How to predict a value in R?
Question
I have a small issue regarding my data. I get this dataframe:
id power hr fr VE VO2 VCO2 PETCO2 percent_VO2 percent_power group
1 AC12-PRD-C1 25 88.75 22.75 22.75 0.73900 0.66700 39.2925 49.34068 21.73913 CHD
2 AC12-PRD-C1 40 93.25 23.00 23.75 0.81975 0.71500 39.6200 54.73210 34.78261 CHD
3 AC12-PRD-C1 55 99.75 22.75 26.75 0.95125 0.85400 41.4100 63.51193 47.82609 CHD
4 AC12-PRD-C1 70 109.75 23.00 32.50 1.07525 1.04700 42.0150 71.79102 60.86957 CHD
5 AC12-PRD-C1 85 118.75 22.75 39.50 1.19900 1.25125 41.8425 80.05341 73.91304 CHD
6 AC12-PRD-C1 100 127.00 26.00 48.25 1.34575 1.51850 41.0950 89.85144 86.95652 CHD
7 AC12-PRD-C1 115 135.75 28.00 55.75 1.49775 1.76025 40.7275 100.00000 100.00000 CHD
8 AL13-PRD-C1 25 69.50 16.50 24.00 0.66125 0.58050 31.2275 41.36691 19.23077 CHD
9 AL13-PRD-C1 40 73.00 17.50 26.50 0.74850 0.66425 32.1025 46.82515 30.76923 CHD
10 AL13-PRD-C1 55 83.25 15.50 29.00 0.85500 0.79425 33.6650 53.48764 42.30769 CHD
11 AL13-PRD-C1 70 93.75 16.00 36.50 0.98450 0.99925 34.5325 61.58899 53.84615 CHD
12 AL13-PRD-C1 85 104.50 16.00 44.75 1.14950 1.23475 34.4225 71.91117 65.38462 CHD
13 AL13-PRD-C1 100 114.25 19.25 55.25 1.34650 1.48375 33.1800 84.23522 76.92308 CHD
14 AL13-PRD-C1 115 125.25 20.75 63.75 1.45100 1.65775 32.6450 90.77260 88.46154 CHD
15 AL13-PRD-C1 130 136.25 24.75 78.00 1.59850 1.89075 30.9000 100.00000 100.00000 CHD
16 BM06-PRD-S1 25 119.25 18.25 19.00 0.61675 0.58225 37.6425 48.87084 25.00000 Sains
17 BM06-PRD-S1 40 126.00 18.00 20.75 0.71700 0.65950 39.2175 56.81458 40.00000 Sains
18 BM06-PRD-S1 55 133.50 20.75 25.00 0.86275 0.82750 41.2150 68.36371 55.00000 Sains
19 BM06-PRD-S1 70 147.25 18.25 29.00 0.98575 1.04550 41.7050 78.11014 70.00000 Sains
20 BM06-PRD-S1 85 158.50 22.25 39.25 1.13000 1.30525 41.1425 89.54041 85.00000 Sains
21 BM06-PRD-S1 100 168.75 27.75 51.00 1.26200 1.61150 38.8925 100.00000 100.00000 Sains
22 CB19-PRD-S1 25 98.75 18.50 25.00 0.88350 0.80475 40.7550 36.15715 13.15789 Sains
23 CB19-PRD-S1 40 98.25 20.00 25.50 0.94575 0.82900 41.4675 38.70473 21.05263 Sains
24 CB19-PRD-S1 55 102.00 19.75 28.50 1.08125 0.95800 42.2775 44.25005 28.94737 Sains
25 CB19-PRD-S1 70 107.50 20.50 34.25 1.24400 1.14275 42.6450 50.91058 36.84211 Sains
26 CB19-PRD-S1 85 111.00 21.25 35.50 1.30475 1.19925 43.3600 53.39677 44.73684 Sains
27 CB19-PRD-S1 100 117.25 21.50 40.25 1.47350 1.42225 44.2650 60.30284 52.63158 Sains
28 CB19-PRD-S1 115 123.00 22.75 47.00 1.67900 1.68475 44.6400 68.71291 60.52632 Sains
