how to draw separate graphs

Question

0 个投票

Good afternoon sir,

Sir I have taken lsqnonlin method to estimate parameters and draw the graphs for prediction.Here i have to draw graphs for 2,3,4 columns but it shows only one column for predictions.sir i request please tell me how to draw graphs for 3rd and 4th columns and also please tell me how to separate graphs.please give me 3 to 5 lines code if possible.

Thanking you sir

the code and graph are

code:

data = [  1  1  0  0
    2  1  0  0
    3  1  0  0
    4  2  0  0
    5  3  0  0
    6  3  0  0
    7  3  0  0
    8  3  0  0
    9  3  0  0
    10  3  0  0
    11  3  0  0
    12  3  0  0
    13  3  0  0
    14  3  0  0
    15  3  1  0
    16  3  1  0
    17  3  1  0
    18  3  2  0
    19  3  2  0
    20  3  2  0
    21  3  2  0
    22  3  3  0
    23  3  3  0
    24  3  3  0
    25  3  3  0
    26  3  3  0
    27  3  3  0
    28  3  3  0
    29  3  3  0
    30  3  3  0
    31  3  3  0
    32  3  3  0
    33  5  3  0
    34  6  3  0
    35  28  3  0
    36  30  3  0
    37  31  3  0
    38  34  3  0
    39  39  3  0
    40  48  3  0
    41  63  4  0
    42  71  4  0
    43  81  4  1
    44  91  10  1
    45  102  10  2
    46  112  13  2
    47  126  14  2
    48  146  15  3
    49  171  15  3
    50  198  20  4
    51  256  23  4
    52  334  23  4
    53  403  23  7
    54  497  25  9
    55  571  40  10
    56  657  43  11
    57  730  50  16
    58  883  75  19
    59  1019  85  24
    60  1139  102  27
    61  1326  137  41
    62  1635  150  47
    63  2059  169  53
    64  2545  191  69
    65  3105  230  83
    66  3684  286  96
    67  4293  329  118
    68  4777  394  134
    69  5350  469  161
    70  5915  565  181
    71  6728  635  227
    72  7599  786  249
    73  8453  972  290
    74  9211  1086  332
    75  10454  1198  359
    76  11485  1365  396
    77  12371  1509  423
    78  13432  1767  449
    79  14354  2040  487
    80  15725  2466  522
    81  17305  2854  560
    82  18544  3273  593
    83  20081  3976  646
    84  21373  4370  682
    85  23040  5012  722
    86  24448  5496  781
    87  26283  5938  825
    88  27890  6523  881
    89  29458  7103  939
    90  31360  7739  1008
    91  33065  8429  1079
    92  34866  9059  1154
    93  37262  10021  1231
    94  39826  10852  1323
    95  42778  11763  1463
    96  46434  12845  1566
    97  49405  14140  1694
    98  53007  15301  1785
    99  56351  16776  1889
    100  59690  17887  1986]
time = data(:,1);
c_data = data(:,[2,3,4]);
beta0 = 0.5;
alpha0 = 0.004;
gamma0 = 0.1;
upsilon0 = 0.13;
epsilon0= 0.07;
lamda0= 0.1;
sigma0= 0.07;
kappa0= 0.03;
nu0 = 0.0001;	
xi0 = 0.0002;
lb =[0,0,0,0,0,0,0,0,0,0]; ub = [1,1,1,1,1,1,1,1,1,1];
p0 = [beta0; alpha0; gamma0;  upsilon0; epsilon0; lamda0; sigma0; kappa0; nu0; xi0 ];
options = optimoptions(@lsqnonlin,'Algorithm','trust-region-reflective');
[p,resnorm,RESIDUAL,exitflag,OUTPUT,LAMBDA,Jacobian] = lsqnonlin(@(p) immanuel(p,time,c_data),p0,lb,ub,options );
disp(p)
Cdata_minus_Cv =immanuel(p,time,c_data);
Cv = c_data-Cdata_minus_Cv;
plot(time,[c_data,Cv])
function  C=immanuel(p,time,c_data)
c0 = [1217378052,100,10,5,3,3,1,1];
[T,Cv]=ode45(@DifEq,time,c0);
    function dC=DifEq(time,c)
        N = 1390000000;
        pi = 150;
        zeta = 0.1;
        eta = 0.2;
        theta = 0.3;
        iota = 0.3;
        delta = 0.1;
        rho = 0.5;
        mu = 0.0000425;
        beta = p(1);
        alpha =p(2);
        gamma = p(3);
        upsilon =p(4);
        epsilon = p(5);
        lamda = p(6);
        sigma = p(7);
        kappa = p(8);
        nu = p(9);	
        xi = p(10);
        dcdt = zeros(8,1);
        dcdt(1) = pi -beta*(zeta*c(3)+eta*c(4)+theta*c(5)+iota*c(6))*(c(1)/N) -mu*c(1);
        dcdt(2) = beta*(zeta*c(3)+eta*c(4)+theta*c(5)+iota*c(6))*(c(1)/N) -(delta+mu)*c(2); 
        dcdt(3) = rho*delta*c(2)-(lamda+gamma+nu+mu)*c(3);
        dcdt(4) = (1-rho)*delta*c(2)-(sigma+kappa+mu)*c(4);
        dcdt(5) = lamda*c(3) + sigma*c(4)-(alpha+upsilon+mu)*c(5);
        dcdt(6) = alpha*c(6)  + kappa*c(4)- (epsilon+xi+mu)*c(6);
        dcdt(7) =  gamma*c(3) + upsilon*c(5) + epsilon*c(6);
        dcdt(8) = nu*c(3)  + xi*c(6);
        dC = dcdt;
    end
C=c_data-Cv(:,2);
end

