Created
January 8, 2021 22:27
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library(tidyverse) | |
library(ggplot2) | |
# create holder data frame | |
data <- data.frame(magic_number=numeric(), equity_fraction=numeric(), | |
mgmt_span=numeric(), ps_fit=numeric(), | |
salary_growth=numeric()) | |
# create ranges for variables | |
s_set <- 1:20 | |
e_set <- c(0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50) | |
g_set <- c(0.06, 0.12, 0.18, 0.24, 0.30) | |
f_set <- 1:10/10 | |
# calculate magic number threshold for all combinations of incentive variables | |
for (v_s in s_set) { | |
for (v_e in e_set) { | |
for (v_g in g_set) { | |
for (v_f in f_set) { | |
temp_data <- data.frame(v_e, v_s, v_f, v_g) | |
temp_data$m <- (v_e * v_s^2 * v_f)/v_g | |
data <- rbind(data, temp_data) | |
} | |
} | |
} | |
} | |
# create scatterplot for single level of E & G | |
df1 <- data %>% filter(e == 0.25 & g == 0.12) | |
combos1 <- df1 %>% filter(m > 135 & m < 155) | |
ggplot(combos1, aes(x=f, y=s)) + geom_point() + | |
geom_smooth(method="lm", aes(color="Exp Model"), formula= (y ~ log(x)), se=FALSE, linetype = 1) + | |
theme_minimal() + | |
theme(legend.position = "none") + | |
labs(x="Project-Skill Fit (1=perfect)", y="Mgmt. Span", | |
title="Mgmt. Span & Skill-Fit Inverse Relationship at Magic Number Threshold (135-155)", | |
subtitle="Different combinations of S & F can produce innovative environments.") | |
# create scatterplot for all levels of E & G | |
combos <- data %>% filter(m > 135 & m < 155) | |
ggplot(combos, aes(x=v_f, y=v_s)) + geom_point() + | |
facet_grid(v_g~v_e) + | |
geom_smooth(method="lm", aes(color="Exp Model"), formula= (y ~ log(x)), se=FALSE, linetype = 1) + | |
theme_minimal() + | |
theme(legend.position = "none") + | |
labs(x="Project-Skill Fit (1=perfect)", y="Mgmt. Span", | |
title="Mgmt. Span & Skill-Fit Inverse Relationship at Magic Number Threshold (135-155)", | |
subtitle="Different combinations of S & F can produce innovative environments.") |
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