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Sample data

dat <- structure(list(location.id = structure(c(198L, 243L, 147L, 114L,152L, 117L, 151L, 129L, 148L, 154L, 233L, 12L, 
            226L, 129L, 146L,126L, 247L, 147L, 80L, 133L, 162L, 147L, 152L, 126L, 36L, 154L,147L, 166L, 132L, 241L, 
            146L, 152L, 141L, 227L, 41L, 187L, 131L,140L, 232L, 155L, 130L, 166L, 222L, 246L, 222L, 129L, 262L, 244L,
            188L, 210L), .Label = c("11001", "11003", "11005", "11006","11007","11008", "14001", "14002", "15002", 
            "15010", "15012", "15014","15015", "15017", "15018", "15019","15021", "15022", "17001",  "17002","17003", 
            "17004", "17005", "17006", "17007", "17008","21008", "21009","21011", "21012", "21013", "21014","21018", 
            "21019", "21020", "21021", "22001", "22002", "22003", "22005","22007", "22008", "22009", "22010","22012", 
            "29001", "29002","29003", "29007", "29023", "31001", "31002","31003", "31006","31011","31017","31018", 
            "31019", "31020", "31021", "31022","31023", "31024","31025","31026", "31027", "31029","31042","31043",
            "31044", "31047", "31048", "31049", "31050", "31051","31054","31057", "31058", "31060","35001","35002", 
            "35003","35004", "35005", "35006", "35007", "35008","35009","35010","35011", "35012","35013","35014", 
            "35015", "35016", "35017", "35018", "35019", "35020","35021","35022", "35023", "35024","35025","35026", "35027", "35028", "35029", "35030", "35031", 
            "35032", "35034", "35035", "35036", "35037", "35038","35039","35040", "35041", "35042","35043","35044", "35046", "35048", 
            "35050", "41001", "41002", "41003", "41004", "41005","41006","41007", "41008", "41009","41010","41011", "41012", "41013", 
            "41014", "41015", "41016", "41017", "41018", "41019","41020","41021", "41022", "41023","41024","41025", "41026", "41027", 
            "41028", "41029", "41030", "41031", "41032", "41033","41034","41035", "41036", "41037","41038","41039", "42001", "42002", 
            "42003", "42004", "42005", "42006", "42007", "42009","42010","42011", "42014", "42019", "42020","43001", "43002", "43003", 
            "43004", "43005", "43006", "43007", "43008", "43009","43010","43011", "43012", "43013", "43014","43015", "43016", "43017", 
            "43018", "43019", "43020", "43021", "43022", "43023","43024","43025", "43026", "43027", "43028","43029", "43030", "43031", 
            "43032", "43033", "43034", "43035", "50001", "50002", "50003","50004", "50005", "50006", "50007","50008", "50009", "50010", 
            "50011", "51001", "51002", "51003", "51004", "51005", "51006","51007", "51008", "51009", "51010","51011", "51012", "51013", 
            "51014", "51015", "51016", "51017", "51018", "51019", "51020","51021", "51022", "52001", "52002", "52003", "52004", "52005", 
            "52006", "52007", "52008", "52009", "52010", "52011", "52012","52013", "52014", "52015", "52016", "52017", "52018", "53001"
            ), class = "factor"), year = structure(c(4L, 3L, 26L, 27L, 14L,19L, 13L, 19L, 9L, 21L, 4L, 20L, 27L, 17L, 25L, 23L, 3L, 13L, 
            10L, 22L, 27L, 27L, 21L, 15L, 26L, 27L, 15L, 18L, 21L, 28L, 22L, 17L, 17L, 26L, 8L, 26L, 13L, 13L, 2L, 19L, 17L, 12L, 5L, 17L, 
            27L, 4L, 5L, 16L, 28L, 26L), .Label = c("1987", "1988", "1989", "1990", "1991", "1992", "1993", "1994", "1995", "1996", "1997", 
            "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", 
            "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", 
            "2014"), class = "factor"), yield = c(731.07, 2264.99,3382.08, 2520.42, 3093.13, 3030.99, 
             2796.10, 2098.87, 2797.54, 3037.78,1980, 2720, 2966.98, 2799.68, 3290.9, 3199.72, 702.70, 2085.21,2396.69, 
            1344, 2975.39, 3475.36, 2918.5, 3470,2864.99, 2849.73, 3398.87, 359.45, 
            2579.98, 2849.49, 2323.80, 2345.07,2798.13, 2931.56, 1199.93, 3170.05, 
            1599.72, 1735.04, 1600.24, 2299.06,2472.64, 1682.35, 1599.93, 2675.57, 
            2595.38, 1989.35, 2182.74, 2883.3,3523.37, 2820.11), dhs.rep = c(0.11, 
            12.94, 6.1, 7.41, 0, 0.03, 0, 0, 0, 4.32, 5.61, 0, 0, 2.01, 0, 1.9, 2.85, 
            0.14, 0.27, 4.16, 0, 1.89, 2.75, 0.98, 0, 0, 0, 0.5, 6.48, 2.95, 0, 0, 
            3, 3.76, 0.15, 0, 0, 0, 0, 0, 2.87,1.83, 0, 6.02, 11.17, 1.15, 0, 12.33, 0, 3.55), nhs.rip = c(0, 
            37.47, 9.47, 21.6, 3.18, 16.08, 0, 0, 0, 0, 3.15, 12.01, 6.16, 0, 0, 34.92, 0.1, 2.58, 
            11.01, 4.91, 0, 19.07, 0, 9.54, 70.28, 2.2, 0.52, 2.16, 0.7,108.32, 0, 0, 1.08, 12.17, 13.73, 0, 4.7, 0.25, 17.15, 0, 0, 
            2.21, 0.78, 26.86, 11.62, 0.99, 0, 72.74, 0.42, 2.01), solar.veg = c(21.57, 20.22, 21.7, 23.2, 22.95, 22.14, 
            21.85, 21.95, 22.44, 22.07, 21.138, 17.37, 16.6, 22.88, 19.82, 22.83, 17.87, 22.11, 
            18.8, 19.97, 17.28, 24.81, 21.57, 21.04, 15.46, 24.49,19.55, 21.12, 19.41, 19.30, 
            18.34, 21.5, 20.94, 17.7, 16.95, 22.13, 21.72, 21.12, 17.235, 21.97, 23.154, 24.24, 21.42, 
            19.17, 23.3216, 19.11, 20.21, 20.66, 24.74, 24.95), elevation = c(232.53,487.04, 269.5, 357.94, 636.83, 
            400.53, 489.75, 585.67, 662.87, 641.73, 353.21, 337.01, 314.9, 421.94, 887.89, 346.45, 321.6, 
            269.5, 388.07, 386.98, 906.96, 503.98, 575.18, 421.59, 450.33, 597.56, 312.68, 401.59, 383.07, 124, 887.89, 687.08, 519.76, 
            527.22, 409.98, 601.31, 528.53, 420.48, 186.81, 858.06, 548.78,532.25, 362.1, 257.96, 362.1, 487.63, 805.64, 453.03, 410.2, 
            34.96)), row.names = c(6645L, 8041L, 4028L, 1999L, 4974L, 2063L,4932L, 2602L, 4409L, 5200L, 7747L, 78L, 7384L, 2734L, 3944L, 
            2255L, 8262L, 4021L, 1368L, 3334L, 5596L, 4286L, 5083L, 2258L, 542L, 5328L, 4153L, 5721L, 3244L, 7965L, 3948L, 4980L, 3778L, 
            7408L, 691L, 6367L, 3121L, 3630L, 7712L, 5370L, 2752L, 5765L, 7169L, 8238L, 7171L, 2621L, 8705L, 8169L, 6425L, 6847L), class = "data.frame")