Graph:

9 个评论
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mallela ankamma rao 2022-6-10

sir this project is mathematical modeling on covid 19.

here 1st column represents days,2nd columns represents confirmed cases,3rd columns represents recovered cases and 4th column represents death cases which are taken from WHO.

In python i knew how to draw how to take curve fitting prediction data against the observed data.

please see code below which belongs to python

fig.add_trace(go.Scatter(x=tspan, y=data[:, 0], mode='markers', name='Observed confirmed', line = dict(dash='dot')))

fig.add_trace(go.Scatter(x=tspan, y=data[:, 1], mode='markers', name='Observed recovered', line = dict(dash='dot')))

fig.add_trace(go.Scatter(x=tspan, y=data[:, 2], mode='markers', name='Observed death', line = dict(dash='dot')))

fig.add_trace(go.Scatter(x=tspan, y=final[:, 0], mode='lines+markers', name='Fitted confirmed'))

fig.add_trace(go.Scatter(x=tspan, y=final[:, 1], mode='lines+markers', name='Fitted recovered'))

fig.add_trace(go.Scatter(x=tspan, y=final[:, 2], mode='lines+markers', name='Fitted death'))

fig.update_layout(title='SEIaIsQHR: Observed vs Fitted',

xaxis_title='Day',

yaxis_title='Counts',

title_x=0.5,

width=1000, height=500)

I need code like this model sir. please help how to draw plot

Torsten 2022-6-10

编辑：Torsten 2022-6-10

Before you plot results, we should first make sure that your code is correct - that's why I insist.

At the moment, you subtract the vector Cv(:,2) from each column of the matrix c_data. Thus, lsqnonlin assumes that columns 2, 3 and 4 of your matrix "data" are repetitions of an experiment and that all three columns should be approximated by Cv(:,2) using one set of parameters. But I think this must be wrong since the values for the three columns differ significantly in size.

So again my question: Is it correct that you want lsqnonlin minimize [c_data(:,1)-Cv(:,2);c_data(:,2)-Cv(:,2);c_data(:,3)-Cv(:,2)] simultaneously ?