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plspm is a Python 3 package dedicated to Partial Least Squares Path Modeling (PLS-PM) analysis. It is a port of the R package plspm, with additional features adopted from the R package seminr
PLSPM (partial least squares path modeling) is a correlation-based structural equation modeling (SEM) algorithm. It allows for estimation of complex cause-effect or prediction models using latent/manifest variables.
PLSPM may be preferred to other SEM methods for several reasons: it is a method that is appropriate for exploratory research, can be used with small-to-medium sample sizes (as well as large data sets), and does not require assumptions of multivariate normality. (See Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic management journal, 20(2), 195-204.) In contrast to covariance-based SEM (CBSEM), goodness of fit is less important, because the purpose of the algorithm is to optimize for prediction of the dependent variable vs. fit of data to a predetermined model. (See "goodness of fit" vs "goodness of model" in Chin, W. W. (2010). How to write up and report PLS analyses. In Handbook of partial least squares (pp. 655-690). Springer, Berlin, Heidelberg.)
You can install the latest version of this package using pip:
python3 -m pip install --user plspm
It's hosted on pypi: https://pypi.org/project/plspm/
plspm expects to get a Pandas DataFrame containing your data. You start by creating a Config object with the details of the model, and then pass it, along with the data and optionally some further configuration, to an instance of Plspm. Use the examples below to get started, or browse the documentation (start with Config and Plspm)
Typical example with a Customer Satisfaction Model
#!/usr/bin/env python3
import pandas as pd, plspm.config as c
from plspm.plspm import Plspm
from plspm.scheme import Scheme
from plspm.mode import Mode
satisfaction = pd.read_csv("file:tests/data/satisfaction.csv", index_col=0)
structure = c.Structure()
structure.add_path(["IMAG"], ["EXPE", "SAT", "LOY"])
structure.add_path(["EXPE"], ["QUAL", "VAL", "SAT"])
structure.add_path(["QUAL"], ["VAL", "SAT"])
structure.add_path(["VAL"], ["SAT"])
structure.add_path(["SAT"], ["LOY"])
config = c.Config(structure.path(), scaled=False)
config.add_lv_with_columns_named("IMAG", Mode.A, satisfaction, "imag")
config.add_lv_with_columns_named("EXPE", Mode.A, satisfaction, "expe")
config.add_lv_with_columns_named("QUAL", Mode.A, satisfaction, "qual")
config.add_lv_with_columns_named("VAL", Mode.A, satisfaction, "val")
config.add_lv_with_columns_named("SAT", Mode.A, satisfaction, "sat")
config.add_lv_with_columns_named("LOY", Mode.A, satisfaction, "loy")
plspm_calc = Plspm(satisfaction, config, Scheme.CENTROID)
print(plspm_calc.inner_summary())
print(plspm_calc.path_coefficients())
This will produce the output:
type r_squared block_communality mean_redundancy ave
EXPE Endogenous 0.335194 0.616420 0.206620 0.616420
IMAG Exogenous 0.000000 0.582269 0.000000 0.582269
LOY Endogenous 0.509923 0.639052 0.325867 0.639052
QUAL Endogenous 0.719688 0.658572 0.473966 0.658572
SAT Endogenous 0.707321 0.758891 0.536779 0.758891
VAL Endogenous 0.590084 0.664416 0.392061 0.664416
IMAG EXPE QUAL VAL SAT LOY
IMAG 0.000000 0.000000 0.000000 0.000000 0.000000 0
EXPE 0.578959 0.000000 0.000000 0.000000 0.000000 0
QUAL 0.000000 0.848344 0.000000 0.000000 0.000000 0
VAL 0.000000 0.105478 0.676656 0.000000 0.000000 0
SAT 0.200724 -0.002754 0.122145 0.589331 0.000000 0
LOY 0.275150 0.000000 0.000000 0.000000 0.495479 0
Example using seminr's mobile industry data set:
mobi = pd.read_csv("file:tests/data/mobi.csv", index_col=0)
structure = c.Structure()
structure.add_path(["Expectation", "Quality"], ["Satisfaction"])
structure.add_path(["Satisfaction"], ["Complaints", "Loyalty"])
