envelope of the K funcition (and its derivative such as L) is very useful for validating a fitted spatial points process model. for instance, I fit a poisson model for a data J1a2, which is as following:
J1a2.points:
# X.1 X Y
1 1 118.544 1638.445
2 2 325.995 1761.223
3 3 681.625 1553.771
4 4 677.392 1816.261
5 5 986.451 1685.016
6 6 1469.093 1354.787
7 7 1608.805 1625.744
8 8 1994.071 1782.391
9 9 1968.669 1375.955
10 10 2362.403 1337.852
11 11 2701.099 1773.924
12 12 2900.083 1820.495
13 13 2963.588 1668.081
14 14 3412.360 1676.549
15 15 3378.490 1456.396
16 16 3721.420 1464.863
17 17 3823.028 1701.951
18 18 4072.817 1790.859
19 19 4089.751 1388.656
20 20 97.375 715.497
21 21 376.799 1033.025
22 22 563.082 1126.166
23 23 935.647 1206.607
24 24 512.277 486.876
25 25 935.647 757.834
26 26 1409.821 410.670
27 27 1435.223 639.290
28 28 1706.180 1045.726
29 29 1968.669 876.378
30 30 2307.365 711.263
31 31 2624.892 897.546
32 32 2654.528 1236.243
33 33 2857.746 423.371
34 34 3039.795 639.290
35 35 3298.050 707.029
36 36 3111.767 1011.856
37 37 3361.555 1227.775
38 38 4047.414 1185.438
39 39 3569.007 508.045
40 40 4250.632 469.942
41 41 4386.110 872.144
42 42 93.141 237.088
43 43 554.614 186.283
44 44 757.832 148.180
45 45 965.283 220.153
46 46 1723.115 296.360
47 47 1744.283 423.371
48 48 1913.631 203.218
49 49 2167.653 292.126
50 50 2629.126 211.685
51 51 3217.610 283.658
52 52 3827.262 325.996
and:
J1a2.Win<-owin(c(0, 4500.42),c(0, 1917.87))
if you draw evelope for the data with Lest:
library(spatstat)
env.data<-envelope(J1a2, Lest,correction="border",
nsim=19, global=TRUE)
plot(env.data,.-r~r, shade=NULL, legend=FALSE,
xlab=expression(paste("r(",mu,"m)")),ylab="L(r)-r", main = "")
the Lest() curve goes out of the envelope. however, if you use Linhom instead of Lest, you will find the Linhom() are all inside of the envelope.
it seems that this suggest a inhomogenous density kernel of the data. so I use y as covariate in fitting:
poisson.J1a2<-ppm(J1a2~1,Poisson(),correction="border")
y1.J1a2<-ppm(J1a2~y,correction="border")
anova(poisson.J1a2,y.J1a2,test="LR") #p=0.6484
I don't find any evidence of a spatial trend of density along y, or x, or their combinations.
then why the Linhom() outperform the Lest() in this case?
furthermore, when should one decide to use Linhom() instead of Lest?
You should first decide whether or not the intensity can be assumed to be constant. To help you with this you can look at kernel density estimates or do formal tests such as a quadrat test etc. If you decide that the intensity can be assumed to be constant you use Lest() if this is not the case you use Linhom().
I'm trying to add error bars to a second curve (using dataset "pmfprofbs01"), but I'm having problems and I couldn't fix this.
There are a few threads on this error, but unfortunately it looks like every other answer is case specific, and I'm not able to overcome this error in my code. I am able to plot a first smoothed curve (stat_smooth) and overlapping errorbars (using geom_errobar). The problem rises when I try to add a second curve to the same graph, for comparison purposes.
With following code, I get the following error: "Error: Aesthetics must be either length 1 or the same as the data (35): ymin, ymax, x, y, colour"
I am looking to add additional errorbars to the second smoothed curve (corresponding to datasets pmfprof01 and pmfprofbs01).
Could someone explain why I keep getting this error? The code works until using the second call of geom_errorbar().
