In [5]:
%matplotlib inline
from numpy import linspace, log, exp
from numpy.random import normal
from scipy.stats import chisqprob
from lmfit import Model
import seaborn as sns
sns.set_context('notebook', font_scale=1)

The likelihood ratio test: Python implementation

For two models, one nested in the other (meaning that the nested model estimated parameters are a subset of the nesting model), the test statistic $D$ is (based on this):

$$ \Lambda = \Big( \Big(\frac{\sum{(X_i - \hat{X_i}(\theta_1))^2}}{\sum{(X_i - \hat{X_i}(\theta_0))^2}}\Big)^{n/2} \Big) \\ D = -2 log \Lambda \\ lim_{n \to \infty} D \sim \chi^2_{df=\Delta} $$

where $\Lambda$ is the likelihood ratio, $D$ is the statistic, $X_{i}$ are the data points, $\hat{X_i}(\theta)$ is the model prediction with parameters $\theta$, $\theta_i$ is the parameters estimation for model $i$, $n$ is the number of data points and $\Delta$ is the difference in number of parameters between the models.

The python implementation below compares between two lmfit.ModelFit objects. These are the results of fitting models to the same data set using the lmfit package.

The function compares between model fit m0 and m1 and assumes that m0 is nested in m1, meaning that the set of varying parameters of m0 is a subset of the varying parameters of m1. The property chisqr of the ModelFit objects is the sum of the square of the residuals of the fit. ndata is the number of data points. nvarys is the number of varying parameters.

In [31]:
def lrtest(m0, m1, alfa=0.05):
    """Perform a likelihood ratio test on two nested models.
    `m0` is nested in `m1`.
    `alfa` is the test significance level (default 5%).
    prefer_m1 - a boolean, should we prefer `m1` over `m0`.
    pval - the test p-value
    D - the test statistic 
    ddf - the number of degrees of freedom
    n0 = m0.ndata
    k0 = m0.nvarys
    chisqr0 = m0.chisqr
    assert chisqr0 > 0
    n1 = m1.ndata
    assert n0 == n1
    k1 = m1.nvarys
    chisqr1 = m1.chisqr
    assert chisqr1 > 0
    Lambda = (m1.chisqr / m0.chisqr)**(n0 / 2.)
    D = -2 * log( Lambda )
    assert D > 0
    ddf = k1 - k0
    assert ddf > 0
    pval = chisqprob(D, ddf)
    prefer_m1 = pval < alfa
    return prefer_m1, pval, D, ddf

Test on a simple model

We test the function on data generated from the equation:

$$ y = b + e^{-a t} + N(0, \sigma^2) $$

where $a$ and $b$ are the parameters, $t$ is the variable, and $N$ is the normal distribution.

We fit a nesting model model_fit1 (the alternative hypothesis of the test). This model estimates both $a$ and $b$. We also fit a nested model model_fit0 (the nul hypothesis of the test) in which either $a$ or $b$ is fixed at an initial value. We than plot both model fits, print the estimated parameters, the test statistic and the p-value of the test.

In [34]:
# test lrt
def lrtest_test(a, b, a_init, b_init, a_vary, b_vary, sig=0.1, N=100, alfa=0.05):
    assert a_vary or b_vary
    assert not(a_vary and b_vary)
    t = linspace(0,1,N)
    f = lambda t,a,b: b + exp(-a*t)
    data = f(t,a,b) + normal(0, sig, N)
    fig, ax = subplots(1,2,sharex=True,sharey=True,figsize=(15,5))
    print "real:", a,b
    model = Model(f)
    params = model.make_params(a=a_init, b=b_init)

    two_var_fit =, t=t, params=params)
    ax[1].set_title("Two variables")
    print "Two variables:",two_var_fit.best_values

    one_var_fit =, t=t, params=params)
    ax[0].set_title("One variable")
    print "One variable:",one_var_fit.best_values

