linear-regression

问题 I'm trying to make a simple linear regression function but continue to encounter a numpy.linalg.linalg.LinAlgError: Singular matrix error Existing function (with debug prints): def makeLLS(inputData, targetData): print "In makeLLS:" print " Shape inputData:",inputData.shape print " Shape targetData:",targetData.shape term1 = np.dot(inputData.T, inputData) term2 = np.dot(inputData.T, targetData) print " Shape term1:",term1.shape print " Shape term2:",term2.shape #print term1 #print term2

问题 I need to attain the slop of a linear regression similar to the way the excel function in the below link is implemented: http://office.microsoft.com/en-gb/excel-help/slope-function-HP010342903.aspx Is there a library in C++ or a simple coded solution someone has created which can do this? I have implemented code according to this formula, however it does not always give me the correct results (taken from here http://easycalculation.com/statistics/learn-regression.php) .... Slope(b) = (NΣXY -

问题 Could someone help me interpret the alias function output for testing for multicollinearity in a multiple regression model. I know some predictor variables in my model are highly correlated, and I want to identify them using the alias table. Model : Score ~ Comments + Pros + Cons + Advice + Response + Value + Recommendation + 6Months + 12Months + 2Years + 3Years + Daily + Weekly + Monthly Complete : (Intercept) Comments Pros Cons Advice Response Value1 UseMonthly1 0 0 0 0 0 0 0

问题 We are using a programming language that does not have a linear regression function in it. We have already implemented a single variable linear equation: y = Ax + B and have simply calculated the A and B coefficents from the data using a solution similar to this Stack Overflow answer. I know this problem gets geometrically harder as variables are added, but for our purposes, we only need to add one more: z = Ax + By + C Does anyone have the closed form equations, or code in any language that

问题 I've been trying to get a prediction for future values in a model I've created. I have tried both OLS in pandas and statsmodels. Here is what I have in statsmodels: import statsmodels.api as sm endog = pd.DataFrame(dframe['monthly_data_smoothed8']) smresults = sm.OLS(dframe['monthly_data_smoothed8'], dframe['date_delta']).fit() sm_pred = smresults.predict(endog) sm_pred The length of the array returned is equal to the number of records in my original dataframe but the values are not the same.

问题 I want to predict a value at a date in the future with simple linear regression, but I can't due to the date format. This is the dataframe I have: data_df = date value 2016-01-15 1555 2016-01-16 1678 2016-01-17 1789 ... y = np.asarray(data_df['value']) X = data_df[['date']] X_train, X_test, y_train, y_test = train_test_split (X,y,train_size=.7,random_state=42) model = LinearRegression() #create linear regression object model.fit(X_train, y_train) #train model on train data model.score(X_train

问题 I have a dataset including categorical variables(binary) and continuous variables. I'm trying to apply a linear regression model for predicting a continuous variable. Can someone please let me know how to check for correlation among the categorical variables and the continuous target variable. Current Code: import pandas as pd df_hosp = pd.read_csv('C:\Users\LAPPY-2\Desktop\LengthOfStay.csv') data = df_hosp[['lengthofstay', 'male', 'female', 'dialysisrenalendstage', 'asthma', \ 'irondef',

问题 My goal is to solve: Kc=y with the pseudo-inverse (i.e. minimum norm solution ): c=K^{+}y such that the model is (hopefully) high degree polynomial model f(x) = sum_i c_i x^i . I am specially interested in the underdetermined case where we have more polynomial features than data (few equation too many variables/unknowns) columns = deg+1 > N = rows . Note K is the vandermode matrix of polynomial features. I was initially using the python function np.linalg.pinv but then I noticed something

问题 I have a dataset (data frame) with 5 columns all containing numeric values. I'm looking to run a simple linear regression for each pair in the dataset. For example, If the columns were named A, B, C, D, E , I want to run lm(A~B), lm(A~C), lm(A~D), ...., lm(D~E) ,... and, then I want to plot the data for each pair along with the regression line. I'm pretty new to R so I'm sort of spinning my wheels on how to actually accomplish this. Should I use ddply ? or lapply ? I'm not really sure how to

问题 I have a dataset (data frame) with 5 columns all containing numeric values. I'm looking to run a simple linear regression for each pair in the dataset. For example, If the columns were named A, B, C, D, E , I want to run lm(A~B), lm(A~C), lm(A~D), ...., lm(D~E) ,... and, then I want to plot the data for each pair along with the regression line. I'm pretty new to R so I'm sort of spinning my wheels on how to actually accomplish this. Should I use ddply ? or lapply ? I'm not really sure how to