R: Calculate linear regression and get slope for "a subset of data"

Question

My goal is to find out half life (from terminal phase if anyone is familiar with Pharmacokinetics)

I have some data containing the following;

1500 rows, with ID being main "key". There is 15 rows per ID. Then I have other columns TIME and CONCENTRATION. Now What I want to do is, for each ID, remove the first TIME (which equals "000" (numeric)), then run lm() function on the remaining 14 rows per ID, and then use abs() to extract the absolute value of the slope, then then save this to a new column named THALF. (If anyone is familiar with Pharmacokinetics maybe there is better way to do this?)

But I have not be able to do this using my limited knowledge of R.

Here is what I've come up with so far:

data_new <- data %>% dplyr::group_by(data $ID) %>% dplyr::filter(data $TIME != 10) %>% dplyr::mutate(THAFL = abs(lm$coefficients[2](data $CONC ~ data $TIME)))

From what I've understood from other Stackoverflow answers, lm$coefficients[2] will extract the slope.

But however, I have not been able to make this work. I get this error from trying to run the code:

Error: Problem with `mutate()` input `..1`. x Input `..1` can't be recycled to size 15. i Input `..1` is `data$ID`. i Input `..1` must be size 15 or 1, not 1500. i The error occurred in group 1: data$ID = "pat1".

Any suggestions on how to solve this? IF you need more info, let me know please.

(Also, if anyone is familiar with Pharmacokinetics, when they ask for half life from terminal phase, do I do lm() from the concentration max ? I Have a column with value for the highest observed concentration at what time. )

Hi Carlton, is possible to share a sample of data maybe using dput(head(yourdata,100))? — Duck
– Duck, Commented Dec 6, 2020 at 14:32
Also, don't specify data$ inside dplyr statements, there's no need for it — Ric S
– Ric S, Commented Dec 6, 2020 at 14:34
I don't think there is enough data in there for you to try something. Do you want all the data? @Duck — CJR
– CJR, Commented Dec 6, 2020 at 14:42
In your sample data TIME is always 10, which means every row gets excluded. Maybe you can share all the rows for a few distinct IDs, say three or four — Ric S
– Ric S, Commented Dec 6, 2020 at 14:43

Ric S · Accepted Answer · 2020-12-06 15:09:30Z

If after the model fitting you still need the observations with TIME == 10, you can try summarising after you group by ID and then using a right join

data %>% filter(TIME != 10) %>% group_by(ID) %>% summarise(THAFL = abs(lm(CONC ~ TIME)$coefficients[2])) %>% right_join(data, by = "ID") # A tibble: 30 x 16 ID THAFL Sex Weight..kg. Height..cm. Age..yrs. T134A A443G G769C G955C A990C TIME CONC LBM `data_combine$ID` CMAX <chr> <dbl> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <dbl> <dbl> <chr> <chr> <dbl> 1 pat1 0.00975 F 50 135 47 0 2 1 2 0 10 0 Under pat1 60 2 pat1 0.00975 F 50 135 47 0 2 1 2 0 20 6.93 Under pat1 60 3 pat1 0.00975 F 50 135 47 0 2 1 2 0 30 12.2 Under pat1 60 4 pat1 0.00975 F 50 135 47 0 2 1 2 0 45 14.8 Under pat1 60 5 pat1 0.00975 F 50 135 47 0 2 1 2 0 60 15.0 Under pat1 60 6 pat1 0.00975 F 50 135 47 0 2 1 2 0 90 12.4 Under pat1 60 7 pat1 0.00975 F 50 135 47 0 2 1 2 0 120 9.00 Under pat1 60 8 pat1 0.00975 F 50 135 47 0 2 1 2 0 150 6.22 Under pat1 60 9 pat1 0.00975 F 50 135 47 0 2 1 2 0 180 4.18 Under pat1 60 10 pat1 0.00975 F 50 135 47 0 2 1 2 0 240 1.82 Under pat1 60 # ... with 20 more rows

If after the model fitting you don't want the rows with TIME == 10 to appear on your dataset, you can use mutate

data %>% filter(TIME != 10) %>% group_by(ID) %>% mutate(THAFL = abs(lm(CONC ~ TIME)$coefficients[2])) # A tibble: 28 x 16 # Groups: ID [2] ID Sex Weight..kg. Height..cm. Age..yrs. T134A A443G G769C G955C A990C TIME CONC LBM `data_combine$ID` CMAX THAFL <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <dbl> <dbl> <chr> <chr> <dbl> <dbl> 1 pat1 F 50 135 47 0 2 1 2 0 20 6.93 Under pat1 60 0.00975 2 pat2 M 75 175 29 0 2 0 0 0 20 6.78 Under pat2 60 0.00835 3 pat1 F 50 135 47 0 2 1 2 0 30 12.2 Under pat1 60 0.00975 4 pat2 M 75 175 29 0 2 0 0 0 30 11.6 Above pat2 60 0.00835 5 pat1 F 50 135 47 0 2 1 2 0 45 14.8 Under pat1 60 0.00975 6 pat2 M 75 175 29 0 2 0 0 0 45 13.5 Under pat2 60 0.00835 7 pat1 F 50 135 47 0 2 1 2 0 60 15.0 Under pat1 60 0.00975 8 pat2 M 75 175 29 0 2 0 0 0 60 13.1 Above pat2 60 0.00835 9 pat1 F 50 135 47 0 2 1 2 0 90 12.4 Under pat1 60 0.00975 10 pat2 M 75 175 29 0 2 0 0 0 90 9.77 Under pat2 60 0.00835 # ... with 18 more rows

Thanks for your answer! If I need to filter additionally so that only rows where TIME >= CMAX, do i add an additional filter() function, or do it in the same filter() i already have?
You can do it in the same filterstatement: filter(TIME != 10 & TIME >= CMAX)

Duck · Accepted Answer · 2020-12-06 15:05:34Z

You can use broom:

library(broom) library(dplyr) #Code data %>% group_by(ID) %>% filter(TIME!=10) %>% do(fit = tidy(lm(CONC ~ TIME, data = .))) %>% unnest(fit) %>% filter(term=='TIME') %>% mutate(estimate=abs(estimate))

Output:

# A tibble: 2 x 6 ID term estimate std.error statistic p.value <chr> <chr> <dbl> <dbl> <dbl> <dbl> 1 pat1 TIME 0.00975 0.00334 -2.92 0.0128 2 pat2 TIME 0.00835 0.00313 -2.67 0.0204

If joining with original data is needed, try:

#Code 2 data <- data %>% left_join(data %>% group_by(ID) %>% filter(TIME!=10) %>% do(fit = tidy(lm(CONC ~ TIME, data = .))) %>% unnest(fit) %>% filter(term=='TIME') %>% mutate(estimate=abs(estimate)) %>% select(c(ID,estimate)))

Similar to @RicS.

Thanks for your answer! If I need to filter additionally so that only rows where TIME >= CMAX, do i add an additional filter() function, or do it in the same filter() i already have?
@Carlton You can add another filter() because if you add in the the first both conditions will be evaluated jointly. You could do filter(TIME!=10|TIME >= CMAX) in only one line!
@Carlton Also, filter(TIME!=10,TIME >= CMAX) will evaluate both conditions jointly!
Thank you for the help once again! Very helpful! I upvoted, but I have to accept the other answer sorry, he replied first. Apologies for that and thank you for the help!

Collectives™ on Stack Overflow

R: Calculate linear regression and get slope for "a subset of data"

2 Answers 2

2 Comments

4 Comments

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

2 Comments

4 Comments

Related