These are my 4 datasets (all used as data frames):
- pmfprof1 and pmfprof01 are the two datasets used for applying the smoothing method.
- pmfprofbs1 and pmfprofbs01 contain additional information based on an error analysis for plotting error bars.
> pmfprof1
Z correctedpmfprof1
1 -1.1023900 -8.025386e-22
2 -1.0570000 6.257110e-02
3 -1.0116000 1.251420e-01
4 -0.9662020 2.143170e-01
5 -0.9208040 3.300960e-01
6 -0.8754060 4.658550e-01
7 -0.8300090 6.113410e-01
8 -0.7846110 4.902430e-01
9 -0.7392140 3.344200e-01
10 -0.6938160 4.002040e-01
11 -0.6484190 1.215460e-01
12 -0.6030210 -1.724360e-01
13 -0.5576240 -6.077170e-01
14 -0.5122260 -1.513420e+00
15 -0.4668290 -2.075330e+00
16 -0.4214310 -2.617160e+00
17 -0.3760340 -3.350500e+00
18 -0.3306360 -4.076220e+00
19 -0.2852380 -4.926540e+00
20 -0.2398410 -5.826390e+00
21 -0.1944430 -6.761300e+00
22 -0.1490460 -7.301530e+00
23 -0.1036480 -7.303880e+00
24 -0.0582507 -7.026800e+00
25 -0.0128532 -6.627960e+00
26 0.0325444 -6.651490e+00
27 0.0779419 -6.919830e+00
28 0.1233390 -6.686490e+00
29 0.1687370 -6.129060e+00
30 0.2141350 -6.120890e+00
31 0.2595320 -6.455160e+00
32 0.3049300 -6.554560e+00
33 0.3503270 -6.983390e+00
34 0.3957250 -7.413500e+00
35 0.4411220 -6.697370e+00
36 0.4865200 -5.477230e+00
37 0.5319170 -4.552890e+00
38 0.5773150 -3.393060e+00
39 0.6227120 -2.449930e+00
40 0.6681100 -2.183190e+00
41 0.7135080 -1.673980e+00
42 0.7589050 -8.003740e-01
43 0.8043030 -2.918780e-01
44 0.8497000 -1.159710e-01
45 0.8950980 9.123767e-22
> pmfprof01
Z correctedpmfprof01
1 -1.25634000 -1.878749e-21
2 -1.20387000 -1.750190e-01
3 -1.15141000 -3.500380e-01
4 -1.09894000 -6.005650e-01
5 -1.04647000 -7.935110e-01
6 -0.99400600 -8.626150e-01
7 -0.94153900 -1.313880e+00
8 -0.88907200 -2.067770e+00
9 -0.83660500 -2.662440e+00
10 -0.78413800 -4.514190e+00
11 -0.73167100 -7.989510e+00
12 -0.67920400 -1.186870e+01
13 -0.62673800 -1.535970e+01
14 -0.57427100 -1.829150e+01
15 -0.52180400 -2.067170e+01
16 -0.46933700 -2.167890e+01
17 -0.41687000 -2.069820e+01
18 -0.36440300 -1.662640e+01
19 -0.31193600 -1.265950e+01
20 -0.25946900 -1.182580e+01
21 -0.20700200 -1.213370e+01
22 -0.15453500 -1.233680e+01
23 -0.10206800 -1.235160e+01
24 -0.04960160 -1.123630e+01
25 0.00286531 -9.086940e+00
26 0.05533220 -6.562710e+00
27 0.10779900 -4.185860e+00
28 0.16026600 -3.087430e+00
29 0.21273300 -2.005150e+00
30 0.26520000 -9.295540e-02
31 0.31766700 1.450360e+00
32 0.37013400 1.123910e+00
33 0.42260100 2.426750e-01
34 0.47506700 1.213370e-01
35 0.52753400 5.265226e-21
> pmfprofbs1
Z correctedpmfprof01 bsmean bssd bsse bsci
1 -1.1023900 -8.025386e-22 0.00000000 0.0000000 0.00000000 0.0000000
2 -1.0570000 6.257110e-02 1.46519200 0.6691245 0.09974719 0.2010273
