summary method for class bestfit produce result summary
containing the formula for the best (chosen) fit and the summary.lm
for that fit.
# S3 method for bestfit summary(object, fit = 1, subset, ...)
| object | an object of class |
|---|---|
| fit | the number of the chosen fit from the combination matrix (defaults
for the best fit found with |
| subset | a specification of the rows to be used: defaults to all rows.
This can be any valid indexing vector (see [.data.frame) for the
rows of data or if that is not supplied, a data frame made up of the
variables used in |
| … | further arguments passed to lm. |
Returns the call for the bestfit function, the best
(chosen) fit number, the lm formula and the lm fit summary
for the best (chosen) fit transformations found by bestfit.
#> Call: #> bestfit.formula(formula = valor ~ ., data = centro_2015@data) #> #> Best (Chosen) Transformations: #> id valor area_total quartos suites garagens dist_b_mar adj_R2 #> 443 1 rsqrt sqrt rsqrt identity sqrt rsqrt 0.9480455 #> #> Best (Chosen) fit LM summary: #> #> Call: #> lm(formula = "rsqrt(valor) ~ sqrt(area_total) + rsqrt(quartos) + identity(suites) + sqrt(garagens) + rsqrt(dist_b_mar) + (padrao)", #> data = centro_2015@data, subset = NULL) #> #> Residuals: #> Min 1Q Median 3Q Max #> -2.144e-04 -5.344e-05 8.870e-07 4.272e-05 1.729e-04 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 1.780e-03 1.301e-04 13.681 < 2e-16 *** #> sqrt(area_total) -2.279e-05 5.295e-06 -4.304 9.81e-05 *** #> rsqrt(quartos) 6.561e-04 1.269e-04 5.169 6.14e-06 *** #> identity(suites) -4.240e-05 2.060e-05 -2.058 0.0459 * #> sqrt(garagens) -2.711e-04 4.426e-05 -6.125 2.62e-07 *** #> rsqrt(dist_b_mar) -2.628e-03 5.099e-04 -5.154 6.45e-06 *** #> padraomedio -2.214e-04 4.586e-05 -4.828 1.85e-05 *** #> padraoalto -2.576e-04 4.605e-05 -5.595 1.52e-06 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 8.428e-05 on 42 degrees of freedom #> (3 observations deleted due to missingness) #> Multiple R-squared: 0.9555, Adjusted R-squared: 0.948 #> F-statistic: 128.7 on 7 and 42 DF, p-value: < 2.2e-16 #> #> NBR-14.653-2 check: #> Minimum number of market data: #> [1] "n = 53 >= 42 --> Grau III" #> Max significance level allowed for each predictor: #> [1] "t máximo = 4.59 % < 10% --> Grau III" #> Max significance level allowed for F-test: #> [1] "p-valor F = 2.79e-24 % < 1% --> Grau III"summary(best_fit, fit = 2)#> Call: #> bestfit.formula(formula = valor ~ ., data = centro_2015@data) #> #> Best (Chosen) Transformations: #> id valor area_total quartos suites garagens dist_b_mar adj_R2 #> 395 2 rsqrt identity rsqrt identity sqrt rsqrt 0.9477222 #> #> Best (Chosen) fit LM summary: #> #> Call: #> lm(formula = "rsqrt(valor) ~ identity(area_total) + rsqrt(quartos) + identity(suites) + sqrt(garagens) + rsqrt(dist_b_mar) + (padrao)", #> data = centro_2015@data, subset = NULL) #> #> Residuals: #> Min 1Q Median 3Q Max #> -2.012e-04 -5.206e-05 -1.442e-06 4.426e-05 1.674e-04 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 1.608e-03 1.176e-04 13.672 < 2e-16 *** #> identity(area_total) -6.702e-07 1.573e-07 -4.261 0.000113 *** #> rsqrt(quartos) 7.006e-04 1.246e-04 5.623 1.38e-06 *** #> identity(suites) -4.472e-05 2.052e-05 -2.180 0.034933 * #> sqrt(garagens) -2.779e-04 4.377e-05 -6.350 1.25e-07 *** #> rsqrt(dist_b_mar) -2.668e-03 5.112e-04 -5.220 5.19e-06 *** #> padraomedio -2.428e-04 4.633e-05 -5.241 4.85e-06 *** #> padraoalto -2.770e-04 4.617e-05 -6.000 3.97e-07 *** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> #> Residual standard error: 8.454e-05 on 42 degrees of freedom #> (3 observations deleted due to missingness) #> Multiple R-squared: 0.9552, Adjusted R-squared: 0.9477 #> F-statistic: 127.9 on 7 and 42 DF, p-value: < 2.2e-16 #> #> NBR-14.653-2 check: #> Minimum number of market data: #> [1] "n = 53 >= 42 --> Grau III" #> Max significance level allowed for each predictor: #> [1] "t máximo = 3.49 % < 10% --> Grau III" #> Max significance level allowed for F-test: #> [1] "p-valor F = 3.17e-24 % < 1% --> Grau III"