Differences in Linear Regression in R and Python
<p>I was trying to match the linear regression R results with that of python</p>

<p>Matching the coefficients for each of independent variable</p>

<p>and below is the code:</p>

<p>Data is uploaded.</p>

<p><a href="https://www.dropbox.com/s/oowe4irm9332s78/X.csv?dl=0" rel="nofollow noreferrer">https://www.dropbox.com/s/oowe4irm9332s78/X.csv?dl=0</a></p>

<p><a href="https://www.dropbox.com/s/79scp54unzlbwyk/Y.csv?dl=0" rel="nofollow noreferrer">https://www.dropbox.com/s/79scp54unzlbwyk/Y.csv?dl=0</a></p>

<p><strong>R code:</strong></p>

<pre><code>#define pathname = " "

X &lt;- read.csv(file.path(pathname,"X.csv"),stringsAsFactors = F)
Y &lt;- read.csv(file.path(pathname,"Y.csv"),stringsAsFactors = F)

d1 = Y$d1

reg_data &lt;- cbind(d1, X)
head(reg_data)

reg_model &lt;- lm(d1 ~ .,data=reg_data)

names(which(is.na(reg_model$coefficients)))

reg_model$coefficients
</code></pre>

<p><strong>R Result</strong></p>

<pre><code>&gt; summary(reg_model)

Call:
lm(formula = d1 ~ ., data = reg_data)

Residuals:
ALL 60 residuals are 0: no residual degrees of freedom!

Coefficients: (14 not defined because of singularities)
             Estimate Std. Error t value Pr(&gt;|t|)
(Intercept) -67.37752         NA      NA       NA
v1            1.30214         NA      NA       NA
v2           -2.93118         NA      NA       NA
v3            7.58902         NA      NA       NA
v4           11.88570         NA      NA       NA
v5            1.60622         NA      NA       NA
v6            3.71528         NA      NA       NA
v7           -9.34627         NA      NA       NA
v8           -3.84694         NA      NA       NA
v9           -2.51332         NA      NA       NA
v10           4.22403         NA      NA       NA
v11          -9.70126         NA      NA       NA
v12                NA         NA      NA       NA
v13           4.67276         NA      NA       NA
v14          -6.57924         NA      NA       NA
v15          -3.68065         NA      NA       NA
v16           5.25168         NA      NA       NA
v17          14.60444         NA      NA       NA
v18          16.00679         NA      NA       NA
v19          24.79622         NA      NA       NA
v20          13.85774         NA      NA       NA
v21           2.16022         NA      NA       NA
v22         -36.65361         NA      NA       NA
v23           2.26554         NA      NA       NA
v24                NA         NA      NA       NA
v25                NA         NA      NA       NA
v26           7.00981         NA      NA       NA
v27           0.88904         NA      NA       NA
v28           0.34400         NA      NA       NA
v29          -5.27597         NA      NA       NA
v30           5.21034         NA      NA       NA
v31           6.79640         NA      NA       NA
v32           2.96346         NA      NA       NA
v33          -1.52702         NA      NA       NA
v34          -2.74632         NA      NA       NA
v35          -2.36952         NA      NA       NA
v36          -7.76547         NA      NA       NA
v37           2.19630         NA      NA       NA
v38           1.63336         NA      NA       NA
v39           0.69485         NA      NA       NA
v40           0.37379         NA      NA       NA
v41          -0.09107         NA      NA       NA
v42           2.06569         NA      NA       NA
v43           1.57505         NA      NA       NA
v44           2.70535         NA      NA       NA
v45           1.17634         NA      NA       NA
v46         -10.51141         NA      NA       NA
v47          -1.15060         NA      NA       NA
v48           2.87353         NA      NA       NA
v49           3.37740         NA      NA       NA
v50          -5.89816         NA      NA       NA
v51           0.85851         NA      NA       NA
v52           3.73929         NA      NA       NA
v53           4.93265         NA      NA       NA
v54           3.45650         NA      NA       NA
v55           0.12382         NA      NA       NA
v56          -0.21171         NA      NA       NA
v57           4.37199         NA      NA       NA
v58           3.21456         NA      NA       NA
v59           0.09012         NA      NA       NA
v60          -0.85414         NA      NA       NA
v61          -3.29856         NA      NA       NA
v62           4.38842         NA      NA       NA
v63                NA         NA      NA       NA
v64                NA         NA      NA       NA
v65                NA         NA      NA       NA
v66                NA         NA      NA       NA
v67                NA         NA      NA       NA
v68                NA         NA      NA       NA
v69                NA         NA      NA       NA
v70                NA         NA      NA       NA
v71                NA         NA      NA       NA
v72                NA         NA      NA       NA
v73                NA         NA      NA       NA