3 -1.0116000 1.251420e-01 1.62453300 0.6368053 0.09492933 0.1913175
4 -0.9662020 2.143170e-01 1.62111600 0.7200497 0.10733867 0.2163269
5 -0.9208040 3.300960e-01 1.44754700 0.7236743 0.10787900 0.2174158
6 -0.8754060 4.658550e-01 1.67509800 0.7148755 0.10656735 0.2147724
7 -0.8300090 6.113410e-01 1.78144200 0.7374481 0.10993227 0.2215539
8 -0.7846110 4.902430e-01 1.73058700 0.7701354 0.11480501 0.2313743
9 -0.7392140 3.344200e-01 0.97430090 0.7809477 0.11641681 0.2346227
10 -0.6938160 4.002040e-01 1.26812000 0.8033838 0.11976139 0.2413632
11 -0.6484190 1.215460e-01 0.93601510 0.7927926 0.11818254 0.2381813
12 -0.6030210 -1.724360e-01 0.63201080 0.8210839 0.12239996 0.2466809
13 -0.5576240 -6.077170e-01 0.05952252 0.8653050 0.12899205 0.2599664
14 -0.5122260 -1.513420e+00 0.57893690 0.8858471 0.13205429 0.2661379
15 -0.4668290 -2.075330e+00 -0.08164613 0.8921298 0.13299086 0.2680255
16 -0.4214310 -2.617160e+00 -1.08074600 0.8906925 0.13277660 0.2675937
17 -0.3760340 -3.350500e+00 -1.67279700 0.9081813 0.13538367 0.2728479
18 -0.3306360 -4.076220e+00 -2.50074900 1.0641550 0.15863486 0.3197076
19 -0.2852380 -4.926540e+00 -3.12062200 1.0639080 0.15859804 0.3196333
20 -0.2398410 -5.826390e+00 -4.47060100 1.1320770 0.16876008 0.3401136
21 -0.1944430 -6.761300e+00 -5.40812700 1.1471780 0.17101120 0.3446504
22 -0.1490460 -7.301530e+00 -6.42419100 1.1685490 0.17419700 0.3510710
23 -0.1036480 -7.303880e+00 -5.79613500 1.1935850 0.17792915 0.3585926
24 -0.0582507 -7.026800e+00 -5.85496900 1.2117630 0.18063896 0.3640539
25 -0.0128532 -6.627960e+00 -6.70480400 1.1961400 0.17831002 0.3593602
26 0.0325444 -6.651490e+00 -8.27106200 1.3376870 0.19941060 0.4018857
27 0.0779419 -6.919830e+00 -8.79402900 1.3582760 0.20247983 0.4080713
28 0.1233390 -6.686490e+00 -8.35947700 1.3673080 0.20382624 0.4107848
29 0.1687370 -6.129060e+00 -8.04437600 1.3921620 0.20753126 0.4182518
30 0.2141350 -6.120890e+00 -8.18588300 1.5220550 0.22689456 0.4572759
31 0.2595320 -6.455160e+00 -8.37217600 1.5436800 0.23011823 0.4637728
32 0.3049300 -6.554560e+00 -8.59346400 1.6276880 0.24264140 0.4890116
33 0.3503270 -6.983390e+00 -8.88378700 1.6557140 0.24681927 0.4974316
34 0.3957250 -7.413500e+00 -9.72709800 1.6569390 0.24700188 0.4977996
35 0.4411220 -6.697370e+00 -9.46033400 1.6378470 0.24415582 0.4920637
36 0.4865200 -5.477230e+00 -8.37590600 1.6262700 0.24243002 0.4885856
37 0.5319170 -4.552890e+00 -7.52867000 1.6617010 0.24771176 0.4992302
38 0.5773150 -3.393060e+00 -6.89192300 1.6667330 0.24846189 0.5007420
39 0.6227120 -2.449930e+00 -6.25115300 1.6670390 0.24850750 0.5008340
40 0.6681100 -2.183190e+00 -6.05373800 1.6720180 0.24924973 0.5023298
41 0.7135080 -1.673980e+00 -5.10526700 1.6668400 0.24847784 0.5007742
