PMC TMS Analyses

#setwd("~/Documents/PMC_TMS_Analysis")
require(ggplot2)
require(xlsx)

data = read.xlsx("Recruitmentcurve_results_MASTER_temp.xlsx", sheetIndex = 4)
data$group = as.factor(data$group)
data = data[data$Task.Difficulty == "4-color",]

Inflection Point 1-way ANOVA

oneway.test(Inf_delta ~ group, data = data, var.equal = T)

    One-way analysis of means

data:  Inf_delta and group
F = 0.5976, num df = 2, denom df = 23, p-value = 0.5584

AUC 1-Way ANOVA

oneway.test(AUC_delta ~ group, data = data, var.equal = T)

    One-way analysis of means

data:  AUC_delta and group
F = 0.1756, num df = 2, denom df = 23, p-value = 0.8401

Slope 1-Way ANOVA

oneway.test(Slope_delta ~ group, data = data, var.equal = T)

    One-way analysis of means

data:  Slope_delta and group
F = 0.30907, num df = 2, denom df = 23, p-value = 0.7371

Plot: Inflection Point 1 point not shown in plot, PMC subject with Inflection = ~120

ggplot(data, aes(x=group, y=Inf_delta, color = group)) + 
  geom_boxplot()+
  geom_dotplot(binaxis='y', stackdir='center') + ylim(-10,10) + 
  theme(text = element_text(size = 24))      

Plot: AUC

ggplot(data, aes(x=group, y=AUC_delta, color = group)) + 
  geom_boxplot()+
  geom_dotplot(binaxis='y', stackdir='center') + 
  theme(text = element_text(size = 24))      

Plot: Slope

#4-color = data[data$]
ggplot(data, aes(x=group, y=Slope_delta, color = group)) + 
  geom_boxplot()+
  geom_dotplot(binaxis='y', stackdir='center') + 
  theme(text = element_text(size = 24))      

Plot: Slope w/ 4-color only

data$Task.Difficulty = factor(data$Task.Difficulty)
four_color = data[data$Task.Difficulty == "4-color",]
ggplot(four_color, aes(x=group, y=Slope_delta, color = group)) + 
  geom_boxplot()+
  geom_dotplot(binaxis='y', stackdir='center') +
  ylab(expression(paste(Delta,"Slope"))) + xlab("TMS Groups") + 
  theme(text = element_text(size = 24))      

#+ ylim(-10,30)

Plot: Slope 2 VS 4 COLOR

data = read.xlsx("Recruitmentcurve_results_MASTER_temp.xlsx", sheetIndex = 4)
data$group = as.factor(data$group)

data$Task.Difficulty = factor(data$Task.Difficulty)
ggplot(data[data$group == "Control",], aes(x=Task.Difficulty, y=Slope_delta, color = Task.Difficulty)) + 
  geom_boxplot()+
  geom_dotplot(binaxis='y', stackdir='center') +
  ylab(expression(paste(Delta,"Slope"))) + xlab("Task Difficulty") + scale_color_manual(values=c("#999999", "#F8766D")) + 
  theme(text = element_text(size = 24))      

#+ ylim(-10,30)

T-test: Controls pre vs post, Inflection

cont_data = read.xlsx("Recruitmentcurve_results_MASTER_temp.xlsx", sheetIndex = 3)
t.test(cont_data$Δ.Inf.., mu = 0, alternative = "two.sided")

    One Sample t-test

data:  cont_data$Δ.Inf..
t = 0.70531, df = 19, p-value = 0.4892
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 -1.987838  4.008476
sample estimates:
mean of x 
 1.010319 

T-test: Controls pre vs post, AUC

t.test(cont_data$Δ.AUC, mu = 0, alternative = "two.sided")

    One Sample t-test

data:  cont_data$Δ.AUC
t = 0.38535, df = 19, p-value = 0.7043
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 -110.5958  160.5095
sample estimates:
mean of x 
 24.95682 

T-test: Controls pre vs post, Slope

t.test(cont_data$Δ.Slope, mu = 0, alternative = "greater")

    One Sample t-test

data:  cont_data$Δ.Slope
t = 1.7231, df = 19, p-value = 0.05055
alternative hypothesis: true mean is greater than 0
95 percent confidence interval:
 -0.003279286          Inf
sample estimates:
mean of x 
 0.934295 

cont_data = read.xlsx("Recruitmentcurve_results_MASTER_temp.xlsx", sheetIndex = 5)
ggplot(cont_data, aes(x=Time, y=Slope, color = Time)) + 
  geom_boxplot()+
  geom_dotplot(binaxis='y', stackdir='center') +
  ylab("Slope") + xlab("Time") + scale_color_manual(values=c("#999999", "#F8766D")) + scale_x_discrete(limits=c("Pre", "Post")) + 
  theme(text = element_text(size = 24))      

Recruitment Curve

recruit = read.xlsx("RecruitmentCurve31.xlsx", sheetIndex = 1)
ggplot(data = recruit, aes(x = Condition, y = mep.Amplitude, color = Time)) +
  geom_smooth(method = "loess") + 
  theme(text = element_text(size = 24))