TBFi = [2993 5036 6150 6919 8862 11488 13545].';
Frequence = [6 24 32 41 59 76 94].';
OrderInterp = 1:11;
Filled = interp1(Order, [TBFi Frequence], OrderInterp(:));

TBFii = Filled(:,1);                                            % Interpolated 'TBFi'
Frequncei = Filled(:,2);                                        % Interpolated 'Frequence'

% FilledTable = table(OrderInterp(:),Filled(:,1),Filled(:,2), 'VariableNames',{'Order','TBFi','Frequence'})

figure
yyaxis left
hyl = plot(Order, TBFi, 'p');
hold on
plot(OrderInterp, Filled(:,1),'+-', 'Color',hyl.Color)
hold off
ylabel('TPi')
yyaxis right
hyr = plot(Order, Frequence, 'p');
hold on
plot(OrderInterp, Filled(:,2), '+-', 'Color',hyr.Color)
hold off
ylabel('Frequence')
grid

I seriously doubt that a linear regression would produce comparable or more accurate results. However if you want to use that approach, the code in my previous Comment is more efficient than using polyfit and polyval, each twice, in order to achieve the same result.

Then:

R = @(TBFi) exp(-((TBFi/9400).^2.2));
F = @(TBFi) 1-exp(-((TBFi/9400).^2.2));
f = @(TBFi) (2.2/9400).*((TBFi/9400).^1.2).*exp(-((TBFi/9400).^2.2));
y = @(TBFi) (2.2/9400).*((TBFi/9400).^1.2);

would produce (again using a table for convenience):

RFfy = table(OrderInterp(:),R(TBFii),F(TBFii),f(TBFii),y(TBFii), 'VariableNames',{'Order','R','F','f','y'})

RFfy = 11×5 table
    Order       R          F            f             y     
    _____    _______    ________    __________    __________

      1      0.92253    0.077475    5.4682e-05    5.9275e-05
      2       0.8574      0.1426    7.2291e-05    8.4314e-05
      3       0.7762      0.2238    8.5905e-05    0.00011067
      4      0.67488     0.32512    9.4933e-05    0.00014067
      5      0.60075     0.39925    9.7339e-05    0.00016203
      6      0.50643     0.49357    9.6069e-05     0.0001897
      7      0.41545     0.58455    9.0594e-05    0.00021806
      8       0.3041      0.6959     7.827e-05    0.00025738
      9      0.21125     0.78875    6.2896e-05    0.00029774
     10      0.15297     0.84703    5.0481e-05    0.00033001
     11      0.10713     0.89287    3.8867e-05    0.00036281

The calculations using ‘Frequencei’ (‘Frequence’ interpolated) wouild go similarly.

.