regression

I've been trying to implement time series prediction tool using support vector regression in python language. I use SVR module from scikit-learn for non-linear Support vector regression. But I have serious problem with prediction of future events. The regression line fits the original function great (from known data) but as soon as I want to predict future steps, it returns value from the last known step. My code looks like this: import numpy as np from matplotlib import pyplot as plt from sklearn.svm import SVR X = np.arange(0,100) Y = np.sin(X) svr_rbf = SVR(kernel='rbf', C=1e5, gamma=1e5) y

I am trying to run a multi-level model on multiply imputed data (created with Amelia); the sample is based on a clustered sample with group = 24, N= 150. library("ZeligMultilevel") ML.model.0 <- zelig(dv~1 + tag(1|group), model="ls.mixed", data=a.out$imputations) summary(ML.model.0) This code produces the following error code: Error in object[[1]]$result$call : $ operator not defined for this S4 class If I run a OLS regression, it works: model.0 <- zelig(dv~1, model="ls", data=a.out$imputations) m.0 <- coef(summary(model.0)) print(m.0, digits = 2) Value Std. Error t-stat p-value [1,] 45 0.34

Original question I want to smooth my explanatory variable, something like Speed data of a vehicle, and then use this smoothed values. I searched a lot, and find nothing that directly is my answer. I know how to calculate the kernel density estimation ( density() or KernSmooth::bkde() ) but I don't know then how to calculate the smoothed values of speed. Re-edited question Thanks to @ZheyuanLi, I am able to better explain what I have and what I want to do. So I have re-edited my question as below. I have some speed measurement of a vehicle during a time, stored as a data frame vehicle : t

When using stat_smooth() with geom_point is there a way to remove the shaded fit region, but only draw its outer bounds? I know I can remove the shaded region with something like: geom_point(aes(x=x, y=y)) + geom_stat(aes(x=x, y=y), alpha=0) but how can I make the outer bounds of it (outer curves) still visible as faint black lines? agstudy You can also use geom_ribbon with fill = NA. gg <- ggplot(mtcars, aes(qsec, wt))+ geom_point() + stat_smooth( alpha=0,method='loess') rib_data <- ggplot_build(gg)$data[[2]] ggplot(mtcars)+ stat_smooth(aes(qsec, wt), alpha=0,method='loess')+ geom_point(aes

I am trying to do this little tutorial on keras about regression: http://machinelearningmastery.com/regression-tutorial-keras-deep-learning-library-python/ Unfortunately I am running into an error I cannot fix. If i just copy and paste the code I get the following error when running this snippet: import numpy import pandas from keras.models import Sequential from keras.layers import Dense from keras.wrappers.scikit_learn import KerasRegressor from sklearn.model_selection import cross_val_score from sklearn.model_selection import KFold from sklearn.preprocessing import StandardScaler from