How to prevent LS means analysis from producing NAs?

Question

I am running an linear model regression analysis script and I am running emmeans (ls means) on my model but I am getting a whole of NA's not sure why... Here is what I have run:

   setwd("C:/Users/wkmus/Desktop/R-Stuff")
    ### yeild-twt
    ASM_Data<-read.csv("ASM_FIELD_18_SUMM_wm.csv",header=TRUE, na.strings=".")
    head(ASM_Data)
    str(ASM_Data)
    ####"NA" values in table are labeled as "." colored orange
    ASM_Data$REP <- as.factor(ASM_Data$REP)
    head(ASM_Data$REP)
    ASM_Data$ENTRY_NO <-as.factor(ASM_Data$ENTRY_NO)
    head(ASM_Data$ENTRY_NO)
    ASM_Data$RANGE<-as.factor(ASM_Data$RANGE)
    head(ASM_Data$RANGE)
    ASM_Data$PLOT_ID<-as.factor(ASM_Data$PLOT_ID)
    head(ASM_Data$PLOT_ID)
    ASM_Data$PLOT<-as.factor(ASM_Data$PLOT)
    head(ASM_Data$PLOT)
    ASM_Data$ROW<-as.factor(ASM_Data$ROW)
    head(ASM_Data$ROW)
    ASM_Data$REP <- as.numeric(as.character(ASM_Data$REP))
    head(ASM_Data$REP)
    ASM_Data$TWT_g.li <- as.numeric(as.character(ASM_Data$TWT_g.li))
    ASM_Data$Yield_kg.ha <- as.numeric(as.character(ASM_Data$Yield_kg.ha))
    ASM_Data$PhysMat_Julian <- as.numeric(as.character(ASM_Data$PhysMat_Julian))
    ASM_Data$flowering <- as.numeric(as.character(ASM_Data$flowering))
    ASM_Data$height <- as.numeric(as.character(ASM_Data$height))
    ASM_Data$CLEAN.WT <- as.numeric(as.character(ASM_Data$CLEAN.WT))
    ASM_Data$GRAV.TEST.WEIGHT <-as.numeric(as.character(ASM_Data$GRAV.TEST.WEIGHT))
    str(ASM_Data)
    
    library(lme4)
    #library(lsmeans)
    library(emmeans)

Here is the data frame:

  > str(ASM_Data)
'data.frame':   270 obs. of  20 variables:
 $ TRIAL_ID         : Factor w/ 1 level "18ASM_OvOv": 1 1 1 1 1 1 1 1 1 1 ...
 $ PLOT_ID          : Factor w/ 270 levels "18ASM_OvOv_002",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ PLOT             : Factor w/ 270 levels "2","3","4","5",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ ROW              : Factor w/ 20 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ RANGE            : Factor w/ 15 levels "1","2","3","4",..: 2 3 4 5 6 7 8 9 10 12 ...
 $ REP              : num  1 1 1 1 1 1 1 1 1 1 ...
 $ MP               : int  1 1 1 1 1 1 1 1 1 1 ...
 $ SUB.PLOT         : Factor w/ 6 levels "A","B","C","D",..: 1 1 1 1 2 2 2 2 2 3 ...
 $ ENTRY_NO         : Factor w/ 139 levels "840","850","851",..: 116 82 87 134 77 120 34 62 48 136 ...
 $ height           : num  74 70 73 80 70 73 75 68 65 68 ...
 $ flowering        : num  133 133 134 134 133 131 133 137 134 132 ...
 $ CLEAN.WT         : num  1072 929 952 1149 1014 ...
 $ GRAV.TEST.WEIGHT : num  349 309 332 340 325 ...
 $ TWT_g.li         : num  699 618 663 681 650 684 673 641 585 646 ...
 $ Yield_kg.ha      : num  2073 1797 1841 2222 1961 ...
 $ Chaff.Color      : Factor w/ 3 levels "Bronze","Mixed",..: 1 3 3 1 1 1 1 3 1 3 ...
 $ CHAFF_COLOR_SCALE: int  2 1 1 2 2 2 2 1 2 1 ...
 $ PhysMat          : Factor w/ 3 levels "6/12/2018","6/13/2018",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ PhysMat_Julian   : num  163 163 163 163 163 163 163 163 163 163 ...
 $ PEDIGREE         : Factor w/ 1 level "OVERLEY/OVERLAND": 1 1 1 1 1 1 1 1 1 1 ...