42 0.7589050 -8.003740e-01 -4.42001600 1.6561830 0.24688918 0.4975725
43 0.8043030 -2.918780e-01 -4.26640200 1.6588970 0.24729376 0.4983878
44 0.8497000 -1.159710e-01 -4.46318500 1.6533830 0.24647179 0.4967312
45 0.8950980 9.123767e-22 -5.17173200 1.6557990 0.24683194 0.4974571
> pmfprofbs01
Z correctedpmfprof01 bsmean bssd bsse bsci
1 -1.25634000 -1.878749e-21 0.000000 0.0000000 0.00000000 0.0000000
2 -1.20387000 -1.750190e-01 2.316589 0.4646486 0.07853995 0.1596124
3 -1.15141000 -3.500380e-01 2.320647 0.4619668 0.07808664 0.1586911
4 -1.09894000 -6.005650e-01 2.635883 0.6519826 0.11020517 0.2239639
5 -1.04647000 -7.935110e-01 2.814679 0.6789875 0.11476983 0.2332404
6 -0.99400600 -8.626150e-01 2.588038 0.7324196 0.12380151 0.2515949
7 -0.94153900 -1.313880e+00 2.033736 0.7635401 0.12906183 0.2622852
8 -0.88907200 -2.067770e+00 2.394285 0.8120181 0.13725611 0.2789380
9 -0.83660500 -2.662440e+00 2.465425 0.9485307 0.16033095 0.3258317
10 -0.78413800 -4.514190e+00 0.998115 1.0177400 0.17202946 0.3496059
11 -0.73167100 -7.989510e+00 -1.585430 1.0502190 0.17751941 0.3607628
12 -0.67920400 -1.186870e+01 -5.740894 1.2281430 0.20759406 0.4218819
13 -0.62673800 -1.535970e+01 -9.325951 1.3289330 0.22463068 0.4565045
14 -0.57427100 -1.829150e+01 -12.010540 1.3279860 0.22447060 0.4561792
15 -0.52180400 -2.067170e+01 -14.672770 1.3296720 0.22475559 0.4567583
16 -0.46933700 -2.167890e+01 -14.912250 1.3192610 0.22299581 0.4531820
17 -0.41687000 -2.069820e+01 -12.850570 1.3288470 0.22461614 0.4564749
18 -0.36440300 -1.662640e+01 -6.093746 1.3497100 0.22814263 0.4636416
19 -0.31193600 -1.265950e+01 -5.210692 1.3602240 0.22991982 0.4672533
20 -0.25946900 -1.182580e+01 -6.041660 1.3818700 0.23357866 0.4746890
21 -0.20700200 -1.213370e+01 -5.765808 1.3854680 0.23418683 0.4759249
22 -0.15453500 -1.233680e+01 -6.985883 1.4025360 0.23707185 0.4817880
23 -0.10206800 -1.235160e+01 -7.152865 1.4224030 0.24042999 0.4886125
24 -0.04960160 -1.123630e+01 -3.600538 1.4122650 0.23871635 0.4851300
25 0.00286531 -9.086940e+00 -0.751673 1.5764920 0.26647578 0.5415439
26 0.05533220 -6.562710e+00 2.852910 1.5535620 0.26259991 0.5336672
27 0.10779900 -4.185860e+00 5.398850 1.5915640 0.26902342 0.5467214
28 0.16026600 -3.087430e+00 6.262459 1.6137360 0.27277117 0.5543377
29 0.21273300 -2.005150e+00 8.047920 1.6283340 0.27523868 0.5593523
30 0.26520000 -9.295540e-02 11.168640 1.6267620 0.27497297 0.5588123
31 0.31766700 1.450360e+00 12.345900 1.6363310 0.27659042 0.5620994
32 0.37013400 1.123910e+00 12.124650 1.6289230 0.27533824 0.5595546
33 0.42260100 2.426750e-01 11.279890 1.6137100 0.27276677 0.5543288
34 0.47506700 1.213370e-01 11.531670 1.6311490 0.27571450 0.5603193
