small change

fabsig · Jun 10, 2022 · cb58a7d · cb58a7d
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Showing 3 changed files with 3 additions and 3 deletions.
diff --git a/R-package/demo/generalized_linear_Gaussian_process_mixed_effects_models.R b/R-package/demo/generalized_linear_Gaussian_process_mixed_effects_models.R
@@ -175,7 +175,7 @@ plot(b, pred_random_effects, xlab="truth", ylab="predicted",
 points(b_random_slope, pred_random_slopes, col=2, pch=2, lwd=1.5)
 points(b_crossed, pred_random_effects_crossed, col=4, pch=4, lwd=1.5)
 
-# Random slope model in which a intercept random effect is dropped / not included
+# Random slope model in which an intercept random effect is dropped / not included
 gp_model <- fitGPModel(group_data = cbind(group,group_crossed), group_rand_coef_data = x,
                        ind_effect_group_rand_coef = 1, drop_intercept_group_rand_effect = c(TRUE,FALSE),
                        likelihood = likelihood, y = y_crossed_random_slope, X = X)

diff --git a/examples/python-guide/generalized_linear_Gaussian_process_mixed_effects_models.py b/examples/python-guide/generalized_linear_Gaussian_process_mixed_effects_models.py
@@ -190,7 +190,7 @@ def simulate_response_variable(lp, rand_eff, likelihood):
 plt.title("Comparison of true and predicted random effects")
 plt.show(block=False)
 
-# Random slope model in which a intercept random effect is dropped / not included
+# Random slope model in which an intercept random effect is dropped / not included
 gp_model = gpb.GPModel(group_data=group_data, group_rand_coef_data=x,
                        ind_effect_group_rand_coef=[1], 
                        drop_intercept_group_rand_effect=[True,False], likelihood=likelihood)

diff --git a/python-package/gpboost/basic.py b/python-package/gpboost/basic.py
@@ -3968,7 +3968,7 @@ def __init__(self, likelihood="gaussian",
             drop_intercept_group_rand_effect : list, numpy 1-D array, pandas Series / one-column DataFrame with bool data or None, optional (default=None)
                 Indicates whether intercept random effects are dropped (only for random coefficients).
                 If drop_intercept_group_rand_effect[k] is True, the intercept random effect number k is dropped / not included.
-                Only random effects with random slopes can be dropped.
+                Only random effects with random slopes can be dropped
             gp_coords : numpy array or pandas DataFrame with numeric data or None, optional (default=None)
                 Coordinates (= inputs / features) for defining Gaussian processes
             gp_rand_coef_data : numpy array or pandas DataFrame with numeric data or None, optional (default=None)