lm(formula, data, subset, weights, na.action=na.omit, method="qr", model=TRUE, singular.ok = TRUE) lm.fit[.null] (x, y, method = "qr", tol = 1e-7, ...) lm.wfit[.null](x, y, w, method = "qr", tol = 1e-7, ...)
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 lm is called from.
|
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 NAs. The default action (na.omit)
is to omit any incomplete observations.
The alternative action na.fail causes lm to
print an error message and terminate if there are any incomplete
observations.
|
model
|
logical. If TRUE (default), the model.frame is also
returned.
|
singular.ok
|
logical, defaulting to
TRUE. FALSE is not yet implemented.
|
method
|
currently, only method="qr" is supported.
|
tol
|
tolerance for the qr decomposition. Default is 1e-7.
|
...
| currently disregarded. |
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
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
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.
lm returns an object of class "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.
summary.lm for summaries and anova.lm for
the ANOVA table.
The generic functions coefficients, effects,
residuals, fitted.values;
lm.influence for regression diagnostics, and
glm for generalized linear models.
## 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)) #- residuals almost identical
plot(lm.D9)# Residuals, Fitted,..