35 0.52753400 5.265226e-21 11.284980 1.6662890 0.28165425 0.5723903
The code for plotting both curves is:
deltamean01<-pmfprofbs01[,"bsmean"]-
pmfprofbs01[,"correctedpmfprof01"]
correctmean01<-pmfprofbs01[,"bsmean"]-deltamean01
deltamean1<-pmfprofbs1[,"bsmean"]-
pmfprofbs1[,"correctedpmfprof1"]
correctmean1<-pmfprofbs1[,"bsmean"]-deltamean1
pl<- ggplot(pmfprof1, aes(x=pmfprof1[,1], y=pmfprof1[,2],
colour="red")) +
list(
stat_smooth(method = "gam", formula = y ~ s(x), size = 1,
colour="chartreuse3",fill="chartreuse3", alpha = 0.3),
geom_line(data=pmfprof1,linetype=4, size=0.5,colour="chartreuse3"),
geom_errorbar(aes(ymin=correctmean1-pmfprofbs1[,"bsci"],
ymax=correctmean1+pmfprofbs1[,"bsci"]),
data=pmfprofbs1,colour="chartreuse3",
width=0.02,size=0.9),
geom_point(data=pmfprof1,size=1,colour="chartreuse3"),
xlab(expression(xi*(nm))),
ylab("PMF (KJ/mol)"),
## GCD
geom_errorbar(aes(ymin=correctmean01-pmfprofbs01[,"bsci"],
ymax=correctmean01+pmfprofbs01[,"bsci"]),
data=pmfprofbs01,
width=0.02,size=0.9),
geom_line(data=pmfprof01,aes(x=pmfprof01[,1],y=pmfprof01[,2]),
linetype=4, size=0.5,colour="darkgreen"),
stat_smooth(data=pmfprof01,method = "gam",aes(x=pmfprof01[,1],pmfprof01[,2]),
formula = y ~ s(x), size = 1,
colour="darkgreen",fill="darkgreen", alpha = 0.3),
theme(text = element_text(size=20),
axis.text.x = element_text(size=20,colour="black"),
axis.text.y = element_text(size=20,colour="black")),
scale_x_continuous(breaks=number_ticks(8)),
scale_y_continuous(breaks=number_ticks(8)),
theme(panel.background = element_rect(fill ='white',
colour='gray')),
theme(plot.background = element_rect(fill='white',
colour='white')),
theme(legend.position="none"),
theme(legend.key = element_blank()),
theme(legend.title = element_text(colour='gray', size=20)),
NULL
)
pl
This is the result of using pl,
[enter image description here][1]
[1]: https://i.stack.imgur.com/x8FjY.png
Thanks in advance for any suggestion,
Im trying Moran's I and respective plot in r. But the plot has only one point. I have no idea of what is going wrong. The code is based on<
http://rstudio-pubs-static.s3.amazonaws.com/9688_a49c681fab974bbca889e3eae9fbb837.html>
my data called "coordenata"
resid x y
1 0.07785411 -53.20342 -22.66700
2 -0.28358702 -53.20389 -22.66864
3 -0.64011338 -53.21392 -22.68122
4 1.22071249 -53.21311 -22.72369
5 0.95734778 -53.28469 -22.75289
6 0.35345302 -53.25822 -22.74850
7 -0.68357738 -53.28344 -22.70694
8 -1.24596010 -53.32950 -22.72872
9 -0.19944162 -53.33669 -22.73561
10 0.67544909 -53.36756 -22.80767
11 0.64002961 -53.35947 -22.79958
12 0.04564233 -53.21889 -22.67419
13 0.01618436 -53.24522 -22.70144
14 -2.65436794 -53.23017 -22.69292
15 0.72096256 -53.25539 -22.69978
16 0.89656515 -53.28489 -22.72222
