I'm trying to fit a curve over (the tail of) the following data:
[1] 1 1 1 1 1 1 2 1 2 2 3 2 1 1 4 3 2 11 6 2 16 7 17 36
[25] 27 39 41 33 42 66 92 138 189 249 665 224 309 247 641 777 671 532 749 506 315 292 281 130
[49] 137 91 40 27 34 19 1
I'm using the following function in R to accomplish this:
nls(y~a*x*exp(-b*x^2),start=list(a=1,b=1),trace=TRUE)
However, I'm getting the following error:
3650202 : 1 1
Error in numericDeriv(form[[3L]], names(ind), env) :
Missing value or an infinity produced when evaluating the model
When using the following, artificial values for x and y, everything works just fine:
y=x*exp(-.5*x^2)+rnorm(length(x),0,0.1)
x
[1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90
[20] 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55 1.60 1.65 1.70 1.75 1.80 1.85
[39] 1.90 1.95 2.00 2.05 2.10 2.15 2.20 2.25 2.30 2.35 2.40 2.45 2.50 2.55 2.60 2.65 2.70 2.75 2.80
[58] 2.85 2.90 2.95 3.00 3.05 3.10 3.15 3.20 3.25 3.30 3.35 3.40 3.45 3.50 3.55 3.60 3.65 3.70 3.75
[77] 3.80 3.85 3.90 3.95 4.00 4.05 4.10 4.15 4.20 4.25 4.30 4.35 4.40 4.45 4.50 4.55 4.60 4.65 4.70
[96] 4.75 4.80 4.85 4.90 4.95 5.00
y
[1] -0.080214106 0.075247488 0.076355116 -0.020087646 0.181314038 0.075832658 0.248303254
[8] 0.364244010 0.453655908 0.347854869 0.514373164 0.384051249 0.618584696 0.515684390
[15] 0.534737770 0.609279111 0.618936091 0.534443863 0.739118585 0.677679546 0.526011452
[22] 0.645645150 0.578274968 0.589619834 0.476186241 0.621638333 0.601663144 0.535981735
[29] 0.518434367 0.581735107 0.423872948 0.445335110 0.340884242 0.317121065 0.342683141
[36] 0.278351104 0.402947372 0.429483276 0.276655872 0.108164828 0.389994138 0.372300257
[43] -0.057320612 0.131271986 0.226212869 0.131171973 0.245970674 0.009926555 0.173465207
[50] 0.141220590 0.280616078 0.108515613 0.117697407 0.130700771 0.058540888 0.251613512
[57] 0.168094899 -0.058382571 0.123306762 -0.048605186 -0.010131767 0.076701962 -0.051982924
[64] 0.058427540 0.144665070 0.063998841 -0.010495697 0.119868854 0.114447318 0.006759691
[71] 0.025041761 -0.178145771 0.041547126 0.122084819 0.034283141 0.209140060 0.197024853
[78] -0.005491966 -0.033260219 -0.028123314 -0.005775553 -0.040781462 0.090024896 0.116390743
[85] -0.017811031 0.094039200 -0.147064060 -0.057249278 0.211587898 -0.066153592 0.032100332
[92] -0.092756136 -0.125906598 0.136937364 0.046453010 0.002000336 -0.134047101 0.089748847
[99] -0.019355567 -0.042158950 0.149594368
Can anyone point out what I'm doing wrong? Thanks for your help.
Well I found the answer to my problem. The starting values for the real data are completely different from the dummy values: a=500 and b=.1 result in a nice fit. Just thought it might be useful to mention that here.
