R: All Samples Appearing As the Same-CodePudding

I am working with the R programming language. I have a dataset which contains a person's height and whether or not they play basketball.

I want to see if on average, people over the 80th percentile (height wise) play basketball.

To do this, I:

I randomly break the data into a 70% group (train) and a 30% group (test)
I calculate the 80th percentile of the train group: using this 80th percentile, I see how many people in the test group play basketball
I calculate on average how accurate I was (on the test group)
I repeat this procedure many times (e.g. 100) and calculate the total average.

Here is the R code that generates the data for this example:

set.seed(123)

height <- rnorm(1000,210,5)
status <- c("basketball", "not_basketball")
basketball_status <- as.character(sample(status, 1000, replace=TRUE, prob=c(0.80, 0.20)))
data_1 <- data.frame(height, basketball_status)

height <- rnorm(1000,190,1)
status <- c("basketball", "not_basketball")
basketball_status <- as.character(sample(status, 1000, replace=TRUE, prob=c(0.8, 0.2)))
data_2 <- data.frame(height, basketball_status)


height <- rnorm(1000,170,5)
status <- c("basketball", "not_basketball")
basketball_status <- as.character(sample(status, 1000, replace=TRUE, prob=c(0.20, 0.80)))
data_3 <- data.frame(height, basketball_status)


my_data <- rbind(data_1, data_2, data_3)

And here is the iterative process:

library(dplyr)

results <- list()
for (i in 1:100) {

  train_i<-sample_frac(my_data, 0.7)

  sid<-as.numeric(rownames(train_i))

  test_i<-my_data[-sid,]
 
  quantiles = data.frame( train_i %>% summarise (quant_1 = quantile(height, 0.80)))
 
 
  test_i$basketball_pred = as.character(ifelse(test_i$height > quantiles$quant_1 , "basketball",   "not_basketball" ))
 
  test_i$accuracy = ifelse(test_i$basketball_pred == test_i$basketball_status, 1, 0)
 
  
 
  results_tmp = data.frame(test_i %>%
                          
                           dplyr::summarize(Mean = mean(accuracy, na.rm=TRUE)))
 
  results_tmp$iteration = i
 
  results_tmp$total_mean = mean(test_i$accuracy)
  results[[i]] <- results_tmp
}

results

results_df <- do.call(rbind.data.frame, results)

But when I run the iterative process, all averages appear the same:

head(results_df)
       Mean iteration total_mean
1 0.8344444         1  0.8344444
2 0.8344444         2  0.8344444
3 0.8344444         3  0.8344444
4 0.8344444         4  0.8344444
5 0.8344444         5  0.8344444
6 0.8344444         6  0.8344444

Question: Does anyone know why this is happening?

Thanks

CodePudding user response：

sid<-as.numeric(rownames(train_i)) is not doing what you expect, I think. You are probably looking to identify which of the original data frame rows are being included in the preceding line train_i<-sample_frac(my_data, 0.7), but it's actually just outputting 1:2100, so that all the steps later on provide identical results each time.

I think if you replace those lines with:

my_data$row = 1:nrow(my_data)
train_i <- sample_frac(my_data, 0.7)
sid <- train_i$row

you'll get results like you're expecting.

         Mean iteration total_mean
1   0.5111111         1  0.5111111
2   0.5244444         2  0.5244444
3   0.5177778         3  0.5177778
4   0.5488889         4  0.5488889
5   0.5322222         5  0.5322222

Full code that works for me:

results <- list()
for (i in 1:100) {
  
  my_data$row = 1:nrow(my_data)
  train_i<-sample_frac(my_data, 0.7)
  sid<-train_i$row
  
  test_i<-my_data[-sid,]
  
  quantiles = data.frame( train_i %>% summarise (quant_1 = quantile(height, 0.80)))
  
  
  test_i$basketball_pred = ifelse(test_i$height > quantiles$quant_1 , "basketball",   "not_basketball" )
  
  test_i$accuracy = ifelse(test_i$basketball_pred == test_i$basketball_status, 1, 0)
  
  results_tmp = data.frame(test_i %>%
                             
                             dplyr::summarize(Mean = mean(accuracy, na.rm=TRUE)))
  
  results_tmp$iteration = i
  
  results_tmp$total_mean = mean(test_i$accuracy)
  results[[i]] <- results_tmp
}

CodePudding user response：

Not an answer - using the answer kindly provided by @ Jon Spring:

results <- list()
for (i in 1:100) {

my_data$row = 1:nrow(my_data)
train_i <- sample_frac(my_data, 0.7)
sid <- train_i$row
 
  quantiles = data.frame( train_i %>% summarise (quant_1 = quantile(height, 0.80)))
 
 
  test_i$basketball_pred = as.character(ifelse(test_i$height > quantiles$quant_1 , "basketball",   "not_basketball" ))
 
  test_i$accuracy = ifelse(test_i$basketball_pred == test_i$basketball_status, 1, 0)
 
  
 
  results_tmp = data.frame(test_i %>%
                          
                           dplyr::summarize(Mean = mean(accuracy, na.rm=TRUE)))
 
  results_tmp$iteration = i
 
  results_tmp$total_mean = mean(test_i$accuracy)
  results[[i]] <- results_tmp
}

results

results_df <- do.call(rbind.data.frame, results)

Here is the final answer:

 head(results_df)
       Mean iteration total_mean
1 0.8344444         1  0.8344444
2 0.8344444         2  0.8344444
3 0.8344444         3  0.8344444
4 0.8344444         4  0.8344444
5 0.8344444         5  0.8344444
6 0.8344444         6  0.8344444

@Jon Spring: The numbers are still the same? Did I understand your answer correctly?

Thank you so much for all your help!