def R_run_loess(x, y, span=0.75):
"""
Predict y as function of x. Takes two numpy vectors.
"""
# Ensure that Inf/-Inf values are substituted
x[utils.where_null(x)] = robj.NA_Real
y[utils.where_null(x)] = robj.NA_Real
data = robj.DataFrame({"x": x, "y": y})
loess_fit = r.loess("y ~ x", data=data, span=span, family="symmetric")
correction_factor = np.array(list(r.predict(loess_fit, x)))
corrected_y = \
np.array(list(y)) - correction_factor
return corrected_y, correction_factor
def R_run_loess(x, y, span=0.75):
"""
Predict y as function of x. Takes two numpy vectors.
"""
# Ensure that Inf/-Inf values are substituted
x[utils.where_null(x)] = robj.NA_Real
y[utils.where_null(x)] = robj.NA_Real
data = robj.DataFrame({"x": x, "y": y})
loess_fit = r.loess("y ~ x", data=data, span=span,
family="symmetric")
correction_factor = np.array(list(r.predict(loess_fit, x)))
corrected_y = \
np.array(list(y)) - correction_factor
return corrected_y, correction_factor
def run_lowess(X, Y, frac=0.75, missing="none"):
"""
Y ~ X lowess.
Parameters:
-----------
X: X values
Y: Y values
frac: fraction of data used to estimate each y-value.
missing: how to handle missing values (by default "drop" them).
"""
X[utils.where_null(X)] = np.nan
Y[utils.where_null(Y)] = np.nan
# Lowess takes Y values first
fitted_Y = lowess(Y, X, return_sorted=False, frac=frac, missing=missing)
return fitted_Y
def run_lowess(X, Y,
frac=0.75,
missing="none"):
"""
Y ~ X lowess.
Parameters:
-----------
X: X values
Y: Y values
frac: fraction of data used to estimate each y-value.
missing: how to handle missing values (by default "drop" them).
"""
X[utils.where_null(X)] = np.nan
Y[utils.where_null(Y)] = np.nan
# Lowess takes Y values first
fitted_Y = lowess(Y, X,
return_sorted=False,
frac=frac,
missing=missing)
return fitted_Y
