Regression
These are the available functions for regression. See each function's help topic in the TERR Language Reference for more information.
Function name | Title description |
---|---|
.lm.fit
|
General fitting for linear (regression) models |
.kappa_tri
|
Compute the Exact or Estimated Condition Number |
add1.glm
|
Add a Single Term to a Linear Model |
add1.lm
|
Add a Single Term to a Linear Model |
anova.glm
|
Analysis of Deviance for Generalized Linear Model Fits |
anova.glmlist
|
Analysis of Deviance for Generalized Linear Model Fits |
cooks.distance
|
Regression Deletion Diagnostics |
cooks.distance.glm
|
Regression Deletion Diagnostics |
cooks.distance.lm
|
Regression Deletion Diagnostics |
covratio
|
Regression Deletion Diagnostics |
dfbeta
|
Regression Deletion Diagnostics |
dfbeta.lm
|
Regression Deletion Diagnostics |
dfbetas
|
Regression Deletion Diagnostics |
dfbetas.lm
|
Regression Deletion Diagnostics |
dffits
|
Regression Deletion Diagnostics |
dummy.coef
|
Extract Original Coefficients from a Linear Model |
dummy.coef.aovlist
|
Extract Original Coefficients from a Linear Model |
dummy.coef.lm
|
Extract Original Coefficients from a Linear Model |
effects
|
Single Degree-of-freedom Effects from a Fitted Model |
effects.glm
|
Single Degree-of-freedom Effects from a Fitted Model |
effects.lm
|
Single Degree-of-freedom Effects from a Fitted Model |
glm
|
Fit a Generalized Linear Model |
glm.control
|
Set Control Parameters for Generalized Linear Model |
glm.fit
|
Fit a GLM without Computing the Model Matrix |
glm.object
|
Generalized Linear Model Object |
hat
|
Hat Diagonal Regression Diagnostic |
hatvalues
|
Regression Deletion Diagnostics |
hatvalues.lm
|
Regression Deletion Diagnostics |
influence
|
Regression Diagnostics |
influence.glm
|
Regression Diagnostics |
influence.lm
|
Regression Diagnostics |
influence.measures
|
Regression Deletion Diagnostics |
isoreg
|
Isotonic / Monotone Regression |
kappa
|
Compute the Exact or Estimated Condition Number |
kappa.default
|
Compute the Exact or Estimated Condition Number |
kappa.lm
|
Compute the Exact or Estimated Condition Number |
kappa.qr
|
Compute the Exact or Estimated Condition Number |
ksmooth
|
Scatter Plot Smoothing |
lm
|
Fit Linear Regression Model |
lm.fit
|
General fitting for linear (regression) models |
lm.influence
|
Regression Diagnostics |
lm.object
|
Linear Least Squares Model Object |
lm.wfit
|
General fitting for linear (regression) models |
lowess
|
Scatter Plot Smoothing |
lsfit
|
Linear Least-Squares Fit |
mlm.object
|
Linear Least Squares Model Object |
poly
|
Compute Orthogonal Polynomials |
polym
|
Compute Orthogonal Polynomials |
predict.nls
|
Predicting from Nonlinear Least Squares Fits |
predict.poly
|
Compute Orthogonal Polynomials |
print.dummy_coef
|
Extract Original Coefficients from a Linear Model |
print.dummy_coef_list
|
Extract Original Coefficients from a Linear Model |
print.infl
|
Regression Deletion Diagnostics |
print.summary.lm
|
Summary Method for Linear Models |
print.summary.mlm
|
Summary Method for Linear Models |
proj
|
Projection Matrix |
proj.aov
|
Projection Matrix |
proj.aovlist
|
Projection Matrix |
proj.default
|
Projection Matrix |
proj.lm
|
Projection Matrix |
rstandard
|
Regression Deletion Diagnostics |
rstandard.glm
|
Regression Deletion Diagnostics |
rstandard.lm
|
Regression Deletion Diagnostics |
rstudent
|
Regression Deletion Diagnostics |
rstudent.glm
|
Regression Deletion Diagnostics |
rstudent.lm
|
Regression Deletion Diagnostics |
stat.anova
|
Add Statistics Columns to an Anova Table |
summary.glm
|
Summary Method for Fitted Generalized Linear Models |
summary.infl
|
Regression Deletion Diagnostics |
summary.lm
|
Summary Method for Linear Models |
summary.mlm
|
Summary Method for Linear Models |
Parent topic: Statistics
Related reference
- Clustering
- Computations Related to Plotting (Statistics)
- Curve (and Surface) Smoothing
- Designed Experiments
- Loess Objects
- Multivariate Techniques
- Non-Linear Regression
- Nonparametric Statistics
- Probability Distributions and Random Numbers
- Regression and Classification Trees
- Robust and Resistant Techniques
- Simple Univariate Statistics
- Statistical Inference
- Statistical Models
- Time Series