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Class of statistical models
In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing
Generalized_linear_model
Statistical model
In statistics, a generalized linear mixed model (GLMM) is an extension to the generalized linear model (GLM) in which the linear predictor contains random
Generalized linear mixed model
Generalized_linear_mixed_model
Concept in statistics
class of vector generalized linear models (VGLMs) was proposed to enlarge the scope of models catered for by generalized linear models (GLMs). In particular
Vector generalized linear model
Vector_generalized_linear_model
Statistical linear model
Nelder, J. A. (January 1, 1983). "An outline of generalized linear models". Generalized Linear Models. Springer US. pp. 21–47. doi:10.1007/978-1-4899-3242-6_2
General_linear_model
Statistics models class
In statistics, a generalized additive model (GAM) is a generalized linear model in which the linear response variable depends linearly on unknown smooth
Generalized_additive_model
Mathematical model for stochastic processes
The generalized functional linear model (GFLM) is an extension of the generalized linear model (GLM) that allows one to regress univariate responses of
Generalized functional linear model
Generalized_functional_linear_model
Statistical modeling method
In statistics, linear regression is a model that estimates the relationship between a scalar response (dependent variable) and one or more explanatory
Linear_regression
Type of statistical model
"linear model" is not usually applied. One example of this is nonlinear dimensionality reduction. General linear model Generalized linear model Linear
Linear_model
hierarchical generalized linear models extend generalized linear models by relaxing the assumption that error components are independent. This allows models to
Hierarchical generalized linear model
Hierarchical_generalized_linear_model
Linear regression model with a single explanatory variable
In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample
Simple_linear_regression
Statistical regression where the dependent variable can take only two values
regression using similar techniques. When viewed in the generalized linear model framework, the probit model employs a probit link function. It is most often
Probit_model
Mathematical model
regression for contingency tables, a type of generalized linear model. The specific applications of log-linear models are where the output quantity lies in the
Log-linear_model
Statistical model containing both fixed effects and random effects
discuss mainly linear mixed-effects models rather than generalized linear mixed models or nonlinear mixed-effects models. Linear mixed models (LMMs) are statistical
Mixed_model
statistics, the generalized linear array model (GLAM) is used for analyzing data sets with array structures. It based on the generalized linear model with the
Generalized linear array model
Generalized_linear_array_model
Type of statistical model
are grouped. These models are also known as hierarchical linear models, linear mixed-effect models, mixed models, nested data models, random coefficient
Multilevel_model
Statistical model for a binary dependent variable
In statistics, a logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent
Logistic_regression
Method of statistical analysis
Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables
Bayesian_linear_regression
Set of statistical processes for estimating the relationships among variables
Fraction of variance unexplained Function approximation Generalized linear model Kriging (a linear least squares estimation algorithm) Local regression Modifiable
Regression_analysis
Time series model
variance, the model is a generalized autoregressive conditional heteroskedasticity (GARCH) model. ARCH models are commonly employed in modeling financial
Autoregressive conditional heteroskedasticity
Autoregressive_conditional_heteroskedasticity
Least squares approximation of linear functions to data
in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals. Numerical methods for linear least
Linear_least_squares
Statistical estimation technique
In statistics, generalized least squares (GLS) is a method used to estimate the unknown parameters in a linear regression model. It is used when there
Generalized_least_squares
Specialized form of regression analysis, in statistics
Google books Dawes, Robyn M. (1979). "The robust beauty of improper linear models in decision making". American Psychologist, volume 34, pages 571-582
Robust_regression
Method for model fitting in statistics
specialization of generalized least squares, when all the off-diagonal entries of the covariance matrix of the errors are null. The fit of a model to a data point
Weighted_least_squares
Statistical model for count data
In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression
Poisson_regression
Measure of goodness of fit for a statistical model
where model-fitting is achieved by maximum likelihood. It plays an important role in exponential dispersion models and generalized linear models. Deviance
Deviance_(statistics)
Class of statistical survival models
Poisson model] is true, but simply use it as a device for deriving the likelihood." McCullagh and Nelder's book on generalized linear models has a chapter
Proportional_hazards_model
Regression model for ordinal dependent variables
Models. New York: Cambridge University Press. pp. 119–124. ISBN 978-0-521-68689-1. Hardin, James; Hilbe, Joseph (2007). Generalized Linear Models and
Ordered_logit
Moving average and polynomial regression method for smoothing data
criterion, thereby extending the local regression method to the Generalized linear model setting; for example binary data, count data or censored data.