    prefer_m1,pval,D,ddf = lrtest(one_var_fit, two_var_fit, alfa)
    print "pval=%.2g, D=%.2g, ddf=%d" % (pval, D, ddf)
    print "Decision with significance level %.2g:" % alfa,
    if prefer_m1:
        print "Two variable model"
        print "One variable model"
    return t,data
In [35]:
real: 1 1
Two variables: {'a': 0.86775531514445625, 'b': 0.97560666838937937}
One variable: {'a': 1, 'b': 1.0118956348766281}
pval=0.19, D=1.7, ddf=1
Decision with significance level 0.05: One variable model
In [36]:
real: 1 1
Two variables: {'a': 0.88198851749015894, 'b': 0.95415310004056564}
One variable: {'a': 1.0278836663608819, 'b': 1}
pval=0.042, D=4.1, ddf=1
Decision with significance level 0.05: Two variable model
In [37]:
real: 1 1
Two variables: {'a': 1.0842648241878579, 'b': 1.0287763184769507}
One variable: {'a': -0.5729794466048922, 'b': 0}
pval=1.2e-72, D=3.2e+02, ddf=1
Decision with significance level 0.05: Two variable model
In [38]:
real: 1 1
Two variables: {'a': 0.9452008165850978, 'b': 0.98442967528266856}
One variable: {'a': 0, 'b': 0.63175416158525943}
pval=5.3e-35, D=1.5e+02, ddf=1
Decision with significance level 0.05: Two variable model
In [39]:
real: 1 1
Two variables: {'a': 0.98872695387898135, 'b': 0.98522534209144352}
One variable: {'a': 0, 'b': 0.62084932431698936}
pval=7.3e-35, D=1.5e+02, ddf=1
Decision with significance level 0.05: Two variable model
In [40]:
real: 1 1
Two variables: {'a': 1.0224399232088139, 'b': 0.99340603684830542}
One variable: {'a': 1.0446412443876858, 'b': 1}
pval=0.8, D=0.065, ddf=1
Decision with significance level 0.05: One variable model
In [41]:
real: 1 1
Two variables: {'a': 0.87089207465018115, 'b': 0.96616302997657855}
One variable: {'a': -0.58724547087449808, 'b': 0}
pval=5e-74, D=3.3e+02, ddf=1
Decision with significance level 0.05: Two variable model
In [42]:
real: 1 1
Two variables: {'a': 1.0285746095656456, 'b': 1.0174174562149918}
One variable: {'a': 1, 'b': 1.0099552907882328}
pval=0.77, D=0.086, ddf=1
Decision with significance level 0.05: One variable model
In [43]:
real: 1 1
Two variables: {'a': 1.0888695144064144, 'b': 1.0317987702059201}
One variable: {'a': 2, 'b': 1.2079536785181528}
pval=3.3e-07, D=26, ddf=1
Decision with significance level 0.05: Two variable model

Compare with R

Here we compare the Python implementation with the R implementation - lmtest::lrtest. The data is generated from a quadratic polynomial:

$$ y = at^2 + bt + c + N(0,\sigma^2) $$

and we fit two models - the nested model is a linear function and the nesting model is a quadratic polynomial.


In order to get this to work you will need to make sure the lmtest package is installed (don't install via RStudio - use regular command line R) and that the R_HOME and R_USER environment variable are set to where R is running from the command line and where the packages are installed to when running from the command line.

In [44]:
%load_ext rpy2.ipython
The rpy2.ipython extension is already loaded. To reload it, use:
  %reload_ext rpy2.ipython
In [45]:
!echo %R_HOME%
!echo %R_USER%
In [47]:
for _ in range(10):
    N = 100
    sig = 0.1
    a,b,c = 0.1,1,1
    t = linspace(0,1,N)
    f = lambda t,a,b,c: c + b*t + a*t**2
    data = f(t,a,b,c) + normal(0, sig, N)
    model = Model(f)
    params = model.make_params(a=0, b=0, c=0)

    model_fit1 =, t=t, params=params)

    model_fit0 =, t=t, params=params)

    prefer_m1,pval,D,ddf = lrtest(model_fit0, model_fit1)
    print "py",
    print pval

    print "R ",
    %Rpush t data
    %R library(lmtest)
    %R lm1=lm(data~t)
    %R lm2=lm(data~t+I(t^2))
    %R pvalue = lrtest(lm2,lm1)[2,5]
    %Rpull pvalue
    print pvalue[0]
py 0.0179882275173
R  0.0179882275173
py 0.537531566876
R  0.537531566876
py 0.269018479047
R  0.269018479047
py 0.495471455839
R  0.495471455839
py 0.546745762151
R  0.546745762151
py 0.662389288421
R  0.662389288421
py 0.534273698101
R  0.534273698101
py 0.0422040728343
R  0.0422040728343
py 0.633756809221
R  0.633756809221
py 0.203276605564
R  0.203276605564


This notebook was written by Yoav Ram and Uri Obolski. The notebook was written to facilitate the discussion on lmfit mailing-list. The latest version can be found at

The code is released with a CC-BY-SA 3.0 license.