config = c.Config(structure.path(), default_scale=Scale.NUM)
config.add_higher_order("Satisfaction", Mode.A, ["Image", "Value"])
config.add_lv_with_columns_named("Expectation", Mode.A, mobi, "CUEX")
config.add_lv_with_columns_named("Quality", Mode.B, mobi, "PERQ")
config.add_lv_with_columns_named("Loyalty", Mode.A, mobi, "CUSL")
config.add_lv_with_columns_named("Image", Mode.A, mobi, "IMAG")
config.add_lv_with_columns_named("Complaints", Mode.A, mobi, "CUSCO")
config.add_lv_with_columns_named("Value", Mode.A, mobi, "PERV")
mobi_pls = Plspm(mobi, config, Scheme.PATH, 100, 0.00000001)
print(plspm_calc.inner_model())
This will produce the output:
from to estimate std error t p>|t|
index
Quality -> Satisfaction Quality Satisfaction 0.743041 0.046318 16.042102 3.633866e-40
Expectation -> Satisfaction Expectation Satisfaction 0.089572 0.046318 1.933832 5.427626e-02
Satisfaction -> Loyalty Satisfaction Loyalty 0.627940 0.049420 12.706272 7.996788e-29
Satisfaction -> Complaints Satisfaction Complaints 0.486696 0.055472 8.773752 2.841768e-16
Example with the classic Russett data (original data set)
#!/usr/bin/env python3
import pandas as pd, plspm.config as c
from plspm.plspm import Plspm
from plspm.scale import Scale
from plspm.scheme import Scheme
from plspm.mode import Mode
russa = pd.read_csv("file:tests/data/russa.csv", index_col=0)
structure = c.Structure()
structure.add_path(["AGRI", "IND"], ["POLINS"])
config = c.Config(structure.path(), default_scale=Scale.NUM)
config.add_lv("AGRI", Mode.A, c.MV("gini"), c.MV("farm"), c.MV("rent"))
config.add_lv("IND", Mode.A, c.MV("gnpr"), c.MV("labo"))
config.add_lv("POLINS", Mode.A, c.MV("ecks"), c.MV("death"), c.MV("demo"), c.MV("inst"))
plspm_calc = Plspm(russa, config, Scheme.CENTROID, 100, 0.0000001)
print(plspm_calc.inner_summary())
print(plspm_calc.effects())
This will produce the output:
type r_squared block_communality mean_redundancy ave
AGRI Exogenous 0.000000 0.739560 0.000000 0.739560
IND Exogenous 0.000000 0.907524 0.000000 0.907524
POLINS Endogenous 0.592258 0.565175 0.334729 0.565175
from to direct indirect total
0 AGRI POLINS 0.225639 0.0 0.225639
1 IND POLINS 0.671457 0.0 0.671457
PLS-PM using data set russa, and different scaling
#!/usr/bin/python3
import pandas as pd, plspm.config as c, plspm.util as util
from plspm.plspm import Plspm
from plspm.scale import Scale
from plspm.scheme import Scheme
from plspm.mode import Mode
russa = pd.read_csv("file:tests/data/russa.csv", index_col=0)
structure = c.Structure()
structure.add_path(["AGRI", "IND"], ["POLINS"])
config = c.Config(structure.path(), default_scale=Scale.NUM)
config.add_lv("AGRI", Mode.A, c.MV("gini"), c.MV("farm"), c.MV("rent"))
config.add_lv("IND", Mode.A, c.MV("gnpr", Scale.ORD), c.MV("labo", Scale.ORD))
config.add_lv("POLINS", Mode.A, c.MV("ecks"), c.MV("death"), c.MV("demo", Scale.NOM), c.MV("inst"))
plspm_calc = Plspm(russa, config, Scheme.CENTROID, 100, 0.0000001)
#!/usr/bin/env python3
import pandas as pd, plspm.config as c
from plspm.plspm import Plspm
from plspm.scale import Scale
from plspm.scheme import Scheme
from plspm.mode import Mode
russa = pd.read_csv("file:tests/data/russa.csv", index_col=0)
russa.iloc[0, 0] = np.NaN
russa.iloc[3, 3] = np.NaN
russa.iloc[5, 5] = np.NaN
structure = c.Structure()
structure.add_path(["AGRI", "IND"], ["POLINS"])
config = c.Config(structure.path(), default_scale=Scale.NUM)
config.add_lv("AGRI", Mode.A, c.MV("gini"), c.MV("farm"), c.MV("rent"))
config.add_lv("IND", Mode.A, c.MV("gnpr"), c.MV("labo"))
config.add_lv("POLINS", Mode.A, c.MV("ecks"), c.MV("death"), c.MV("demo"), c.MV("inst"))
plspm_calc = Plspm(russa, config, Scheme.CENTROID, 100, 0.0000001)
Jez Humble (humble at google.com)
Nicole Forsgren (nicolefv at github.com)
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