This is the head of ASM Data:

 head(ASM_Data)
    `TRIAL_ID        PLOT_ID PLOT ROW RANGE REP MP SUB.PLOT ENTRY_NO height flowering CLEAN.WT GRAV.TEST.WEIGHT TWT_g.li`
    1 18ASM_OvOv 18ASM_OvOv_002    2   1     2   1  1        A      965     74       133   1071.5           349.37      699
    2 18ASM_OvOv 18ASM_OvOv_003    3   1     3   1  1        A      931     70       133    928.8           309.13      618
    3 18ASM_OvOv 18ASM_OvOv_004    4   1     4   1  1        A      936     73       134    951.8           331.70      663
    4 18ASM_OvOv 18ASM_OvOv_005    5   1     5   1  1        A      983     80       134   1148.6           340.47      681
    5 18ASM_OvOv 18ASM_OvOv_006    6   1     6   1  1        B      926     70       133   1014.0           324.95      650
    6 18ASM_OvOv 18ASM_OvOv_007    7   1     7   1  1        B      969     73       131   1076.6           342.09      684
      Yield_kg.ha Chaff.Color CHAFF_COLOR_SCALE   PhysMat PhysMat_Julian         PEDIGREE
    1        2073      Bronze                 2 6/12/2018            163 OVERLEY/OVERLAND
    2        1797       White                 1 6/12/2018            163 OVERLEY/OVERLAND
    3        1841       White                 1 6/12/2018            163 OVERLEY/OVERLAND
    4        2222      Bronze                 2 6/12/2018            163 OVERLEY/OVERLAND
    5        1961      Bronze                 2 6/12/2018            163 OVERLEY/OVERLAND
    6        2082      Bronze                 2 6/12/2018            163 OVERLEY/OVERLAND

I am looking at a linear model dealing with test weight.

This is what I ran:

ASM_Data$TWT_g.li <- as.numeric(as.character((ASM_Data$TWT_g.li))) head(ASM_Data$TWT_g.li)

ASM_YIELD_1 <- lm(TWT_g.li~ENTRY_NO + REP + SUB.BLOCK, data=ASM_Data)
anova(ASM_YIELD_1)
summary(ASM_YIELD_1)
emmeans(ASM_YIELD_1, "ENTRY_NO") ###########ADJ. MEANS

I get an output for anova

anova(ASM_YIELD_1)
Analysis of Variance Table

Response: TWT_g.li
           Df Sum Sq Mean Sq  F value  Pr(>F)    
ENTRY_NO  138 217949    1579   7.0339 < 2e-16 ***
REP         1  66410   66410 295.7683 < 2e-16 ***
SUB.BLOCK   4   1917     479   2.1348 0.08035 .  
Residuals 125  28067     225                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

but for emmeans I get something like this:

ENTRY_NO emmean SE df asymp.LCL asymp.UCL
 840      nonEst NA NA        NA        NA
 850      nonEst NA NA        NA        NA
 851      nonEst NA NA        NA        NA
 852      nonEst NA NA        NA        NA
 853      nonEst NA NA        NA        NA
 854      nonEst NA NA        NA        NA
 855      nonEst NA NA        NA        NA
 857      nonEst NA NA        NA        NA
 858      nonEst NA NA        NA        NA
 859      nonEst NA NA        NA        NA

I do have outliers in my data which is indicated by a "." in my data but that's the only thing I can think of which is off.

When I run with(ASM_Data, table(ENTRY_NO, REP, SUB.BLOCK))

this is what I have:

