When applying a function to different subsets of data, for 1 out of 3 subsets the"Error: $ operator is invalid for atomic vectors" occurs

Question

I am trying to run a single linear regression model analysis on a large dataset. This dataset contains several grouping factors, for each of which I want to run the analysis in an automatized manner.

I have a code that should automatically fit linear models according to the grouping factor "species", "performance" and "functional groups"; place them in a list, and extract all interesting variables from a lm summary, writing them into a table. I have attached the code below.

My problem now is that the code works perfectly fine for the entire dataset. When I split the dataset into three subsets according to grouping factor "region", the code works fine for two of the subsets, but does not for the third. Instead, I get the following:

Error: $ operator is invalid for atomic vectors.

A traceback showed me that the problem lies within nascar2; when I start extracting the coefficients using summary(x)$.., psSE. When I try to extract the values using psSE<- unlist(lapply(h.models, function(x) coef(x)[2,2])); R does run the code without error, but won't write any output.

The structure of all three subsets of data is the same, and I have not changed anything in the code. Why could this be? And how do I fix this?

Thanks in advance!

Small extract of my dataset:

dput(D2)
structure(list(Sample_ID = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 
20L, 21L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 
135L, 136L, 137L, 138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 
146L, 147L, 148L, 149L, 375L, 376L, 377L, 378L, 379L, 380L, 381L, 
382L, 383L, 384L, 385L, 386L, 387L, 388L, 389L, 390L, 391L, 392L, 
393L, 394L, 395L, 397L, 396L, 398L), .Label = c("AEG01_2015_DG", 
"AEG01_2015_TO", "AEG01_2015_TR", "AEG03_2015_AE", "AEG03_2015_DG", 
"AEG03_2015_LC", "AEG03_2015_TO", "AEG03_2015_TR", "AEG04_2015_AO", 
"AEG04_2015_DG", "AEG04_2015_TO", "AEG04_2015_TR", "AEG05_2015_DG", 
"AEG05_2015_TO", "AEG05_2015_TR", "AEG06_2015_DG", "AEG07_2015_AO", 
"AEG09_2015_AE", "AEG09_2015_AO", "AEG09_2015_LC", "AEG10_2015_AO", 
"AEG11_2015_DG", "AEG11_2015_TR", "AEG12_2015_DG", "AEG12_2015_TO", 
"AEG12_2015_TR", "AEG13_2015_DG", "AEG13_2015_TO", "AEG13_2015_TR", 
"AEG14_2015_DG", "AEG14_2015_TO", "AEG14_2015_TR", "AEG15_2015_DG", 
"AEG15_2015_TO", "AEG15_2015_TR", "AEG16_2015_AO", "AEG16_2015_DG", 
"AEG16_2015_TO", "AEG16_2015_TR", "AEG17_2015_AE", "AEG17_2015_AO", 
"AEG17_2015_DG", "AEG17_2015_LC", "AEG17_2015_TO", "AEG18_2015_DG", 
"AEG18_2015_TO", "AEG18_2015_TR", "AEG19_2015_AO", "AEG19_2015_DG", 
"AEG19_2015_TO", "AEG19_2015_TR", "AEG20_2015_DG", "AEG20_2015_TO", 
"AEG21_2015_DG", "AEG21_2015_TO", "AEG21_2015_TR", "AEG22_2015_AE", 
"AEG22_2015_AO", "AEG22_2015_DG", "AEG22_2015_LC", "AEG23_2015_AO", 
