lm {stats}  R Documentation 
lm
is used to fit linear models.
It can be used to carry out regression,
single stratum analysis of variance and
analysis of covariance (although aov
may provide a more
convenient interface for these).
lm(formula, data, subset, weights, na.action, method = "qr", model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE, contrasts = NULL, offset, ...)
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. If not found in data , the variables are taken
from environment(formula) , typically the environment from which
lm is called. 
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. If specified, weighted least squares is used
with weights weights (that is, minimizing sum(w*e^2) );
otherwise ordinary least squares is used. 
na.action 
a function which indicates what should happen
when the data contain NA s. The default is set by
the na.action setting of options , and is
na.fail if that is unset. The “factoryfresh”
default is na.omit . Another possible value is
NULL , no action. 
method 
the method to be used; for fitting, currently only
method = "qr" is supported; method = "model.frame" returns
the model frame (the same as with model = TRUE , see below). 
model, x, y, qr 
logicals. If TRUE the corresponding
components of the fit (the model frame, the model matrix, the
response, the QR decomposition) are returned. qr=FALSE is for
Scompatibility and currently unimplemented.

singular.ok 
logical. If FALSE (the default in S but
not in R) a singular fit is an error. 
contrasts 
an optional list. See the contrasts.arg
of model.matrix.default . 
offset 
this can be used to specify an a priori
known component to be included in the linear predictor
during fitting. An offset term can be included in the
formula instead or as well, and if both are specified their sum is used. 
... 
additional arguments to be passed to the low level regression fitting functions (see below). 
Models for lm
are specified symbolically. A typical model has
the form response ~ 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 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
.
If response
is a matrix a linear model is fitted separately by
leastsquares to each column of the matrix.
See model.matrix
for some further details. The terms in
the formula will be reordered so that main effects come first,
followed by the interactions, all secondorder, all thirdorder and so
on: to avoid this pass a terms
object as the formula.
A formula has an implied intercept term. To remove this use either
y ~ x  1
or y ~ 0 + x
. See formula
for
more details of allowed formulae.
lm
calls the lower level functions lm.fit
, etc,
see below, for the actual numerical computations. For programming
only, you may consider doing likewise.
All of weights
, subset
and offset
are evaluated
in the same way as variables in formula
, that is first in
data
and then in the environment of formula
.
lm
returns an object of class
"lm"
or for
multiple responses of class c("mlm", "lm")
.
The functions summary
and anova
are used to
obtain and print a summary and analysis of variance table of the
results. The generic accessor functions coefficients
,
effects
, fitted.values
and residuals
extract
various useful features of the value returned by lm
.
An object of class "lm"
is a list containing at least the
following components:
coefficients 
a named vector of coefficients 
residuals 
the residuals, that is response minus fitted values. 
fitted.values 
the fitted mean values. 
rank 
the numeric rank of the fitted linear model. 
weights 
(only for weighted fits) the specified weights. 
df.residual 
the residual degrees of freedom. 
call 
the matched call. 
terms 
the terms object used. 
contrasts 
(only where relevant) the contrasts used. 
xlevels 
(only where relevant) a record of the levels of the factors used in fitting. 
y 
if requested, the response used. 
x 
if requested, the model matrix used. 
model 
if requested (the default), the model frame used. 
In addition, nonnull fits will have components assign
,
effects
and (unless not requested) qr
relating to the linear
fit, for use by extractor functions such as summary
and
effects
.
Considerable care is needed when using lm
with time series.
Unless na.action = NULL
, the time series attributes are
stripped from the variables before the regression is done. (This is
necessary as omitting NA
s would invalidate the time series
attributes, and if NA
s are omitted in the middle of the series
the result would no longer be a regular time series.)
Even if the time series attributes are retained, they are not used to
line up series, so that the time shift of a lagged or differenced
regressor would be ignored. It is good practice to prepare a
data
argument by ts.intersect(..., dframe = TRUE)
,
then apply a suitable na.action
to that data frame and call
lm
with na.action = NULL
so that residuals and fitted
values are time series.
Offsets specified by offset
will not be included in predictions
by predict.lm
, whereas those specified by an offset term
in the formula will be.
The design was inspired by the S function of the same name described in Chambers (1992). The implementation of model formula by Ross Ihaka was based on Wilkinson & Rogers (1973).
Chambers, J. M. (1992) Linear models. Chapter 4 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
Wilkinson, G. N. and Rogers, C. E. (1973) Symbolic descriptions of factorial models for analysis of variance. Applied Statistics, 22, 392–9.
summary.lm
for summaries and anova.lm
for
the ANOVA table; aov
for a different interface.
The generic functions coef
, effects
,
residuals
, fitted
, vcov
.
predict.lm
(via predict
) for prediction,
including confidence and prediction intervals.
lm.influence
for regression diagnostics, and
glm
for generalized linear models.
The underlying low level functions,
lm.fit
for plain, and lm.wfit
for weighted
regression fitting.
## Annette Dobson (1990) "An Introduction to Generalized Linear Models". ## 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)) # residuals almost identical opar < par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(lm.D9, las = 1) # Residuals, Fitted, ... par(opar) ## model frame : stopifnot(identical(lm(weight ~ group, method = "model.frame"), model.frame(lm.D9)))