linear-regression

问题 I am trying to make and test a linear model as follows: lm_model <- lm(Purchase ~., data = train) lm_prediction <- predict(lm_model, test) This results in the following error, stating that the Product_Category_1 column has values that exist in the test data frame but not the train data frame): factor Product_Category_1 has new levels 7, 9, 14, 16, 17, 18 However, if I check these they definitely look to appear in both data frames: > nrow(subset(train, Product_Category_1 == "7")) [1] 2923 >

问题 I am building a quadratic model with lm in R: y <- data[[1]] x <- data[[2]] x2 <- x^2 quadratic.model = lm(y ~ x + x2) Now I want to display both the predicted values and the actual values on a plot. I tried this: par(las=1,bty="l") plot(y~x) P <- predict(quadratic.model) lines(x, P) but the line comes up all squiggely. Maybe it has to do with the fact that it's quadratic? Thanks for any help. 回答1: You need order() : P <- predict(quadratic.model) plot(y~x) reorder <- order(x) lines(x[reorder]

问题 I am building a quadratic model with lm in R: y <- data[[1]] x <- data[[2]] x2 <- x^2 quadratic.model = lm(y ~ x + x2) Now I want to display both the predicted values and the actual values on a plot. I tried this: par(las=1,bty="l") plot(y~x) P <- predict(quadratic.model) lines(x, P) but the line comes up all squiggely. Maybe it has to do with the fact that it's quadratic? Thanks for any help. 回答1: You need order() : P <- predict(quadratic.model) plot(y~x) reorder <- order(x) lines(x[reorder]

问题 First off, I am pretty new to this so my method/thinking may be wrong, I have imported a xlsx data set into a data frame using R and R studio. I want to be able to loop through the column names to get all of the variables with exactly " 10 " in them in order to run a simple linear regression. So here's my code: indx <- grepl('_10_', colnames(data)) #list returns all of the true values in the data set col10 <- names(data[indx]) #this gives me the names of the columns I want Here is the for

问题 I can't seem to find any python libraries that do multiple regression. The only things I find only do simple regression. I need to regress my dependent variable (y) against several independent variables (x1, x2, x3, etc.). For example, with this data: print 'y x1 x2 x3 x4 x5 x6 x7' for t in texts: print "{:>7.1f}{:>10.2f}{:>9.2f}{:>9.2f}{:>10.2f}{:>7.2f}{:>7.2f}{:>9.2f}" / .format(t.y,t.x1,t.x2,t.x3,t.x4,t.x5,t.x6,t.x7) (output for above:) y x1 x2 x3 x4 x5 x6 x7 -6.0 -4.95 -5.87 -0.76 14.73 4

问题 Let's say I have some generic dataset for which an OLS regression is the best choice. So, I generate a model with some first-order terms and decide to use Caret in R for my regression coefficient estimates and error estimates. In caret, this ends up being: k10_cv = trainControl(method="cv", number=10) ols_model = train(Y ~ X1 + X2 + X3, data = my_data, trControl = k10_cv, method = "lm") From there, I can pull out regression information using summary(ols_model) and can also pull some more

问题 Data Set: I have collected tablespace growth of my database and trying to use it to predict the growth. Dataset has data from year 2009 to 2017. I tried many ways but unable to use the date format for processing. Got errors and all of them are related to date time types. Can you please suggest how i can use this dataset to predict the growth. One of the errors: TypeError: Cannot cast array data from dtype('M8[ns]') to dtype('float64') according to the rule 'safe' TS_SIZE FETCH_DATE 34911.99

问题 Is it at all possible to use the lm() function with a matrix? Or maybe, the correct question is: "Is it possible to dynamically create formulas in R?" I am creating a function whose output is a matrix and the number of columns in the matrix is not fixed = it depends on the inputs of the user. I want to fit an OLS model using the data in the matrix. - The first column represents the dependent variable - The other columns are the independent variables. Using the lm function requires a formula,

问题 In linear regression I've always seen the situation where I have many features and I use them to predict a single output, for example f1 f2 f3 f4 --> y1 f1 f2 f3 f4 --> y2 and so on... I want to know if there is something where the predicted value i.e. y1 is actually a vector not a single value 回答1: Yes, pretty much every regression method (neural networks, support vector regressors, random forest regressors, ....) works just fine for multidimensional output. Including linear regression. In

问题 I'm facing issues in fitting a simple y= 4x1 line with 2 data points using pytorch. While running the inference code, the model seems to output same value to any input which is strange. Pls find the code attached along with the data files used by me. Appreciate any help here. import torch import numpy as np import pandas as pd df = pd.read_csv('data.csv') test_data = pd.read_csv('test_data.csv') inputs = df[['x1']] target = df['y'] inputs = torch.tensor(inputs.values).float() target = torch