"AEG23_2015_DG", "AEG23_2015_LC", "AEG23_2015_TO", "AEG23_2015_TR", 
"AEG24_2015_DG", "AEG24_2015_TO", "AEG24_2015_TR", "AEG25_2015_AE", 
"AEG25_2015_TO", "AEG26_2015_AO", "AEG27_2015_AE", "AEG27_2015_AO", 
"AEG27_2015_LC", "AEG28_2015_AE", "AEG28_2015_AO", "AEG28_2015_LC", 
"AEG29_2015_AO", "AEG29_2015_DG", "AEG29_2015_TO", "AEG30_2015_AO", 
"AEG30_2015_LC", "AEG32_2015_AE", "AEG32_2015_AO", "AEG33_2015_AE", 
"AEG33_2015_AO", "AEG33_2015_LC", "AEG34_2015_TR", "AEG35_2015_AO", 
"AEG35_2015_DG", "AEG35_2015_TO", "AEG35_2015_TR", "AEG36_2015_AO", 
"AEG36_2015_DG", "AEG36_2015_TO", "AEG36_2015_TR", "AEG37_2015_DG", 
"AEG37_2015_TO", "AEG37_2015_TR", "AEG39_2015_DG", "AEG39_2015_TO", 
"AEG41_2015_AO", "AEG41_2015_DG", "AEG41_2015_TO", "AEG41_2015_TR", 
"AEG44_2015_AO", "AEG44_2015_DG", "AEG44_2015_TO", "AEG45_2015_AO", 
"AEG45_2015_DG", "AEG45_2015_TO", "AEG45_2015_TR", "AEG46_2015_AO", 
"AEG46_2015_DG", "AEG46_2015_TO", "AEG46_2015_TR", "AEG47_2015_AE", 
"AEG47_2015_LC", "AEG48_2015_LC", "AEG49_2015_AE", "AEG49_2015_AO", 
"AEG49_2015_DG", "AEG49_2015_LC", "AEG50_2015_DG", "AEG50_2015_TO", 
"AEG50_2015_TR", "HEG01_2015_DG", "HEG01_2015_TO", "HEG01_2015_TR", 
"HEG02_2015_DG", "HEG02_2015_TO", "HEG03_2015_DG", "HEG03_2015_TO", 
"HEG03_2015_TR", "HEG04_2015_LC", "HEG04_2015_TO", "HEG04_2015_TP", 
"HEG04_2015_TR", "HEG05_2015_AE", "HEG05_2015_DG", "HEG05_2015_PL", 
"HEG05_2015_TO", "HEG05_2015_TP", "HEG05_2015_TR", "HEG06_2015_DG", 
"HEG06_2015_HL", "HEG06_2015_LC", "HEG06_2015_PL", "HEG06_2015_TO", 
"HEG06_2015_TP", "HEG06_2015_TR", "HEG07_2015_DG", "HEG07_2015_LC", 
"HEG07_2015_PL", "HEG07_2015_TO", "HEG07_2015_TP", "HEG07_2015_TR", 
"HEG08_2015_DG", "HEG08_2015_PL", "HEG08_2015_TO", "HEG08_2015_TP", 
"HEG08_2015_TR", "HEG09_2015_DG", "HEG09_2015_TO", "HEG10_2015_AE", 
"HEG10_2015_DG", "HEG10_2015_PL", "HEG10_2015_TO", "HEG11_2015_AE", 
"HEG11_2015_DG", "HEG11_2015_LC", "HEG11_2015_PL", "HEG12_2015_DG", 
"HEG12_2015_PL", "HEG12_2015_TO", "HEG13_2015_PL", "HEG13_2015_TO", 
"HEG13_2015_TP", "HEG13_2015_TR", "HEG14_2015_DG", "HEG14_2015_LC", 
"HEG14_2015_PL", "HEG14_2015_TO", "HEG14_2015_TP", "HEG14_2015_TR", 
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"HEG15_2015_TR", "HEG16_2015_AE", "HEG16_2015_DG", "HEG16_2015_PL", 
"HEG16_2015_TO", "HEG16_2015_TP", "HEG16_2015_TR", "HEG17_2015_AO", 
"HEG17_2015_DG", "HEG17_2015_HL", "HEG17_2015_LC", "HEG17_2015_PL", 
"HEG17_2015_TO", "HEG18_2015_AE", "HEG18_2015_DG", "HEG18_2015_LC", 
"HEG18_2015_PL", "HEG18_2015_TO", "HEG19_2015_AE", "HEG19_2015_AO", 
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"HEG19_2015_TP", "HEG19_2015_TR", "HEG20_2015_AE", "HEG20_2015_AO", 
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"HEG21_2015_TP", "HEG22_2015_HL", "HEG22_2015_PL", "HEG22_2015_TO", 
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"HEG23_2015_TR", "HEG24_2015_PL", "HEG24_2015_TO", "HEG24_2015_TP", 
