lm {base} | R Documentation |
Fitting Linear Models
Description
lm
is used to fit linear models.
It can be used to carry out regression,
single stratum analysis of variance and
analysis of covariance.
Models for lm
are specified symbolically.
A typical model has the form
reponse ~ terms
where response
is the (numeric)
response vector and terms
is a series of terms which
specifies a linear predictor for response
.
A terms specification of the form first+second
indicates all the terms in first
together
with all the terms in second
with duplicates
removed.
A specification of the form first:second
indicates the
the set of terms obtained by taking the interactions of
all terms in first
with all terms in second
.
The specification first*second
indicates the cross
of first
and second
.
This is the same as first+second+first:second
.
Usage
lm(formula, data, subset, weights, na.action=na.omit,
method="qr", model=TRUE, singular.ok = TRUE)
anova(lm.obj, ...)
summary(lm.obj, correlation = FALSE)
coefficients(lm.obj)
deviance(lm.obj)
df.residual(lm.obj)
effects(lm.obj)
fitted.values(lm.obj)
residuals(lm.obj)
weights(lm.obj)
lm.fit(x, y, method = "qr", tol = 1e-7, ...)
lm.wfit(x, y, w, method = "qr", tol = 1e-7, ...)
Arguments
formula |
a symbolic description of the model to be fit. The details of model specification are given below. |
data |
an optional data frame containing the variables
in the model. By default the variables are taken from
the environment which |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
weights |
an optional vector of weights to be used in the fitting process. |
na.action |
a function which indicates what should happen
when the data contain |
model |
logical. If |
singular.ok |
logical, defaulting to
|
lm.obj |
an object of class |
method |
currently, only |
tol |
tolerance for |
... |
currently, disregarded. |
Value
lm
returns an object of class lm
.
The function summary
can be used to obtain or print
a summary of the results and the function anova
and be used to produce and analysis of variance table.
The generic accessor functions coefficients
,
effects
, fitted.values
and residuals
can be used to extract various useful features of the
value returned by lm
.
See Also
anova
for the ANOVA table,
coefficients
, effects
, fitted.values
,
glm
for generalized linear models,
lm.influence
for regression diagnostics,
residuals
, summary
.
Examples
## Annette Dobson (1990) "An Introduction to Statistical Modelling".
## Page 9: Plant Weight Data.
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2,10,20,labels=c("Ctl","Trt"))
weight <- c(ctl,trt)
anova(lm.D9 <- lm(weight~group))
summary(lm.D90 <- lm(weight ~ group -1))# omitting intercept
summary(resid(lm.D9) - resid(lm.D90)) #- residual are practically identical