regression

For the simple example below, you can see that there are certain points that are identified in the ensuing plots. How can I extract the row numbers identified in these plots, especially the Normal Q-Q plot? set.seed(2016) maya <- data.frame(rnorm(100)) names(maya)[1] <- "a" maya$b <- rnorm(100) mara <- lm(b~a, data=maya) plot(mara) I tried using str(mara) to see if I could find a list there, but I can't see any of the numbers from the Normal Q-Q plot there. Thoughts? I have edited your question using set.seed(2016) for reproducibility. To answer your question, I need to explain how to produce

I am working on a big data set having over 300K elements, and running some regression analysis trying to estimate a parameter called Rate using the predictor variable Distance. I have the regression equation. Now I want to get the confidence and prediction intervals. I can easily get the confidence intervals for the coefficients by the command: > confint(W1500.LR1, level = 0.95) 2.5 % 97.5 % (Intercept) 666.2817393 668.0216072 Distance 0.3934499 0.3946572 which gives me the upper and lower bounds for the CI of the coefficients. Now I want to get the same upper and lower bounds for the

Fitting a linear-regression model using least squares on my training dataset works fine. library(Matrix) library(tm) library(glmnet) library(e1071) library(SparseM) library(ggplot2) trainingData <- read.csv("train.csv", stringsAsFactors=FALSE,sep=",", header = FALSE) testingData <- read.csv("test.csv",sep=",", stringsAsFactors=FALSE, header = FALSE) lm.fit = lm(as.factor(V42)~ ., data = trainingData) linearMPrediction = predict(lm.fit,newdata = testingData, se.fit = TRUE) mean((linearMPrediction$fit - testingData[,20:41])^2) linearMPrediction$residual.scale However, when i try to fit a ridge

301_PyTorch中文教程：回归分析-Regression 更多参考: https://morvanzhou.github.io/tutorials/ 油管频道: https://www.youtube.com/user/MorvanZhou 依赖软件包 torch matplotlib import torch import torch.nn.functional as F import matplotlib.pyplot as plt %matplotlib inline torch.manual_seed(1) # reproducible <torch._C.Generator at 0x7f2c68165e90> x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1) # x data (tensor), shape=(100, 1) y = x.pow(2) + 0.2*torch.rand(x.size()) # noisy y data (tensor), shape=(100, 1) plt.scatter(x.data.numpy(), y.data.numpy()) plt.show() x[:10] tensor([[-1.0000], [-0.9798], [-0.9596], [-0.9394],

Say I want to run regressions per group whereby I want to use the last 5 year data as input for that regression. Then, for each next year, I would like to "shift" the input for that regression by one year (i.e., 4 observations). From those regressions I want to extract both the R2 and the fitted values/residuals, which I then need in subsequent regressions that follow similar notions. I have some code working using loops, but it is not really elegant nor efficient for large datasets. I assume there must be a nice plyr way for resolving this issue. # libraries # library(dplyr) library(broom) #

Given time-series data, I want to find the best fitting logarithmic curve. What are good libraries for doing this in either Python or SQL? Edit: Specifically, what I'm looking for is a library that can fit data resembling a sigmoid function, with upper and lower horizontal asymptotes. If your data were categorical, then you could use a logistic regression to fit the probabilities of belonging to a class (classification). However, I understand you are trying to fit the data to a sigmoid curve, which means you just want to minimize the mean squared error of the fit. I would redirect you to the

I have tried to apply this QA: "efficient looping logistic regression in R" to my own problem but I cannot quite make it work. I haven't tried to use apply, but I was told by a few people that a for loop is the best here (if someone believes otherwise please feel free to explain!) I think this problem is pretty generalizeable and not too esoteric for the forum. This is what I want to achieve: I have a dataset with 3 predictor variables (gender, age, race) and a dependent variable (a proportion) for 86 genetic positions for several people. I want to run bivariate linear regressions for each