"HEG24_2015_TR", "HEG25_2015_AE", "HEG25_2015_DG", "HEG25_2015_HL", 
"HEG25_2015_LC", "HEG25_2015_PL", "HEG25_2015_TO", "HEG25_2015_TP", 
"HEG26_2015_AE", "HEG26_2015_DG", "HEG26_2015_PL", "HEG26_2015_TO", 
"HEG27_2015_AE", "HEG27_2015_DG", "HEG27_2015_PL", "HEG27_2015_TO", 
"HEG27_2015_TP", "HEG28_2015_AE", "HEG28_2015_DG", "HEG28_2015_PL", 
"HEG28_2015_TO", "HEG28_2015_TP", "HEG29_2015_AE", "HEG29_2015_LC", 
"HEG29_2015_PL", "HEG29_2015_TO", "HEG29_2015_TP", "HEG30_2015_AE", 
"HEG30_2015_DG", "HEG30_2015_LC", "HEG30_2015_PL", "HEG30_2015_TO", 
"HEG30_2015_TP", "HEG30_2015_TR", "HEG31_2015_DG", "HEG31_2015_PL", 
"HEG31_2015_TO", "HEG31_2015_TP", "HEG31_2015_TR", "HEG32_2015_DG", 
"HEG32_2015_PL", "HEG32_2015_TO", "HEG32_2015_TP", "HEG32_2015_TR", 
"HEG33_2015_DG", "HEG33_2015_PL", "HEG33_2015_TO", "HEG33_2015_TP", 
"HEG33_2015_TR", "HEG34_2015_DG", "HEG34_2015_LC", "HEG34_2015_PL", 
"HEG34_2015_TO", "HEG34_2015_TP", "HEG34_2015_TR", "HEG35_2015_DG", 
"HEG35_2015_PL", "HEG35_2015_TO", "HEG35_2015_TP", "HEG35_2015_TR", 
"HEG36_2015_AE", "HEG36_2015_DG", "HEG36_2015_TO", "HEG36_2015_TP", 
"HEG37_2015_AE", "HEG37_2015_DG", "HEG37_2015_PL", "HEG37_2015_TO", 
"HEG38_2015_HL", "HEG38_2015_LC", "HEG38_2015_PL", "HEG38_2015_TO", 
"HEG38_2015_TP", "HEG38_2015_TR", "HEG39_2015_DG", "HEG39_2015_PL", 
"HEG39_2015_TO", "HEG39_2015_TP", "HEG40_2015_PL", "HEG40_2015_TO", 
"HEG41_2015_AE", "HEG41_2015_DG", "HEG41_2015_HL", "HEG41_2015_LC", 
"HEG41_2015_PL", "HEG42_2015_AE", "HEG42_2015_AO", "HEG42_2015_DG", 
"HEG42_2015_HL", "HEG42_2015_LC", "HEG42_2015_PL", "HEG42_2015_TO", 
"HEG43_2015_AE", "HEG43_2015_DG", "HEG43_2015_PL", "HEG43_2015_TO", 
"HEG43_2015_TP", "HEG43_2015_TR", "HEG44_2015_AE", "HEG44_2015_DG", 
"HEG44_2015_PL", "HEG44_2015_TO", "HEG44_2015_TP", "HEG45_2015_AE", 
"HEG45_2015_AO", "HEG45_2015_DG", "HEG45_2015_LC", "HEG45_2015_PL", 
"HEG45_2015_TO", "HEG45_2015_TP", "HEG45_2015_TR", "HEG46_2015_AE", 
"HEG46_2015_DG", "HEG46_2015_LC", "HEG46_2015_TO", "HEG47_2015_DG", 
"HEG47_2015_PL", "HEG47_2015_TO", "HEG47_2015_TP", "HEG48_2015_AE", 
"HEG48_2015_DG", "HEG48_2015_LC", "HEG48_2015_PL", "HEG48_2015_TO", 
"HEG48_2015_TP", "HEG49_2015_DG", "HEG49_2015_PL", "HEG49_2015_TO", 
"HEG50_2015_AO", "HEG50_2015_DG", "HEG50_2015_LC", "HEG50_2015_PL", 
"HEG50_2015_TO", "HEG50_2015_TP", "SEG01_2015_DG", "SEG01_2015_HL", 
"SEG01_2015_TO", "SEG02_2015_DG", "SEG02_2015_HL", "SEG02_2015_TO", 
"SEG02_2015_TR", "SEG03_2015_DG", "SEG03_2015_TO", "SEG04_2015_DG", 
"SEG04_2015_HL", "SEG04_2015_TO", "SEG05_2015_DG", "SEG05_2015_HL", 
"SEG06_2015_HL", "SEG06_2015_PL", "SEG06_2015_TO", "SEG06_2015_TR", 
"SEG07_2015_DG", "SEG07_2015_TO", "SEG08_2015_DG", "SEG08_2015_HL", 
"SEG08_2015_PL", "SEG08_2015_TO", "SEG08_2015_TP", "SEG08_2015_TR", 
"SEG09_2015_DG", "SEG09_2015_HL", "SEG09_2015_TO", "SEG09_2015_TR", 
