<commit_before>''' Test results of OLS against R '''

from numpy.testing import *

import numpy as np

import nipy.fixes.scipy.stats.models as SSM

from exampledata import y, x

def assert_model_similar(res1, res2):
    ''' Test if models have similar parameters '''
    assert np.allclose(res1.beta, res2.beta)
    assert np.allclose(res1.resid, res2.resid)
    assert np.allclose(res1.predict, res2.predict)
    assert np.allclose(res1.df_resid, res2.df_resid)

# FIXME: TypeError: test_model_class() takes exactly 2 arguments (0 given)
@dec.knownfailureif(True)    
def test_model_class(model_class, r_model_type):
    results = model_class(x).fit(y)
    r_results = WrappedRModel(y, x, r_model_type)
    r_results.assert_similar(results)

# FIXME: ImportError: No module named rpy
@dec.knownfailureif(True)
def test_using_rpy():
    from rpy import r
    from rmodelwrap import RModel
    # Test OLS
    ols_res = SSM.regression.ols_model(x).fit(y)
    rlm_res = RModel(y, x, r.lm)
    assert_model_similar(ols_res, rlm_res)

# FIXME: NameError: global name 'rlm_res' is not defined
@dec.knownfailureif(True)
def test1():
    # Standard GLM
    glm_res = SSM.glm(x).fit(y)
    assert_model_similar(glm_res, rlm_res)
    # Which should be same as each other
    assert_model_similar(ols_res, glm_res)<commit_msg>complete test of R's output for one OLS fit<commit_after>
import numpy as np
from nipy.fixes.scipy.stats.models.regression import OLSModel
import nipy.testing as niptest
import csv
import scipy.stats

from exampledata import x, y

Rscript = '''
d = read.table('data.csv', header=T, sep=' ')

y.lm = lm(Y ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + X10 + X11 + X12 + X13 + X14, data=d)
print(summary(y.lm))

y.lm2 = lm(Y ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + X10 + X11 + X12 + X13 + X14 - 1, data=d)
print(summary(y.lm2))

SSE = sum(resid(y.lm)^2)
SST = sum((d$Y - mean(d$Y))^2)
SSR = SST - SSE

print(data.frame(SSE, SST, SSR))

MSE = SSE / y.lm$df.resid
MST = SST / (length(d$Y) - 1)
MSR = SSR / (length(d$Y) - y.lm$df.resid - 1)

print(data.frame(MSE, MST, MSR))
'''

# lines about "Signif. codes" were deleted due to a character encoding issue

Rresults = \
"""
These are the results from fitting the model in R, i.e. running the commands Rscript in R
A few things to note, X8 is a column of 1s,
so by not including a '-1' in the formula, X8 gets
thrown out of the model, with its coefficients
being the "(Intercept)" term. An alternative is to use "-1"
in the formula, but then R gives nonsensical F, R2 and adjusted R2
values. This means that R2, R2a and F cannot fully be trusted in R.

In OLSModel, we have checked whether a column of 1s is in the column
space, in which case the F, R2, and R2a are seneible.

> source('test.R')
[1] "Without using '-1'"
[1] "------------------"

Call:
lm(formula = Y ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + 
    X10 + X11 + X12 + X13 + X14, data = d)

Residuals:
      Min        1Q    Median        3Q       Max 
-2.125783 -0.567850  0.004305  0.532145  2.372263 

Coefficients: (1 not defined because of singularities)
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2.603e+02  8.226e-01 316.463  < 2e-16 ***
X1           1.439e-02  2.649e-02   0.543   0.5881    
X2          -6.975e+00  1.022e+01  -0.683   0.4963    
X3           4.410e+01  5.740e+00   7.682 6.42e-12 ***
X4           3.864e+00  5.770e+00   0.670   0.5044    
X5           2.458e+02  4.594e+02   0.535   0.5937    
X6           9.789e+02  3.851e+02   2.542   0.0124 *  
X7           1.339e+03  8.418e+02   1.591   0.1145    
X8                  NA         NA      NA       NA    
X9          -1.955e-02  1.539e-02  -1.270   0.2066    
X10          7.042e-05  2.173e-04   0.324   0.7465    
X11         -3.743e-08  6.770e-07  -0.055   0.9560    
X12          3.060e-06  2.094e-06   1.461   0.1469    
X13          1.440e-06  1.992e-06   0.723   0.4711    
X14         -1.044e-05  7.215e-06  -1.448   0.1505    
---

Residual standard error: 0.8019 on 112 degrees of freedom
Multiple R-squared: 0.5737,Adjusted R-squared: 0.5242 
F-statistic: 11.59 on 13 and 112 DF,  p-value: 1.818e-15 

[1] "Using '-1'"
[1] "------------------"

Call:
lm(formula = Y ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 + 
    X10 + X11 + X12 + X13 + X14 - 1, data = d)

Residuals:
      Min        1Q    Median        3Q       Max 
-2.125783 -0.567850  0.004305  0.532145  2.372263 

Coefficients:
      Estimate Std. Error t value Pr(>|t|)    
X1   1.439e-02  2.649e-02   0.543   0.5881    
X2  -6.975e+00  1.022e+01  -0.683   0.4963    
X3   4.410e+01  5.740e+00   7.682 6.42e-12 ***
X4   3.864e+00  5.770e+00   0.670   0.5044    
X5   2.458e+02  4.594e+02   0.535   0.5937    
X6   9.789e+02  3.851e+02   2.542   0.0124 *  
X7   1.339e+03  8.418e+02   1.591   0.1145    
X8   2.603e+02  8.226e-01 316.463  < 2e-16 ***
X9  -1.955e-02  1.539e-02  -1.270   0.2066    
X10  7.042e-05  2.173e-04   0.324   0.7465    
X11 -3.743e-08  6.770e-07  -0.055   0.9560    
X12  3.060e-06  2.094e-06   1.461   0.1469    
X13  1.440e-06  1.992e-06   0.723   0.4711    
X14 -1.044e-05  7.215e-06  -1.448   0.1505    
---