 with(ASM_Data, table(ENTRY_NO,REP,SUB.BLOCK))
, , SUB.BLOCK = A

        REP
ENTRY_NO 1 2
     840 0 0
     850 0 0
     851 0 0
     852 0 0
     853 0 0
     854 0 0
     855 0 0
     857 0 0
     858 0 0
     859 0 0
     860 0 0
     861 0 0
     862 0 0
     863 1 0
     864 0 0
     865 1 0
     866 1 0
     867 0 0
     868 0 0
     869 1 0
     870 1 0
     871 0 0
     872 0 0
     873 0 0
     874 0 0
     875 0 0
     876 0 0
     877 0 0
     878 0 0
     879 1 0
     880 0 0
     881 0 0
     882 0 0
     883 0 0
     884 0 0
     885 1 0
     886 0 0
     887 1 0
     888 1 0
     889 1 0
     890 0 0
     891 1 0
     892 0 0
     893 0 0
     894 0 0
     895 0 0
     896 1 0
     897 0 0
     898 0 0
     899 0 0
     900 1 0
     901 1 0
     902 0 0
     903 0 0
     904 1 0
     905 1 0
     906 0 0
     907 1 0
     908 1 0
     909 0 0
     910 0 0
     911 0 0
     912 0 0
     913 0 0
     914 0 0
     915 0 0
     916 1 0
     917 0 0
     918 0 0
     919 1 0
     920 0 0
     921 0 0
     922 0 0
     923 1 0
     924 0 0
     925 0 0
     926 0 0
     927 1 0
     928 0 0
     929 0 0
     930 0 0
     931 1 0
     932 0 0
     933 0 0
     934 0 0
     935 0 0
     936 1 0
     937 0 0
     938 1 0
     939 1 0
     940 0 0
     941 1 0
     942 0 0
     943 1 0
     944 0 0
     945 0 0
     946 0 0
     947 0 0
     948 1 0
     949 0 0
     950 1 0
     951 0 0
     952 0 0
     953 0 0
     954 0 0
     955 1 0
     956 1 0
     957 1 0
     958 1 0
     959 0 0
     960 0 0
     961 0 0
     962 0 0
     963 0 0
     964 0 0
     965 1 0
     966 0 0
     967 1 0
     968 0 0
     969 0 0
     970 1 0
     971 0 0
     972 0 0
     973 0 0
     974 1 0
     975 0 0
     976 0 0
     977 0 0
     978 1 0
     979 0 0
     980 0 0
     981 0 0
     982 0 0
     983 1 0
     984 1 0
     985 0 0
     986 1 0
     987 3 0
     988 0 0

, , SUB.BLOCK = B

        REP
ENTRY_NO 1 2
     840 0 0
     850 0 0
     851 0 0
     852 0 0
     853 1 0
     854 0 0
     855 0 0
     857 0 0
     858 0 0
     859 0 0
     860 0 0
     861 1 0
     862 0 0
     863 0 0
     864 0 0
     865 0 0
     866 0 0
     867 0 0
     868 0 0
     869 0 0
     870 0 0
     871 1 0
     872 0 0
     873 0 0
     874 0 0
     875 0 0
     876 1 0
     877 1 0
     878 1 0
     879 0 0
     880 1 0
     881 0 0
     882 1 0
     883 1 0
     884 1 0
     885 0 0
     886 0 0
     887 0 0
     888 0 0
     889 0 0
     890 1 0
     891 0 0
     892 1 0
     893 1 0
     894 1 0
     895 1 0
     896 0 0
     897 1 0
     898 0 0
     899 0 0
     900 0 0
     901 0 0
     902 1 0
     903 0 0
     904 0 0
     905 0 0
     906 0 0
     907 0 0
     908 0 0
     909 1 0
     910 0 0
     911 1 0
     912 0 0
     913 1 0
     914 0 0
     915 0 0
     916 0 0
     917 0 0
     918 0 0
     919 0 0
     920 1 0
     921 1 0
     922 0 0
     923 0 0
     924 0 0
     925 1 0
     926 1 0
     927 0 0
     928 0 0
     929 0 0
     930 1 0
     931 0 0
     932 1 0
     933 0 0
     934 1 0
     935 0 0
     936 0 0
     937 1 0
     938 0 0
     939 0 0
     940 1 0
     941 0 0
     942 0 0
     943 0 0
     944 0 0
     945 1 0
     946 0 0
     947 1 0
     948 0 0
     949 0 0
     950 0 0
     951 1 0
     952 0 0
     953 0 0
     954 1 0
     955 0 0
     956 0 0
     957 0 0
     958 0 0
     959 1 0
     960 0 0
     961 0 0
     962 1 0
     963 0 0
     964 0 0
     965 0 0
     966 0 0
     967 0 0
     968 0 0
     969 1 0
     970 0 0
     971 0 0
     972 0 0
     973 0 0
     974 0 0
     975 0 0
     976 1 0
     977 1 0
     978 0 0
     979 0 0
     980 0 0
     981 1 0
     982 1 0
     983 0 0
     984 0 0
     985 3 0
     986 0 0
     987 1 0
     988 1 0

