diff --git a/horizont/lda.py b/horizont/lda.py
index 0c688f5..5ec04c6 100644
--- a/horizont/lda.py
+++ b/horizont/lda.py
@@ -104,7 +104,7 @@ def __init__(self, n_topics=None, n_iter=1000, alpha=0.1, eta=0.01, random_state
         self.n_iter = n_iter
         self.alpha = alpha
         self.eta = eta
-        self.random_state = random_state
+        self.random_state = sklearn.utils.check_random_state(random_state)
         rng = sklearn.utils.check_random_state(random_state)
         # random numbers that are reused
         self._rands = rng.rand(1000)
@@ -138,6 +138,11 @@ def _fit(self, X):
         random_state = sklearn.utils.check_random_state(self.random_state)
         X = np.atleast_2d(sklearn.utils.as_float_array(X))
         self._initialize(X, random_state)
+        self._run_fitting_iterations()
+        return self
+
+    def _run_fitting_iterations(self):
+        random_state = sklearn.utils.check_random_state(self.random_state)
         for it in range(self.n_iter):
             if it % 10 == 0:
                 self._print_status(it)
@@ -151,11 +156,27 @@ def _fit(self, X):
         self.theta_ = self.ndz_ + self.alpha
         self.theta_ /= np.sum(self.theta_, axis=1, keepdims=True)
 
-        # delete attributes no longer needed after fitting
-        del self.WS
-        del self.DS
-        del self.ZS
+    def continue_fitting(self, n_iter):
+        """Continues fitting the model after calling `fit` or `fit_transform.
+
+        Parameters
+        ----------
+        n_iter: int, number of iterations to perform
+        """
+        if not hasattr(self, 'WS'):
+            raise Exception('Must call `fit` or `fit_transform` first.')
+        self.n_iter = n_iter
+        self._run_fitting_iterations()
         return self
+        
+    def delete_temp_vars(self):
+        """Delete attributes no longer needed after fitting"""
+        if hasattr(self, 'WS'):
+            del self.WS
+        if hasattr(self, 'DS'):
+            del self.DS
+        if hasattr(self, 'ZS'):
+            del self.ZS
 
     def _print_status(self, iter):
         ll = self.loglikelihood()