summary.lm {stats} R Documentation

## Summarizing Linear Model Fits

### Description

summary method for class "lm".

### Usage

## S3 method for class 'lm'
summary(object, correlation = FALSE, symbolic.cor = FALSE, ...)

## S3 method for class 'summary.lm'
print(x, digits = max(3, getOption("digits") - 3),

### Value

The function summary.lm computes and returns a list of summary statistics of the fitted linear model given in object, using the components (list elements) "call" and "terms" from its argument, plus

 residuals the weighted residuals, the usual residuals rescaled by the square root of the weights specified in the call to lm. coefficients a p \times 4 matrix with columns for the estimated coefficient, its standard error, t-statistic and corresponding (two-sided) p-value. Aliased coefficients are omitted. aliased named logical vector showing if the original coefficients are aliased. sigma the square root of the estimated variance of the random error \hat\sigma^2 = \frac{1}{n-p}\sum_i{w_i R_i^2}, where R_i is the i-th residual, residuals[i]. df degrees of freedom, a 3-vector (p, n-p, p*), the first being the number of non-aliased coefficients, the last being the total number of coefficients. fstatistic (for models including non-intercept terms) a 3-vector with the value of the F-statistic with its numerator and denominator degrees of freedom. r.squared R^2, the ‘fraction of variance explained by the model’, R^2 = 1 - \frac{\sum_i{R_i^2}}{\sum_i(y_i- y^*)^2}, where y^* is the mean of y_i if there is an intercept and zero otherwise. adj.r.squared the above R^2 statistic ‘adjusted’, penalizing for higher p. cov.unscaled a p \times p matrix of (unscaled) covariances of the \hat\beta_j, j=1, \dots, p. correlation the correlation matrix corresponding to the above cov.unscaled, if correlation = TRUE is specified. symbolic.cor (only if correlation is true.) The value of the argument symbolic.cor. na.action from object, if present there.

The model fitting function lm, summary.

Function coef will extract the matrix of coefficients with standard errors, t-statistics and p-values.

### Examples


##-- Continuing the  lm(.) example:
coef(lm.D90)  # the bare coefficients
sld90 <- summary(lm.D90 <- lm(weight ~ group -1))  # omitting intercept
sld90
coef(sld90)  # much more

## model with *aliased* coefficient:
lm.D9. <- lm(weight ~ group + I(group != "Ctl"))
Sm.D9. <- summary(lm.D9.)
Sm.D9. #  shows the NA NA NA NA  line
stopifnot(length(cc <- coef(lm.D9.)) == 3, is.na(cc[3]),
dim(coef(Sm.D9.)) == c(2,4), Sm.D9.\$df == c(2, 18, 3))


[Package stats version 4.2.0 Index]