Local_regression
Method for solving certain optimization problems
|}^{2}.} IRLS is used to find the maximum likelihood estimates of a generalized linear model, and in robust regression to find an M-estimator, as a way of mitigating
Iteratively reweighted least squares
Iteratively_reweighted_least_squares
Method used in statistics, pattern recognition, and other fields
in that they both look for linear combinations of variables which best explain the data. LDA explicitly attempts to model the difference between the classes
Linear_discriminant_analysis
Statistical method
variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables
Partial least squares regression
Partial_least_squares_regression
Statistical estimation method
probabilities less than zero or greater than one. Generalized linear model § Binary data Fractional model For a detailed example, refer to: Tetsuo Yai, Seiji
Binary_regression
Statistical technique to aid interpretation of data
Non-normal distribution for errors: in the simplest cases, a generalized linear model might be applicable. Unit root: taking first (or occasionally second)
Linear_trend_estimation
Regression analysis
negatively. Mathematics portal Non-linear least squares Curve fitting Generalized linear model Local regression Response modeling methodology Genetic programming
Nonlinear_regression
Approximation method in statistics
Non-linear least squares is the form of least squares analysis used to fit a set of m observations with a model that is non-linear in n unknown parameters
Non-linear_least_squares
Statistical model
discriminate between the fixed and the random effects models. Consider the linear unobserved effects model for N {\displaystyle N} observations and T {\displaystyle
Fixed_effects_model
Regression models accounting for possible errors in independent variables
generalized to discrete variables with more than two possible values.) Linear errors-in-variables models were studied first, probably because linear models
Errors-in-variables_model
Statistics concept
nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown
Polynomial_regression
Estimation procedure for correlated data
In statistics, a generalized estimating equation (GEE) is used to estimate the parameters of a generalized linear model with a possible unmeasured correlation
Generalized estimating equation
Generalized_estimating_equation
Design of tasks
Douglas C.; Vining, G. Geoffrey; Robinson, Timothy J. (2010). Generalized linear models : with applications in engineering and the sciences (2 ed.). Hoboken
Design_of_experiments
Approximation method in statistics
linear or ordinary least squares and nonlinear least squares, depending on whether or not the model functions are linear in all unknowns. The linear least-squares
Least_squares
Statistics concept
Applied linear models with SAS (Online-Ausg. ed.). Cambridge: Cambridge University Press. ISBN 9780521761598. "7.3: Types of Outliers in Linear Regression"
Errors_and_residuals
queuing systems The inverse-gamma distribution The generalized gamma distribution The generalized Pareto distribution The Gamma/Gompertz distribution
List of probability distributions
List_of_probability_distributions
Type of mathematical model
being 1.5 meters tall. We could formalize that relationship in a linear regression model, like this: heighti = b0 + b1agei + εi, where b0 is the intercept
Statistical_model
Statistical relationship
− 1 , 1 ] {\displaystyle [-1,1]} . The odds ratio is generalized by the logistic model to model cases where the dependent variables are discrete and there
Correlation
Statistical model extension
a functional additive model (FAM) can be viewed as an extension of a generalized functional linear model where the linearity assumption between the response
Functional_additive_model
Regression analysis for modeling ordinal data
ranking learning. Ordinal regression can be performed using a generalized linear model (GLM) that fits both a coefficient vector and a set of thresholds
Ordinal_regression
Statistical method that summarizes and/or integrates data from multiple sources
from the literature. The generalized integration model (GIM) is a generalization of the meta-analysis. It allows that the model fitted on the individual
Meta-analysis
Aphorism in statistics
accurate, simpler models can still provide valuable insights if applied judiciously. In their 1983 book on generalized linear models, Peter McCullagh and
All_models_are_wrong
Task of selecting a statistical model from a set of candidate models
information criterion (QIC), for model selection where the log-likelihood is replaced by the quasi log-likelihood (e.g., generalized estimating equations) Structural