29 CB19-PRD-S1 130 129.50 24.50 52.50 1.79075 1.87950 44.3425 73.28627 68.42105 Sains
30 CB19-PRD-S1 145 135.50 25.25 59.50 1.96000 2.13525 44.7300 80.21281 76.31579 Sains
31 CB19-PRD-S1 160 145.25 26.75 64.50 2.04050 2.28350 43.8825 83.50726 84.21053 Sains
32 CB19-PRD-S1 175 151.25 30.50 83.00 2.34425 2.76050 41.6025 95.93820 92.10526 Sains
33 CB19-PRD-S1 190 161.75 33.75 92.25 2.44350 2.96850 40.0400 100.00000 100.00000 Sains
34 CC14-PRD-S1 20 102.50 19.00 18.25 0.59250 0.54825 37.7175 49.26211 22.22222 Sains
35 CC14-PRD-S1 30 110.25 18.75 19.75 0.66100 0.60325 38.5800 54.95739 33.33333 Sains
36 CC14-PRD-S1 40 113.25 18.50 20.75 0.74350 0.66025 39.2950 61.81667 44.44444 Sains
37 CC14-PRD-S1 50 122.50 20.00 23.50 0.87875 0.77325 40.5650 73.06173 55.55556 Sains
38 CC14-PRD-S1 60 126.25 17.50 26.25 0.94350 0.89375 41.3525 78.44523 66.66667 Sains
39 CC14-PRD-S1 70 132.00 16.50 28.00 0.99675 0.98525 42.7575 82.87258 77.77778 Sains
40 CC14-PRD-S1 80 145.00 18.50 32.75 1.11425 1.16275 42.5025 92.64186 88.88889 Sains
41 CC14-PRD-S1 90 153.50 19.50 37.25 1.20275 1.32700 42.0975 100.00000 100.00000 Sains
42 DA03-PRD-C1 20 75.50 20.00 24.25 0.92550 0.78100 41.8375 45.37877 15.38462 CHD
43 DA03-PRD-C1 30 77.00 21.50 26.75 1.02925 0.87750 41.8625 50.46580 23.07692 CHD
44 DA03-PRD-C1 40 79.50 22.00 29.50 1.11675 0.97200 42.1025 54.75607 30.76923 CHD
45 DA03-PRD-C1 50 81.25 22.75 31.00 1.19725 1.05425 42.9525 58.70311 38.46154 CHD
46 DA03-PRD-C1 60 86.25 21.75 32.00 1.24775 1.11750 44.0800 61.17921 46.15385 CHD
47 DA03-PRD-C1 70 89.50 26.50 39.00 1.42625 1.35300 43.6350 69.93136 53.84615 CHD
48 DA03-PRD-C1 80 91.75 27.00 43.25 1.54225 1.50750 43.5800 75.61902 61.53846 CHD
49 DA03-PRD-C1 90 95.25 28.00 46.25 1.59425 1.61425 43.5800 78.16867 69.23077 CHD
50 DA03-PRD-C1 100 96.75 29.50 51.75 1.69675 1.76925 43.2775 83.19441 76.92308 CHD
51 DA03-PRD-C1 110 99.50 29.75 52.75 1.77600 1.86750 44.5050 87.08017 84.61538 CHD
52 DA03-PRD-C1 120 104.75 34.75 64.50 1.94525 2.17975 42.3325 95.37877 92.30769 CHD
53 DA03-PRD-C1 130 109.00 37.75 72.75 2.03950 2.35750 40.0550 100.00000 100.00000 CHD
54 DA24-PRD-S1 25 88.00 18.50 15.75 0.53500 0.45075 37.2200 40.33170 21.73913 Sains
55 DA24-PRD-S1 40 93.25 18.50 16.25 0.58450 0.47775 38.3375 44.06332 34.78261 Sains
56 DA24-PRD-S1 55 103.75 19.00 20.25 0.76875 0.65450 40.1875 57.95326 47.82609 Sains
57 DA24-PRD-S1 70 119.00 20.75 28.00 0.98200 0.95525 41.5175 74.02940 60.86957 Sains
58 DA24-PRD-S1 85 133.25 22.75 34.75 1.09975 1.18325 41.4125 82.90614 73.91304 Sains