Residual standard error: NaN on 0 degrees of freedom
Multiple R-squared:      1, Adjusted R-squared:    NaN 
F-statistic:   NaN on 59 and 0 DF,  p-value: NA
</code></pre>

<p><strong>Python Code:</strong></p>

<pre><code>Y = pd.read_csv(pathname+"Y.csv")
X = pd.read_csv(pathname+"X.csv")

lr = LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
lr.fit(X, Y['d1'])

(list(zip(lr.coef_, X)))

lr.intercept_
</code></pre>

<p><strong>Python Result:</strong></p>

<pre><code>intercept = 29.396033164254106

[(-2.4463986167806304, 'v1'),
 (-1.6293010275307021, 'v2'),
 (0.89089949009506508, 'v3'),
 (-3.1021251646895251, 'v4'),
 (-1.7707078771936109, 'v5'),
 (-2.0474705122225636, 'v6'),
 (-1.5537181337496202, 'v7'),
 (-1.6391241229716156, 'v8'),
 (-1.2981646048517046, 'v9'),
 (0.89221826294889328, 'v10'),
 (-0.56694104645951571, 'v11'),
 (2.042810365310288e-14, 'v12'),
 (-2.0312478672439052, 'v13'),
 (-1.5617121392788413, 'v14'),
 (0.4583365939498274, 'v15'),
 (0.8840538748922967, 'v16'),
 (-5.5952681002058871, 'v17'),
 (2.4937042448512892, 'v18'),
 (0.45806845189176543, 'v19'),
 (-1.1648810657830406, 'v20'),
 (-1.7800004329275585, 'v21'),
 (-5.0132817522704816, 'v22'),
 (3.6862778096189266, 'v23'),
 (2.7533531010703882e-14, 'v24'),
 (1.2150003225741557e-14, 'v25'),
 (0.94669823515018103, 'v26'),
 (-0.3082823207975679, 'v27'),
 (0.53619247380957358, 'v28'),
 (-1.1339902793546781, 'v29'),
 (1.9657159583080186, 'v30'),
 (-0.63200501460653324, 'v31'),
 (1.4741013580918978, 'v32'),
 (-2.4448418291953313, 'v33'),
 (-2.0787115960875036, 'v34'),
 (0.22492914212063603, 'v35'),
 (-0.75136276693004922, 'v36'),
 (1.2838658951186761, 'v37'),
 (0.5816277993227944, 'v38'),
 (-0.11270569554555088, 'v39'),
 (-0.13430982360936233, 'v40'),
 (-3.3189296496897662, 'v41'),
 (-0.452575588270415, 'v42'),
 (6.1329755709937519, 'v43'),
 (0.61559185634634817, 'v44'),
 (-1.206315459828555, 'v45'),
 (-3.7452010299772009, 'v46'),
 (-1.1472174665136678, 'v47'),
 (2.8960489381172652, 'v48'),
 (0.0090220136972478659, 'v49'),
 (-5.264918363314754, 'v50'),
 (1.2194758337662015, 'v51'),
 (2.78655271320092, 'v52'),
 (3.106513852668896, 'v53'),
 (3.5181252502607929, 'v54'),
 (-0.34426523770507278, 'v55'),
 (-0.48792823932479878, 'v56'),
 (0.12284460490031779, 'v57'),
 (1.6860388628044991, 'v58'),
 (1.2823067194737174, 'v59'),
 (2.8352263554153665, 'v60'),
 (-1.304336378501032, 'v61'),
 (0.55226132316435139, 'v62'),
 (1.5416988124754771, 'v63'),
 (-0.2605804175310813, 'v64'),
 (1.2489066081702334, 'v65'),
 (-0.44469553013696161, 'v66'),
 (-1.4102990055550157, 'v67'),
 (3.8150423259022639, 'v68'),
 (0.12039684410168072, 'v69'),
 (-1.340699466779357, 'v70'),
 (1.7066389124439212, 'v71'),
 (0.50470752944860442, 'v72'),
 (1.0024872633969766, 'v73')]
</code></pre>

<p>But it is not matching.Please help.</p>

<p>Note:It matched for below example</p>

<p><a href="http://davidcoallier.com/blog/linear-regression-from-r-to-python/" rel="nofollow noreferrer">http://davidcoallier.com/blog/linear-regression-from-r-to-python/</a></p>