print and summary method for "relate" class.
# S3 method for relate print(x, ...)
Arguments
| x | an object of class "relate", usually, a result of a call to relate(). |
|---|---|
| ... | further arguments passed to or from other methods. |
Details
print.relate() tries to be smart about formatting four kinds of relate. summary.relate() tries to be smart about formatting four kinds of relate.
See also
Examples
if (FALSE) { # If the target variable is a categorical variable categ <- target_by(heartfailure, death_event) # If the variable of interest is a categorical variable cat_cat <- relate(categ, hblood_pressure) # Print bins class object cat_cat summary(cat_cat) } # \donttest{ # If the target variable is a categorical variable categ <- target_by(heartfailure, death_event) # If the variable of interest is a numerical variable cat_num <- relate(categ, sodium) cat_num#> # A tibble: 3 x 27 #> variable death_event n na mean sd se_mean IQR skewness kurtosis #> <chr> <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 sodium No 203 0 137. 3.98 0.280 4.5 -1.22 6.49 #> 2 sodium Yes 96 0 135. 5.00 0.510 5.25 -0.677 2.08 #> 3 sodium total 299 0 137. 4.41 0.255 6 -1.05 4.12 #> # … with 17 more variables: p00 <dbl>, p01 <dbl>, p05 <dbl>, p10 <dbl>, #> # p20 <dbl>, p25 <dbl>, p30 <dbl>, p40 <dbl>, p50 <dbl>, p60 <dbl>, #> # p70 <dbl>, p75 <dbl>, p80 <dbl>, p90 <dbl>, p95 <dbl>, p99 <dbl>, #> # p100 <dbl>summary(cat_num)#> variable death_event n na mean #> Length:3 No :1 Min. : 96.0 Min. :0 Min. :135.4 #> Class :character Yes :1 1st Qu.:149.5 1st Qu.:0 1st Qu.:136.0 #> Mode :character total:1 Median :203.0 Median :0 Median :136.6 #> Mean :199.3 Mean :0 Mean :136.4 #> 3rd Qu.:251.0 3rd Qu.:0 3rd Qu.:136.9 #> Max. :299.0 Max. :0 Max. :137.2 #> sd se_mean IQR skewness #> Min. :3.983 Min. :0.2552 Min. :4.500 Min. :-1.2189 #> 1st Qu.:4.198 1st Qu.:0.2674 1st Qu.:4.875 1st Qu.:-1.1335 #> Median :4.412 Median :0.2795 Median :5.250 Median :-1.0481 #> Mean :4.466 Mean :0.3484 Mean :5.250 Mean :-0.9812 #> 3rd Qu.:4.707 3rd Qu.:0.3950 3rd Qu.:5.625 3rd Qu.:-0.8624 #> Max. :5.002 Max. :0.5105 Max. :6.000 Max. :-0.6766 #> kurtosis p00 p01 p05 #> Min. :2.081 Min. :113.0 Min. :120.8 Min. :127.0 #> 1st Qu.:3.100 1st Qu.:113.0 1st Qu.:122.3 1st Qu.:128.5 #> Median :4.120 Median :113.0 Median :123.9 Median :130.0 #> Mean :4.229 Mean :114.0 Mean :123.6 Mean :129.3 #> 3rd Qu.:5.304 3rd Qu.:114.5 3rd Qu.:125.0 3rd Qu.:130.5 #> Max. :6.488 Max. :116.0 Max. :126.0 Max. :131.0 #> p10 p20 p25 p30 #> Min. :130.0 Min. :132.0 Min. :133.0 Min. :134.0 #> 1st Qu.:131.0 1st Qu.:133.0 1st Qu.:133.5 1st Qu.:134.5 #> Median :132.0 Median :134.0 Median :134.0 Median :135.0 #> Mean :131.7 Mean :133.5 Mean :134.2 Mean :135.0 #> 3rd Qu.:132.5 3rd Qu.:134.2 3rd Qu.:134.8 3rd Qu.:135.5 #> Max. :133.0 Max. :134.4 Max. :135.5 Max. :136.0 #> p40 p50 p60 p70 #> Min. :134.0 