Getting Started

Samplics is a python package for selecting, weighting and analyzing sample obtained from complex sampling design.

Installation

pip install samplics

if both Python 2.x and python 3.x are installed on your computer, you may have to use:

pip3 install samplics

Dependencies

Python versions 3.10.x or newer and the following packages:

• numpy
• scipy
• polars
• statsmodels

Usage

To select a sample of primary sampling units using PPS method, we can use a code similar to:

import samplics
from samplics.sampling import SampleSelection

psu_frame = pd.read_csv("psu_frame.csv")
psu_sample_size = {"East":3, "West": 2, "North": 2, "South": 3}

pps_design = SampleSelection(
    method="pps-sys", stratification=True, with_replacement=False
    )

frame["psu_prob"] = pps_design.inclusion_probs(
    psu_frame["cluster"],
    psu_sample_size,
    psu_frame["region"],
    psu_frame["number_households_census"]
    )

To adjust the design sample weight for nonresponse, we can use a code similar to:

import samplics
from samplics.weighting import SampleWeight

status_mapping = {
    "in": "ineligible",
    "rr": "respondent",
    "nr": "non-respondent",
    "uk":"unknown"
    }

full_sample["nr_weight"] = SampleWeight().adjust(
    samp_weight=full_sample["design_weight"],
    adjust_class=full_sample["region"],
    resp_status=full_sample["response_status"],
    resp_dict=status_mapping
    )

import samplics
from samplics.estimation import TaylorEstimator, ReplicateEstimator

zinc_mean_str = TaylorEstimator("mean").estimate(
    y=nhanes2f["zinc"],
    samp_weight=nhanes2f["finalwgt"],
    stratum=nhanes2f["stratid"],
    psu=nhanes2f["psuid"],
    remove_nan=True,
)

ratio_wgt_hgt = ReplicateEstimator("brr", "ratio").estimate(
    y=nhanes2brr["weight"],
    samp_weight=nhanes2brr["finalwgt"],
    x=nhanes2brr["height"],
    rep_weights=nhanes2brr.loc[:, "brr_1":"brr_32"],
    remove_nan=True,
)

Contributing

Support the project

License

Open source MIT