17 1.85358579 -53.33069 -22.79161
18 -0.03590077 -53.33200 -22.78336
19 0.32348975 -53.33494 -22.78586
20 2.06771402 -53.37781 -22.77869
21 -1.02190709 -53.30492 -22.77244
22 -2.02813250 -53.53917 -22.79856
23 -1.20702445 -53.53858 -22.79406
24 -1.24091732 -53.55272 -22.80536
25 -1.13491596 -53.56181 -22.82914
26 -0.82934613 -53.56422 -22.83417
27 1.23418758 -53.60017 -22.85531
28 -1.72808514 -53.65900 -22.97828
29 -0.02144049 -53.65908 -22.97497
30 0.49174568 -53.64597 -22.95439
31 -0.54408149 -53.64217 -22.91033
32 -0.37111342 -53.61447 -22.86269
33 -0.31121931 -53.27153 -22.70036
34 0.32419211 -53.30308 -22.72183
35 1.57980287 -53.33053 -22.72947
36 -1.91156060 -53.34633 -22.74722
37 -0.79036645 -53.23667 -22.68925
the code
coordinates(coordenata)<-c("x","y")
fit2<-correlog(coordenata$x,coordenata$y,coordenata$resid,increment=5,resamp=100,quiet=T)
plot(fit2)
Thanks in advance for any help!
I am trying to compile a code that runs a model on each column and saves each prediction into a data.frame.
I am probably missing a basic step because the result only saves data for the last prediction. It would be great if someone could give me a tip.
Here is the code:
for(i in 1:ncol(temp[,1:101])){
pred=(exp(predict(gam(temp[,i]~s(w),gamma=1.4,data=temp))))
prediction[i]<- as.data.frame(cbind(pred=pred))
}
Here is a short look of the data and result:
temp2
tdat.V1 tdat.V2 tdat.V3 tdat.V4 tdat.V5 w
9 0.2468596 0.2468596 -0.47226384 -0.47226384 -0.69767176 9
10 -0.3298719 -0.3298719 -0.61766160 -0.61766160 -1.05190065 10
11 0.2636122 0.2636122 -0.16966523 -0.16966523 -0.98531224 11
12 1.1036205 1.1036205 0.46601526 0.46601526 -0.35346974 12
13 1.1337664 1.1337664 -0.31946816 -0.31946816 -0.78722896 13
14 1.0441290 1.0441290 -0.19397040 -0.19397040 -0.99997758 14
15 0.5904416 0.5904416 -0.49903362 -0.49903362 -1.29327665 15
16 0.2704478 0.2704478 -0.33188601 -0.33188601 -0.89020267 16
17 0.4905354 0.4905354 0.26849660 0.26849660 -0.22608949 17
18 1.4072215 1.4072215 1.43372101 1.43372101 0.74552152 18
19 -0.3510362 -0.3510362 -0.65455175 -0.65455175 -0.67979925 19
20 -0.9471780 -0.9471780 -0.99449245 -0.99449245 -0.94800264 20
21 0.5601007 0.5601007 -0.41078889 -0.41078889 -0.70911666 21
22 0.6337811 0.6337811 0.11769665 0.11769665 -0.37718872 22
23 1.1154420 1.1154420 0.52692499 0.52692499 0.26777430 23
24 0.1314404 0.1314404 0.02146546 0.02146546 -0.03748099 24
25 0.2262661 0.2262661 0.14216196 0.14216196 -0.19273456 25
26 1.7767008 1.7767008 1.19683315 1.19683315 0.55529405 26
27 2.0070761 2.0070761 1.70737151 1.70737151 0.90322033 27
28 2.2252446 2.2252446 1.35160191 1.35160191 0.98155994 28
29 1.7452878 1.7452878 0.86052298 0.86052298 1.27898872 29
30 0.2071554 0.2071554 0.55612163 0.55612163 0.64726184 30
