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 × 27
#> described_variables death_event n na mean sd se_mean IQR skewness
#> <chr> <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sodium No 203 0 137. 3.98 0.280 4.5 -1.22
#> 2 sodium Yes 96 0 135. 5.00 0.510 5.25 -0.677
#> 3 sodium total 299 0 137. 4.41 0.255 6 -1.05
#> # ℹ 18 more variables: kurtosis <dbl>, 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)
#> described_variables 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 p99
#> Min. :138.2 Min. :139.0 Min. :141.0 Min. :143.0 Min. :145
#> 1st Qu.:139.1 1st Qu.:139.5 1st Qu.:141.1 1st Qu.:143.5 1st Qu.:145
#> Median :140.0 Median :140.0 Median :141.2 Median :144.0 Median :145
#> Mean :139.4 Mean :139.7 Mean :141.2 Mean :143.7 Mean :145
#> 3rd Qu.:140.0 3rd Qu.:140.0 3rd Qu.:141.3 3rd Qu.:144.0 3rd Qu.:145
#> Max. :140.0 Max. :140.0 Max. :141.5 Max. :144.0 Max. :145
#> p100
#> Min. :146.0
#> 1st Qu.:147.0
#> Median :148.0
#> Mean :147.3
#> 3rd Qu.:148.0
#> 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)
# }