# summary.speedlm: Methods to summarize Linear Models fits In speedglm: Fitting Linear and Generalized Linear Models to Large Data Sets

## Description

`summary` method for class 'speedlm'.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10``` ```## S3 method for class 'speedlm' summary(object, correlation = FALSE,...) ## S3 method for class 'speedlm' coef(object,...) ## S3 method for class 'speedlm' vcov(object,...) ## S3 method for class 'speedlm' logLik(object,...) ## S3 method for class 'speedlm' AIC(object,...,k = 2) ```

## Arguments

 `object` an object of class 'speedlm'. `correlation` logical. Do you want to print the correlation matrix? By default it is false. `k` numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC. `...` further optional arguments

## Value

 `coefficients` the matrix of coefficients, standard errors, t-statistics and two-side p-values. `rdf` degrees of freedom of the fitted model. It is a component from `object`. `call` the component from `object`. `r.squared` R^2, the fraction of variance explained by the model. `adj.r.squared` the "adjusted" R^2 statistic, penalizing for higher p. `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. `f.pvalue` p-value of the F-statistic. `RSS` Residual sum of squares. `var.res` estimated variance of residuals. `rank` the component from `object`. `correlation` (only if `correlation` is true) the correlations of the estimated parameters. `...` the results from the functions `logLik`, `AIC` and `vcov`.

## Author(s)

Marco ENEA

 ```1 2 3 4 5 6 7``` ```y <- rnorm(100,1.5,1) x <- round(matrix(rnorm(200), 100, 2), digits = 3) colnames(x) <- c("s1","s2") da <- data.frame(y, x) m <- speedlm(y ~ s1 + s2,da) summary(m) ```