Hi @MRB3855 , thanks for your reply!
Here shares my dataset and JMP/Python scripts and outputs for checking,
For JMP
Fit Model(
Y( :Y1 ),
Effects( :A, :B, :C, :D ),
Random Effects( :Subject ),
NoBounds( 1 ),
Personality( "Standard Least Squares" ),
Method( "REML" ),
Emphasis( "Effect Leverage" ),
Run(
:Y1 << {Summary of Fit( 1 ), Analysis of Variance( 0 ),
Parameter Estimates( 1 ), Scaled Estimates( 0 ),
Plot Actual by Predicted( 0 ), Plot Regression( 0 ),
Plot Residual by Predicted( 0 ), Plot Studentized Residuals( 0 ),
Plot Effect Leverage( 0 ), Plot Residual by Normal Quantiles( 0 ),
{:A << {LSMeans Student's t( 0.05 )}}}
)
);
For Python
def fit_model(df, y_col):
"""Fit Y ~ A(Sum) + B(Sum) + C(Sum) + D(Sum) + (1|Subject) via REML."""
# Note: column "C" conflicts with patsy's C() function,
# so we temporarily rename it for formula construction.
df_tmp = df.rename(columns={'C': '_C_'})
formula = '1 + C(A, Sum) + C(B, Sum) + C(_C_, Sum) + C(D, Sum)'
X_mat = dmatrix(formula, df_tmp)
design_info = X_mat.design_info
X = pd.DataFrame(np.asarray(X_mat), columns=design_info.column_names)
y = df[y_col].values
groups = df['Subject']
model = MixedLM(y, X, groups=groups)
result = model.fit(reml=True, method='powell')
return result, X, design_info, df_tmp
def compute_lsmeans_A(result, X, df_tmp, design_info):
"""Compute LS Means for factor A (Sum coding → intercept + effect)."""
fe = result.fe_params.values
n_fe = len(fe)
col_names = list(X.columns)
target_prefix = 'C(A, Sum)'
target_col_idx = [i for i, c in enumerate(col_names) if target_prefix in c]
base_contrast = np.zeros(n_fe)
base_contrast[0] = 1.0 # intercept
levels = sorted(df_tmp['A'].unique())
lsmeans = {}
for level in levels:
sr = df_tmp.iloc[0:1].copy()
sr['A'] = level
X_single = np.asarray(build_design_matrices([design_info], sr)[0])
c = base_contrast.copy()
for idx in target_col_idx:
c[idx] = X_single[0, idx]
lsmeans[level] = float(c @ fe)
return lsmeans
I'd appreciate any comments/suggestions you might have.