Residual standard error: 0.8019 on 112 degrees of freedom
Multiple R-squared:     1,Adjusted R-squared:     1 
F-statistic: 9.399e+05 on 14 and 112 DF,  p-value: < 2.2e-16 

       SSE      SST      SSR
1 72.02328 168.9401 96.91685
       MSE      MST      MSR
1 0.643065 1.351521 7.455142
> 


"""

def myround(f, decimals=4):
    """
    Round to 4 decimal places, but 
    avoid 0.12399999999 stuff
    """
    v = np.round(f, decimals=decimals)


m = OLSModel(x)
r = m.fit(y)

def test_results():
    # results hand compared with R's printout

    yield niptest.assert_equal, '%0.4f' % r.R2, '0.5737'
    yield niptest.assert_equal, '%0.4f' % r.R2_adj, '0.5242'

    f = r.F_overall
    yield niptest.assert_equal, '%0.2f' % f['F'], '11.59'
    yield niptest.assert_equal, f['df_num'], 13
    yield niptest.assert_equal, f['df_den'], 112
    yield niptest.assert_equal, '%0.3e' % f['p_value'], '1.818e-15'

    thetas = []
    sds = []
    ts = []
    ps = []

    # entries with '*****' are not tested as they were a different format

    resultstr = \
'''
X1   1.439e-02  2.649e-02   0.543   0.5881    
X2  -6.975e+00  1.022e+01  -0.683   0.4963    
X3   4.410e+01  5.740e+00   7.682   ******
X4   3.864e+00  5.770e+00   0.670   0.5044    
X5   2.458e+02  4.594e+02   0.535   0.5937    
X6   9.789e+02  3.851e+02   2.542   0.0124 
X7   1.339e+03  8.418e+02   1.591   0.1145    
X8   2.603e+02  8.226e-01 316.463   ******
X9  -1.955e-02  1.539e-02  -1.270   0.2066    
X10  7.042e-05  2.173e-04   0.324   0.7465    
X11 -3.743e-08  6.770e-07  -0.055   0.9560    
X12  3.060e-06  2.094e-06   1.461   0.1469    
X13  1.440e-06  1.992e-06   0.723   0.4711    
X14 -1.044e-05  7.215e-06  -1.448   0.1505    
X1   1.439e-02  2.649e-02   0.543   0.5881    
X2  -6.975e+00  1.022e+01  -0.683   0.4963    
X3   4.410e+01  5.740e+00   7.682   ****** 
X4   3.864e+00  5.770e+00   0.670   0.5044    
X5   2.458e+02  4.594e+02   0.535   0.5937    
X6   9.789e+02  3.851e+02   2.542   0.0124   
X7   1.339e+03  8.418e+02   1.591   0.1145    
X8   2.603e+02  8.226e-01 316.463   ******
X9  -1.955e-02  1.539e-02  -1.270   0.2066    
X10  7.042e-05  2.173e-04   0.324   0.7465    
X11 -3.743e-08  6.770e-07  -0.055   0.9560    
X12  3.060e-06  2.094e-06   1.461   0.1469    
X13  1.440e-06  1.992e-06   0.723   0.4711    
X14 -1.044e-05  7.215e-06  -1.448   0.1505    
'''

    for row in resultstr.strip().split('\n'):
        row = row.strip()
        _, th, sd, t, p = row.split()
        thetas.append(th)
        sds.append(sd)
        ts.append(t)
        ps.append(p)

    for th, thstr in zip(r.theta, thetas):
        yield niptest.assert_equal, '%0.3e' % th, thstr

    for sd, sdstr in zip([np.sqrt(r.vcov(column=i)) for i in range(14)], sds):
        yield niptest.assert_equal, '%0.3e' % sd, sdstr

    for t, tstr in zip([r.t(column=i) for i in range(14)], ts):
        yield niptest.assert_equal, '%0.3f' % t, tstr

    for p, pstr in zip([2*scipy.stats.t.sf(np.fabs(r.t(column=i)), r.df_resid) for i in range(14)], ps):
        if pstr.find('*') < 0:
            yield niptest.assert_equal, '%0.4f' % p, pstr

    yield niptest.assert_equal, "%0.5f" % r.SSE, "72.02328"
    yield niptest.assert_equal, "%0.4f" % r.SST, "168.9401"
    yield niptest.assert_equal, "%0.5f" % r.SSR, "96.91685"

    yield niptest.assert_equal, "%0.6f" % r.MSE, "0.643065"
    yield niptest.assert_equal, "%0.6f" % r.MST, "1.351521"
    yield niptest.assert_equal, "%0.6f" % r.MSR, "7.455142"

    yield niptest.assert_equal, "%0.4f" % np.sqrt(r.MSE), "0.8019"


# this is the file "data.csv" referred to in Rscript above

Rdata = '''
Y X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14
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2.573856564029982792e+02 -1.247753847628743987e-02 8.132393396825286086e-03 -4.413603363412710312e-02 3.174380286547619917e-02 1.507591026246031356e-03 -8.321096135079367661e-04 -5.268108768253958792e-04 1.000000000000000000e+00 2.027260000000000062e+00 4.109783107600000207e+00 8.331598902713176713e+00 -0.000000000000000000e+00 -0.000000000000000000e+00 -0.000000000000000000e+00
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