Thank you everyone for your replies! Just to clarify, here is what I am trying to do:
I wrote this loop that evaluates the following "function: (it's actually a "loop") 100 times, at randomly selected inputs for "random_1, random_2, random_3, random_4, split_1, split_2, split_3":
#load library
library(dplyr)
library(data.table)
set.seed(123)
# create some data for this example
a1 = rnorm(1000,100,10)
b1 = rnorm(1000,100,5)
c1 = sample.int(1000, 1000, replace = TRUE)
train_data = data.frame(a1,b1,c1)
####
results_table <- data.frame()
for (i in 1:100 ) {
#generate random numbers
random_1 = runif(1, 80, 120)
random_2 = runif(1, random_1, 120)
random_3 = runif(1, 85, 120)
random_4 = runif(1, random_3, 120)
#bin data according to random criteria
train_data <- train_data %>% mutate(cat = ifelse(a1 <= random_1 & b1 <= random_3, "a", ifelse(a1 <= random_2 & b1 <= random_4, "b", "c")))
train_data$cat = as.factor(train_data$cat)
#new splits
a_table = train_data %>%
filter(cat == "a") %>%
select(a1, b1, c1, cat)
b_table = train_data %>%
filter(cat == "b") %>%
select(a1, b1, c1, cat)
c_table = train_data %>%
filter(cat == "c") %>%
select(a1, b1, c1, cat)
split_1 = runif(1,0, 1)
split_2 = runif(1, 0, 1)
split_3 = runif(1, 0, 1)
#calculate 60th quantile ("quant") for each bin
table_a = data.frame(a_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_1)))
table_b = data.frame(b_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_2)))
table_c = data.frame(c_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_3)))
#create a new variable ("diff") that measures if the quantile is bigger tha the value of "c1"
table_a$diff = ifelse(table_a$quant > table_a$c1,1,0)
table_b$diff = ifelse(table_b$quant > table_b$c1,1,0)
table_c$diff = ifelse(table_c$quant > table_c$c1,1,0)
#group all tables
final_table = rbind(table_a, table_b, table_c)
#create a table: for each bin, calculate the average of "diff"
final_table_2 = data.frame(final_table %>%
group_by(cat) %>%
summarize(
mean = mean(diff)
))
#add "total mean" to this table
final_table_2 = data.frame(final_table_2 %>% add_row(cat = "total", mean = mean(final_table$diff)))
#format this table: add the random criteria to this table for reference
final_table_2$random_1 = random_1
final_table_2$random_2 = random_2
final_table_2$random_3 = random_3
final_table_2$random_4 = random_4
final_table_2$split_1 = split_1
final_table_2$split_2 = split_2
final_table_2$split_3 = split_3
final_table_2$iteration_number = i
results_table <- rbind(results_table, final_table_2)
final_results = dcast(setDT(results_table), iteration_number + random_1 + random_2 + random_3 + random_4 + split_1 + split_2 + split_3 ~ cat, value.var = 'mean')
}
Here is how the results of the above code look like:
head(final_results)
iteration_number random_1 random_2 random_3 random_4 split_1 split_2 split_3 a b c total
1: 1 95.67371 111.8133 94.00313 102.0569 0.84045638 0.6882731 0.7749321 0.82051282 0.6870229 0.7734554 0.730
2: 2 92.31360 110.0762 106.46871 109.5343 0.24615922 0.8777580 0.7847697 0.24731183 0.8777429 0.7840909 0.744
3: 3 81.02645 110.4645 116.42006 119.6172 0.11943576 0.9762721 0.9100522 0.14285714 0.9758162 0.9103448 0.943
4: 4 90.35986 116.7089 114.15588 116.7231 0.07675141 0.8661540 0.3236617 0.08139535 0.8658065 0.3207547 0.702
5: 5 89.28374 114.7103 119.70448 119.7725 0.08881443 0.6351936 0.8565509 0.09027778 0.6349614 0.8461538 0.573
6: 6 87.35767 103.8575 97.44462 116.0414 0.48372890 0.2319129 0.2701634 0.47368421 0.2326333 0.2711370 0.255
Here is my question: Instead of evaluating the above function at "randomly selected points", I want to evaluate this function at points defined within the a grid.
First, I defined the gird:
#grid_2
random_1 <- seq(80,100,5)
random_2 <- seq(85,120,5)
random_3 <- seq(85,120,5)
random_4 <- seq(90,120,5)
split_1 = seq(0.4,1,0.2)
split_2 = seq(0.4,1,0.2)
split_3 = seq(0.4,1,0.2)
DF_1 <- expand.grid(random_1 , random_2, random_3, random_4, split_1, split_2, split_3)
> head(DF_1)
Var1 Var2 Var3 Var4 Var5 Var6 Var7
1 80 85 85 90 0.4 0.4 0.4
2 85 85 85 90 0.4 0.4 0.4
3 90 85 85 90 0.4 0.4 0.4
4 95 85 85 90 0.4 0.4 0.4
5 100 85 85 90 0.4 0.4 0.4
6 80 90 85 90 0.4 0.4 0.4
Next, I converted that "loop" into a "function"
results_table <- data.frame()
grid_function <- function(random_1, random_2, random_3, random_4, split_1, split_2, split_3) {
#bin data according to random criteria
train_data <- train_data %>% mutate(cat = ifelse(a1 <= random_1 & b1 <= random_3, "a", ifelse(a1 <= random_2 & b1 <= random_4, "b", "c")))
train_data$cat = as.factor(train_data$cat)
#new splits
a_table = train_data %>%
filter(cat == "a") %>%
select(a1, b1, c1, cat)
b_table = train_data %>%
filter(cat == "b") %>%
select(a1, b1, c1, cat)
c_table = train_data %>%
filter(cat == "c") %>%
select(a1, b1, c1, cat)
#calculate random quantile ("quant") for each bin
table_a = data.frame(a_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_1)))
table_b = data.frame(b_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_2)))
table_c = data.frame(c_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_3)))
#create a new variable ("diff") that measures if the quantile is bigger tha the value of "c1"
table_a$diff = ifelse(table_a$quant > table_a$c1,1,0)
table_b$diff = ifelse(table_b$quant > table_b$c1,1,0)
table_c$diff = ifelse(table_c$quant > table_c$c1,1,0)
#group all tables
final_table = rbind(table_a, table_b, table_c)
#create a table: for each bin, calculate the average of "diff"
final_table_2 = data.frame(final_table %>%
group_by(cat) %>%
summarize(
mean = mean(diff)
))
#add "total mean" to this table
final_table_2 = data.frame(final_table_2 %>% add_row(cat = "total", mean = mean(final_table$diff)))
#format this table: add the random criteria to this table for reference
final_table_2$random_1 = random_1
final_table_2$random_2 = random_2
final_table_2$random_3 = random_3
final_table_2$random_4 = random_4
final_table_2$split_1 = split_1
final_table_2$split_2 = split_2
final_table_2$split_3 = split_3
final_table_2$iteration_number = i
results_table <- rbind(results_table, final_table_2)
final_results = dcast(setDT(results_table), iteration_number + random_1 + random_2 + random_3 + random_4 + split_1 + split_2 + split_3 ~ cat, value.var = 'mean')
}
Problem: Instead of evaluating the function at randomly selected points, such as " 95.67371 111.8133 94.00313 102.0569 0.84045638 0.6882731 0.7749321 0.82051282 0.6870229 0.7734554 0.730" , I want to evaluate the function at points defined within the grid, e.g. " 80 85 85 90 0.4 0.4 0.4"
Can you please show me how to do this?
Thank you so much for your help!