Related
I am trying to plot an LDA analysis using partimat function from klaRpackage in R and I am getting this warning message Error in partimat.default(x, grouping, ...) : at least two classes required I am pasting here part of the data to make a reproducible example:
abrev Linfoprolif CORT Testo FDL Ac.GRO ifn.g il.4
1 A 2.00 0.53 1.54 1.65 8 192 68
2 A 13.91 0.65 1.34 2.27 6 195 58
3 A 15.65 0.50 0.07 0.97 5 280 67
4 A 4.96 1.51 1.45 2.54 3 30 48
5 A 0.00 3.18 0.01 0.95 3 60 71
6 A 36.23 0.28 0.88 3.63 7 320 50
7 A 9.15 1.20 0.16 1.32 1 52 74
8 A 17.63 1.68 1.29 1.86 1 47 53
9 A 6.52 2.36 0.03 0.92 4 51 75
113 B 20.48 0.38 1.27 2.40 4 278 52
114 B 12.10 0.83 0.11 0.53 2 146 41
115 B 61.56 0.07 0.09 1.09 9 305 52
116 B 35.06 0.59 0.05 0.67 4 220 54
117 B 33.48 0.68 0.99 1.24 3 102 58
118 B 20.56 0.94 0.06 1.71 3 58 45
119 B 26.46 0.12 0.29 1.60 3 210 55
120 B 24.91 0.56 0.11 0.55 5 108 56
121 B 29.22 0.42 2.60 1.55 3 84 69
122 B 19.30 1.63 0.02 0.78 3 62 69
123 B 14.45 0.22 0.79 1.89 4 245 59
373 D 27.13 0.23 1.03 4.23 6 261 100
374 D 0.00 0.43 0.08 15.34 1 58 69
375 D 17.42 0.27 2.07 7.09 5 184 80
376 D 37.34 0.91 0.08 6.18 6 210 81
377 D 28.19 0.20 3.34 6.82 6 269 105
378 D 8.53 0.61 0.05 5.31 4 98 115
I followed the code posted here like this:
partimat(abrev ~ Linfoprolif + CORT + Testo + FDL+Ac.GRO,+ ifn.g + ifn.g, data=d_e_disc, method="lda")
I can't find my error. Any help is wecome
Your response variable abrev must be factor , so you have to make it of class factor
d_e_disc $abrev <- as.factor(d_e_disc $abrev)
# then apply your code above
#Mohamed Desouky found your problem, abrev should be a factor! Also, there is a small typo in your formula (","), So here you can see a reproducible example to make sure you can reproduce your problem:
library(klaR)
partimat(factor(abrev) ~ Linfoprolif + CORT + Testo + FDL+Ac.GRO + ifn.g + ifn.g, data=d_e_disc, method="lda")
Created on 2022-07-11 by the reprex package (v2.0.1)
Just trying to some basic matrix algebra in R and I'm getting some weird results that I don't completely understand.
So, my data looks like this:
Wt LvrWt Dose Y
1 176 6.5 0.88 0.42
2 176 9.5 0.88 0.25
3 190 9.0 1.00 0.56
4 176 8.9 0.88 0.23
5 200 7.2 1.00 0.23
6 167 8.9 0.83 0.32
7 188 8.0 0.94 0.37
8 195 10.0 0.98 0.41
9 176 8.0 0.88 0.33
10 165 7.9 0.84 0.38
11 158 6.9 0.80 0.27
12 148 7.3 0.74 0.36
13 149 5.2 0.75 0.21
14 163 8.4 0.81 0.28
15 170 7.2 0.85 0.34
16 186 6.8 0.94 0.28
17 146 7.3 0.73 0.30
18 181 9.0 0.90 0.37
19 149 6.4 0.75 0.46
And here is the code I'm using:
# Creating the X matrix
Xmatrix <- subset(questionOneA, select = -c(Y))
Xmatrix <- matrix(Xmatrix)
Xmatrix <- sapply(Xmatrix, as.numeric)
is.numeric(Xmatrix)
# Transposing the x matrix
Xtranspose <- t(Xmatrix)
Xtranspose <- matrix(Xtranspose)
is.numeric(Xtranspose)
The output of Xmatrix seems correct:
V1 V2 V3
1 176 6.5 0.88
2 176 9.5 0.88
3 190 9.0 1.00
4 176 8.9 0.88
5 200 7.2 1.00
6 167 8.9 0.83
7 188 8.0 0.94
8 195 10.0 0.98
9 176 8.0 0.88
10 165 7.9 0.84
11 158 6.9 0.80
12 148 7.3 0.74
13 149 5.2 0.75
14 163 8.4 0.81
15 170 7.2 0.85
16 186 6.8 0.94
17 146 7.3 0.73
18 181 9.0 0.90
19 149 6.4 0.75
However, the output of Xtranspose seems strange to me:
V1
1 176.00
2 6.50
3 0.88
4 176.00
5 9.50
6 0.88
7 190.00
8 9.00
9 1.00
10 176.00
11 8.90
12 0.88
13 200.00
14 7.20
15 1.00
16 167.00
17 8.90
18 0.83
19 188.00
20 8.00
21 0.94
22 195.00
23 10.00
24 0.98
25 176.00
26 8.00
27 0.88
28 165.00
29 7.90
30 0.84
31 158.00
32 6.90
33 0.80
34 148.00
35 7.30
36 0.74
37 149.00
38 5.20
39 0.75
40 163.00
41 8.40
42 0.81
43 170.00
44 7.20
45 0.85
46 186.00
47 6.80
48 0.94
49 146.00
50 7.30
51 0.73
52 181.00
53 9.00
54 0.90
55 149.00
56 6.40
57 0.75
I was expecting an output with 3 rows and 19 columns. What's happened here that I'm not understanding?
