dlookr (version 0.3.9)

relate: Relationship between target variable and variable of interest

Description

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

Usage

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.

Descriptive statistic information

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

• mean : arithmetic average

• sd : standard devation

• 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

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

    • contegency table

    • c("xtabs", "table") class

  • predictor: numerical variable

    • descriptive statistic for each levles and total observation.

• target variable: numerical variable

  • predictor: categorical variable

    • ANOVA test. "lm" class.

  • predictor: numerical variable

    • simple linear model. "lm" class.

See Also

print.relate, plot.relate.

Examples

# NOT RUN {
# Generate data for the example
carseats <- ISLR::Carseats
carseats[sample(seq(NROW(carseats)), 20), "Income"] <- NA
carseats[sample(seq(NROW(carseats)), 5), "Urban"] <- NA

# If the target variable is a categorical variable
categ <- target_by(carseats, US)

# If the variable of interest is a numarical variable
cat_num <- relate(categ, Sales)
cat_num
summary(cat_num)
plot(cat_num)

# If the variable of interest is a categorical variable
cat_cat <- relate(categ, ShelveLoc)
cat_cat
summary(cat_cat)
plot(cat_cat)

##---------------------------------------------------
# If the target variable is a categorical variable
num <- target_by(carseats, Sales)

# If the variable of interest is a numarical variable
num_num <- relate(num, Price)
num_num
summary(num_num)
plot(num_num)

# If the variable of interest is a categorical variable
num_cat <- relate(num, ShelveLoc)
num_cat
summary(num_cat)
plot(num_cat)
# }