, , SUB.BLOCK = C

        REP
ENTRY_NO 1 2
     840 0 0
     850 0 0
     851 0 0
     852 0 0
     853 0 0
     854 0 0
     855 0 0
     857 1 0
     858 0 0
     859 1 0
     860 0 0
     861 0 0
     862 1 0
     863 0 0
     864 0 0
     865 0 0
     866 0 0
     867 0 0
     868 0 0
     869 0 0
     870 0 0
     871 0 0
     872 1 0
     873 0 0
     874 0 0
     875 0 0
     876 0 0
     877 0 0
     878 0 0
     879 0 0
     880 0 0
     881 1 0
     882 0 0
     883 0 0
     884 0 0
     885 0 0
     886 1 0
     887 0 0
     888 0 0
     889 0 0
     890 0 0
     891 0 0
     892 0 0
     893 0 0
     894 0 0
     895 0 0
     896 0 0
     897 0 0
     898 1 0
     899 1 0
     900 0 0
     901 0 0
     902 0 0
     903 1 0
     904 0 0
     905 0 0
     906 1 0
     907 0 0
     908 0 0
     909 0 0
     910 1 0
     911 0 0
     912 1 0
     913 0 0
     914 1 0
     915 1 0
     916 0 0
     917 1 0
     918 1 0
     919 0 0
     920 0 0
     921 0 0
     922 1 0
     923 0 0
     924 1 0
     925 0 0
     926 0 0
     927 0 0
     928 1 0
     929 1 0
     930 0 0
     931 0 0
     932 0 0
     933 1 0
     934 0 0
     935 1 0
     936 0 0
     937 0 0
     938 0 0
     939 0 0
     940 0 0
     941 0 0
     942 1 0
     943 0 0
     944 1 0
     945 0 0
     946 1 0
     947 0 0
     948 0 0
     949 1 0
     950 0 0
     951 0 0
     952 1 0
     953 1 0
     954 0 0
     955 0 0
     956 0 0
     957 0 0
     958 0 0
     959 0 0
     960 1 0
     961 1 0
     962 0 0
     963 1 0
     964 1 0
     965 0 0
     966 1 0
     967 0 0
     968 1 0
     969 0 0
     970 0 0
     971 1 0
     972 1 0
     973 1 0
     974 0 0
     975 1 0
     976 0 0
     977 0 0
     978 1 0
     979 2 0
     980 0 0
     981 0 0
     982 0 0
     983 0 0
     984 0 0
     985 1 0
     986 3 0
     987 0 0
     988 0 0

, , SUB.BLOCK = D

        REP
ENTRY_NO 1 2
     840 0 0
     850 0 0
     851 0 0
     852 0 1
     853 0 0
     854 0 0
     855 0 0
     857 0 0
     858 0 1
     859 0 0
     860 0 1
     861 0 0
     862 0 0
     863 0 0
     864 0 1
     865 0 0
     866 0 0
     867 0 0
     868 0 0
     869 0 0
     870 0 0
     871 0 0
     872 0 0
     873 0 0
     874 0 0
     875 0 1
     876 0 0
     877 0 0
     878 0 1
     879 0 0
     880 0 1
     881 0 1
     882 0 1
     883 0 1
     884 0 1
     885 0 0
     886 0 0
     887 0 0
     888 0 0
     889 0 0
     890 0 0
     891 0 0
     892 0 1
     893 0 0
     894 0 0
     895 0 0
     896 0 0
     897 0 1
     898 0 0
     899 0 1
     900 0 0
     901 0 0
     902 0 1
     903 0 0
     904 0 0
     905 0 0
     906 0 0
     907 0 0
     908 0 0
     909 0 0
     910 0 0
     911 0 0
     912 0 0
     913 0 1
     914 0 1
     915 0 1
     916 0 0
     917 0 1
     918 0 1
     919 0 0
     920 0 0
     921 0 1
     922 0 1
     923 0 0
     924 0 0
     925 0 0
     926 0 0
     927 0 0
     928 0 0
     929 0 1
     930 0 1
     931 0 0
     932 0 0

Can someone please give me an idea of what is going wrong??

Thanks !

Can you show `head(ASM_Data)` in your question? It looks like `ENTRY_NO` may be an ID number, with only 1 observation per ENTRY_NO? — Marius, Nov 20 '18 at 00:42
Where is the variable `SUB.BLOCK`? It’s in the model but not in the data. — Russ Lenth, Nov 21 '18 at 04:47

Russ Lenth · Answer 1 · 2018-11-22T06:21:46.400

1

EMMs are obtained by averaging predictions over 2 reps and 5 blocks (or maybe more?). Look at

coef(ASM_YIELD_1)

If any of the rep or block effects are NA, then you can’t estimate all of the rep or block effects, and that makes the average of them non-estimable.

You can see exactly which factor combinations are non-estimable by doing:

summary(ref_grid(ASM_YIELD_1))

addendum

Here is a reformatting of the tables I requested in comments:

ENTRY   ---------- BLOCK -------------
 NO      A        B        C        D

 840    0 0      0 0      0 0      0 0
 850    0 0      0 0      0 0      0 0
 851    0 0      0 0      0 0      0 0
 852    0 0      0 0      0 0      0 1
 853    0 0      1 0      0 0      0 0
 854    0 0      0 0      0 0      0 0
 855    0 0      0 0      0 0      0 0
 857    0 0      0 0      1 0      0 0
 858    0 0      0 0      0 0      0 1
 859    0 0      0 0      1 0      0 0
    ... etc ...

