I have some personal dataset. So I split it into variable to predict and predictors. Following is the syntax: library(Cubist) str(A) 'data.frame': 6038 obs. of 3 variables: $ ads_return_count : num 7 10 10 4 10 10 10 10 10 9 ... $ actual_cpc : num 0.
I have following code to find eta-squared values for regression model: > mod = lm(mpg~., mtcars) > summary(mod) Call: lm(formula = mpg ~ ., data = mtcars) Residuals: Min 1Q Median 3Q Max -3.4506 -1.6044 -0.1196 1.2193 4.6271 Coefficients: Estimate S
I am looking to create some code that will out-of-sample forecast the HAR-RV model. The model itself is formulated as the following, and the betas are estimated through HAC-OLS or Newey-West. Where weekly and monthly are 5 and 22 daily averages of th
This might take a little to explain but here goes. For the sake of this example lets say I have 2 columns that relate to real world observed data (RealX) and 4 columns that relate to predictions (ModX) from a model output of that real world data. To
I am trying to understand the different behaviors of these two smoothing functions when given apparently equivalent inputs. My understanding was that locpoly just takes a fixed bandwidth argument, while locfit can also include a varying part in its s
I recently started learning how to use the python library theano and using various examples provided in the documentation and this blog post(http://underflow.fr/ai/lets-play-with-theano-547) I wrote the code for batch gradient descent. However, I am
I am running a binary logistic regression in SPSS, to test the effect of e.g. TV advertisements on the probability of a consumer to buy a product. My problem is that with the formula of binary logistic regression: P=1/(1+e^(-(a+b*Adv)) ) the maximum
I saw this answer from Jayden a while ago about adding regression equation to a plot, which I found very useful. But I don't want to display R^2, so I changed the code a bit to this: lm_eqn = function(m) { l <- list(a = format(coef(m)[1], digits =
I have the following data: dput(dat) structure(list(Band = c(1930, 1930, 1930, 1930, 1930, 1930, 1930, 1930, 1930, 1930, 1930, 1930, 1930, 1930, 1930, 1930, 1930, 1930 ), Reflectance = c(25.296494, 21.954657, 18.981184, 15.984661, 14.381341, 12.48537
I want to learn multiple linear regression model for my data frame. Below is demo code. Everywhere on internet I only found as such code where target variable can be learned with other variables but on complete data set. I do not specifically found k
Does the scikit-learn Ridge regression include the intercept coefficient in the regularization term, and if so, is there a way to run ridge regression without regularizing the intercept? Suppose I fit a Ridge Regression: from sklearn import linear_mo
I have a data-set which has columns as x1 x2 x3 x4 x5 y all of them has integer / float value and Y values ranges from 98,000 to 1,10,000 If I want to find the relationship between x1 and y , x2 and y ... x5 and y and come up with y = A.x1+c how shou
I am confused. I input a .csv file in R and want to fit a linear multivariate regression model. However, R declares all my obvious numeric variables to be factors and my categorial variables to be integers. Therefore, I cannot fit the model. Does any
I'm incrementally up the parameters of WLS regression functions using statsmodels. I have a 10x3 dataset X that I declared like this: X = np.array([[1,2,3],[1,2,3],[4,5,6],[1,2,3],[4,5,6],[1,2,3],[1,2,3],[4,5,6],[4,5,6],[1,2,3]]) This is my dataset,
I would like to have the correlation coefficient R instead of coefficient of determination r2. I wonder if there is a way to get it from the regression analyses. t = c(1,2,5,4,8,7,5,1,2,5,4,1,2,1,5) t1 = c(1,2,4,4,5,3,7,5,6,8,7,1,2,1,5) y = c(1,2,1,4
To analyze the data of an eyetracking experiment I preprocessed the data using Matlab and now I wanna conduct regression analysis in R. OLS Regression and quantile regression to be specific. For a single test person in this case "vp31" I started like
So I'm taking N bootstrap samples and training N logistic regression classifiers on these samples. Each classifier gives me some probability of being in a binary class and then I average these N probabilities to get a final prediction. My question is
Does any one know how I could use the same relevel method in a linear regression (lm) in order to combine several categorical levels as the reference cell. In my case, my categorical variable of car Type has levels Coupe, Convertable, Wagon, Sedan an
I am trying to use matlab to obtain line equations from multiple matrices. I have three matrices A,B,C,all are of same size (5000 by 2000 ); For x axis, it is always X=[10,15,20]； For y axis, it would be a matrices like this [A(i,j), B(i,j),C(i,J)].
I want to use the new bootMer() feature of the new lme4 package (the developer version currently). I am new to R and don't know which function should I write for its FUN argument. It says it needs a numerical vector, but I have no idea what that func