Any help would be appreciated!
You should use as.matrix instead of matrix to convert to matrix, also this can be done in fewer steps.
Xmatrix <- subset(questionOneA, select = -Y)
Xmatrix <- as.matrix(Xmatrix)
Xtranspose <- t(Xmatrix)
Xmatrix
# Wt LvrWt Dose
#1 176 6.5 0.88
#2 176 9.5 0.88
#3 190 9.0 1.00
#4 176 8.9 0.88
#5 200 7.2 1.00
#6 167 8.9 0.83
#7 188 8.0 0.94
#8 195 10.0 0.98
#9 176 8.0 0.88
#10 165 7.9 0.84
#11 158 6.9 0.80
#12 148 7.3 0.74
#13 149 5.2 0.75
#14 163 8.4 0.81
#15 170 7.2 0.85
#16 186 6.8 0.94
#17 146 7.3 0.73
#18 181 9.0 0.90
#19 149 6.4 0.75
Xtranspose
# 1 2 3 4 5 6 7 8
#Wt 176.00 176.00 190 176.00 200.0 167.00 188.00 195.00
#LvrWt 6.50 9.50 9 8.90 7.2 8.90 8.00 10.00
#Dose 0.88 0.88 1 0.88 1.0 0.83 0.94 0.98
# 9 10 11 12 13 14 15 16
#Wt 176.00 165.00 158.0 148.00 149.00 163.00 170.00 186.00
#LvrWt 8.00 7.90 6.9 7.30 5.20 8.40 7.20 6.80
#Dose 0.88 0.84 0.8 0.74 0.75 0.81 0.85 0.94
# 17 18 19
#Wt 146.00 181.0 149.00
#LvrWt 7.30 9.0 6.40
#Dose 0.73 0.9 0.75
See what matrix(Xmatrix) returns :
matrix(Xmatrix)
# [,1]
#[1,] Integer,19
#[2,] Numeric,19
#[3,] Numeric,19
Just check the output from each of your steps, and you will see the matrix becomes a "one column" matrix after this step:
Xtranspose <- matrix(Xtranspose)
This function creates a matrix. If you see the manual of the matrix function you will see that it defaults to nrow=1 and ncol=1.
Your matrix obviously has more elements than would fit in a 1x1 matrix, but creating a matrix isn't really what you would want to do at this point, you would just make sure that the 2-dimensional structure you have, is a matrix, for which as.matrix is better. (But unecessary, it already is a matrix.)
Though I will say, the manual does not explain this specific happening well enough. It does not clearly say what happens if you give matrix() a matrix as input data that has more elements than would fit in the given number of rows and columns you want.
Though it does say this, which is probably applicable to your case:
When coercing a vector, it produces a one-column matrix, and promotes the names (if any) of the vector to the rownames of the matrix.
This is also what you see.
I have the following data set (which I import from a 6-column Excel file with a .csv file extension):
treas <- read.csv(file = 'treas.csv', header = TRUE, stringsAsFactors = FALSE)
2YR 3YR 5YR 7YR 10YR 30YR
0.41 0.85 1.65 2.18 2.6 3.43
0.41 0.85 1.65 2.2 2.61 3.45
0.4 0.82 1.63 2.17 2.59 3.44
0.41 0.86 1.66 2.19 2.6 3.44
0.43 0.88 1.69 2.22 2.62 3.45
0.45 0.93 1.71 2.24 2.64 3.47
0.44 0.91 1.7 2.23 2.65 3.47
0.42 0.88 1.66 2.17 2.58 3.41
0.45 0.93 1.7 2.21 2.6 3.41
0.49 0.95 1.71 2.21 2.61 3.4
0.51 0.99 1.77 2.27 2.66 3.44
0.48 0.95 1.71 2.21 2.61 3.43
0.48 0.94 1.71 2.22 2.64 3.47
0.5 0.94 1.71 2.22 2.63 3.44
0.48 0.96 1.72 2.23 2.63 3.45
0.49 0.95 1.7 2.19 2.59 3.41
0.48 0.92 1.68 2.17 2.57 3.38
0.46 0.9 1.64 2.14 2.53 3.35
0.45 0.88 1.64 2.14 2.54 3.36
0.47 0.88 1.62 2.13 2.53 3.34
0.47 0.9 1.66 2.17 2.58 3.4
0.49 0.95 1.71 2.22 2.64 3.46
0.52 0.98 1.74 2.25 2.65 3.47
0.52 1 1.74 2.24 2.63 3.44
0.51 0.99 1.7 2.19 2.58 3.38
0.51 0.97 1.68 2.17 2.57 3.37
0.46 0.93 1.66 2.15 2.55 3.38
0.48 0.92 1.65 2.13 2.53 3.34
0.48 0.95 1.68 2.17 2.55 3.36
When I call the cov() method on the the treas data frame, I see the following error message:
Error: is.numeric(x) || is.logical(x) is not TRUE
To check the data types, I use:
sapply(treas, typeof)
The result is:
2YR 3YR 5YR 7YR 10YR 30YR
"character" "character" "character" "character" "character" "character"
Calling str(treas) reveals:
str(treas)
'data.frame': 1252 obs. of 6 variables:
$ 2YR : Factor w/ 235 levels ".","0.34","0.35",..: 8 8 7 8 10 12 11 9 12 16 ...
