R: LS Means Analysis produces NAs?

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 !

edited Nov 22 '18 at 5:28

asked Nov 20 '18 at 0:33

wardah m

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 0:42

Hi~ let me add to the post.

– wardah m
Nov 20 '18 at 0:46

Where is the variable SUB.BLOCK? It’s in the model but not in the data.

– rvl
Nov 21 '18 at 4:47

Hi Sub.Plot is renamed as Sub.Block in my script.

– wardah m
Nov 21 '18 at 22:45

add a comment |

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 !

edited Nov 22 '18 at 5:28

asked Nov 20 '18 at 0:33

wardah m

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 0:42

Hi~ let me add to the post.

– wardah m
Nov 20 '18 at 0:46

Where is the variable SUB.BLOCK? It’s in the model but not in the data.

– rvl
Nov 21 '18 at 4:47

Hi Sub.Plot is renamed as Sub.Block in my script.

– wardah m
Nov 21 '18 at 22:45

add a comment |

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 !

edited Nov 22 '18 at 5:28

asked Nov 20 '18 at 0:33

wardah m

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 !

r linear-regression lsmeans

edited Nov 22 '18 at 5:28

asked Nov 20 '18 at 0:33

wardah m

edited Nov 22 '18 at 5:28

asked Nov 20 '18 at 0:33

wardah m

edited Nov 22 '18 at 5:28

asked Nov 20 '18 at 0:33

wardah m

asked Nov 20 '18 at 0:33

wardah m

asked Nov 20 '18 at 0:33

wardah m

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 0:42

Hi~ let me add to the post.

– wardah m
Nov 20 '18 at 0:46

Where is the variable SUB.BLOCK? It’s in the model but not in the data.

– rvl
Nov 21 '18 at 4:47

Hi Sub.Plot is renamed as Sub.Block in my script.

– wardah m
Nov 21 '18 at 22:45

add a comment |

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 0:42

Hi~ let me add to the post.

– wardah m
Nov 20 '18 at 0:46

Where is the variable SUB.BLOCK? It’s in the model but not in the data.

– rvl
Nov 21 '18 at 4:47

Hi Sub.Plot is renamed as Sub.Block in my script.

– wardah m
Nov 21 '18 at 22:45

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 0:42

Hi~ let me add to the post.

– wardah m
Nov 20 '18 at 0:46

Where is the variable SUB.BLOCK? It’s in the model but not in the data.

– rvl
Nov 21 '18 at 4:47

Hi Sub.Plot is renamed as Sub.Block in my script.

– wardah m
Nov 21 '18 at 22:45

add a comment |

2 Answers
2

active

oldest

votes

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.

answered Nov 22 '18 at 17:41

rvl

1,9552613

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

add a comment |

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 6:21

answered Nov 21 '18 at 4:57

rvl

1,9552613

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 1:51

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?

– rvl
Nov 22 '18 at 2:12

@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 3:21

@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 3:24

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.

– rvl
Nov 22 '18 at 4:06

|
show 6 more comments

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2 Answers
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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

> sapply(junk, class)

      trt       rep       blk         y 

 "factor" "numeric"  "factor" "numeric"

> 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

> 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.

answered Nov 22 '18 at 17:41

rvl

1,9552613

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

add a comment |

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

> sapply(junk, class)

      trt       rep       blk         y 

 "factor" "numeric"  "factor" "numeric"

> 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

> 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.

answered Nov 22 '18 at 17:41

rvl

1,9552613

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

add a comment |

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

> sapply(junk, class)

      trt       rep       blk         y 

 "factor" "numeric"  "factor" "numeric"

> 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

> 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.

answered Nov 22 '18 at 17:41

rvl

1,9552613

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

> sapply(junk, class)

      trt       rep       blk         y 

 "factor" "numeric"  "factor" "numeric"

> 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

> 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.

answered Nov 22 '18 at 17:41

rvl

1,9552613

answered Nov 22 '18 at 17:41

rvl

1,9552613

answered Nov 22 '18 at 17:41

rvl

1,9552613

answered Nov 22 '18 at 17:41

rvl

1,9552613

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

add a comment |

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

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

add a comment |

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 ...

edited Nov 22 '18 at 6:21

answered Nov 21 '18 at 4:57

rvl

1,9552613

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 1:51

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?

– rvl
Nov 22 '18 at 2:12

@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 3:21

@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 3:24

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.

– rvl
Nov 22 '18 at 4:06

|
show 6 more comments

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 ...

edited Nov 22 '18 at 6:21

answered Nov 21 '18 at 4:57

rvl

1,9552613

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 1:51

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?

– rvl
Nov 22 '18 at 2:12

@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 3:21

@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 3:24

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.

– rvl
Nov 22 '18 at 4:06

|
show 6 more comments

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 ...

edited Nov 22 '18 at 6:21

answered Nov 21 '18 at 4:57

rvl

1,9552613

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 ...

edited Nov 22 '18 at 6:21

answered Nov 21 '18 at 4:57

rvl

1,9552613

edited Nov 22 '18 at 6:21

answered Nov 21 '18 at 4:57

rvl

1,9552613

answered Nov 21 '18 at 4:57

rvl

1,9552613

answered Nov 21 '18 at 4:57

rvl

1,9552613

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 1:51

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?

– rvl
Nov 22 '18 at 2:12

@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 3:21

@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 3:24

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.

– rvl
Nov 22 '18 at 4:06

|
show 6 more comments

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 1:51

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?

– rvl
Nov 22 '18 at 2:12

@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 3:21

@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 3:24

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.

– rvl
Nov 22 '18 at 4:06

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 1:51

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?

– rvl
Nov 22 '18 at 2:12

@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 3:21

@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 3:21

@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 3:24

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.

– rvl
Nov 22 '18 at 4:06

|
show 6 more comments

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