Model_selection
Measure of covariance of components of a random vector
{\boldsymbol {\Sigma }}} , the so-called generalized variance. Applied to one vector, the covariance matrix maps a linear combination c of the random variables
Covariance_matrix
Statistical method
to other statistical models including generalized linear models, generalized estimating equations, proportional hazards models, and M-estimators. Lasso's
Lasso_(statistics)
Mathematical model used for classification or regression
descent family) Examples of discriminative models include: Logistic regression, a type of generalized linear regression used for predicting binary or categorical
Discriminative_model
Statistic which divides a data set into 100 parts and analyzes it as a percentage
{\displaystyle C={\tfrac {1}{2}}(1+\xi )} where ξ is the shape of the Generalized extreme value distribution which is the extreme value limit of the sampled
Percentile
Parametric model in survival analysis
\theta } . This reduces the accelerated failure time model to regression analysis (typically a linear model) where − log ( θ ) {\displaystyle -\log(\theta
Accelerated failure time model
Accelerated_failure_time_model
Numerical measure of a statistical relationship between variables
correlation coefficient is a numerical measure of some type of linear correlation, meaning a linear function between two variables. The variables may be two
Correlation_coefficient
Sequence of data points over time
predictions derived from non-linear models, over those from linear models, as for example in nonlinear autoregressive exogenous models. Further references on
Time_series
Statistical model
the model effects are random variables. It is a kind of hierarchical linear model, which assumes that the data being analysed are drawn from a hierarchy
Random_effects_model
N-th root of the product of n numbers
the exponentiation to return to the original scale, i.e., it is the generalized f-mean with f ( x ) = log x {\displaystyle f(x)=\log x} . A logarithm
Geometric_mean
Statistics model
In statistics, a linear probability model (LPM) is a special case of a binary regression model. Here the dependent variable for each observation takes
Linear_probability_model
Topics referred to by the same term
refer to: Generalized linear model, a generalization of ordinary linear regression General linear model, a generalization of multiple linear regression
GLM
Theorem related to ordinary least squares
estimator across samples) within the class of linear unbiased estimators, if the errors in the linear regression model are uncorrelated, have equal variances
Gauss–Markov_theorem
Statistical hypothesis test
decryption was successful with high probability. This method can be generalized for solving modern cryptographic problems. In bioinformatics, the chi-squared
Chi-squared_test
Probabilistic problem-solving algorithm
Chia-Ming (March 15, 2021). "Improvement of generalized finite difference method for stochastic subsurface flow modeling". Journal of Computational Physics. 429
Monte_Carlo_method
Collection of statistical models
produce a derived linear model, very similar to the textbook model discussed previously. The test statistics of this derived linear model are closely approximated
Analysis_of_variance
Statistical matching technique
Campbell, D. T. (2002). Experimental and Quasi-experimental Designs for Generalized Causal Inference. Boston: Houghton Mifflin. ISBN 978-0-395-61556-0. Pearl
Propensity_score_matching
Statistical hypothesis test
data set in a regression analysis follows the simpler of two proposed linear models that are nested within each other. Multiple-comparison testing is conducted
F-test
Measure of the joint variability
random variables. The sign of the covariance shows the tendency in the linear relationship between the variables. Covariance is positive when variables
Covariance
Statistical model used in time series analysis
"Recent results for linear time series models with non independent innovations", in Duchesne, P.; Remillard, B. (eds.), Statistical Modeling and Analysis for
Autoregressive moving-average model
Autoregressive_moving-average_model
Regression for more than two discrete outcomes
regression” Darroch, J.N. & Ratcliff, D. (1972). "Generalized iterative scaling for log-linear models". The Annals of Mathematical Statistics. 43 (5):
Multinomial logistic regression
Multinomial_logistic_regression
Statistical modeling technique
"quantreg: Quantile Regression". R Project. 2018-12-18. "gbm: Generalized Boosted Regression Models". R Project. 2019-01-14. "quantregForest: Quantile Regression