This is extremely sparse data. I think there are two more blocks not shown. But I see very few instances where a given ENTRY_NO is observed in more than one rep or block. So I think it is seriously over-fitting to try to account for rep or block effects in this model.

MAYBE omitting REP from the model will make it work. MAYBE re-fitting the model with factor(REP) in place of REP will enable emmeans to detect a nesting structure. Otherwise, there's some really subtle dependence in the blocking structure and treatments, and I don't know what to suggest.

edited Nov 22 '18 at 06:21

answered Nov 21 '18 at 04:57

Russ Lenth

5,922
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Hi ~ I cannot tell much from this but I get the same result for my other traits as well (flowering, height etc.) each emmean comparison gets a nonEst. I just think this is really strange. – wardah m Nov 22 '18 at 01:51
Yes — changing the response variable will make no difference whatsoever, because this is due to linear dependence in the factor levels. Did you print that summary of the reference grid that I suggested? – Russ Lenth Nov 22 '18 at 02:12
@rvi `coef(ASM_YIELD_1) (Intercept) ENTRY_NO982 ENTRY_NO983 ENTRY_NO984 ENTRY_NO985 1.39184456 -20.85391334 16.60855561 7.69139728 9.63711948 -2.29160352 -18.46223843 -26.07993162 ENTRY_NO986 ENTRY_NO987 ENTRY_NO988 REP SUB.BLOCKB SUB.BLOCKC SUB.BLOCKD SUB.BLOCKE 8.97955019 6.69982467 -47.13796781 37.71682387 13.06799679 5.98303157 6.10034916 3.78288879 SUB.BLOCKF NA` – wardah m Nov 22 '18 at 03:21
@rvi Yes I seem to have it for Block F , how can I change the code to omit the na so that this can work? – wardah m Nov 22 '18 at 03:24
I’d suggest taking REP out of the model. My guess it is providing completely redundant info and that’s why things can’t be estimated. But I am just guessing because you still haven’t provided the second set of details I mentioned. – Russ Lenth Nov 22 '18 at 04:06
@rvi is the second the summary ref grid...? – wardah m Nov 22 '18 at 04:27
@rvi `> summary(ref_grid(ASM_YIELD_1)) ENTRY_NO REP SUB.BLOCK prediction SE df 840 1.501859 A nonEst NA NA 850 1.501859 A nonEst NA NA 851 1.501859 A nonEst NA NA 852 1.501859 A nonEst NA NA` this is how it is for all entries. – wardah m Nov 22 '18 at 04:29
Do this: `with(ASM_Data, table(ENTRY_NO, REP, SUB.BLOCK))`. Please do not put the results in a comment, because (like your previous replies) it will be nearly unreadable. Edit your question and paste the entire results in there; then mark all those results and click the {} icon so it is formatted as computer output. Also, please do not change any of the output like you did in the data listing. I'll try to remember to look at it tomorrow. BTW, my last initial is "l" as in "ell" – Russ Lenth Nov 22 '18 at 04:44
Do you promise me that you're not actually using an ancient version of **lsmeans** and not a recent version of **emmeans**? – Russ Lenth Nov 22 '18 at 04:48
Yes I assure you I ran `emmeans` not `lsmeans`. I have mentioned it in the script above. Your suggestion is a the of the question along with the table. Thank you for the advice. Sorry about mistyping your username. – wardah m Nov 22 '18 at 05:29
@wardahm please see my additional answer. – Russ Lenth Nov 22 '18 at 17:41

score 0 · Accepted Answer · answered Nov 22 '18 at 17:41

I've been able to create a situation like this. Consider this dataset:

> junk
   trt rep blk           y
1    A   1   1 -1.17415687
2    B   1   1 -0.20084854
3    C   1   1  0.64797806
4    A   1   2 -1.69371434
5    B   1   2 -0.35835442
6    C   1   2  1.35718782
7    A   1   3  0.20510482
8    B   1   3  1.00857651
9    C   1   3 -0.20553167
10   A   2   4  0.31261523
11   B   2   4  0.47989115
12   C   2   4  1.27574085
13   A   2   5 -0.79209520
14   B   2   5  1.07151315
15   C   2   5 -0.04222769
16   A   2   6 -0.80571767
17   B   2   6  0.80442988
18   C   2   6  1.73526561

This has 6 complete blocks, separately labeled with 3 blocks per rep. Not obvious, but true, is that rep is a numeric variable having values 1 and 2, while blk is a factor having 6 levels 1 -- 6:

> sapply(junk, class)
      trt       rep       blk         y 
 "factor" "numeric"  "factor" "numeric"

With this complete dataset, I have no problem obtaining EMMs for modeling situations parallel to what was used in the original posting. However, if I use only a subset of these data, it is different. Consider:

> samp
[1]  1  2  3  5  8 11 13 15 16

> junk.lm = lm(y ~ trt + rep + blk, data = junk, subset = samp)
> emmeans(junk.lm, "trt")
 trt emmean SE df asymp.LCL asymp.UCL
 A   nonEst NA NA        NA        NA
 B   nonEst NA NA        NA        NA
 C   nonEst NA NA        NA        NA

Results are averaged over the levels of: blk 
Confidence level used: 0.95

Again, recall that rep is numeric in this model. If instead, I make rep a factor:

> junk.lmf = lm(y ~ trt + factor(rep) + blk, data = junk, subset = samp)
> emmeans(junk.lmf, "trt")
NOTE: A nesting structure was detected in the fitted model:
    blk %in% rep
If this is incorrect, re-run or update with `nesting` specified
 trt     emmean        SE df  lower.CL upper.CL
 A   -0.6262635 0.4707099  1 -6.607200 5.354673
 B    0.0789780 0.3546191  1 -4.426885 4.584841
 C    0.6597377 0.5191092  1 -5.936170 7.255646

Results are averaged over the levels of: blk, rep 
Confidence level used: 0.95

We get non-NA estimates, in part because it is able to detect the fact that blk is nested in rep, and thus performs the EMM computations separately in each rep. Note in the annotations in this last output that averaging is done over the 2 reps and 6 blocks; whereas in fiber.lm averaging is done only over blocks, while rep, a numeric variable, is set at its average. Compare the reference grids for the two models:

> ref_grid(junk.lm)
'emmGrid' object with variables:
    trt = A, B, C
    rep = 1.4444
    blk = 1, 2, 3, 4, 5, 6

> ref_grid(junk.lmf)
'emmGrid' object with variables:
    trt = A, B, C
    rep = 1, 2
    blk = 1, 2, 3, 4, 5, 6
Nesting structure:  blk %in% rep

An additional option is to avoid the nesting issue by simply omitting rep from the model:

> junk.lm.norep = lm(y ~ trt + blk, data = junk, subset = samp)
> emmeans(junk.lm.norep, "trt")
 trt     emmean        SE df  lower.CL upper.CL
 A   -0.6262635 0.4707099  1 -6.607200 5.354673
 B    0.0789780 0.3546191  1 -4.426885 4.584841
 C    0.6597377 0.5191092  1 -5.936170 7.255646

Results are averaged over the levels of: blk 
Confidence level used: 0.95

Note that exactly the same results are produced. The reason is the levels of blk already predict the levels of rep, so there is no need for it to be in the model.

In summary:

The situation is due in part to the fact that there are missing data
and in part because rep was in the model as a numeric predictor rather than a factor.
In your situation, I suggest re-fitting the model with factor(REP) instead of REPas a numeric predictor. This may be enough to produce estimates.
If, indeed, as in my example, the SUB.BLOCK levels predict the REP levels, just leave REP out of the model altogether.

Great ! This helped alot! I will keep my REP but change it to a factor. Appreciate you patience and guidance! — wardah m, Nov 22 '18 at 21:07

score 0 · Answer 3 · answered Jul 24 '23 at 17:51

emmeans can be picky when variables have been dropped from the model due to rank deficiencies. They are actually not estimated, but still live in the data stored in the fit object= emmeans is using and will in a way poison it.

The remedy is to recalculate the model, while eliminating the bad variables from the model, i.e. from the formula.

Consider the following example for lme4::lmer, where we are interested in the interaction st:d1.

fo <- y ~ st*(d1*d5*d6 + d2 + d3 + d4) + a1 + x + (1 | id) + (1 | trial)

mm1 <- lme4::lmer(fo, dat, REML=FALSE)
# fixed-effect model matrix is rank deficient so dropping 4 columns / coefficients
# boundary (singular) fit: see help('isSingular')

Four variables were dropped, and emmeans throws NAs.

emmeans(mm1, ~ st*d1, nuisance='x')
 # st d1 emmean    SE  df lower.CL upper.CL
 #  0  0   2.17 0.190 122     1.79     2.54
 #  1  0   2.25 0.294 241     1.67     2.83
 #  0  1 nonEst    NA  NA       NA       NA
 #  1  1 nonEst    NA  NA       NA       NA

Now, we just build a new reduced formula based on the names of the actual fitted coefficients, maybe something like the following using fixef and ranef and refit the model.

