def multiReg(x, y, colList, alpha):
    #d = rpy.r.data_frame(y)
    newX = []
    d = {'Y':y}
    model_string = "Y~"
    #names = ["Y"]
    for i in colList:
      name = "X" + str(i) 
      #names.append( name )
      d[name] = x[i]
      newX.append(x[i])
      model_string = model_string + name + "+"
    print d
    #rpy.r.colnames(d) = names
    model_string = model_string + "1"
    model = rpy.r(model_string)

    n = rpy.sqrt( len( y ) )
    norm = rpy.r.qnorm( 1. - ( alpha/200. ) )
    Rlm = rpy.with_mode(rpy.NO_CONVERSION, rpy.r.lm)
    reg = Rlm(model, data = d)
    result = rpy.r.summary(reg)
    print result
    coef =result['coefficients']
    r2 = result['r.squared']
    r2adj = result['adj.r.squared']
    ic = result['sigma']*norm/n
    regressor = coef[:, 0]

    data = {'regressor':regressor[1:], 'intercept':regressor[0], 'r2':r2, 'adj_r2':r2adj, 'ic':ic, 'x':newX, 'y':y}
    return data
Example #2
0
def regression(x, y, regmodel, alpha):
    d = rpy.r.data_frame(X=x, Y=y)
    model = regmodel
    #reg = rpy.r.lm(model, data = d)
    n = rpy.sqrt(len(x))
    norm = rpy.r.qt(1. - (alpha / 200.), len(x))
    Rlm = rpy.with_mode(rpy.NO_CONVERSION, rpy.r.lm)
    reg2 = Rlm(model, data=d)
    result = rpy.r.summary(reg2)
    coef = result['coefficients']
    r2 = result['r.squared']
    r2adj = result['adj.r.squared']
    ic = result['sigma'] * norm / n
    try:
        slope = coef[1][0]
        intercept = coef[0][0]
    except IndexError:
        slope = coef[0][0]
        intercept = 0

    data = {
        'slope': slope,
        'intercept': intercept,
        'r2': r2,
        'adj_r2': r2adj,
        'ic': ic,
        'x': x,
        'y': y
    }
    return data
Example #3
0
def multiReg(x, y, colList, alpha):
    #d = rpy.r.data_frame(y)
    newX = []
    d = {'Y': y}
    model_string = "Y~"
    #names = ["Y"]
    for i in colList:
        name = "X" + str(i)
        #names.append( name )
        d[name] = x[i]
        newX.append(x[i])
        model_string = model_string + name + "+"
    print d
    #rpy.r.colnames(d) = names
    model_string = model_string + "1"
    model = rpy.r(model_string)

    n = rpy.sqrt(len(y))
    norm = rpy.r.qnorm(1. - (alpha / 200.))
    Rlm = rpy.with_mode(rpy.NO_CONVERSION, rpy.r.lm)
    reg = Rlm(model, data=d)
    result = rpy.r.summary(reg)
    print result
    coef = result['coefficients']
    r2 = result['r.squared']
    r2adj = result['adj.r.squared']
    ic = result['sigma'] * norm / n
    regressor = coef[:, 0]

    data = {
        'regressor': regressor[1:],
        'intercept': regressor[0],
        'r2': r2,
        'adj_r2': r2adj,
        'ic': ic,
        'x': newX,
        'y': y
    }
    return data
def regression(x, y, regmodel, alpha ): 
    d = rpy.r.data_frame(X=x, Y=y)
    model = regmodel
    #reg = rpy.r.lm(model, data = d)
    n = rpy.sqrt( len( x ) )
    norm = rpy.r.qt( 1. - ( alpha/200. ), len(x) )
    Rlm = rpy.with_mode(rpy.NO_CONVERSION, rpy.r.lm)
    reg2 = Rlm(model, data = d)
    result = rpy.r.summary(reg2)
    coef = result['coefficients']
    r2 = result['r.squared']
    r2adj = result['adj.r.squared']
    ic = result[ 'sigma' ]*norm/n
    try:
      slope = coef[1][0]
      intercept = coef[0][0]
    except IndexError :
      slope = coef[0][0]
      intercept = 0

    data = {'slope':slope, 'intercept':intercept, 'r2':r2,
            'adj_r2':r2adj, 'ic':ic, 'x':x, 'y':y}
    return data