$ 3YR : chr w/ 219 levels ".","0.66","0.69",..: 18 18 15 19 21 26 24 21 26 28 ...
$ 5YR : chr w/ 207 levels ".","0.94","0.95",..: 67 67 65 68 71 73 72 68 72 73 ...
$ 7YR : chr w/ 192 levels ".","1.19","1.20",..: 96 98 95 97 100 102 101 95 99 99 ...
$ 10YR : chr w/ 178 levels ".","1.37","1.38",..: 118 119 117 118 120 122 123 116 118 119 ...
$ 30YR : chr w/ 125 levels ".","2.11","2.14",..: 121 123 122 122 123 125 125 120 120 119 ...
I've tried to force the data frame to numeric using:
lapply(treas, as.numeric)
But, doing so results in:
Warning messages:
1: In lapply(treas, as.numeric) : NAs introduced by coercion
2: In lapply(treas, as.numeric) : NAs introduced by coercion
3: In lapply(treas, as.numeric) : NAs introduced by coercion
4: In lapply(treas, as.numeric) : NAs introduced by coercion
5: In lapply(treas, as.numeric) : NAs introduced by coercion
6: In lapply(treas, as.numeric) : NAs introduced by coercion
Then, I still get the same error when calling cov(treas):
Error: is.numeric(x) || is.logical(x) is not TRUE
Anyone see what I'm doing incorrectly here? Thanks!
I would like to figure out which is the winning unit of a node in the kohonen plot
library(kohonen)
set.seed(0)
data("wines")
wines <- scale(wines)
som_grid <- somgrid(8, 6, "hexagonal")
som_model <- som(wines, som_grid)
plot(som_model)
The plot will look like this:
And you may know in which cluster the observation will lie with
head(data.frame(cbind(wines,unit= som_model$unit.classif)))
alcohol malic.acid ash ash.alkalinity magnesium tot..phenols flavonoids non.flav..phenols proanth col..int. col..hue
1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05
2 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03
3 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86
4 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.32 1.04
5 14.20 1.76 2.45 15.2 112 3.27 3.39 0.34 1.97 6.75 1.05
6 14.39 1.87 2.45 14.6 96 2.50 2.52 0.30 1.98 5.25 1.02
OD.ratio proline unit
1 3.40 1050 24
2 3.17 1185 46
3 3.45 1480 48
4 2.93 735 4
5 2.85 1450 48
6 3.58 1290 47
But I would like to retrieve this unit information in the plot, like putting a text in the nodes with this unit number in the same way that identify function does, but automatically. Thanks in advance!
I have a data.frame with 178 rows and 14 columns. When I print it into the R-console, it only shows me 71 rows, despite the max.print option being set to 1000 rows.
Could anyone please explain why max.print option doesn't work to print full dataset in R console? And how can I do that?
I use R 3.4.1 on MacOS.