"SEG10_2015_DG", "SEG10_2015_HL", "SEG10_2015_TO", "SEG11_2015_DG", 
"SEG11_2015_HL", "SEG11_2015_TO", "SEG12_2015_DG", "SEG12_2015_HL", 
"SEG12_2015_TO", "SEG13_2015_AO", "SEG13_2015_HL", "SEG13_2015_PL", 
"SEG13_2015_TO", "SEG13_2015_TR", "SEG14_2015_DG", "SEG14_2015_HL", 
"SEG14_2015_TO", "SEG14_2015_TR", "SEG15_2015_DG", "SEG15_2015_HL", 
"SEG16_2015_DG", "SEG16_2015_HL", "SEG16_2015_TO", "SEG17_2015_DG", 
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"SEG37_2015_LC", "SEG37_2015_PL", "SEG37_2015_TO", "SEG37_2015_TP", 
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"SEG45_2015_DG", "SEG45_2015_HL", "SEG45_2015_PL", "SEG45_2015_TO", 
"SEG45_2015_TP", "SEG45_2015_TR", "SEG46_2015_DG", "SEG46_2015_PL", 
"SEG46_2015_TO", "SEG46_2015_TR", "SEG47_2015_DG", "SEG47_2015_PL", 
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"SEG50_2015_HL", "SEG50_2015_LC", "SEG50_2015_PL", "SEG50_2015_TO", 
"SEG50_2015_TP"), class = "factor"), Region = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L), .Label = c("AEG", "HEG", "SEG"), class = "factor"), 
    Plot = structure(c(1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 
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    1L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 
    5L, 6L, 6L, 6L, 6L, 6L, 50L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
    3L, 3L, 4L, 4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 8L, 8L, 
    8L), .Label = c("1", "2", "3", "4", "5", "6", "7", "8", "9", 
    "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", 
    "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", 
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    "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", 
    "50"), class = "factor"), Plot_ID = structure(c(1L, 1L, 1L, 
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    7L, 7L, 8L, 43L, 44L, 44L, 44L, 45L, 45L, 46L, 46L, 46L, 
    47L, 47L, 47L, 47L, 48L, 48L, 48L, 48L, 48L, 48L, 49L, 49L, 
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    96L, 97L, 97L, 97L, 98L, 98L, 99L, 99L, 99L, 99L, 100L, 100L, 
    101L, 101L, 101L), .Label = c("AEG01", "AEG03", "AEG04", 
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    "SEG44", "SEG45", "SEG46", "SEG47", "SEG48", "SEG49", "SEG50"
    ), class = "factor"), Species = structure(c(3L, 7L, 9L, 1L, 
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    1L, 3L, 6L, 7L, 8L, 9L, 3L, 4L, 5L, 6L, 7L, 8L, 3L, 4L, 7L, 
    3L, 4L, 7L, 9L, 3L, 7L, 3L, 4L, 7L, 3L, 4L, 4L, 6L, 7L, 9L, 
    3L, 7L, 4L, 3L, 6L), .Label = c("Agrimonia eupatoria", "Anthoxanthum odoratum", 
    "Dactylis glomerata", "Holcus lanatus", "Lotus corniculatus", 
    "Plantago lanceolata", "Taraxacum officinalis", "Trifolium pratense", 
    "Trifolium repens"), class = "factor"), Spec_ID = structure(c(3L, 
    7L, 9L, 1L, 3L, 5L, 7L, 9L, 2L, 3L, 7L, 9L, 3L, 7L, 9L, 3L, 
