How to fit a smooth curve to my data in R

Question

I m trying to draw a smooth curve in R  I have the following simple toy data    gt  x   1   1  2  3  4  5  6  7  8  9 10  gt  y   1   2  4  6  8  7 12 14 16 18 20   Now when I plot it with a standard command it looks bumpy and edgy  of course    gt  plot x y  type  l   lwd 2  col  red     How can I make the curve smooth so that the 3 edges are rounded using estimated values  I know there are many methods to fit a smooth curve but I m not sure which one would be most appropriate for this type of curve and how you would write it in R

User · Answer

I didn t see this method shown  so if someone else is looking to do this I found that ggplot documentation suggested a technique for using the gam method that produced similar results to loess when working with small data sets    library ggplot2  x  lt - 1 10 y  lt - c 2 4 6 8 7 8 14 16 18 20   df  lt - data frame x y  r  lt - ggplot df  aes x   x  y   y     geom smooth method    gam   formula   y   s x  bs    cs    geom point   r   First with the loess method and auto formula Second with the gam method with suggested formula

User · Answer

Maybe smooth spline is an option  You can set a smoothing parameter  typically between 0 and 1  here  smoothingSpline   smooth spline x  y  spar 0 35  plot x y  lines smoothingSpline    you can also use predict on smooth spline objects  The function comes with base R  see   smooth spline for details

User · Answer

LOESS is a very good approach  as Dirk said   Another option is using Bezier splines  which may in some cases work better than LOESS if you don t have many data points   Here you ll find an example  http   rosettacode org wiki Cubic bezier curves R    x  y  the x and y coordinates of the hull points   n  the number of points in the curve  bezierCurve  lt - function x  y  n 10            outx  lt - NULL     outy  lt - NULL      i  lt - 1     for  t in seq 0  1  length out n                     b  lt - bez x  y  t          outx i   lt - b x         outy i   lt - b y          i  lt - i 1                return  list x outx  y outy          bez  lt - function x  y  t            outx  lt - 0     outy  lt - 0     n  lt - length x -1     for  i in 0 n                    outx  lt - outx   choose n  i    1-t   n-i   t i x i 1          outy  lt - outy   choose n  i    1-t   n-i   t i y i 1                 return  list x outx  y outy            Example usage x  lt - c 4 6 4 5 6 7  y  lt - 1 6 plot x  y   o   pch 20  points bezierCurve x y 20   type  l   col  red

User · Answer

I like loess   a lot for smoothing   x  lt - 1 10 y  lt - c 2 4 6 8 7 12 14 16 18 20  lo  lt - loess y x  plot x y  lines predict lo   col  red   lwd 2    Venables and Ripley s MASS book has an entire section on smoothing that also covers splines and polynomials -- but loess   is just about everybody s favourite

User · Answer

the qplot   function in the ggplot2 package is very simple to use and provides an elegant solution that includes confidence bands   For instance   qplot x y  geom  smooth   span  0 5    produces

User · Answer

In order to get it REALLY smoooth     x  lt - 1 10 y  lt - c 2 4 6 8 7 8 14 16 18 20  lo  lt - loess y x  plot x y  xl  lt - seq min x  max x    max x  - min x   1000  lines xl  predict lo xl   col  red   lwd 2    This style interpolates lots of extra points and gets you a curve that is very smooth   It also appears to be the the approach that ggplot takes  If the standard level of smoothness is fine you can just use   scatter smooth x  y

User · Answer

The other answers are all good approaches   However  there are a few other options in R that haven t been mentioned  including lowess and approx  which may give better fits or faster performance   The advantages are more easily demonstrated with an alternate dataset   sigmoid  lt - function x      y lt -1  1 exp - 15  x-100      return y     dat lt -data frame x rnorm 5000  30 100  dat y lt -as numeric as logical round sigmoid dat x  rnorm 5000   3 0      Here is the data overlaid with the sigmoid curve that generated it     This sort of data is common when looking at a binary behavior among a population   For example  this might be a plot of whether or not a customer purchased something  a binary 1 0 on the y-axis  versus the amount of time they spent on the site  x-axis      A large number of points are used to better demonstrate the performance differences of these functions   Smooth  spline  and smooth spline all produce gibberish on a dataset like this with any set of parameters I have tried  perhaps due to their tendency to map to every point  which does not work for noisy data   The loess  lowess  and approx functions all produce usable results  although just barely for approx   This is the code for each using lightly optimized parameters   loessFit  lt - loess y x  dat  span   0 6  loessFit  lt - data frame x loessFit x y loessFit fitted  loessFit  lt - loessFit order loessFit x     approxFit  lt - approx dat n   15   lowessFit  lt -data frame lowess dat f    6 iter 1     And the results   plot dat col  gray   curve sigmoid 0 200 add TRUE col  blue    lines lowessFit col  red   lines loessFit col  green   lines approxFit col  purple   legend 150  6         legend c  Sigmoid   Loess   Lowess   Approx           lty c 1 1          lwd c 2 5 2 5  col c  blue   green   red   purple        As you can see  lowess produces a near perfect fit to the original generating curve   Loess is close  but experiences a strange deviation at both tails   Although your dataset will be very different  I have found that other datasets perform similarly  with both loess and lowess capable of producing good results   The differences become more significant when you look at benchmarks    gt  microbenchmark  microbenchmark loess y x  dat  span   0 6  approx dat n   20  lowess dat f    6 iter 1  times 20  Unit  milliseconds                            expr        min         lq       mean     median        uq        max neval cld   loess y   x  dat  span   0 6  153 034810 154 450750 156 794257 156 004357 159 23183 163 117746    20   c             approx dat  n   20    1 297685   1 346773   1 689133   1 441823   1 86018   4 281735    20 a    lowess dat  f   0 6  iter   1    9 637583  10 085613  11 270911  11 350722  12 33046  12 495343    20  b    Loess is extremely slow  taking 100x as long as approx   Lowess produces better results than approx  while still running fairly quickly  15x faster than loess    Loess also becomes increasingly bogged down as the number of points increases  becoming unusable around 50 000     EDIT   Additional research shows that loess gives better fits for certain datasets   If you are dealing with a small dataset or performance is not a consideration  try both functions and compare the results

User · Answer

In ggplot2 you can do smooths in a number of ways  for example   library ggplot2  ggplot mtcars  aes wt  mpg     geom point       geom smooth method    gam   formula   y   poly x  2    ggplot mtcars  aes wt  mpg     geom point       geom smooth method    loess   span   0 3  se   FALSE

[r] How to fit a smooth curve to my data in R?

Examples related to r

Examples related to plot

Examples related to curve-fitting