How to use the pymer4.models.Lmer function in pymer4

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))

import pandas as pd
import seaborn as sns
from pymer4.models import Lmer
from pymer4.utils import get_resource_path

df = pd.read_csv(os.path.join(get_resource_path(),'sample_data.csv'))
df.head()
print("Hello World!")

#######################################################################
# Estimate a model
# ----------------
#
# Initialize linear model with random intercepts, slopes and their correlation

model = Lmer('DV ~ IV2 + (IV2|Subject)',data=df)

#########################################################################
# Fit it

model.fit()

#######################################################################
# Inspect clusters
# --------------------------
#
# We can look at the 'Subject' level parameters easily
# Each row here is a unique Subject's random intercept and slope

model.fixef.head()

# We can also plot these values with respect to the population parameters

import os
import pandas as pd
import seaborn as sns
from pymer4.models import Lmer
from pymer4.utils import get_resource_path

df = pd.read_csv(os.path.join(get_resource_path(),'sample_data.csv'))
df.head()

#######################################################################
# Estimate a model
# ----------------
#
# Initialize linear model with random intercepts, slopes and their correlation

model = Lmer('DV ~ IV2 + (IV2|Subject)',data=df)

#########################################################################
# Fit it

model.fit()

#######################################################################
# Inspect clusters
# --------------------------
#
# We can look at the 'Subject' level parameters easily
# Each row here is a unique Subject's random intercept and slope

model.fixef.head()

# We can also plot these values with respect to the population parameters

).astype(model_atts['data_atts'][dtype_name])
                    # Check if the list already exists if so just append to it
                    if hasattr(model, item_name):
                        current_items = getattr(model, item_name)
                        if current_items is not None:
                            current_items += [df]
                            setattr(model, item_name, current_items)
                        else:
                            setattr(model, item_name, [df])
                    # Otherwise create it
                    else:
                        setattr(model, item_name, [df])
                    # Add to the list of completed items
                    completed.extend([item_name, vals_name, idx_name, dtype_name])
        # Now deal with model object in R if needed
        if isinstance(model, Lmer):
            filename = filepath.split('.')[0]
            model.model_obj = base.readRDS(f"{filename}.rds")
        return model
    else:
        raise IOError("filepath must end with .h5 or .hdf5")

Function for loading pymer4 models. A file path ending in .h5 or .hdf5 should be provided. For Lmer models an additional filepath.robj should be located in the same directory.

    Args:
        model (pymer4.models): an instance of a pymer4 model
        filepath (str): full filepath string ending with .h5 or .hd5f 
    """
    
    if filepath.endswith(".h5") or filepath.endswith('.hdf5'):
        if not os.path.exists(filepath):
            raise IOError("File not found!")
        
        # Load h5 first
        model_atts = dd.io.load(filepath)
        # Figure out what kind of model we're dealing with
        if model_atts['simple_atts']['model_class'] == 'Lmer':
            model = Lmer('', [])
        elif model_atts['simple_atts']['model_class'] == 'Lm2':
            model = Lm2('', [] , '')
        elif model_atts['simple_atts']['model_class'] == 'Lm':
            model = Lm('', [])
        
        # Set top level attributes
        for k, v in model_atts['simple_atts'].items():
            if k != 'model_class':
                setattr(model, k, v)
        # Make sure the model formula is a python string string so that rpy2 doesn't complain
        model.formula = str(model.formula)
        
        # Set data attributes
        # Container for already set items
        completed = []
        for k, v in model_atts['data_atts'].items():