    2L, 1L, 2L, 5L, 2L, 9L, 3L, 7L, 9L, 3L, 7L, 3L, 7L, 9L, 5L, 
    7L, 8L, 9L, 1L, 3L, 6L, 7L, 8L, 9L, 3L, 4L, 5L, 6L, 7L, 8L, 
    3L, 4L, 7L, 3L, 4L, 7L, 9L, 3L, 7L, 3L, 4L, 7L, 3L, 4L, 4L, 
    6L, 7L, 9L, 3L, 7L, 4L, 3L, 6L), .Label = c("Agr_eup", "Ant_odo", 
    "Dac_glo", "Hol_lan", "Lot_cor", "Pla_lan", "Tar_off", "Tri_pra", 
    "Tri_rep"), class = "factor"), Functional.Group = structure(c(1L, 
    2L, 3L, 2L, 1L, 3L, 2L, 3L, 1L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
    1L, 2L, 1L, 3L, 1L, 3L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, 3L, 3L, 
    2L, 3L, 3L, 2L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 3L, 2L, 2L, 3L, 
    1L, 1L, 2L, 1L, 1L, 2L, 3L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 
    2L, 2L, 3L, 1L, 2L, 1L, 1L, 2L), .Label = c("grass", "herb", 
    "legume"), class = "factor"), Performance = structure(c(3L, 
    3L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 
    3L, 2L, 3L, 1L, 3L, 2L, 3L, 2L, 3L, 3L, 2L, 1L, 2L, 3L, 2L, 
    3L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 
    2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L), .Label = c("Loser", "Neutral", 
    "Winner"), class = "factor"), C.NIRSCN = c(434100, 428000, 
    429900, 496800, 434400, 498900, 426800, 429000, 444700, 432100, 
    426400, 430000, 436200, 429300, 430300, NA, 464400, 459300, 
    438100, 421100, 444800, 431300, 430000, 427000, 428800, NA, 
    425500, 432800, 426900, 430300, 452000, 426400, NA, 428000, 
    447900, 437200, NA, 426300, NA, 427600, 427900, 434400, 460400, 
    NA, 424800, 434700, 431500, 425200, 426900, 433900, 420600, 
    430600, 430500, 440900, 429900, 424600, 428100, 427100, 441200, 
    428400, 422700, 426100, 428600, 428000, 434200, 425900, 430400, 
    431100, 427600), C.2014 = c(83.25, 83.25, 83.25, 62.89, 62.89, 
    62.89, 62.89, 62.89, 45.85, 45.85, 45.85, 45.85, 91.34, 91.34, 
    91.34, 78.08, 83.68, 71.9, 71.9, 71.9, 91.71, 68.78, 43.63, 
    43.63, 43.63, 31.83, 31.83, 29.6, 29.6, 29.6, 52.58, 52.58, 
    52.58, 52.58, 39.54, 39.54, 39.54, 39.54, 39.54, 39.54, 19.13, 
    19.13, 19.13, 19.13, 19.13, 32.53, 211.9, 211.9, 211.9, 181.54, 
    181.54, 181.54, 181.54, 132.14, 132.14, 210.72, 210.72, 210.72, 
    177.4, 177.4, 301.46, 301.46, 301.46, 301.46, 176.75, 176.75, 
    157.57, 157.57, 157.57)), row.names = c("1", "1.1", "1.2", 
"3", "3.1", "3.2", "3.3", "3.4", "4", "4.1", "4.2", "4.3", "5", 
"5.1", "5.2", "6", "7", "9", "9.1", "9.2", "10", "50.2", "51", 
"51.1", "51.2", "52", "52.1", "53", "53.1", "53.2", "54", "54.1", 
"54.2", "54.3", "55", "55.1", "55.2", "55.3", "55.4", "55.5", 
"56", "56.1", "56.2", "56.3", "56.4", "100.5", "101", "101.1", 
"101.2", "102", "102.1", "102.2", "102.3", "103", "103.1", "104", 
"104.1", "104.2", "105", "105.1", "106", "106.1", "106.2", "106.3", 
"107", "107.1", "108", "108.1", "108.2"), class = "data.frame")