Quantile_regression
Nonparametric measure of rank correlation
Pearson's correlation assesses linear relationships, Spearman's correlation assesses monotonic relationships (whether linear or not). If there are no repeated
Spearman's rank correlation coefficient
Spearman's_rank_correlation_coefficient
Statistical hypothesis test for forecasting
test based on the GARCH (generalized auto-regressive conditional heteroscedasticity) type of integer-valued time series models is available in many areas
Granger_causality
Mathematical descriptions of the properties of certain cells in the nervous system
related to linear-nonlinear-Poisson cascade models (also called Generalized Linear Model). The estimation of parameters of probabilistic neuron models such
Biological_neuron_model
Number of values in the final calculation of a statistic that are free to vary
Tibshirani (1990), Generalized additive models, CRC Press, (p. 54) and (eq.(B.1), p. 305)) Simon N. Wood (2006), Generalized additive models: an introduction
Degrees of freedom (statistics)
Degrees_of_freedom_(statistics)
Family of probability distributions related to the normal distribution
distribution functions used in generalized linear models (GLM), a class of model that encompasses many of the commonly used regression models in statistics. Examples
Exponential_family
Statistical property of collections of time series data
trends). In such cases, the variables may drift in the short run, but their linear combination is stationary, implying that they move together over time and
Cointegration
Probability distribution
for a generalized linear model (GLM) using a Bayesian formulation. A dual-link GLM based on the CMP distribution has been developed, and this model has
Conway–Maxwell–Poisson distribution
Conway–Maxwell–Poisson_distribution
Algorithmically generated data that have a similar distribution as sampled data
constructing a statistical model. In a linear regression line example, the original data can be plotted, and a best fit linear line can be created from
Synthetic_data
Generates a forecast of future values of a time series
Seasonal, Holt's Linear Trend, Brown's Linear Trend, Damped Trend, Winters' Additive, and Winters' Multiplicative in the Time-Series modeling procedure within
Exponential_smoothing
Visualization method for regularization
The L-curve is a graphical tool used in the numerical treatment of linear inverse problems. It displays how the size of a regularized solution varies relative
L-curve
Function related to statistics and probability theory
Statistical Modelling and Inference Using Likelihood. Oxford University Press. ISBN 978-0-19-850765-9. Wen Hsiang Wei. "Generalized Linear Model - course
Likelihood_function
Statistical method for handling multiple comparisons
PMID 21243075. Sarkar SK (2007). "Stepup procedures controlling generalized FWER and generalized FDR". The Annals of Statistics. 35 (6): 2405–20. arXiv:0803
False_discovery_rate
Probability distribution
The generalized normal distribution (GND) or generalized Gaussian distribution (GGD) is either of two parametric families of continuous probability distributions
Generalized normal distribution
Generalized_normal_distribution
Correlation of a signal with a time-shifted copy of itself, as a function of shift
with k degrees of freedom. Responses to nonzero autocorrelation include generalized least squares and the Newey–West HAC estimator (Heteroskedasticity and
Autocorrelation
Mathematical decision rule
such alternatives. We denote the posterior generalized distribution function by F {\displaystyle F} . A "linear" loss function, with a > 0 {\displaystyle
Bayes_estimator
Method for estimating the unknown parameters in a linear regression model
least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model by the principle of least squares:
Ordinary_least_squares
Plot using the dispersal of scattered dots to show the relationship between variables
each cell plots a scatter plot of two dimensions.[citation needed] A generalized scatter plot matrix offers a range of displays of paired combinations
Scatter_plot
Statistical phenomenon
such a line that minimizes the sum of squared residuals of the linear regression model. In other words, numbers α and β solve the following minimization
Regression_toward_the_mean
Measure of variation in statistics
Σ {\displaystyle \mathbf {\Sigma } } . S {\displaystyle \mathbf {S} } linearly scales a random vector in multiple dimensions in the same way that σ {\displaystyle
Standard_deviation
How many standard deviations apart from the mean an observed datum is
