lm.summaries {base} R Documentation

## Accessing Linear Model Fits

### Description

All these functions are `methods` for class `lm` or `summary.lm` and `anova.lm` objects.

### Usage

```anova(object, ...)
anovalist.lm(object, ..., test = NULL)
summary(object, correlation = FALSE)
coefficients(x) ; coef(x)
df.residual(x)
family(x)
formula(x)
fitted.values(x)
residuals(x, type = c("working", "pearson", "deviance"), ...)
weights(x)
plot(x)

print(summary.lm.obj, digits = max(3, getOption("digits") - 3),
symbolic.cor = p > 4,
signif.stars= getOption("show.signif.stars"), ...)
```

### Arguments

 `object, x` an object of class `lm`, usually, a result of a call to `lm`.

### Details

`print.summary.lm` tries to be smart about formatting the coefficients, standard errors, etc. and additionally gives ``significance stars'' if `signif.stars` is `TRUE`.

`anova.lm` produces an analysis of variance (`anova`) table.

The generic accessor functions `coefficients`, `effects`, `fitted.values` and `residuals` can be used to extract various useful features of the value returned by `lm`.

### Value

The function `summary.lm` computes and returns a list of summary statistics of the fitted linear model given in `lm.obj`, 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 x 4 matrix with columns for the estimated coefficient, its standard error, t-statistic and corresponding (two-sided) p-value. `sigma` the square root of the estimated variance of the random error sigma^2 = 1/(n-p) Sum(R[i]^2), where R[i] is the i-th residual, `residuals[i]`. `df` degrees of freedom, a 3-vector (p, n-p, p*). `fstatistic` 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 - Sum(R[i]^2) / Sum((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 x p matrix of (unscaled) covariances of the coef[j], j=1, ..., p. `correlation` the correlation matrix corresponding to the above `cov.unscaled`, if `correlation = TRUE` is specified.

### Warning

The comparison between two or more models by `anova` or `anovalist.lm` will only be valid if they are fitted to the same dataset. This may be a problem if there are missing values and R's default of `na.action = na.omit` is used.

The model fitting function `lm`.

`anova` for the ANOVA table, `coefficients`, `deviance`, `effects`, `fitted.values`, `glm` for generalized linear models, `lm.influence` for regression diagnostics, `weighted.residuals`, `residuals`, `residuals.glm`, `summary`.

### 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

## The 2 basic regression diagnostic plots [plot.lm(.) is preferred]
plot(resid(lm.D90), fitted(lm.D90))# Tukey-Anscombe's
abline(h=0, lty=2, col = 'gray')

qqnorm(residuals(lm.D90))
```

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