31 1.7144228 1.7144228 0.74949354 0.74949354 0.42433658 31
32 0.2533343 0.2533343 -0.11861726 -0.11861726 -0.63511376 32
33 0.6176735 0.6176735 0.29274750 0.29274750 -0.20402280 33
34 1.0868382 1.0868382 1.19325652 1.19325652 1.57309478 34
35 1.7051584 1.7051584 0.00151082 0.00151082 -0.95416617 35
> for(i in 1:ncol(temp2[,1:4])){
+
+ pred=(exp(predict(gam(temp2[,i]~s(w),gamma=1.4,data=temp2))))
+ prediction <- as.data.frame(cbind(pred=pred))
+ }
> print(prediction)
pred
9 0.6969407
10 0.7291379
11 0.7628225
12 0.7980632
13 0.8349320
14 0.8735040
15 0.9138580
16 0.9560762
17 1.0002448
18 1.0464540
19 1.0947979
20 1.1453751
21 1.1982889
22 1.2536473
23 1.3115630
24 1.3721544
25 1.4355449
26 1.5018639
27 1.5712468
28 1.6438349
29 1.7197765
30 1.7992264
31 1.8823468
32 1.9693071
33 2.0602847
34 2.1554654
35 2.2550432
Given a set of (x,y) coordinates, how can I solve for x, from y. If you were to plot the coordinates, they would be non-linear, but pretty close to exponential. I tried approx(), but it is way off. Here is example data. In this scenario, how could I solve for y == 50?
V1 V3
1 5.35 11.7906
2 10.70 15.0451
3 16.05 19.4243
4 21.40 20.7885
5 26.75 22.0584
6 32.10 25.4367
7 37.45 28.6701
8 42.80 30.7500
9 48.15 34.5084
10 53.50 37.0096
11 58.85 39.3423
12 64.20 41.5023
13 69.55 43.4599
14 74.90 44.7299
15 80.25 46.5738
16 85.60 47.7548
17 90.95 49.9749
18 96.30 51.0331
19 101.65 52.0207
20 107.00 52.9781
21 112.35 53.8730
22 117.70 54.2907
23 123.05 56.3025
24 128.40 56.6949
25 133.75 57.0830
26 139.10 58.5051
27 144.45 59.1440
28 149.80 60.0687
29 155.15 60.6627
30 160.50 61.2313
31 165.85 61.7748
32 171.20 62.5587
33 176.55 63.2684
34 181.90 63.7085
35 187.25 64.0788
36 192.60 64.5807
37 197.95 65.2233
38 203.30 65.5331
39 208.65 66.1200
40 214.00 66.6208
41 219.35 67.1952
42 224.70 67.5270
43 230.05 68.0175
44 235.40 68.3869
45 240.75 68.7485
46 246.10 69.1878
47 251.45 69.3980
48 256.80 69.5899
49 262.15 69.7382
50 267.50 69.7693
51 272.85 69.7693
52 278.20 69.7693
53 283.55 69.7693
54 288.90 69.7693
I suppose the problem you have is that approx solves for y given x, while you are talking about solving for x given y. So you need to switch your variables x and y when using approx:
df <- read.table(textConnection("
V1 V3
85.60 47.7548
90.95 49.9749
96.30 51.0331
101.65 52.0207
"), header = TRUE)
approx(x = df$V3, y = df$V1, xout = 50)
# $x
# [1] 50
#
# $y
# [1] 91.0769
Also, if y is exponential with respect to x, then you have a linear relationship between x and log(y), so it makes more sense to use a linear interpolator between x and log(y), then take the exponential to get back to y:
exp(approx(x = df$V3, y = log(df$V1), xout = 50)$y)
# [1] 91.07339