In [9]:

    

library(dplyr)
library(ggplot2)
library(knitr)


    

    




Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

In [3]:

    

data<-read.csv("../RESULTS_bin/SummaryRsqs.csv",header = TRUE, sep = ",")
head(data)
attach(data)


    

    






motion_thresh
med_rsq
CI_95
med_icc
CI_95_icc
n
age_l
age_u

	
1
50.00000000
 0.86749487
 0.15140379
 0.93437604
 0.07460912
20.00000000
 6.00000000
18.00000000
	
2
65.00000000
 0.90981842
 0.14721701
 0.95548640
 0.06453941
30.00000000
 6.00000000
18.00000000
	
3
 60.00000000
  0.97270180
  0.03760561
  0.98622421
  0.01718400
100.00000000
  6.00000000
 18.00000000
	
4
35.00000000
 0.96734236
 0.05291633
 0.98365908
 0.02506813
80.00000000
 6.00000000
18.00000000
	
5
60.00000000
 0.95366882
 0.07605641
 0.97697349
 0.03599832
60.00000000
 6.00000000
18.00000000
	
6
70.00000000
 0.93266660
 0.07137071
 0.96621653
 0.03895027
40.00000000
 6.00000000
18.00000000

In [4]:

    

table(data$motion_thresh)


    

    






 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 
10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 

In [5]:

    

table(data$n)


    

    






 10  20  30  40  50  60  70  80  90 100 
 16  16  16  16  16  16  16  16  16  16 

In [10]:

    

ggplot(data, aes(x=motion_thresh, y=med_rsq, group=n, color=n))+geom_point() +scale_colour_gradientn(colours=rainbow(4))


    

In [11]:

    

aplot <-ggplot(data, aes(x=motion_thresh, y=med_rsq, group=n, color=n)) +scale_colour_gradientn(colours=rainbow(4))+
geom_errorbar(aes(ymin=med_rsq-CI_95/2, 
			  ymax=med_rsq+CI_95/2), 
		      width=0, alpha=.6)+geom_point()+
		geom_line(alpha=.5)


print(aplot)
#ggsave(aplot, filename='/Users/Jlnrd/Desktop/mean_by_perc_fd.jpg',  width=6, height=6)#, units='in', dpi=150)