def test_lmer_opt_passing():
df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
model = Lmer("DV ~ IV2 + (IV2|Group)", data=df)
opt_opts = "optCtrl = list(ftol_abs=1e-8, xtol_abs=1e-8)"
model.fit(summarize=False, control=opt_opts)
estimates = np.array([10.301072, 0.682124])
assert np.allclose(model.coefs["Estimate"], estimates, atol=0.001)
assert len(model.warnings) == 0
df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
model = Lmer("DV ~ IV2 + (IV2|Group)", data=df)
opt_opts = "optCtrl = list(ftol_abs=1e-4, xtol_abs=1e-4)"
model.fit(summarize=False, control=opt_opts)
assert len(model.warnings) >= 1
def test_logistic_lmm():
df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
model = Lmer("DV_l ~ IV1+ (IV1|Group)", data=df, family="binomial")
model.fit(summarize=False)
assert model.coefs.shape == (2, 13)
estimates = np.array([-0.16098421, 0.00296261])
assert np.allclose(model.coefs["Estimate"], estimates, atol=0.001)
assert isinstance(model.fixef, pd.core.frame.DataFrame)
assert model.fixef.shape == (47, 2)
assert isinstance(model.ranef, pd.core.frame.DataFrame)
assert model.ranef.shape == (47, 2)
assert np.allclose(model.coefs.loc[:, "Estimate"], model.fixef.mean(), atol=0.01)
# Test prediction
assert np.allclose(model.predict(model.data, use_rfx=True), model.data.fits)
assert np.allclose(
model.predict(model.data, use_rfx=True, pred_type="link"),
logit(model.data.fits),
)
# Test RFX only
model = Lmer("DV_l ~ 0 + (IV1|Group)", data=df, family="binomial")
model.fit(summarize=False)
assert model.fixef.shape == (47, 2)
model = Lmer("DV_l ~ 0 + (IV1|Group) + (1|IV3)", data=df, family="binomial")
model.fit(summarize=False)
assert isinstance(model.fixef, list)
assert model.fixef[0].shape == (47, 2)
assert model.fixef[1].shape == (3, 2)
def test_gaussian_lmm():
df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
model = Lmer("DV ~ IV3 + IV2 + (IV2|Group) + (1|IV3)", data=df)
opt_opts = "optimizer='Nelder_Mead', optCtrl = list(FtolAbs=1e-8, XtolRel=1e-8)"
model.fit(summarize=False, control=opt_opts)
assert model.coefs.shape == (3, 8)
estimates = np.array([12.04334602, -1.52947016, 0.67768509])
assert np.allclose(model.coefs["Estimate"], estimates, atol=0.001)
assert isinstance(model.fixef, list)
assert model.fixef[0].shape == (47, 3)
assert model.fixef[1].shape == (3, 3)
assert isinstance(model.ranef, list)
assert model.ranef[0].shape == (47, 2)
assert model.ranef[1].shape == (3, 1)
assert model.ranef_corr.shape == (1, 3)
assert model.ranef_var.shape == (4, 3)
assert np.allclose(model.coefs.loc[:, "Estimate"], model.fixef[0].mean(), atol=0.01)
# Test prediction
assert np.allclose(model.predict(model.data, use_rfx=True), model.data.fits)
# Smoketest for simulate
model.simulate(2)
model.simulate(2, use_rfx=True)
# Smoketest for old_optimizer
model.fit(summarize=False, old_optimizer=True)
def test_logistic_lmm():
df = pd.read_csv(os.path.join(get_resource_path(), 'sample_data.csv'))
model = Lmer('DV_l ~ IV1+ (IV1|Group)', data=df, family='binomial')
model.fit(summarize=False)
assert model.coefs.shape == (2, 13)
estimates = np.array([-0.16098421, 0.00296261])
assert np.allclose(model.coefs['Estimate'], estimates, atol=.001)
assert isinstance(model.fixef, pd.core.frame.DataFrame)
assert model.fixef.shape == (47, 2)
assert isinstance(model.ranef, pd.core.frame.DataFrame)
assert model.ranef.shape == (47, 2)
assert np.allclose(model.coefs.loc[:, 'Estimate'],
model.fixef.mean(),
atol=.01)
# Test prediction
assert np.allclose(model.predict(model.data, use_rfx=True),
model.data.fits)
assert np.allclose(
model.predict(model.data, use_rfx=True, pred_type='link'),
logit(model.data.fits))
def test_gaussian_lm():
df = pd.read_csv(os.path.join(get_resource_path(), 'sample_data.csv'))
model = Lm('DV ~ IV1 + IV3', data=df)
model.fit(summarize=False)
assert model.coefs.shape == (3, 8)
estimates = np.array([42.24840439, 0.24114414, -3.34057784])
assert np.allclose(model.coefs['Estimate'], estimates, atol=.001)
# Test robust SE against statsmodels
standard_se = np.array([6.83783939, 0.30393886, 3.70656475])
assert np.allclose(model.coefs['SE'], standard_se, atol=.001)
hc0_se = np.array([7.16661817, 0.31713064, 3.81918182])
model.fit(robust='hc0', summarize=False)
