# summary.lm

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Percentile

##### Summarizing Linear Model Fits

summary method for class "lm".

Keywords
models, regression
##### Usage
# S3 method for lm
summary(object, correlation = FALSE, symbolic.cor = FALSE, …)# S3 method for 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.

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.

##### Aliases
• summary.lm
• summary.mlm
• print.summary.lm
##### Examples
library(stats) # NOT RUN { ##-- 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 3.6.2, License: Part of R 3.6.2

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