jax.scipy.stats.sem

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jax.scipy.stats.sem#

jax.scipy.stats.sem(a, axis=0, ddof=1, nan_policy='propagate', *, keepdims=False)[source]#

Compute the standard error of the mean.

JAX implementation of scipy.stats.sem().

Parameters:
  • a (jax.typing.ArrayLike) – arraylike

  • axis (int | None) – optional integer. If not specified, the input array is flattened.

  • ddof (int) – integer, default=1. The degrees of freedom in the SEM computation.

  • nan_policy (str) – str, default=”propagate”. JAX supports only “propagate” and “omit”.

  • keepdims (bool) – bool, default=False. If true, reduced axes are left in the result with size 1.

Returns:

array

Return type:

Array

Examples

>>> x = jnp.array([2, 4, 1, 1, 3, 4, 4, 2, 3])
>>> with jnp.printoptions(precision=2, suppress=True):
...   jax.scipy.stats.sem(x)
Array(0.41, dtype=float32)

For multi dimensional arrays, sem computes standard error of mean along axis=0:

>>> x1 = jnp.array([[1, 2, 1, 3, 2, 1],
...                 [3, 1, 3, 2, 1, 3],
...                 [1, 2, 2, 3, 1, 2]])
>>> with jnp.printoptions(precision=2, suppress=True):
...   jax.scipy.stats.sem(x1)
Array([0.67, 0.33, 0.58, 0.33, 0.33, 0.58], dtype=float32)

If axis=1, standard error of mean will be computed along axis 1.

>>> with jnp.printoptions(precision=2, suppress=True):
...   jax.scipy.stats.sem(x1, axis=1)
Array([0.33, 0.4 , 0.31], dtype=float32)

If axis=None, standard error of mean will be computed along all the axes.

>>> with jnp.printoptions(precision=2, suppress=True):
...   jax.scipy.stats.sem(x1, axis=None)
Array(0.2, dtype=float32)

By default, sem reduces the dimension of the result. To keep the dimensions same as that of the input array, the argument keepdims must be set to True.

>>> with jnp.printoptions(precision=2, suppress=True):
...   jax.scipy.stats.sem(x1, axis=1, keepdims=True)
Array([[0.33],
       [0.4 ],
       [0.31]], dtype=float32)

Since, by default, nan_policy='propagate', sem propagates the nan values in the result.

>>> nan = jnp.nan
>>> x2 = jnp.array([[1, 2, 3, nan, 4, 2],
...                 [4, 5, 4, 3, nan, 1],
...                 [7, nan, 8, 7, 9, nan]])
>>> with jnp.printoptions(precision=2, suppress=True):
...   jax.scipy.stats.sem(x2)
Array([1.73,  nan, 1.53,  nan,  nan,  nan], dtype=float32)

If nan_policy='omit`, sem omits the nan values and computes the error for the remainging values along the specified axis.

>>> with jnp.printoptions(precision=2, suppress=True):
...   jax.scipy.stats.sem(x2, nan_policy='omit')
Array([1.73, 1.5 , 1.53, 2.  , 2.5 , 0.5 ], dtype=float32)