And here is my code:

# Region subsets 
DA <- subset(D2, Region == "AEG")
DH <- subset(D2, Region == "HEG")
DS <- subset(D2, Region == "SEG")

slopeSubTable <- function(organism, resource, data = DS){
  hd <- na.omit(subset(DS, select = c(organism, resource, "Spec_ID", "Plot_ID", "Region","Performance", "Functional.Group")))

  nascar2 <- as.formula(paste("scale(log(", organism, ")) ~ scale(log(", resource, ")) | Spec_ID"))
  motocross3 <- as.formula(paste("scale(log(", organism, ")) ~ scale(log(", resource, ")) | Performance"))
  soapboxderby4 <-as.formula(paste("scale(log(", organism, ")) ~ scale(log(", resource, ")) | Functional.Group"))

  h.models <- list(lmList(nascar2, data = hd, pool = F),
    lmList(motocross3, data = hd, pool = F),
    lmList(soapboxderby4, data = hd, pool = F))


  group.name <- unlist(lapply(h.models, function (x) rownames(coef(x)[2]))) 
  spec <- substr(group.name, 1, 7) 
  region <- substr(group.name, 9, 11)
  nmod<- unlist(h.models, function (x) length(summary(x)[[3]]))
  estimate <- unlist(lapply(h.models, function (x) coef(x)[[1]]))
  csSE <- unlist(lapply(h.models, function(x) coef(x)[1,2]))
  ctval <- unlist(lapply(h.models, function(x) coef(x)[1,3]))
  cp <- unlist(lapply(h.models, function(x) coef(x)[1,4]))
  slope <- unlist(lapply(h.models, function (x) coef(x)[[2]]))
  psSE<- unlist(lapply(h.models, function(y) sapply(y, function(x) summary(x)$coefficients[2,2])))
  ptval <- unlist(lapply(h.models, function(y) sapply(y, function(x) summary(x)$coefficients[2,3])))
  prsq <- unlist(lapply(h.models, function (y) sapply(y, function(x) summary(x)$r.squared)))
  pp <- unlist(lapply(h.models, function (y) sapply(y, function(x) summary(x)$coefficients[2,4])))
  multR2 <- unlist(lapply(h.models, function (y) sapply(y, function(x) summary(x)$r.squared)))
  adjR2 <- unlist(lapply(h.models, function(y) sapply(y,function(x) summary(x)$adj.r.squared)))
  Fstat <- unlist(lapply(h.models, function(y) sapply(y,function(x) summary(x)$fstatistic[1])))


  dpd <- data.frame(stoichio = rep(paste(organism), length(group.name)),
                    id = interaction(as.factor(rep(paste(organism), length(group.name))), as.factor(group.name)),
                    spec,
                    region,
                    group.name,
                    estimate,
                    #csSE,
                    #ctval,
                    #cp,
                    #starlets = pStars(cp),
                    slope,
                    psSE,
                    ptval,
                    prsq,
                    pp,
                    starlets = pStars(pp),
                    multR2,
                    adjR2,
                    Fstat)

  return(dpd)
}

H_C_SCH <- slopeSubTable("C.NIRSCN", "C.2014")

Answer 1

Ultimately, your error appears data-specific by the specific subset and not a coding issue. Possibly one of the regions' models failed to be properly estimated due to inadequate factors, approximation limitations, or other needed data requirements.

Consider wrapping the problem lines with tryCatch to return NA for any issues. From my review, the problem lines appear with the summary()$coefficients lines which may return NULL for failed estimations. Also, note lme4 actually raises warnings for your attention.

  psSE<- unlist(lapply(h.models, function(y) sapply(y, function(x) 
    tryCatch(summary(x)$coefficients[2,2], error = function(e) NA))))

  ptval <- unlist(lapply(h.models, function(y) sapply(y, function(x) 
    tryCatch(summary(x)$coefficients[2,3], error = function(e) NA))))

  prsq <- unlist(lapply(h.models, function (y) sapply(y, function(x) 
    tryCatch(summary(x)$r.squared, error = function(e) NA))))

  pp <- unlist(lapply(h.models, function (y) sapply(y, function(x) 
    tryCatch(summary(x)$coefficients[2,4], error = function(e) NA))))

  multR2 <- unlist(lapply(h.models, function (y) sapply(y, function(x) 
    tryCatch(summary(x)$r.squared, error = function(e) NA))))

  adjR2 <- unlist(lapply(h.models, function(y) sapply(y,function(x) 
    tryCatch(summary(x)$adj.r.squared, error = function(e) NA))))

  Fstat <- unlist(lapply(h.models, function(y) sapply(y,function(x) 
    tryCatch(summary(x)$fstatistic[1], error = function(e) NA))))

Once function runs, you can review the generated data frame to locate the NAs and investigate the data further by specific rows.

---

By the way, consider by to pass all Region subsets into your method and avoid the need to manually subset. The result will be a named list of data frames which is more manageable then many separate data frames flooding your global environment.

slopeSubTable <- function(organism, resource, data) {
  hd <- na.omit(subset(data, select = c(organism, resource, "Spec_ID", "Plot_ID", 
                                        "Region","Performance", "Functional.Group")))    
  ...
  return(dpd)
}

# BUILD LIST OF DATA FRAMES
df_list <- by(D2, D2$Region, function(sub) slopeSubTable("C.NIRSCN", "C.2014", sub))

# EQUIVALENT CALL WITHOUT `function`
df_list <- by(D2, D2$Region, slopeSubTable, organism = "C.NIRSCN", resource = "C.2014")    

head(df_list$AEG)
head(df_list$HEG)
head(df_list$SEG)