The data has a response variable called 'yield' and predictors which are certain climate variables, elevation, location.id and year. I am trying to run a the implementation of MARS from earth package following this vignette:

http://www.milbo.org/doc/earth-notes.pdf

In this implementation, I do not want to run the interactions between elevation, location.id and year with any of the other variables. In this vignette, there is an example that if I want to exclude interaction between two variables, I can do this as follows;

  PREDICTORS <- c("elevation","location.id","year") # these variables are not allowed to interact with any variables in PARENTS
  PARENTS <- names(dat)[-3] # the third column is my response variables so I am removing it

  no.int <- function(degree, pred, parents, namesx){
       if (degree > 1) {
        predictor <- namesx[pred]
        parents <- namesx[parents != 0]
       if((any(predictor %in% PREDICTORS) && any(parents %in% PARENTS)) ||
        (any(predictor %in% PARENTS) && any(parents %in% PREDICTORS))) {
        return(FALSE)
     }
     }
     TRUE
    }


   library(earth)
   fit.yld <- earth(x = dat[,PARENTS], y = dat[,3], keepxy = T, degree = 2, allowed = no.int,pmethod = "none")
   summary(fit.yld)

I want to test 2-way interactions that's why I set degree = 2. However, I want to exclude elevation, location and year to interact with each other as well as with any of the climate variables in the model. In the vignette on page 21, bottom para it says that using the allowed argument: It prevents the specified predictor from being used in interaction terms

If I run the above function, I still see some terms where location and years are interacting. However, the function should be able to stop any sort of interaction between these two variables.

I understand this is quite a specific topic regarding a package so I would really appreciate some help.

Thanks

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Some of your predictors are factors, and you therefore have to specify the factor level names in the PREDICTORS and PARENTS variables. So incorporate location.id11003 location.id11005 location.id11006 etc. into your PARENTS and/or PREDICTOR variables.

Remember that a factor like location.id gets expanded internally to a set of indicator columns in the earth x matrix (as mentioned in the earth vignette Section 5.1 "Factors in the predictors"). So the model building code internally in earth doesn't actually see the a variable named location.id. Instead it sees a bunch of variables location.id11003 location.id11005 location.id11006 etc.

These are the variable names that are passed to the no.int function, and that are matched against PARENTS and PREDICTORS. To see this, temporarily add a trace print statement inside your no.int function that prints the pred and parents.

More general comments:

I think your code would be less confusing if you used explicit names in rather than constructs like names(dat)[-3], and possibly consider using the formula interface to earth, to make it as obvious as possible what your're trying to achieve.

If you run earth with trace=1, you will see after expanding factors, your x matrix is 50 x 294. That means it is wider than it is tall. Not good---will easily cause overfitting or meaningless models: since there are so many variables to choose from, we can easily find variables that have spurious correlation with the response.

Your definition of location.id is confusing. Why does .Label have so many more values than the c() vector?

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