Description Usage Arguments Value Author(s) References See Also Examples
Summarize, print, and extract objects from slm
objects.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ## S3 method for class 'slm'
summary(object, correlation, ...)
## S3 method for class 'mslm'
summary(object, ...)
## S3 method for class 'slm'
print(x, digits, ...)
## S3 method for class 'summary.slm'
print(x, digits, symbolic.cor, signif.stars, ...)
## S3 method for class 'slm'
fitted(object, ...)
## S3 method for class 'slm'
residuals(object, ...)
## S3 method for class 'slm'
coef(object, ...)
## S3 method for class 'slm'
extractAIC(fit, scale = 0, k = 2, ...)
## S3 method for class 'slm'
deviance(object, ...)
|
object,x,fit |
object of class |
digits |
minimum number of significant digits to be used for most numbers. |
scale |
optional numeric specifying the scale parameter of the model, see 'scale' in 'step'. Currently only used in the '"lm"' method, where 'scale' specifies the estimate of the error variance, and 'scale = 0' indicates that it is to be estimated by maximum likelihood. |
k |
numeric specifying the "weight" of the equivalent degrees of freedom ('edf') part in the AIC formula. |
symbolic.cor |
logical; if |
signif.stars |
logical; if |
correlation |
logical; if |
... |
additional arguments passed to methods. |
print.slm
and print.summary.slm
return invisibly.
fitted.slm
, residuals.slm
, and coef.slm
return the corresponding components of the slm
object.
extractAIC.slm
and deviance.slm
return the AIC
and deviance values of the fitted object.
Roger Koenker
Koenker, R and Ng, P. (2002). SparseM: A Sparse Matrix Package for R,
http://www.econ.uiuc.edu/~roger/research/home.html
1 2 3 4 5 6 7 8 9 10 11 12 13 | data(lsq)
X <- model.matrix(lsq) #extract the design matrix
y <- model.response(lsq) # extract the rhs
X1 <- as.matrix(X)
slm.time <- system.time(slm(y~X1-1) -> slm.o) # pretty fast
cat("slm time =",slm.time,"\n")
cat("slm Results: Reported Coefficients Truncated to 5 ","\n")
sum.slm <- summary(slm.o)
sum.slm$coef <- sum.slm$coef[1:5,]
sum.slm
fitted(slm.o)[1:10]
residuals(slm.o)[1:10]
coef(slm.o)[1:10]
|
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
slm time = 0.145 0.02 0.164 0 0
slm Results: Reported Coefficients Truncated to 5
Call:
slm(formula = y ~ X1 - 1)
Residuals:
Min 1Q Median 3Q Max
-1.952e-01 -1.400e-02 5.329e-15 1.442e-02 1.783e-01
Coefficients:
Estimate Std. Error t value Pr(>|t|)
[1,] 823.3613 0.1274 6460.4 <2e-16 ***
[2,] 340.1156 0.1711 1987.3 <2e-16 ***
[3,] 472.9760 0.1379 3429.6 <2e-16 ***
[4,] 349.3175 0.1743 2004.0 <2e-16 ***
[5,] 187.5595 0.2100 893.3 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03789 on 1138 degrees of freedom
Multiple R-Squared: 1, Adjusted R-squared: 1
F-statistic: 4.504e+07 on 712 and 1138 DF, p-value: 0
[1] 64.040350 5.889029 64.069648 5.937578 76.388826 64.171021 76.414288
[8] 64.196320 64.214065 7.658347
[1] 0.027275686 -0.005630865 -0.041783949 0.020139128 0.022659984
[6] 0.020583686 -0.037168247 -0.023604889 -0.012130877 0.023302606
[1] 823.36129 340.11555 472.97601 349.31746 187.55954 159.05176 -54.88358
[8] 497.65120 574.75533 584.40348
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