The relationship between the target variable and the variable of interest (predictor) is briefly analyzed.

relate(.data, predictor)

# S3 method for target_df
relate(.data, predictor)

Arguments

.data

a target_df.

predictor

variable of interest. predictor.

See vignette("relate") for an introduction to these concepts.

Value

An object of the class as relate. Attributes of relate class is as follows.

  • target : name of target variable

  • predictor : name of predictor

  • model : levels of binned value.

  • raw : table_df with two variables target and predictor.

Details

Returns the four types of results that correspond to the combination of the target variable and the data type of the variable of interest.

  • target variable: categorical variable

    • predictor: categorical variable

      • contingency table

      • c("xtabs", "table") class

    • predictor: numerical variable

      • descriptive statistic for each levels and total observation.

  • target variable: numerical variable

    • predictor: categorical variable

      • ANOVA test. "lm" class.

    • predictor: numerical variable

      • simple linear model. "lm" class.

Descriptive statistic information

The information derived from the numerical data describe is as follows.

  • mean : arithmetic average

  • sd : standard deviation

  • se_mean : standrd error mean. sd/sqrt(n)

  • IQR : interqurtle range (Q3-Q1)

  • skewness : skewness

  • kurtosis : kurtosis

  • p25 : Q1. 25% percentile

  • p50 : median. 50% percentile

  • p75 : Q3. 75% percentile

  • p01, p05, p10, p20, p30 : 1%, 5%, 20%, 30% percentiles

  • p40, p60, p70, p80 : 40%, 60%, 70%, 80% percentiles

  • p90, p95, p99, p100 : 90%, 95%, 99%, 100% percentiles

See also

Examples

# \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. :148.0
plot(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 39
summary(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 = 0.17
plot(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)
# If the variable of interest is a categorical variable num_cat <- relate(num, smoking) num_cat
#> 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 1.07301
summary(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)
# Not allow typographic plot(num_cat, typographic = FALSE)
# }