assert np.allclose(model.coefs['SE'], hc0_se, atol=.001)
hc3_se = np.array([7.22466699, 0.31971942, 3.84863701])
model.fit(robust='hc3', summarize=False)
assert np.allclose(model.coefs['SE'], hc3_se, atol=.001)
# Test bootstrapping
model.fit(summarize=False, conf_int='boot')
assert model.ci_type == 'boot (500)'
# Test permutation
model.fit(summarize=False, permute=500)
assert model.sig_type == 'permutation (500)'
def test_gaussian_lmm():
df = pd.read_csv(os.path.join(get_resource_path(), 'sample_data.csv'))
model = Lmer('DV ~ IV3 + IV2 + (IV2|Group) + (1|IV3)', data=df)
model.fit(summarize=False)
assert model.coefs.shape == (3, 8)
estimates = np.array([12.04334602, -1.52947016, 0.67768509])
assert np.allclose(model.coefs['Estimate'], estimates, atol=.001)
assert isinstance(model.fixef, list)
assert model.fixef[0].shape == (47, 3)
assert model.fixef[1].shape == (3, 3)
assert isinstance(model.ranef, list)
assert model.ranef[0].shape == (47, 2)
assert model.ranef[1].shape == (3, 1)
assert model.ranef_corr.shape == (1, 3)
assert model.ranef_var.shape == (4, 3)
assert np.allclose(model.coefs.loc[:, 'Estimate'],
model.fixef[0].mean(),
atol=.01)
# Test prediction
assert np.allclose(model.predict(model.data, use_rfx=True),
model.data.fits)
def test_glmer_opt_passing():
np.random.seed(1)
df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
df["DV_int"] = np.random.randint(1, 10, df.shape[0])
m = Lmer("DV_int ~ IV3 + (1|Group)", data=df, family="poisson")
m.fit(summarize=False,
control="optCtrl = list(FtolAbs=1e-1, FtolRel=1e-1, maxfun=10)")
assert len(m.warnings) >= 1
def test_anova():
np.random.seed(1)
data = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
data["DV_l2"] = np.random.randint(0, 4, data.shape[0])
model = Lmer("DV ~ IV3*DV_l2 + (IV3|Group)", data=data)
model.fit(summarize=False)
out = model.anova()
assert out.shape == (3, 7)
def test_poisson_lmm():
np.random.seed(1)
df = pd.read_csv(os.path.join(get_resource_path(), 'sample_data.csv'))
df['DV_int'] = np.random.randint(1, 10, df.shape[0])
m = Lmer('DV_int ~ IV3 + (1|Group)', data=df, family='poisson')
m.fit(summarize=False)
assert m.family == 'poisson'
assert m.coefs.shape == (2, 7)
assert 'Z-stat' in m.coefs.columns
def test_gaussian_lm():
df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
model = Lm("DV ~ IV1 + IV3", data=df)
model.fit(summarize=False)
assert model.coefs.shape == (3, 8)
estimates = np.array([42.24840439, 0.24114414, -3.34057784])
assert np.allclose(model.coefs["Estimate"], estimates, atol=0.001)
# Test robust SE against statsmodels
standard_se = np.array([6.83783939, 0.30393886, 3.70656475])
assert np.allclose(model.coefs["SE"], standard_se, atol=0.001)
hc0_se = np.array([7.16661817, 0.31713064, 3.81918182])
model.fit(robust="hc0", summarize=False)
assert np.allclose(model.coefs["SE"], hc0_se, atol=0.001)
hc1_se = np.array([7.1857547, 0.31797745, 3.82937992])
# hc1 is the default
model.fit(robust=True, summarize=False)
assert np.allclose(model.coefs["SE"], hc1_se, atol=0.001)
hc2_se = np.array([7.185755, 0.317977, 3.829380])
model.fit(robust="hc1", summarize=False)
assert np.allclose(model.coefs["SE"], hc2_se, atol=0.001)
hc3_se = np.array([7.22466699, 0.31971942, 3.84863701])
model.fit(robust="hc3", summarize=False)
assert np.allclose(model.coefs["SE"], hc3_se, atol=0.001)
hac_lag1_se = np.array([8.20858448, 0.39184764, 3.60205873])
model.fit(robust="hac", summarize=False)
assert np.allclose(model.coefs["SE"], hac_lag1_se, atol=0.001)
# Test bootstrapping
model.fit(summarize=False, conf_int="boot")
assert model.ci_type == "boot (500)"
# Test permutation
model.fit(summarize=False, permute=500)
assert model.sig_type == "permutation (500)"
# Test WLS
df_two_groups = df.query("IV3 in [0.5, 1.0]").reset_index(drop=True)
x = df_two_groups.query("IV3 == 0.5").DV.values
y = df_two_groups.query("IV3 == 1.0").DV.values
# Fit new a model using a categorical predictor with unequal variances (WLS)
model = Lm("DV ~ IV3", data=df_two_groups)
model.fit(summarize=False, weights="IV3")
assert model.estimator == "WLS"
# Make sure welch's t-test lines up with scipy
wls = np.abs(model.coefs.loc["IV3", ["T-stat", "P-val"]].values)
scit = np.abs(ttest_ind(x, y, equal_var=False))
assert all([np.allclose(a, b) for a, b in zip(wls, scit)])