Min. :135.5 Min. :136.0 Min. :138.0 #> 1st Qu.:135.0 1st Qu.:136.2 1st Qu.:137.0 1st Qu.:138.5 #> Median :136.0 Median :137.0 Median :138.0 Median :139.0 #> Mean :135.7 Mean :136.5 Mean :137.3 Mean :138.7 #> 3rd Qu.:136.5 3rd Qu.:137.0 3rd Qu.:138.0 3rd Qu.:139.0 #> Max. :137.0 Max. :137.0 Max. :138.0 Max. :139.0 #> p75 p80 p90 p95 #> Min. :138.2 Min. :139.0 Min. :141.0 Min. :143.0 #> 1st Qu.:139.1 1st Qu.:139.5 1st Qu.:141.1 1st Qu.:143.5 #> Median :140.0 Median :140.0 Median :141.2 Median :144.0 #> Mean :139.4 Mean :139.7 Mean :141.2 Mean :143.7 #> 3rd Qu.:140.0 3rd Qu.:140.0 3rd Qu.:141.3 3rd Qu.:144.0 #> Max. :140.0 Max. :140.0 Max. :141.5 Max. :144.0 #> p99 p100 #> Min. :145.0 Min. :146.0 #> 1st Qu.:145.0 1st Qu.:147.0 #> Median :145.0 Median :148.0 #> Mean :145.0 Mean :147.3 #> 3rd Qu.:145.0 3rd Qu.:148.0 #> Max. :145.1 Max. :148.0plot(cat_num)# If the variable of interest is a categorical variable cat_cat <- relate(categ, hblood_pressure) cat_cat#> hblood_pressure #> death_event No Yes #> No 137 66 #> Yes 57 39summary(cat_cat)#> Call: xtabs(formula = formula_str, data = data, addNA = TRUE) #> Number of cases in table: 299 #> Number of factors: 2 #> Test for independence of all factors: #> Chisq = 1.8827, df = 1, p-value = 0.17plot(cat_cat)##--------------------------------------------------- # If the target variable is a numerical variable num <- target_by(heartfailure, creatinine) # If the variable of interest is a numerical variable num_num <- relate(num, sodium) num_num#> #> Call: #> lm(formula = formula_str, data = data) #> #> Coefficients: #> (Intercept) sodium #> 7.45097 -0.04433 #>summary(num_num)#> #> Call: #> lm(formula = formula_str, data = data) #> #> Residuals: #> Min 1Q Median 3Q Max #> -1.0433 -0.4329 -0.2443 0.0557 7.8454 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 7.45097 1.82610 4.080 5.79e-05 *** #> sodium -0.04433 0.01336 -3.319 0.00102 ** #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Residual standard error: 1.018 on 297 degrees of freedom #> Multiple R-squared: 0.03576, Adjusted R-squared: 0.03251 #> F-statistic: 11.01 on 1 and 297 DF, p-value: 0.001017 #>plot(num_num)#> Analysis of Variance Table #> #> Response: creatinine #> Df Sum Sq Mean Sq F value Pr(>F) #> smoking 1 0.24 0.23968 0.2234 0.6368 #> Residuals 297 318.68 1.07301summary(num_cat)#> #> Call: #> lm(formula = formula(formula_str), data = data) #> #> Residuals: #> Min 1Q Median 3Q Max #> -0.9133 -0.4527 -0.2527 0.0170 8.0473 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 1.41335 0.07270 19.440 <2e-16 *** #> smokingYes -0.06064 0.12831 -0.473 0.637 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Residual standard error: 1.036 on 297 degrees of freedom #> Multiple R-squared: 0.0007515, Adjusted R-squared: -0.002613 #> F-statistic: 0.2234 on 1 and 297 DF, p-value: 0.6368 #>plot(num_cat)# }
