predict

I would like to plot a model with ggplot2. I have estimated a robust variance-covariance matrix which I would like to use when estimating the confidence interval. Can I tell ggplot2 to use my VCOV, or, alternatively, can I somehow force predict.lm to use my VCOV matrix? A dummy example: source("http://people.su.se/~ma/clmclx.R") df <- data.frame(x1 = rnorm(100), x2 = rnorm(100), y = rnorm(100), group = as.factor(sample(1:10, 100, replace=T))) lm1 <- lm(y ~ x1 + x2, data = df) coeftest(lm1) ## outputs coef.test, but can be modified to output VCOV clx(lm1, 1, df$group) It would be relatively

working with a data frame x Date Val 1/1/2012 7 2/1/2012 9 3/1/2012 20 4/1/2012 24 5/1/2012 50 a <- seq(as.Date(tail(x, 1)$Date), by="month", length=5) a <- data.frame(a) x.lm <- lm(x$Val ~ x$Date) x.pre<-predict(x.lm, newdata=a) I am getting this erro: Warning message: 'newdata' had 5 rows but variable(s) found have 29 rows what am I doing wrong? here is the dput output: dput(x) structure(list(Date = structure(c(14610, 14641, 14669, 14700, 14730, 14761, 14791, 14822, 14853, 14883, 14914, 14944, 14975, 15006, 15034, 15065, 15095, 15126, 15156, 15187, 15218, 15248, 15279, 15309, 15340, 15371,

I've fitted a logistic regression model that predicts the a binary outcome vs from mpg ( mtcars dataset). The plot is shown below. How can I determine the mpg value for any particular vs value? For example, I'm interested in finding out what the mpg value is when the probability of vs is 0.50. Appreciate any help anyone can provide! model <- glm(vs ~ mpg, data = mtcars, family = binomial) ggplot(mtcars, aes(mpg, vs)) + geom_point() + stat_smooth(method = "glm", method.args = list(family = "binomial"), se = FALSE) The easiest way to calculate predicted values from your model is with the predict

Does anyone have a nice clean way to get predict behavior for felm models? library(lfe) model1 <- lm(data = iris, Sepal.Length ~ Sepal.Width + Species) predict(model1, newdata = data.frame(Sepal.Width = 3, Species = "virginica")) # Works model2 <- felm(data = iris, Sepal.Length ~ Sepal.Width | Species) predict(model2, newdata = data.frame(Sepal.Width = 3, Species = "virginica")) # Does not work As a workaround, you could combine felm , getfe , and demeanlist as follows: library(lfe) lm.model <- lm(data=demeanlist(iris[, 1:2], list(iris$Species)), Sepal.Length ~ Sepal.Width) fe <- getfe(felm

I'm following this tutorial to make this ML prediction: import numpy as np import matplotlib.pyplot as plt from matplotlib import style style.use("ggplot") from sklearn import svm x = [1, 5, 1.5, 8, 1, 9] y = [2, 8, 1.8, 8, 0.6, 11] plt.scatter(x,y) plt.show() X = np.array([[1,2], [5,8], [1.5,1.8], [8,8], [1,0.6], [9,11]]) y = [0,1,0,1,0,1] X.reshape(1, -1) clf = svm.SVC(kernel='linear', C = 1.0) clf.fit(X,y) print(clf.predict([0.58,0.76])) I'm using Python 3.6 and I get error "Expected 2D array, got 1D array instead:" I think the script is for older versions, but I don't know how to convert

I am trying to use R to create a linear model and use that to predict some values. The subject matter is baseball stats. If I do this: obp <- lm(offense$R ~ offense$OBP) predict(obp, newdata=data.frame(OBP=0.5), interval="predict") I get the error: Warning message: 'newdata' had 1 row but variables found have 20 rows. However, if I do this: attach(offense) obp <- lm(R ~ OBP) predict(obp, newdata=data.frame(OBP=0.5), interval="predict") It works as expected and I get one result. What is the difference between the two? If I just print OBP and offense$OBP, they look the same. In the first case,

unfortunately I have problems using predict() in the following simple example: library(e1071) x <- c(1:10) y <- c(0,0,0,0,1,0,1,1,1,1) test <- c(11:15) mod <- svm(y ~ x, kernel = "linear", gamma = 1, cost = 2, type="C-classification") predict(mod, newdata = test) The result is as follows: > predict(mod, newdata = test) 1 2 3 4 <NA> <NA> <NA> <NA> <NA> <NA> 0 0 0 0 0 1 1 1 1 1 Can anybody explain why predict() only gives the fitted values of the training sample (x,y) and does not care about the test-data? Thank you very much for your help! Richard It looks like this is because you misuse the