def test_gamma_lmm():
np.random.seed(1)
df = pd.read_csv(os.path.join(get_resource_path(), 'sample_data.csv'))
df['DV_g'] = np.random.uniform(1, 2, size=df.shape[0])
m = Lmer('DV_g ~ IV3 + (1|Group)', data=df, family='gamma')
m.fit(summarize=False)
assert m.family == 'gamma'
assert m.coefs.shape == (2, 7)
def test_inverse_gaussian_lmm():
np.random.seed(1)
df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
df["DV_g"] = np.random.uniform(1, 2, size=df.shape[0])
m = Lmer("DV_g ~ IV3 + (1|Group)", data=df, family="inverse_gaussian")
m.fit(summarize=False)
assert m.family == "inverse_gaussian"
assert m.coefs.shape == (2, 7)
def test_gaussian_lm2():
df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
model = Lm2("DV ~ IV3 + IV2", group="Group", data=df)
model.fit(summarize=False)
assert model.coefs.shape == (3, 8)
estimates = np.array([16.11554138, -1.38425772, 0.59547697])
assert np.allclose(model.coefs["Estimate"], estimates, atol=0.001)
assert model.fixef.shape == (47, 3)
# Test bootstrapping and permutation tests
model.fit(permute=500, conf_int="boot", n_boot=500, summarize=False)
assert model.ci_type == "boot (500)"
assert model.sig_type == "permutation (500)"
def test_post_hoc():
np.random.seed(1)
df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
model = Lmer("DV ~ IV1*IV3*DV_l + (IV1|Group)", data=df, family="gaussian")
model.fit(
factors={"IV3": ["0.5", "1.0", "1.5"], "DV_l": ["0", "1"]}, summarize=False
)
marginal, contrasts = model.post_hoc(marginal_vars="IV3", p_adjust="dunnet")
assert marginal.shape[0] == 3
assert contrasts.shape[0] == 3
marginal, contrasts = model.post_hoc(marginal_vars=["IV3", "DV_l"])
assert marginal.shape[0] == 6
assert contrasts.shape[0] == 15
def test_install():
"""
Quick function to test installation by import a lmm object and fitting a quick model.
"""
try:
from pymer4.models import Lmer
from pymer4.utils import get_resource_path
import os
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
df = pd.read_csv(os.path.join(get_resource_path(), 'sample_data.csv'))
model = Lmer('DV ~ IV3 + (1|Group)', data=df)
model.fit(summarize=False)
print("Pymer4 installation working successfully!")
except Exception as e:
print("Error! {}".format(e))
def test_post_hoc():
np.random.seed(1)
df = pd.read_csv(os.path.join(get_resource_path(), 'sample_data.csv'))
model = Lmer('DV ~ IV1*IV3*DV_l + (IV1|Group)', data=df, family='gaussian')
model.fit(factors={
'IV3': ['0.5', '1.0', '1.5'],
'DV_l': ['0', '1']
},
summarize=False)
marginal, contrasts = model.post_hoc(marginal_vars='IV3',
p_adjust='dunnet')
assert marginal.shape[0] == 3
assert contrasts.shape[0] == 3
marginal, contrasts = model.post_hoc(marginal_vars=['IV3', 'DV_l'])
assert marginal.shape[0] == 6
assert contrasts.shape[0] == 15
def test_poisson_lmm():
np.random.seed(1)
df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
df["DV_int"] = np.random.randint(1, 10, df.shape[0])
m = Lmer("DV_int ~ IV3 + (1|Group)", data=df, family="poisson")
m.fit(summarize=False)
assert m.family == "poisson"
assert m.coefs.shape == (2, 7)
assert "Z-stat" in m.coefs.columns
# Test RFX only
model = Lmer("DV_int ~ 0 + (IV1|Group)", data=df, family="poisson")
model.fit(summarize=False)
assert model.fixef.shape == (47, 2)
model = Lmer("DV_int ~ 0 + (IV1|Group) + (1|IV3)", data=df, family="poisson")
model.fit(summarize=False)
assert isinstance(model.fixef, list)
assert model.fixef[0].shape == (47, 2)
assert model.fixef[1].shape == (3, 2)
################################################################################
# ANOVA tables and orthogonal contrasts
# -------------------------------------
# Because ANOVA is just regression, :code:`pymer4` can estimate ANOVA tables with F-results using the :code:`.anova()` method on a fitted model. This will compute a Type-III SS table given the coding scheme provided when the model was initially fit. Based on the distribution of data across factor levels and the specific coding-scheme used, this may produce invalid Type-III SS computations. For this reason the :code:`.anova()` method has a :code:`force-orthogonal=True` argument that will reparameterize and refit the model using orthogonal polynomial contrasts prior to computing an ANOVA table.
