regression

I am having a problem setting up a panel data model. Here is some sample data: library(plm) id <- c(1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2) year <- c(1999,1999,1999,1999,2000,2000,2000,2000,1999,1999,1999,1999,2000,2000,2000,2000) qtr <- c(1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4) y <- rnorm(16, mean=0, sd=1) x <- rnorm(16, mean=0, sd=1) data <- data.frame(id=id,year=year,qtr=qtr,y_q=paste(year,qtr,sep="_"),y=y,x=x) I run the following regression using 'id' as the individual index and 'year' as the time index: reg1 <- plm(y ~ x, data=data,index=c("id", "year"), model="within",effect="time") Unfortunately, I

I'm curious what the value field is in the nodes of the decision tree produced by Graphviz when used for regression. I understand that this is the number of samples in each class that are separated by a split when using decision tree classification but I'm not sure what it means for regression. My data has a 2 dimensional input and a 10 dimensional output. Here is an example of what a tree looks like for my regression problem: produced using this code & visualized with webgraphviz # X = (n x 2) Y = (n x 10) X_test = (m x 2) input_scaler = pickle.load(open("../input_scaler.sav","rb")) reg =

I have searched stack overflow and have not found any question that really is the same as mine because none really have more than one independent variable. Basically I have an array of datapoints and I want to be able to find a regression equation for those data points. The code I have so far looks like this: (w,x,z are the independent variables and y is the dependent variable) var dataPoints = [{ "w" : 1, "x" : 2, "z" : 1, "y" : 7 }, { "w" : 2, "x" : 1, "z" : 4, "y" : 5 }, { "w" : 1, "x" : 5, "z" : 3, "y" : 2 }, { "w" : 4, "x" : 3, "z" : 5, "y" : 15 }]; I would like a function that would

I'm trying to understand how to use categorical data as features in sklearn.linear_model 's LogisticRegression . I understand of course I need to encode it. What I don't understand is how to pass the encoded feature to the Logistic regression so it's processed as a categorical feature, and not interpreting the int value it got when encoding as a standard quantifiable feature. (Less important) Can somebody explain the difference between using preprocessing.LabelEncoder() , DictVectorizer.vocabulary or just encoding the categorical data yourself with a simple dict? Alex A.'s comment here touches

I'm trying to run a multiple OLS regression using statsmodels and a pandas dataframe. There are missing values in different columns for different rows, and I keep getting the error message: ValueError: array must not contain infs or NaNs I saw this SO question, which is similar but doesn't exactly answer my question: statsmodel.api.Logit: valueerror array must not contain infs or nans What I would like to do is run the regression and ignore all rows where there are missing variables for the variables I am using in this regression. Right now I have: import pandas as pd import numpy as np import