59 DA24-PRD-S1 100 145.00 27.50 45.75 1.25900 1.49700 39.1475 94.91142 86.95652 Sains
60 DA24-PRD-S1 115 155.25 36.50 64.75 1.32650 1.72500 33.0275 100.00000 100.00000 Sains
61 DB22-PRD-S1 25 93.00 17.50 18.25 0.72050 0.58275 40.1275 61.43679 29.41176 Sains
62 DB22-PRD-S1 40 94.75 17.25 21.25 0.86825 0.74600 42.4000 74.03539 47.05882 Sains
63 DB22-PRD-S1 55 111.50 18.00 22.25 0.92350 0.79550 43.7575 78.74654 64.70588 Sains
64 DB22-PRD-S1 70 117.50 19.50 24.75 1.01075 0.91000 45.1775 86.18631 82.35294 Sains
65 DB22-PRD-S1 85 142.50 24.00 29.50 1.17275 1.13175 47.3850 100.00000 100.00000 Sains
66 DB42-PRD-C1 25 81.75 20.75 21.25 0.74350 0.62050 38.7075 34.75111 14.28571 CHD
67 DB42-PRD-C1 40 88.25 22.25 20.75 0.76575 0.60475 39.6275 35.79107 22.85714 CHD
68 DB42-PRD-C1 55 93.00 22.50 26.00 0.96075 0.79100 40.7000 44.90535 31.42857 CHD
69 DB42-PRD-C1 70 98.75 23.50 30.75 1.09750 0.96300 41.6050 51.29703 40.00000 CHD
70 DB42-PRD-C1 85 104.75 23.50 34.00 1.21975 1.10725 42.6325 57.01098 48.57143 CHD
71 DB42-PRD-C1 100 112.75 23.25 38.50 1.34525 1.30025 43.8750 62.87684 57.14286 CHD
72 DB42-PRD-C1 115 119.00 23.75 46.75 1.50625 1.56775 43.4775 70.40196 65.71429 CHD
73 DB42-PRD-C1 130 127.00 25.50 53.75 1.67550 1.80400 42.3750 78.31269 74.28571 CHD
74 DB42-PRD-C1 145 133.25 30.75 70.50 1.86325 2.16450 39.3975 87.08810 82.85714 CHD
75 DB42-PRD-C1 160 139.50 34.75 82.75 2.04650 2.43400 37.6425 95.65319 91.42857 CHD
76 DB42-PRD-C1 175 147.50 39.25 98.25 2.13950 2.67125 34.8550 100.00000 100.00000 CHD
77 DL18-PRD-S1 25 109.25 23.75 17.25 0.61300 0.54200 43.3100 24.77769 12.19512 Sains
78 DL18-PRD-S1 40 115.75 23.25 22.00 0.85525 0.76325 43.7425 34.56952 19.51220 Sains
79 DL18-PRD-S1 55 124.75 26.50 25.75 0.98000 0.89000 44.8275 39.61196 26.82927 Sains
80 DL18-PRD-S1 70 129.25 19.50 29.75 1.07600 1.08100 45.2350 43.49232 34.14634 Sains
81 DL18-PRD-S1 85 136.25 24.00 33.75 1.28700 1.25450 46.6250 52.02102 41.46341 Sains
82 DL18-PRD-S1 100 143.50 24.00 38.75 1.46150 1.47350 47.3825 59.07437 48.78049 Sains
83 DL18-PRD-S1 115 149.25 22.25 44.50 1.59325 1.68575 46.8925 64.39976 56.09756 Sains
84 DL18-PRD-S1 130 156.75 27.25 50.75 1.69650 1.83225 46.1575 68.57316 63.41463 Sains
85 DL18-PRD-S1 145 162.50 26.00 56.25 1.88400 2.10250 46.5550 76.15198 70.73171 Sains
86 DL18-PRD-S1 160 169.50 29.50 65.00 1.99800 2.31325 45.0225 80.75990 78.04878 Sains
87 DL18-PRD-S1 175 173.25 28.50 71.75 2.14250 2.52175 44.5875 86.60065 85.36585 Sains
88 DL18-PRD-S1 190 178.00 32.50 81.00 2.31275 2.75500 43.0400 93.48222 92.68293 Sains