Here is a data example:
1 1 14.23 1.71 2.43 15.6 127 2.80 3.06 0.28 2.29 5.640000 1.040 3.92 1065
2 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.380000 1.050 3.40 1050
3 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.680000 1.030 3.17 1185
4 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.800000 0.860 3.45 1480
5 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.320000 1.040 2.93 735
6 1 14.20 1.76 2.45 15.2 112 3.27 3.39 0.34 1.97 6.750000 1.050 2.85 1450
7 1 14.39 1.87 2.45 14.6 96 2.50 2.52 0.30 1.98 5.250000 1.020 3.58 1290
8 1 14.06 2.15 2.61 17.6 121 2.60 2.51 0.31 1.25 5.050000 1.060 3.58 1295
9 1 14.83 1.64 2.17 14.0 97 2.80 2.98 0.29 1.98 5.200000 1.080 2.85 1045
10 1 13.86 1.35 2.27 16.0 98 2.98 3.15 0.22 1.85 7.220000 1.010 3.55 1045
11 1 14.10 2.16 2.30 18.0 105 2.95 3.32 0.22 2.38 5.750000 1.250 3.17 1510
12 1 14.12 1.48 2.32 16.8 95 2.20 2.43 0.26 1.57 5.000000 1.170 2.82 1280
13 1 13.75 1.73 2.41 16.0 89 2.60 2.76 0.29 1.81 5.600000 1.150 2.90 1320
14 1 14.75 1.73 2.39 11.4 91 3.10 3.69 0.43 2.81 5.400000 1.250 2.73 1150
15 1 14.38 1.87 2.38 12.0 102 3.30 3.64 0.29 2.96 7.500000 1.200 3.00 1547
16 1 13.63 1.81 2.70 17.2 112 2.85 2.91 0.30 1.46 7.300000 1.280 2.88 1310
17 1 14.30 1.92 2.72 20.0 120 2.80 3.14 0.33 1.97 6.200000 1.070 2.65 1280
18 1 13.83 1.57 2.62 20.0 115 2.95 3.40 0.40 1.72 6.600000 1.130 2.57 1130
19 1 14.19 1.59 2.48 16.5 108 3.30 3.93 0.32 1.86 8.700000 1.230 2.82 1680
20 1 13.64 3.10 2.56 15.2 116 2.70 3.03 0.17 1.66 5.100000 0.960 3.36 845
21 1 14.06 1.63 2.28 16.0 126 3.00 3.17 0.24 2.10 5.650000 1.090 3.71 780
22 1 12.93 3.80 2.65 18.6 102 2.41 2.41 0.25 1.98 4.500000 1.030 3.52 770
23 1 13.71 1.86 2.36 16.6 101 2.61 2.88 0.27 1.69 3.800000 1.110 4.00 1035
24 1 12.85 1.60 2.52 17.8 95 2.48 2.37 0.26 1.46 3.930000 1.090 3.63 1015
25 1 13.50 1.81 2.61 20.0 96 2.53 2.61 0.28 1.66 3.520000 1.120 3.82 845
26 1 13.05 2.05 3.22 25.0 124 2.63 2.68 0.47 1.92 3.580000 1.130 3.20 830
27 1 13.39 1.77 2.62 16.1 93 2.85 2.94 0.34 1.45 4.800000 0.920 3.22 1195
28 1 13.30 1.72 2.14 17.0 94 2.40 2.19 0.27 1.35 3.950000 1.020 2.77 1285
29 1 13.87 1.90 2.80 19.4 107 2.95 2.97 0.37 1.76 4.500000 1.250 3.40 915
30 1 14.02 1.68 2.21 16.0 96 2.65 2.33 0.26 1.98 4.700000 1.040 3.59 1035
31 1 13.73 1.50 2.70 22.5 101 3.00 3.25 0.29 2.38 5.700000 1.190 2.71 1285
32 1 13.58 1.66 2.36 19.1 106 2.86 3.19 0.22 1.95 6.900000 1.090 2.88 1515
33 1 13.68 1.83 2.36 17.2 104 2.42 2.69 0.42 1.97 3.840000 1.230 2.87 990
34 1 13.76 1.53 2.70 19.5 132 2.95 2.74 0.50 1.35 5.400000 1.250 3.00 1235
35 1 13.51 1.80 2.65 19.0 110 2.35 2.53 0.29 1.54 4.200000 1.100 2.87 1095
36 1 13.48 1.81 2.41 20.5 100 2.70 2.98 0.26 1.86 5.100000 1.040 3.47 920
37 1 13.28 1.64 2.84 15.5 110 2.60 2.68 0.34 1.36 4.600000 1.090 2.78 880
