After using this code:
output.lm = count_all %>% group_by(year.y) %>% do(tidy(lm(ENS~total.p, data = .)))%>% filter(term == "total.p")output.lmI got the following Error:
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 0 (non-NA) casesWhy is that?
My data is quite big, but this is the dput:
dput(count_all$total.p)c(7.1, NA, NA, 4.84, 3.87, NA, NA, NA, NA, NA, NA, NA, NA, 4.19, NA, NA, 2.29, 4.52, NA, NA, 4.84, NA, 2.94, NA, NA, 4.84, NA, 3.55, 11.61, 6.13, 2.52, 2.74, 2.39, 4.19, 3.23, 4.19, 3.23, NA, NA, 3.55, 4.84, 5.48, 3.55, 2.35, 4.52, 4.84, 5.81, 4.19, 3.06, 5.16, 2.1, 5.81, 2.19, NA, 3.87, 3.16, 6.13, 2.52, 2.52, NA, 2.19, 3.23, 4.19, 3.55, 2.9, 1.65, 1.87, 1.68, 3.06, 3.87, 2.06, 2.42, 2.29, 0.68, 1.42, 3.23, 2.35, 3.87, 4.19, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 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2.581, 2.71, 3.226, 3.129, 2.742, 1.742, 1.968, 2.548, 2.548, 2.613, 2.097, 2.419, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 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2.63, 2.77, 2.87, 2.63, 1.87, 1.87, 2.1, 1.8, 1.87, 2.73, 2.93, 2.37, 2.6, 2.37, 2.5, 4.33, 2.17, 2.4, 2.9, 2.17, 5.33, 4.67, 6, 6.45, 5.48, 7.74, 3.55, 3.03, 3.55, 5.16, 3.87, 3.87, 2.42, 2.84, 1.84, 1.77, 1.81, 2.35, 3.55, 2.48, 2.35, 3.55, 2.23, 2.55, 2.26, 2.74, 2.29, 1.77, 2.52, 2.19, 1.61, 2, 1.87, 1.71, 2.45, 2.97, 3.55, 2.68, 3.55, 3.23, 2.35, 3.1, 2.84, 3.23, 3.23, 2.77, 2.48, 2.16, 4.52, 1.94, 1.71, 3.55, 2.97, 2.61, 3.1, 2.94, 2.61, 2.77, 2.06, 2.32, 2.71, 1.58, NA, 2.06, NA, 1.94, 1.58, 1.94, 2, 1.65, 3.55, 2.29, 1.68, 1.39, 1.94, 0.97, 1.32, 1.35, 1.52, 2.06, 1.71, 2.32, 3.16, 2.13, 2.77, 2.48, 2.71, 3.55, 2.68, 3.23, 2.48, 1.74, 2.1, 1.61, 2.29, 1.74, 2.35, 3.87, 3.13, 2.52, 2.13, 2.94, 3.03, 4.19, 2.39, 4.52, 2.03, 2.55, 2.23, 2.94, 4.52, 2.55, 2.9, 3.55, 3, 3.06, 2.29, 5.81, 2.03, 2.52, 1.68, 1.68, 2.26, 1.16, 1.71, 1.39, 2.16, 2.81, 2.74, 2.1, 2.77, 2.61, 3.23, 2.61, 2.9, 3, 3.23, 3.23, 2.94, 2.48, 2.16, 2.55, 2.42, 2.71, 2.68, 2.26, 2.45, 2.06, 3.55, 4.52, 4.19, 3.23, 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2.55, 2.23, 2.03, 1.68, 1.81, 1.13, 2.9, 1.61, 3.19, 2.06, 1.94, 2.1, 2.16, 2.29, 2.32, 2.42, 3.55, 3.87, 3.87, 2.71, 3.23, 3.87, 3.23, 2.81, 1.97, 2.06, 2.42, 3.23, 3.55, 2.52, 3.55, 3.55, 3.55, 3.87, 4.52, 2.74, 2.06, 2.35, 2.65, 2.26, 2.97, 1.68, 2.13, 2.03, 2.84, 1.87, 2.42, 4.52, 5.81, 3.23, 2.68, 3.23, 