Michael; Nachtsheim, Christopher; Neter, John (204), Applied Linear Regression Models (Fourth ed.), McGraw Hill, ISBN 978-0073014661 {{citation}}: ISBN
Standard_score
Unit of information
"Evidence of unreliable data and poor data provenance in clinical prediction model research and clinical practice". BMC Medicine. doi:10.1186/s12916-026-04981-y
Data
Simultaneous observation and analysis of more than one outcome variable
simultaneously to changes in others. For linear relations, regression analyses here are based on forms of the general linear model. Some suggest that multivariate
Multivariate_statistics
Statistical technique
residuals and W is a weighting matrix. In linear least squares the model contains equations which are linear in the parameters appearing in the parameter
Total_least_squares
Diagnostic plot of binary classifier ability
that illustrates the performance of a binary classifier model (although it can be generalized to multiple classes) at varying threshold values. ROC analysis
Receiver operating characteristic
Receiver_operating_characteristic
Relative measure of dispersion expressed as the ratio of standard deviation to the mean
error in the production process). However, data that are linear or even logarithmically non-linear and include a continuous range for the independent variable
Coefficient_of_variation
Statistical test that compares goodness of fit
Karl-Rudolf (1988). Parameter Estimation and Hypothesis Testing in Linear Models. New York: Springer. p. 306. ISBN 0-387-18840-1. Silvey, S.D. (1970)
Likelihood-ratio_test
Class of statistical models
mixed-effects models constitute a class of statistical models generalizing linear mixed-effects models. Like linear mixed-effects models, they are particularly
Nonlinear_mixed-effects_model
Estimator for quality of a statistical model
to the estimation of the conditional Akaike information in generalized linear mixed models", Electronic Journal of Statistics, 8: 201–225, doi:10.1214/14-EJS881
Akaike_information_criterion
Method of estimating the parameters of a statistical model, given observations
procedure is standard in the estimation of many methods, such as generalized linear models. Although popular, quasi-Newton methods may converge to a stationary
Maximum_likelihood_estimation
GENERALIZED LINEAR-MODEL
GENERALIZED LINEAR-MODEL
Surname or Lastname
English
English : occupational name for a whitewasher, Middle English limer, lymer, an agent derivative of Old English līm ‘lime’.
Surname or Lastname
Swedish
Swedish : ornamental name from lind ‘lime tree’ + either the German suffix -er denoting an inhabitant, or the surname suffix -ér, derived from the Latin adjectival ending -er(i)us.English (mainly southeastern) : variant of Lind 2.German : habitational name from any of numerous places called Linden or Lindern, named with German Linden ‘lime trees’.
Female
English
English name probably derived from Germanic lindi, LINDA means "serpent."Â In some cases, it may have been derived from the Spanish word for "pretty."
Boy/Male
Sikh
Love unending
Male
Scandinavian
Scandinavian form of Old Norse Einarr, EINAR means "lone warrior."
Male
English
Irish Anglicized form of Gaelic Fionnbarr, FINBAR means "fair-headed."
Surname or Lastname
English (Cornish)
English (Cornish) : habitational name from a place named with Cornish lan ‘church’. In England this surname is now found chiefly in the southern counties of Wiltshire and Hampshire, and Berkshire; it has no doubt moved there from Cornwall.
Female
English
Variant spelling of English Linsey, LINSAY means "Lincoln's wetlands."
Boy/Male
Hindu
The Sun
Boy/Male
Irish
Meaning “â€fair-haired,â€â€ the name has been popular since the sixth century when St. Finbar came to an area of Cork that was being tormented by a serpent. The people begged him to do something to help them. One night he went to where the serpent was sleeping and sprinkled it with holy water. The angry serpent tore and devoured the land until she slithered into the sea at Cork Harbor. The track she left behind filled with water and became the River Lee and that’s why St. Finbar is the patron saint of Cork. It is said that the sun didn’t set for two weeks after Finbar’s death.
Surname or Lastname
English
English : metronymic from Line.
Boy/Male
Hindu
Lingam
Surname or Lastname
English (Devon; of Cornish origin)
English (Devon; of Cornish origin) : topographic name for someone who lived by a menhir, i.e. a tall standing stone erected in prehistoric times (Cornish men ‘stone’ + hir ‘long’).
Male
Yiddish
 Variant spelling of Yiddish Lieber, LIBER means "beloved." Compare with another form of Liber.