#
# Here we first estimate a mode with dummy-coded categories and suppress the summary output of :code:`.fit()`. Then we use :code:`.anova()` to examine the F-test results.
# import basic libraries and sample data
import os
import pandas as pd
from pymer4.utils import get_resource_path
from pymer4.models import Lmer
# IV3 is a categorical predictors with 3 levels in the sample data
df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
# # We're going to fit a multi-level regression using the
# categorical predictor (IV3) which has 3 levels
model = Lmer("DV ~ IV3 + (1|Group)", data=df)
# Using dummy-coding; suppress summary output
model.fit(factors={"IV3": ["1.0", "0.5", "1.5"]}, summarize=False)
# Get ANOVA table
print(model.anova())
################################################################################
# Type III SS inferences will only be valid if data are fully balanced across levels or if contrasts between levels are orthogonally coded and sum to 0. Below we tell :code:`pymer4` to respecify our contrasts to ensure this before estimating the ANOVA. :code:`pymer4` also saves the last set of contrasts used priory to forcing orthogonality.
#
# Because the sample data is balanced across factor levels and there are not interaction terms, in this case orthogonal contrast coding doesn't change the results.
def test_gaussian_lmm():
df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
model = Lmer("DV ~ IV3 + IV2 + (IV2|Group) + (1|IV3)", data=df)
opt_opts = "optimizer='Nelder_Mead', optCtrl = list(FtolAbs=1e-8, XtolRel=1e-8)"
model.fit(summarize=False, control=opt_opts)
assert model.coefs.shape == (3, 8)
estimates = np.array([12.04334602, -1.52947016, 0.67768509])
assert np.allclose(model.coefs["Estimate"], estimates, atol=0.001)
assert isinstance(model.fixef, list)
assert (model.fixef[0].index.astype(int) == df.Group.unique()).all()
assert (model.fixef[1].index.astype(float) == df.IV3.unique()).all()
assert model.fixef[0].shape == (47, 3)
assert model.fixef[1].shape == (3, 3)
assert isinstance(model.ranef, list)
assert model.ranef[0].shape == (47, 2)
assert model.ranef[1].shape == (3, 1)
assert (model.ranef[1].index == ["0.5", "1", "1.5"]).all()
assert model.ranef_corr.shape == (1, 3)
assert model.ranef_var.shape == (4, 3)
assert np.allclose(model.coefs.loc[:, "Estimate"],
model.fixef[0].mean(),
atol=0.01)
# Test prediction
assert np.allclose(model.predict(model.data, use_rfx=True),
model.data.fits)
# Test simulate
out = model.simulate(2)
assert isinstance(out, pd.DataFrame)
assert out.shape == (model.data.shape[0], 2)
out = model.simulate(2, use_rfx=True)
assert isinstance(out, pd.DataFrame)
assert out.shape == (model.data.shape[0], 2)
# Smoketest for old_optimizer
model.fit(summarize=False, old_optimizer=True)
# test fixef code for 1 fixed effect
model = Lmer("DV ~ IV3 + IV2 + (IV2|Group)", data=df)
model.fit(summarize=False, control=opt_opts)
assert (model.fixef.index.astype(int) == df.Group.unique()).all()
assert model.fixef.shape == (47, 3)
assert np.allclose(model.coefs.loc[:, "Estimate"],
model.fixef.mean(),
atol=0.01)
# test fixef code for 0 fixed effects
model = Lmer("DV ~ (IV2|Group) + (1|IV3)", data=df)
model.fit(summarize=False, control=opt_opts)
assert isinstance(model.fixef, list)
assert (model.fixef[0].index.astype(int) == df.Group.unique()).all()
assert (model.fixef[1].index.astype(float) == df.IV3.unique()).all()
assert model.fixef[0].shape == (47, 2)
assert model.fixef[1].shape == (3, 2)
def data():
return pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
