sumry is a generic function for the BAQM package used to produce summaries of the results of certain model fitting functions.
Examples
sumry(penguins)
#> species island bill_len bill_dep flipper_len body_mass
#> n.val 344 344 342 342 342 342
#> n.na 0 0 2 2 2 2
#> min n.lvl : 3 n.lvl : 3 32.1 13.1 172 2700
#> Q1 Adelie :152 Biscoe :168 39.2 15.45 190 3550
#> median Gentoo :124 Dream :124 44.45 17.3 197 4050
#> mean Chnstrp: 68 Torgrsn: 52 43.92 17.15 200.9 4202
#> Q3 48.5 18.7 214 4800
#> max 59.6 21.5 231 6300
#> std.dev 5.46 1.975 14.06 802
#> sex year
#> n.val 333 344
#> n.na 11 0
#> min n.lvl : 2 2007
#> Q1 male :168 2007
#> median female:165 2008
#> mean 2008
#> Q3 2009
#> max 2009
#> std.dev 0.8184
sumry(data.frame(a = rnorm(100),
b = c(NA, 1:98, NA),
c = sample(letters[4:6], 100, TRUE)),
transpose = TRUE, pad = 1)
#> n.val n.na min Q1 median mean Q3 max std.dev
#> a 100 0 -1.914 -0.6432 -0.1679 0.009043 0.6221 2.308 0.9815
#> b 98 2 1 25 49.5 49.5 74 98 28.43
#> c n.lvl:3 0 3 d:39 f:35 e:26
sumry(lm(Sepal.Length ~ ., data = iris))
#>
#> Summary Statistics:
#> Value Performance Measure Err(Resids) Metric
#> Observations = 150 R-Squared = 0.86731 MAPE = 0.041785
#> F-Statistic = 188.25 Adj-R2 = 0.86271 MAD = 0.24286
#> Pr(b's=0) = <2e-16 *** Std.Err.Est = 0.30683 RMSE = 0.30063
#>
#> Analysis of Variance:
#> Deg.Frdm Sum.of.Sqs Mean.Sum.Sqs F.statistic p-value(F)
#> Regression 5 88.612 17.722370 188.25 <2e-16 ***
#> Error(Resids) 144 13.556 0.094142
#> Total 149 102.168
#>
#> Coefficients:
#> Coefficient Std.Error t-stat p-value VIF
#> (Intercept) 2.17127 0.279794 7.7602 1.43e-12 ***
#> Sepal.Width 0.49589 0.086070 5.7615 4.87e-08 *** 2.2275
#> Petal.Length 0.82924 0.068528 12.1009 < 2e-16 *** 23.1616
#> Petal.Width -0.31516 0.151196 -2.0844 0.03889 * 21.0214
#> Species_versicolor -0.72356 0.240169 -3.0127 0.00306 ** 20.4234
#> Species_virginica -1.02350 0.333726 -3.0669 0.00258 ** 39.4344
#>
#> Signif.Levels: 0 ‘***’ 0.001 ‘** ’ 0.01 ‘ * ’ 0.05 ‘ . ’ 0.1 ‘ ’ 1
#>
#> Summary of Min 1Q Mean Median 3Q Max
#> Residuals: -0.7942 -0.2187 <3e-14 0.008987 0.2025 0.731
#>
#> Call: lm(formula = Sepal.Length ~ ., data = iris)
sumry(leaps::regsubsets(mpg ~ ., data = mtcars, nbest = 2))
#>
#> Call: eval(expr, envir)
#> _k_i.best rsq adjr2 see cp cyl disp hp drat wt qsec vs am gear
#> 1 1 ( 1 ) 0.7528 0.7446 3.045882 11.63 *
#> 2 1 ( 2 ) 0.7262 0.7171 3.205902 15.90 *
#> 3 2 ( 1 ) 0.8302 0.8185 2.567516 1.22 * *
#> 4 2 ( 2 ) 0.8268 0.8148 2.593412 1.77 * *
#> 5 3 ( 1 ) 0.8497 0.8336 2.458846 0.10 * * *
#> 6 3 ( 2 ) 0.8431 0.8263 2.511548 1.15 * * *
#> 7 4 ( 1 ) 0.8579 0.8368 2.434828 0.79 * * * *
#> 8 4 ( 2 ) 0.8568 0.8356 2.443813 0.96 * * *
#> 9 5 ( 1 ) 0.8637 0.8375 2.429291 1.85 * * * * *
#> 10 5 ( 2 ) 0.8608 0.8340 2.455386 2.32 * * * *
#> 11 6 ( 1 ) 0.8667 0.8347 2.450251 3.37 * * * * * *
#> 12 6 ( 2 ) 0.8664 0.8343 2.453246 3.42 * * * * * *
#> 13 7 ( 1 ) 0.8681 0.8296 2.487705 5.15 * * * * * * *
#> 14 7 ( 2 ) 0.8676 0.8290 2.492404 5.23 * * * * * * *
#> 15 8 ( 1 ) 0.8687 0.8230 2.535340 7.05 * * * * * * *
#> 16 8 ( 2 ) 0.8685 0.8227 2.537497 7.09 * * * * * * * *
#> carb
#> 1
#> 2
#> 3
#> 4
#> 5
#> 6
#> 7
#> 8 *
#> 9
#> 10 *
#> 11
#> 12
#> 13
#> 14
#> 15 *
#> 16