2.58, 2.52, 2.81, 1.52, 2.77, 1.26, 3.23, 1.94, 1.58, 1.55, 2.1, 1.84, 1.45, 1.42, NA, 2.29, 2.13, 3.16, 2.39, 2.74, 4.52, 2.9, 2.77, 1.9, 1.52, 3.55, 2.61, 4.52, 2.13, 2.06, 3.23, 2.23, 3.23, 3, 2.94, 1.58, 2.23, 6.77, 2.71, 4.19, 4.19, 5.16, 5.81, 5.81, 2.68, 4.19, 4.19, 3.55, 5.48, 2.77, 3.55, 2.03, 1.58, 2.77, 1.94, 2.48, 2.45, 3.1, 2.29, 1.84, 1.77, 2, 1.45, 2.03, 1.9, 2.48, 2.06, 2.26, 3.87, 2.13, 1.74, 2.48, 2.48, 2.23, 2.06, 2.1, 2.81, 2.87, 2.55, 2.03, 2.19, 1.71, 1.61, 1.81, 1.9, 2.26, 1.65, 1.77, 2.16, 1.97, 1.55, 2.1, 2.16, 2, 2.35, 2.45, 3.1, 3.55, 1.19, 2.97, 2.87, 1.94, 1.23, 1.29, 1.35, 1.06, 1.71, 1.29, 2.35, 1.23, 1.16, 1.42, 1.74, 2.48, 1.94, 1.42, 2, 1.58, 1.35, 3, 1.84, 1.87, 2.87, 2.1, 1.94, 2.29, 2.65, 1.84, 2.23, 1.97, 2.84, 2.48, 2.68, 2.84, 1.42, 2.45, 1.42, 1.48, 1.71, 1.32, 1.84, 2.26, 2.52, 1.9, 1.61, 2.23, 1.55, 2.19, 1.52, 1.65, 2.16, 1.97, 1.48, 0.74, 1.55, 1.52, 1.32, 0.74, 1.1, 1.39, 1.29, 1.77, 2.1, 1.45, 1.26, 1.55, 1.68, 0.94, 0.74, 0.77, 1.58, 0.77, 2.23, 2.13, 1.9, 1.13, 1.26, 1.65, 1.84, 1.94, 1.39, 1.35, 1.55, 1.81, 1.87, 1.45, 1.39, 1.74, 1.81, 1.55, 2.13, 1.48, 1.87, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3.871, NA, NA, 3.097, 2.968, 3.226, 6.774, 2.742, 2.935, 3.097, 4.839, 4.516, 3.871, 2.71, 2.194, 1.806, 2.097, 2.677, 2.839, 2.323, 2.968, 1.548, 2.581, 2.581, 3.097, 2.742, 2.613, 1.548, 1.387, 1.387, 2.387, 2.613, 1.516, 2.161, 5.161, 2.871, 2.226, 2.613, 2.194, 1.484, 2.613, 3.161, 3.032, 3.871, 3.548, 1.548, 1.742, 3.194, 2.29, 1.613, 5.484, 2.71, NA, 3.871, 1.645, 2.032, 1.645, 1.806, 2.194, 3.226, 3.548, 4.194, 2.935, 4.839, 2.194, 2.774, 2.839, 2.452, 1.548, 1.903)So I tried to built a reproducible example. First the edited R-code and the packages:
library(tidyverse)library(reshape2)library(vegan)library(broom)library(metafor)library(lubridate)library(cowplot)library(psych)library(lme4)count_all |> na.omit() |> dplyr::group_map(~lm(ENS~total.p, data = .x))lme4::lmList(ENS~total.p | year.y, count_all) |> broom.mixed::tidy()At this point: Warning message: In lme4::lmList(ENS ~ total.p | year.y, count_all) : in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...): NA (6 times)
The main part:
output.lm = count_all %>% group_by(year.y) %>% do(tidy(lm(ENS~total.p, data = .)))%>% filter(term == "total.p")output.lmThe dput from total.p is already above.
str(count_all$ENS)str(count_all$year.y)str(count_all)