Using predict.nls to see how well my model fits my data











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So I originally create a plot of my data using different points to show different combinations of factors.



plot(m, y, xlab='Body mass (kg)', ylab='Maximum oxygen uptake (litres/min)', 
ylim=c(0.24,6.6), type='n')
points(m[s==0 & v==0], y[s==0 & v==0], pch=16, cex=0.6)
points(m[s==0 & v>0], y[s==0 & v>0], pch=1, cex=0.6)
points(m[s==1 & v==0], y[s==1 & v==0], pch=17, cex=0.6)
points(m[s==1 & v>0], y[s==1 & v>0], pch=2, cex=0.6)


I have then fitted a non-linear model to my data using nls.



fitness0.st <- list(a0=2/3, a1=0, a2=0, a3=0, b0=coefs[1], b1=coefs[2], 
b2=coefs[3], b3=coefs[4], g0=coefs[5], g1=coefs[6], g2=coefs[7],
g3=coefs[8])
fitness0.nls <- nls(y ~ m^(a0+a1*s+a2*v+a3*sv)*(b0+b1*s+b2*v+b3*sv+
(g0+g1*s+g2*v+g3*sv)*a), start = fitness0.st)


And now I want to consider how well my model fits the data. I have produced the residual plots for my model using



plot(m,residuals(fitness0.nls), xlab='Body mass (kg)', ylab='Residuals', 
xlim=c(30,130))
plot(fitted(fitness0.nls), residuals(fitness0.nls), xlab='Fitted values',
ylab='Residuals', pch=20)
qqnorm(residuals(fitness0.nls), pch=20, main='Normal QQ-plot')
qqline(residuals(fitness0.nls))


But I would also like to create a pot to show the fit of my model using predict but I am unsure how to do this. Can anyone help?










share|improve this question


























    up vote
    0
    down vote

    favorite












    So I originally create a plot of my data using different points to show different combinations of factors.



    plot(m, y, xlab='Body mass (kg)', ylab='Maximum oxygen uptake (litres/min)', 
    ylim=c(0.24,6.6), type='n')
    points(m[s==0 & v==0], y[s==0 & v==0], pch=16, cex=0.6)
    points(m[s==0 & v>0], y[s==0 & v>0], pch=1, cex=0.6)
    points(m[s==1 & v==0], y[s==1 & v==0], pch=17, cex=0.6)
    points(m[s==1 & v>0], y[s==1 & v>0], pch=2, cex=0.6)


    I have then fitted a non-linear model to my data using nls.



    fitness0.st <- list(a0=2/3, a1=0, a2=0, a3=0, b0=coefs[1], b1=coefs[2], 
    b2=coefs[3], b3=coefs[4], g0=coefs[5], g1=coefs[6], g2=coefs[7],
    g3=coefs[8])
    fitness0.nls <- nls(y ~ m^(a0+a1*s+a2*v+a3*sv)*(b0+b1*s+b2*v+b3*sv+
    (g0+g1*s+g2*v+g3*sv)*a), start = fitness0.st)


    And now I want to consider how well my model fits the data. I have produced the residual plots for my model using



    plot(m,residuals(fitness0.nls), xlab='Body mass (kg)', ylab='Residuals', 
    xlim=c(30,130))
    plot(fitted(fitness0.nls), residuals(fitness0.nls), xlab='Fitted values',
    ylab='Residuals', pch=20)
    qqnorm(residuals(fitness0.nls), pch=20, main='Normal QQ-plot')
    qqline(residuals(fitness0.nls))


    But I would also like to create a pot to show the fit of my model using predict but I am unsure how to do this. Can anyone help?










    share|improve this question
























      up vote
      0
      down vote

      favorite









      up vote
      0
      down vote

      favorite











      So I originally create a plot of my data using different points to show different combinations of factors.



      plot(m, y, xlab='Body mass (kg)', ylab='Maximum oxygen uptake (litres/min)', 
      ylim=c(0.24,6.6), type='n')
      points(m[s==0 & v==0], y[s==0 & v==0], pch=16, cex=0.6)
      points(m[s==0 & v>0], y[s==0 & v>0], pch=1, cex=0.6)
      points(m[s==1 & v==0], y[s==1 & v==0], pch=17, cex=0.6)
      points(m[s==1 & v>0], y[s==1 & v>0], pch=2, cex=0.6)


      I have then fitted a non-linear model to my data using nls.



      fitness0.st <- list(a0=2/3, a1=0, a2=0, a3=0, b0=coefs[1], b1=coefs[2], 
      b2=coefs[3], b3=coefs[4], g0=coefs[5], g1=coefs[6], g2=coefs[7],
      g3=coefs[8])
      fitness0.nls <- nls(y ~ m^(a0+a1*s+a2*v+a3*sv)*(b0+b1*s+b2*v+b3*sv+
      (g0+g1*s+g2*v+g3*sv)*a), start = fitness0.st)


      And now I want to consider how well my model fits the data. I have produced the residual plots for my model using



      plot(m,residuals(fitness0.nls), xlab='Body mass (kg)', ylab='Residuals', 
      xlim=c(30,130))
      plot(fitted(fitness0.nls), residuals(fitness0.nls), xlab='Fitted values',
      ylab='Residuals', pch=20)
      qqnorm(residuals(fitness0.nls), pch=20, main='Normal QQ-plot')
      qqline(residuals(fitness0.nls))


      But I would also like to create a pot to show the fit of my model using predict but I am unsure how to do this. Can anyone help?










      share|improve this question













      So I originally create a plot of my data using different points to show different combinations of factors.



      plot(m, y, xlab='Body mass (kg)', ylab='Maximum oxygen uptake (litres/min)', 
      ylim=c(0.24,6.6), type='n')
      points(m[s==0 & v==0], y[s==0 & v==0], pch=16, cex=0.6)
      points(m[s==0 & v>0], y[s==0 & v>0], pch=1, cex=0.6)
      points(m[s==1 & v==0], y[s==1 & v==0], pch=17, cex=0.6)
      points(m[s==1 & v>0], y[s==1 & v>0], pch=2, cex=0.6)


      I have then fitted a non-linear model to my data using nls.



      fitness0.st <- list(a0=2/3, a1=0, a2=0, a3=0, b0=coefs[1], b1=coefs[2], 
      b2=coefs[3], b3=coefs[4], g0=coefs[5], g1=coefs[6], g2=coefs[7],
      g3=coefs[8])
      fitness0.nls <- nls(y ~ m^(a0+a1*s+a2*v+a3*sv)*(b0+b1*s+b2*v+b3*sv+
      (g0+g1*s+g2*v+g3*sv)*a), start = fitness0.st)


      And now I want to consider how well my model fits the data. I have produced the residual plots for my model using



      plot(m,residuals(fitness0.nls), xlab='Body mass (kg)', ylab='Residuals', 
      xlim=c(30,130))
      plot(fitted(fitness0.nls), residuals(fitness0.nls), xlab='Fitted values',
      ylab='Residuals', pch=20)
      qqnorm(residuals(fitness0.nls), pch=20, main='Normal QQ-plot')
      qqline(residuals(fitness0.nls))


      But I would also like to create a pot to show the fit of my model using predict but I am unsure how to do this. Can anyone help?







      r statistics predict nls






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      asked Nov 22 at 1:15









      Christie

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