seems to work fine Or, you can use loess(..., degree=1). This produces a very similar, but not quite identical result to lowess(...) code :
set.seed(1) # for reproducibility
y<rnorm(100)
x<rgamma(100,2,2)
plot(x,y)
points(x,loess(y~x,data.frame(x,y),degree=1)$fitted,pch=20,col="red")
lines(lowess(y~x))
qplot(x,y)+stat_smooth(se=F,degree=1)+
theme_bw()+
geom_point(data=as.data.frame(lowess(y~x)),aes(x,y),col="red")
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Matlab scatter plot with lowess curve
By : Xelibu
Date : March 29 2020, 07:55 AM
This might help you This is the simplest code I can come up with. This assumes you have your data in x and y vectors. code :
%adjust bins accordingly, or add a line that calculates them based on range.
bins = 0.5:0.01:0.5;
nBins = length(bins);
for index = 1:(nBins1)
binVec = (x >= bins(index) & x < bins(index+1));
lowess(index) = mean(y(binvec));
end
%note that bins are shifted by one half step.
plot(x,y,'.',bins+0.005,lowess,'r');

Predicting via Lowess in R (OR reconciling Loess & Lowess)
By : user3383612
Date : March 29 2020, 07:55 AM
Any of those help I don't know how to "reconcile" those two functions but I have used the cobs package (COnstrained BSplines Nonparametric Regression Quantiles ) with some success for similar tasks. The default quantile is the (local) median or 0.5 quantile. In this dataset the default choices for span or kernel width seem very appropriate. code :
require(cobs)
Loading required package: cobs
Package cobs (1.30) attached. To cite, see citation("cobs")
Rbs < cobs(x=dat$experience,y=dat$salary, constraint= "increase")
qbsks2():
# Performing general knot selection ...
#
# Deleting unnecessary knots ...
Rbs
#COBS regression spline (degree = 2) from call:
# cobs(x = dat$experience, y = dat$salary, constraint = "increase")
#{tau=0.5}quantile; dimensionality of fit: 5 from {5}
#x$knots[1:4]: 0.999966, 5.000000, 15.000000, 35.000034
plot(Rbs, lwd = 2.5)
help(predict.cobs)
predict(Rbs, z=seq(0,40,by=5))
z fit
[1,] 0 21519.83
[2,] 5 25488.71
[3,] 10 30653.44
[4,] 15 32773.21
[5,] 20 33295.84
[6,] 25 33669.14
[7,] 30 33893.12
[8,] 35 33967.78
[9,] 40 33893.12

R: how to plot a cox regression model survival curves (treated vs control curves) using ggplot2?
By : Thiago Willians Gome
Date : March 29 2020, 07:55 AM
Does that help There are some functions for this, if you look it up. For example, the ggsurv function from the GGally package seems to do this. You can find a tutorial on using this function here. Also, with some knowledge of ggplot2, you can adapt the code of the function to however it suits you better.

How to plot two curves with error bars using R ggplot2.qplot
By : lenzhao
Date : March 29 2020, 07:55 AM
this will help How can I put two graphs with error bars on one graph using ggplot.qplot. , Note I tweaked the given vectors to create the data frames. code :
library(ggplot2)
library(dplyr)
library(tidyr)
library(magrittr)
time_points = c(15, 30, 60, 90, 120, 150, 180)
control_data = c(1,2,3,4,5,6,7)
control_sd = c(0, 1, 0.2, 0.3, 0.4, 0.5, 0.6)
treated_data = c(9,8,7,6,5,4,3)
treated_sd = c(0.1, 0.4, 0.6, 0.8, 0.8, 0.9, 1.5)
df < data.frame(time=time_points,
cd=control_data,
td=treated_data,
csd=control_sd,
tsd=treated_sd)
df %>%
# stack the control and treated columns using tidyr's gather
# from here we distinguish the different series by the type column
gather(type,value,cd,td) %>%
# append a column for error bar ymin, depending on stacked type
mutate(ymin=ifelse(type=='cd',valuecsd,valuetsd)) %>%
# append a column for error bar ymax, depending on stacked type
mutate(ymax=ifelse(type=='cd',value+csd,value+tsd)) %>%
# pass the revised data frame to ggplot with the computed aesthetics
ggplot(aes(x=time,y=value,color=type,ymin=ymin,ymax=ymax)) +
geom_errorbar() +
geom_point()

Scatterplot matrix with lowess smoother
By : Cyril Mallet
Date : March 29 2020, 07:55 AM
it should still fix some issue What would the Python code be for a scatterplot matrix with lowess smoothers similar to the following one? , I adapted the pandas scatter_matrix function and got a decent result:

