Python-Multiple Lineare Regression mit OLS-code mit bestimmten Daten?

Ich bin mit der ols.py code heruntergeladen scipy Kochbuch (der download ist im ersten Absatz mit der Fett-OLS), aber ich brauche, um zu verstehen, eher als die Verwendung von zufälligen Daten für die ols-Funktion zu tun, die eine multiple lineare regression.

Habe ich eine bestimmte abhängige variable y, und drei erklärende Variablen. Jedes mal, wenn ich versuche in meine Variablen an die Stelle der Zufallsvariablen, es gibt mir die Fehlermeldung:

TypeError: dieser Konstruktor nimmt keine Argumente an.

Kann jemand helfen? Ist dies möglich?

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Hier ist eine Kopie der ols-code, den ich versuche zu verwenden, zusammen mit der Variablen, die ich versuche zu input

from __future__ import division
from scipy import c_, ones, dot, stats, diff
from scipy.linalg import inv, solve, det
from numpy import log, pi, sqrt, square, diagonal
from numpy.random import randn, seed
import time

class ols:
"""
Author: Vincent Nijs (+ ?)

Email: v-nijs at kellogg.northwestern.edu

Last Modified: Mon Jan 15 17:56:17 CST 2007

Dependencies: See import statement at the top of this file

Doc: Class for multi-variate regression using OLS

Input:
     dependent variable
    y_varnm = string with the variable label for y
    x = independent variables, note that a constant is added by default
    x_varnm = string or list of variable labels for the independent variables

Output:
    There are no values returned by the class. Summary provides printed output.
    All other measures can be accessed as follows:

    Step 1: Create an OLS instance by passing data to the class

        m = ols(y,x,y_varnm = 'y',x_varnm = ['x1','x2','x3','x4'])

    Step 2: Get specific metrics

        To print the coefficients: 
            >>> print m.b
        To print the coefficients p-values: 
            >>> print m.p

"""

y = [29.4, 29.9, 31.4, 32.8, 33.6, 34.6, 35.5, 36.3, 37.2, 37.8, 38.5, 38.8,
            38.6, 38.8, 39, 39.7, 40.6, 41.3, 42.5, 43.9, 44.9, 45.3, 45.8, 46.5,
            77.1, 48.2, 48.8, 50.5, 51, 51.3, 50.7, 50.7, 50.6, 50.7, 50.6, 50.7]
        #tuition
x1 = [376, 407, 438, 432, 433, 479, 512, 543, 583, 635, 714, 798, 891,
            971, 1045, 1106, 1218, 1285, 1356, 1454, 1624, 1782, 1942, 2057, 2179,
            2271, 2360, 2506, 2562, 2700, 2903, 3319, 3629, 3874, 4102, 4291]
        #research and development
x2 = [28740.00, 30952.00, 33359.00, 35671.00, 39435.00, 43338.00, 48719.00, 55379.00, 63224.00,
            72292.00, 80748.00, 89950.00, 102244.00, 114671.00, 120249.00, 126360.00, 133881.00, 141891.00,
            151993.00, 160876.00, 165350.00, 165730.00, 169207.00, 183625.00, 197346.00, 212152.00, 226402.00, 
            267298.00, 277366.00, 276022.00, 288324.00, 299201.00, 322104.00, 347048.00, 372535.00,
            397629.00]
        #one/none parents 
x3 = [11610, 12143, 12486, 13015, 13028, 13327, 14074, 14094, 14458, 14878, 15610, 15649,
            15584, 16326, 16379, 16923, 17237, 17088, 17634, 18435, 19327, 19712, 21424, 21978,
            22684, 22597, 22735, 22217, 22214, 22655, 23098, 23602, 24013, 24003, 21593, 22319]


def __init__(self,y,x1,y_varnm = 'y',x_varnm = ''):
"""
Initializing the ols class. 
"""
self.y = y
#self.x1 = c_[ones(x1.shape[0]),x1]
self.y_varnm = y_varnm
if not isinstance(x_varnm,list): 
    self.x_varnm = ['const'] + list(x_varnm)
else:
    self.x_varnm = ['const'] + x_varnm

# Estimate model using OLS
self.estimate()

def estimate(self):

# estimating coefficients, and basic stats
self.inv_xx = inv(dot(self.x.T,self.x))
xy = dot(self.x.T,self.y)
self.b = dot(self.inv_xx,xy)                    # estimate coefficients

self.nobs = self.y.shape[0]                     # number of observations
self.ncoef = self.x.shape[1]                    # number of coef.
self.df_e = self.nobs - self.ncoef              # degrees of freedom, error 
self.df_r = self.ncoef - 1                      # degrees of freedom, regression 

self.e = self.y - dot(self.x,self.b)            # residuals
self.sse = dot(self.e,self.e)/self.df_e         # SSE
self.se = sqrt(diagonal(self.sse*self.inv_xx))  # coef. standard errors
self.t = self.b / self.se                       # coef. t-statistics
self.p = (1-stats.t.cdf(abs(self.t), self.df_e)) * 2    # coef. p-values

self.R2 = 1 - self.e.var()/self.y.var()         # model R-squared
self.R2adj = 1-(1-self.R2)*((self.nobs-1)/(self.nobs-self.ncoef))   # adjusted R-square

self.F = (self.R2/self.df_r) / ((1-self.R2)/self.df_e)  # model F-statistic
self.Fpv = 1-stats.f.cdf(self.F, self.df_r, self.df_e)  # F-statistic p-value

def dw(self):
"""
Calculates the Durbin-Waston statistic
"""
de = diff(self.e,1)
dw = dot(de,de) / dot(self.e,self.e);

return dw

def omni(self):
"""
Omnibus test for normality
"""
return stats.normaltest(self.e) 

def JB(self):
"""
Calculate residual skewness, kurtosis, and do the JB test for normality
"""

# Calculate residual skewness and kurtosis
skew = stats.skew(self.e) 
kurtosis = 3 + stats.kurtosis(self.e) 

# Calculate the Jarque-Bera test for normality
JB = (self.nobs/6) * (square(skew) + (1/4)*square(kurtosis-3))
JBpv = 1-stats.chi2.cdf(JB,2);

return JB, JBpv, skew, kurtosis

def ll(self):
"""
Calculate model log-likelihood and two information criteria
"""

# Model log-likelihood, AIC, and BIC criterion values 
ll = -(self.nobs*1/2)*(1+log(2*pi)) - (self.nobs/2)*log(dot(self.e,self.e)/self.nobs)
aic = -2*ll/self.nobs + (2*self.ncoef/self.nobs)
bic = -2*ll/self.nobs + (self.ncoef*log(self.nobs))/self.nobs

return ll, aic, bic

def summary(self):
"""
Printing model output to screen
"""

# local time & date
t = time.localtime()

# extra stats
ll, aic, bic = self.ll()
JB, JBpv, skew, kurtosis = self.JB()
omni, omnipv = self.omni()

# printing output to screen
                                                                                         print                                                                                         '\n=============================================================================='
print "Dependent Variable: " + self.y_varnm
print "Method: Least Squares"
print "Date: ", time.strftime("%a, %d %b %Y",t)
print "Time: ", time.strftime("%H:%M:%S",t)
print '# obs:               %5.0f' % self.nobs
print '# variables:     %5.0f' % self.ncoef 
print '=============================================================================='
print 'variable     coefficient     std. Error      t-statistic     prob.'
print '=============================================================================='
for i in range(len(self.x_varnm)):
    print '''% -5s          % -5.6f     % -5.6f     % -5.6f     % -5.6f'''  %            tuple([self.x_varnm[i],self.b[i],self.se[i],self.t[i],self.p[i]]) 
print '=============================================================================='
print 'Models stats                         Residual stats'
print '=============================================================================='
print 'R-squared            % -5.6f         Durbin-Watson stat  % -5.6f' %              tuple([self.R2, self.dw()])
print 'Adjusted R-squared   % -5.6f         Omnibus stat        % -5.6f' % tuple([self.R2adj, omni])
print 'F-statistic          % -5.6f         Prob(Omnibus stat)  % -5.6f' % tuple([self.F, omnipv])
print 'Prob (F-statistic)   % -5.6f         JB stat             % -5.6f' % tuple([self.Fpv, JB])
print 'Log likelihood       % -5.6f         Prob(JB)            % -5.6f' % tuple([ll, JBpv])
print 'AIC criterion        % -5.6f         Skew                % -5.6f' % tuple([aic, skew])
print 'BIC criterion        % -5.6f         Kurtosis            % -5.6f' % tuple([bic, kurtosis])
print '=============================================================================='

if __name__ == '__main__':

##########################
### testing the ols class
##########################

# intercept is added, by default
m = ols(y,x1,y_varnm = 'y',x_varnm = ['x1','x2','x3'])
m.summary()

InformationsquelleAutor RenCam | 2011-09-17