(fo_new <- reformulate(c(names(lme4::fixef(mm1))[-1], 
                        sprintf('(1 | %s)', names(lme4::ranef(mm1)))), 'y'))
# y ~ st + d1 + d5 + d6 + d2 + d3 + d4 + a1 + x + d1:d6 + d5:d6 + 
#     st:d1 + st:d5 + st:d6 + st:d2 + st:d3 + st:d4 + st:d1:d6 + 
#     st:d5:d6 + (1 | id) + (1 | trial)

mm2 <- lmerTest::lmer(fo_new, dat, REML=FALSE)
# boundary (singular) fit: see help('isSingular')

stopifnot(all.equal(mm1, mm2, check.attributes=FALSE))

emmeans(mm2, ~ st*d1, nuisance='x')
#  st d1 emmean    SE  df lower.CL upper.CL
#   0  0   2.17 0.190 122     1.79     2.54
#   1  0   2.25 0.294 241     1.67     2.83
#   0  1   1.95 0.276 109     1.40     2.49
#   1  1   2.05 0.405 192     1.25     2.85

Now, emmeans is happy! :-)

Data:

dat <- structure(list(id = c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 
4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 
9L, 10L, 10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 13L, 13L, 13L, 
14L, 14L, 14L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 17L, 17L, 18L, 
18L, 18L, 19L, 19L, 19L, 20L, 20L, 20L, 21L, 21L, 21L, 22L, 22L, 
22L, 23L, 23L, 23L, 24L, 24L, 24L, 25L, 25L, 25L, 26L, 26L, 26L, 
27L, 27L, 27L, 28L, 28L, 28L, 29L, 29L, 29L, 30L, 30L, 30L, 31L, 
31L, 31L, 32L, 32L, 32L, 33L, 33L, 33L, 34L, 34L, 34L, 35L, 35L, 
35L, 36L, 36L, 36L, 37L, 37L, 37L, 38L, 38L, 38L, 39L, 39L, 39L, 
40L, 40L, 40L, 41L, 41L, 41L, 42L, 42L, 42L, 43L, 43L, 43L, 44L, 
44L, 44L, 45L, 45L, 45L, 46L, 46L, 46L, 47L, 47L, 47L, 48L, 48L, 
48L, 49L, 49L, 49L, 50L, 50L, 50L), trial = c(1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
2L, 3L), y = c(1.01837990914119, 1.72281053171597, 1.16038933631774, 
1.84741932777425, 2.15377758211187, 1.44708378268237, 1.32050688356017, 
1.1999393743433, 0.832090772863633, 2.28263010547248, 2.06830807951508, 
3.50064060945826, 2.42965506902417, 1.03344739854432, 2.05249031229156, 
0.624779485406468, 1.34116637631539, 1.69179421074866, 2.27044714140095, 
2.06580159558889, 2.04603679365979, 0.901719165871983, 2.00970305604673, 
2.93104632786378, 1.24723492562883, 2.2271756343974, 1.89879628580628, 
1.83117598856832, 1.70053079532615, 0.626989628785855, 1.984818811227, 
2.50882095372023, 1.27518728336047, 2.24074403417244, 2.14151176453018, 
3.35995098569363, 1.67432260454106, 1.98175867928662, 1.50605033454511, 
2.09774834852059, 1.55073131078313, 1.50766945430263, 2.5248624762036, 
2.57990655332647, 3.42721524313865, 1.68378025917067, 1.47995712419544, 
1.40645873307514, 1.88003440125559, 1.75934122735018, 1.74337693067683, 
1.59747338520177, 1.87679172340406, 1.44208371784719, 2.40275743710848, 
1.5953665217543, 1.55694381354539, 1.64035264679494, 2.48353008255997, 
2.96289269075876, 2.42962806107927, 2.48371741479921, 2.7316101041616, 
2.63939066505726, 2.53641034022768, 2.68671573577324, 1.14285030411344, 
1.20831594497024, 1.27194419545619, 2.09603774330159, 2.23081630563127, 
2.1347438360067, 1.57698407162643, 3.39478557588238, 2.96186354727387, 
2.58239199435352, 2.44241126873145, 2.40546940767434, 1.8969624697088, 
1.2204109755261, 2.20827256882076, 2.3955852344866, 2.34308978876196, 
2.69400711592567, 2.55368414291743, 2.87307348748506, 2.81160389034124, 
2.2251307233081, 2.64579766646207, 2.59041510869758, 1.20044150410878, 
2.5341592223955, 2.11832088659712, 1.37165442217995, 2.48946142326411, 
1.54165246908483, 1.75713292575393, 1.21135423153261, 2.27906786981473, 
0.581949400859875, 2.55432238775076, 0.710437338297285, 1.33730281943567, 
2.81473012051573, 2.38333136367104, 1.69657968891281, 1.55813806156551, 
2.37063978439338, 1.51354489173799, 1.42676809793259, 2.17830007077276, 
2.41163222093621, 3.19393552098512, 2.2541858233295, 1.49809418369717, 
1.12314590398162, 1.97842147508821, 1.14419489276718, 2.29833044665594, 
2.27649799747862, 2.70221763225851, 2.36443763286132, 1.96428137039684, 
2.13762019810797, 1.99990443254389, 2.11636116477743, 1.83950578665097, 
2.55374771744304, 2.1427802527118, 2.64727022373539, 1.96764061562098, 
2.60351192397739, 2.04165177429036, 1.47050617438957, 1.38831574244936, 
0.650957101476655, 1.16840843162526, 2.58234556181071, 1.67146970510712, 
1.96595469079416, 1.72075555640435, 0.889489197804286, 2.14802605910079, 
1.55781298621079, 0.884775340440683, 1.5837912340036, 2.27268991271172, 
