# summary.lm

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##### Summarizing Linear Model Fits

summary method for class "lm".

Keywords
models, regression
##### Usage
## S3 method for class 'lm':
summary(object, correlation = FALSE, symbolic.cor = FALSE, \dots)## 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
• residualsthe weighted residuals, the usual residuals rescaled by the square root of the weights specified in the call to lm.
• coefficientsa $p \times 4$ matrix with columns for the estimated coefficient, its standard error, t-statistic and corresponding (two-sided) p-value. Aliased coefficients are omitted.
• aliasednamed logical vector showing if the original coefficients are aliased.
• sigmathe 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].
• dfdegrees 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.squaredthe above $R^2$ statistic adjusted, penalizing for higher $p$.
• cov.unscaleda $p \times p$ matrix of (unscaled) covariances of the $\hat\beta_j$, $j=1, \dots, p$.
• correlationthe 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.actionfrom 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.

##### Aliases
• summary.lm
• summary.mlm
• print.summary.lm
##### Examples
library(stats) utils::example("lm", echo = FALSE) ##-- 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))
Documentation reproduced from package stats, version 3.3, License: Part of R 3.3

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