Girl/Female
Irish
Eimear possessed the “Six Gifts of Womanhood†– “beauty, a gentle voice, sweet words, wisdom, needlework and chastity!†She was bethrothed to the warrior Cuchulainn (read the legend) when they were children and they loved each other very deeply. But Cuchulainn had “a wandering eye†and Eimear endured this, realizing “everything new is fair,†but when he made love to Fand, wife of the sea god Manannan, Eimear confronted the lovers. After seeing the strength of Fand’s love she offered to withdraw. Touched by this display of unselfishness, Fand left Cuchulainn and returned to the sea. When Cuchulainn died Eimear spoke movingly and lovingly at his graveside.
Male
Greek
(ΑἰνÎας) Variant spelling of Greek AineÃas, AINEAS means "praiseworthy."
Female
Scottish
Variant spelling of Scottish Lilias, LILEAS means "lily."
Surname or Lastname
English
English : habitational name from Lingart, Lancashire, or Lingards Wood in Marsden, West Yorkshire, both named from Old English līn ‘flax’ + garðr ‘enclosure’.
Surname or Lastname
English
English : variant of Lanier 1.Dutch : variant of Leonard.Jewish (western Ashkenazic) : name taken by someone who was good at chanting the Pentateuch at public worship in the synagogue or who regularly did so, from West Yiddish layner ‘reader’ (a derivative of West Yiddish laynen ‘to read’, which comes ultimately from Latin legere ‘to read’).Jewish (Ashkenazic) : occupational name for a flax grower or merchant, from German Lein ‘flax’ + agent suffix -er.
Surname or Lastname
English
English : variant of Lingard.French : occupational name for a maker of or dealer in linen goods, from Old French linge ‘linen (goods)’ (see Linge 1).
GENERALIZED LINEAR-MODEL
GENERALIZED LINEAR-MODEL
Boy/Male
Hindu, Indian
Lord of Happiness
Girl/Female
American, Christian, German, Greek, Hebrew
Noble Kind; Nobility; Rational; Great Happiness; Form of Alice
Girl/Female
Indian, Tamil
Red Lotus Flower
Girl/Female
Tamil
Brightness
Boy/Male
Indian, Telugu
Inherent; Inscribed into Something; Within Something
Girl/Female
Indian
Gleefulness
Boy/Male
Christian & English(British/American/Australian)
Bright Minded
Boy/Male
Gujarati, Hindu, Indian, Sanskrit
Human Being
Boy/Male
Australian, Basque, Danish, Finnish, Hebrew
Rock
Boy/Male
Indian, Tamil
God
GENERALIZED LINEAR-MODEL
GENERALIZED LINEAR-MODEL
GENERALIZED LINEAR-MODEL
GENERALIZED LINEAR-MODEL
GENERALIZED LINEAR-MODEL
a.
Of or pertaining to a line; consisting of lines; in a straight direction; lineal.
a.
Formed by right lines; rectilineal; as, a right-lined angle.
a.
Descending in a direct line from an ancestor; hereditary; derived from ancestors; -- opposed to collateral; as, a lineal descent or a lineal descendant.
p. pr. & vb. n.
of Generalize
n.
One who adjusts things to a line or lines or brings them into line.
n.
A generalized concept of magnitude.
v. t.
To mark with a line or lines; to cover with lines; as, to line a copy book.
v. t.
To make universal; to generalize.
n.
One who lines, as, a liner of shoes.
a.
Of, pertaining to, or included by, two lines; as, bilinear coordinates.
n.
Made of linen; as, linen cloth; a linen stocking.
a.
Linear.
adv.
In a linear manner; with lines.
a.
Of a linear shape.
a.
In the direction of a line; of or pertaining to a line; measured on, or ascertained by, a line; linear; as, lineal magnitude.
a.
Comprising structural characters which are separated in more specialized forms; synthetic; as, a generalized type.
imp. & p. p.
of Generalize
a.
Composed of lines; delineated; as, lineal designs.
n.
A dealer in linen; a linen draper.
a.
Like a line; narrow; of the same breadth throughout, except at the extremities; as, a linear leaf.