38 1 13.05 1.65 2.55 18.0 98 2.45 2.43 0.29 1.44 4.250000 1.120 2.51 1105
39 1 13.07 1.50 2.10 15.5 98 2.40 2.64 0.28 1.37 3.700000 1.180 2.69 1020
40 1 14.22 3.99 2.51 13.2 128 3.00 3.04 0.20 2.08 5.100000 0.890 3.53 760
41 1 13.56 1.71 2.31 16.2 117 3.15 3.29 0.34 2.34 6.130000 0.950 3.38 795
42 1 13.41 3.84 2.12 18.8 90 2.45 2.68 0.27 1.48 4.280000 0.910 3.00 1035
43 1 13.88 1.89 2.59 15.0 101 3.25 3.56 0.17 1.70 5.430000 0.880 3.56 1095
44 1 13.24 3.98 2.29 17.5 103 2.64 2.63 0.32 1.66 4.360000 0.820 3.00 680
45 1 13.05 1.77 2.10 17.0 107 3.00 3.00 0.28 2.03 5.040000 0.880 3.35 885
46 1 14.21 4.04 2.44 18.9 111 2.85 2.65 0.30 1.25 5.240000 0.870 3.33 1080
47 1 14.38 3.59 2.28 16.0 102 3.25 3.17 0.27 2.19 4.900000 1.040 3.44 1065
48 1 13.90 1.68 2.12 16.0 101 3.10 3.39 0.21 2.14 6.100000 0.910 3.33 985
49 1 14.10 2.02 2.40 18.8 103 2.75 2.92 0.32 2.38 6.200000 1.070 2.75 1060
50 1 13.94 1.73 2.27 17.4 108 2.88 3.54 0.32 2.08 8.900000 1.120 3.10 1260
51 1 13.05 1.73 2.04 12.4 92 2.72 3.27 0.17 2.91 7.200000 1.120 2.91 1150
52 1 13.83 1.65 2.60 17.2 94 2.45 2.99 0.22 2.29 5.600000 1.240 3.37 1265
53 1 13.82 1.75 2.42 14.0 111 3.88 3.74 0.32 1.87 7.050000 1.010 3.26 1190
54 1 13.77 1.90 2.68 17.1 115 3.00 2.79 0.39 1.68 6.300000 1.130 2.93 1375
55 1 13.74 1.67 2.25 16.4 118 2.60 2.90 0.21 1.62 5.850000 0.920 3.20 1060
56 1 13.56 1.73 2.46 20.5 116 2.96 2.78 0.20 2.45 6.250000 0.980 3.03 1120
57 1 14.22 1.70 2.30 16.3 118 3.20 3.00 0.26 2.03 6.380000 0.940 3.31 970
58 1 13.29 1.97 2.68 16.8 102 3.00 3.23 0.31 1.66 6.000000 1.070 2.84 1270
59 1 13.72 1.43 2.50 16.7 108 3.40 3.67 0.19 2.04 6.800000 0.890 2.87 1285
60 2 12.37 0.94 1.36 10.6 88 1.98 0.57 0.28 0.42 1.950000 1.050 1.82 520
61 2 12.33 1.10 2.28 16.0 101 2.05 1.09 0.63 0.41 3.270000 1.250 1.67 680
62 2 12.64 1.36 2.02 16.8 100 2.02 1.41 0.53 0.62 5.750000 0.980 1.59 450
63 2 13.67 1.25 1.92 18.0 94 2.10 1.79 0.32 0.73 3.800000 1.230 2.46 630
64 2 12.37 1.13 2.16 19.0 87 3.50 3.10 0.19 1.87 4.450000 1.220 2.87 420
65 2 12.17 1.45 2.53 19.0 104 1.89 1.75 0.45 1.03 2.950000 1.450 2.23 355
66 2 12.37 1.21 2.56 18.1 98 2.42 2.65 0.37 2.08 4.600000 1.190 2.30 678
67 2 13.11 1.01 1.70 15.0 78 2.98 3.18 0.26 2.28 5.300000 1.120 3.18 502
68 2 12.37 1.17 1.92 19.6 78 2.11 2.00 0.27 1.04 4.680000 1.120 3.48 510
69 2 13.34 0.94 2.36 17.0 110 2.53 1.30 0.55 0.42 3.170000 1.020 1.93 750
70 2 12.21 1.19 1.75 16.8 151 1.85 1.28 0.14 2.50 2.850000 1.280 3.07 718
71 2 12.29 1.61 2.21 20.4 103 1.10 1.02 0.37 1.46 3.050000 0.906 1.82 870
[ reached getOption("max.print") -- omitted 107 rows ]```
options(max.print = 99999)
try this command
Type this code at the start of your R code. Worked for me:
options(max.print = .Machine$integer.max)