89 DL18-PRD-S1 205 184.75 37.00 99.00 2.47400 3.11700 39.5725 100.00000 100.00000 Sains
I would like to get a specific Y value. Is it possible to predict ay value for a specific x value (here 70 %) for each participant ? I have more values but I just selected some of them to make it simpler. I am also joining a picture of the plot I got.
by using this code:
df_sum %>%
filter(percent_power>= 0 & percent_power < 75) %>%
ggscatter(x = "percent_power", y = "PETCO2", color = "group") +
stat_cor(aes(color = group), label.x = 15, label.y = c(20,25)) +
stat_regline_equation(label.x = 15, label.y = c(18,23),
formula = y ~ x,
aes(color = group, label = paste(..eq.label.., ..adj.rr.label.., sep = "~~~~")),) +
geom_smooth(aes(colour=group), method = "lm", formula = y ~ x) +
xlab("Percentage of power (%)") +
ylab(expression(paste("PETC", O[2]," (mmHg)")))
Thank you for your help!
1 answers
solution1
0 2020-02-28 16:30:23
Maybe I misunderstand Your question, but as @camille and @MrFlick pointed out this could be done using the predict() function. In detail.
# Fit some model, for example:
m1 <- lm(PETCO2 ~ percent_power + factor(group), data = df_sum)
Now, you can get the predicted values ($\\hat{y}_i$) for your intial observations using the predict() function:
df_sum %>%
mutate(petco2_hat = predict(m1, newdata = dta)) %>%
select(id, PETCO2, petco2_hat, percent_power, group)
# A tibble: 89 x 5
id PETCO2 petco2_hat percent_power group
<fct> <dbl> <dbl> <dbl> <fct>
1 AC12-PRD-C1 39.3 39.6 21.7 CHD
2 AC12-PRD-C1 39.6 39.6 34.8 CHD
3 AC12-PRD-C1 41.4 39.7 47.8 CHD
4 AC12-PRD-C1 42.0 39.7 60.9 CHD
5 AC12-PRD-C1 41.8 39.7 73.9 CHD
6 AC12-PRD-C1 41.1 39.7 87.0 CHD
7 AC12-PRD-C1 40.7 39.7 100 CHD
8 AL13-PRD-C1 31.2 39.6 19.2 CHD
9 AL13-PRD-C1 32.1 39.6 30.8 CHD
10 AL13-PRD-C1 33.7 39.7 42.3 CHD
# … with 79 more rows
On the other hand, if you just want to get the predicted values for some new points, hand the new values over to the predict() function in a similar manner:
predict(m1, newdata = tibble(percent_power = 70.0, group = "CHD"))
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.
Related Question
how to predict value for future date in r
How to predict with multinom() in R
How can I predict a single value from a linear regression in R?
How to predict survival probabilities in R?
How to predict using glm in R?
R predict - How to format output
How to predict compositional data in R
how to predict svar models in r?
Can't predict a single value in R shiny
R predict function returning NULL value
Related Tags