1.90433670807436, 2.97569071967808, 2.67673634576096), st = c(0L, 
0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 
1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 
0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 
0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 
1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 
0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 
0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 
1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 
0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 
0L, 1L, 0L, 0L, 1L), d1 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), d2 = c(0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 
0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 
1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 
1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 
0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 
0L, 0L, 1L, 1L, 1L), d3 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), d4 = c(0L, 
0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L), d5 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), d6 = c(0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L), a1 = c(0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 
0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 
1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 
1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 
0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 
1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 
1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 
0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 
1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 
1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L), x = c(21.693378534168, 
21.693378534168, 21.693378534168, 27.5635733655654, 27.5635733655654, 
27.5635733655654, 21.0235442230478, 21.0235442230478, 21.0235442230478, 
37.3397954637185, 37.3397954637185, 37.3397954637185, 36.9168151924387, 
36.9168151924387, 36.9168151924387, 19.1212821025401, 19.1212821025401, 
19.1212821025401, 32.3830615049228, 32.3830615049228, 32.3830615049228, 
23.3126252391376, 23.3126252391376, 23.3126252391376, 24.4104024292901, 
24.4104024292901, 24.4104024292901, 21.1630470496602, 21.1630470496602, 
21.1630470496602, 26.090356990695, 26.090356990695, 26.090356990695, 
36.2644856846891, 36.2644856846891, 36.2644856846891, 26.431828352157, 
26.431828352157, 26.431828352157, 26.3781595965847, 26.3781595965847, 
26.3781595965847, 39.3826594613492, 39.3826594613492, 39.3826594613492, 
18.0264230361208, 18.0264230361208, 18.0264230361208, 20.3397673121653, 
20.3397673121653, 20.3397673121653, 30.3645347533748, 30.3645347533748, 
30.3645347533748, 18.3756379187107, 18.3756379187107, 18.3756379187107, 
31.2602149215527, 31.2602149215527, 31.2602149215527, 29.8046363908798, 
29.8046363908798, 29.8046363908798, 35.0474634887651, 35.0474634887651, 
35.0474634887651, 20.7696465491317, 20.7696465491317, 20.7696465491317, 
26.4675482031889, 26.4675482031889, 26.4675482031889, 39.5598319787532, 
39.5598319787532, 39.5598319787532, 33.5386927942745, 33.5386927942745, 
33.5386927942745, 26.0694283158518, 26.0694283158518, 26.0694283158518, 
27.0705276266672, 27.0705276266672, 27.0705276266672, 38.1853067930788, 
38.1853067930788, 38.1853067930788, 37.9851826457307, 37.9851826457307, 
37.9851826457307, 33.9107203702442, 33.9107203702442, 33.9107203702442, 
20.2714434023947, 20.2714434023947, 20.2714434023947, 26.8274163762107, 
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Just to be clear. What is characterized as "being picky" is actually "handling the situation appropriately." The reason you get the `NA`s is because the model cannot uniquely estimate those cases. There is lots of other software that would not check estimability, and thus blithely return a misleading answer. Yes, those rank deficiencies poison things - that's a good analogy. So be thankful that `emmeans` is picky enough to employ a food taster! — Russ Lenth, Jul 26 '23 at 02:16
@RussLenth Of course we can be glad that `emmeans` is so "picky", thanks for pointing it out! — jay.sf, Jul 26 '23 at 19:20

How to prevent LS means analysis from producing NAs?

3 Answers3

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