quote in R. How to go arround max

Question

I have to quote very long string in R. I use quote function. But, it return only certain length of tags to the console. Does anyone know the way to go around that.

S <- quote(prfl1 * X1 + prfl2 * X2 + prfl3 * X3 + prfl4 * X4 + prfl5 * X5 + prfl6 * X6 + prfl7 * X7 + prfl8 * X8 + prfl9 * X9 + prfl10 * X10 + prfl11 * X11 + prfl12 * X12 + prfl13 * X13 + prfl14 * X14 + prfl15 * X15 + prfl16 * X16 + prfl17 * X17 + prfl18 * X18 + prfl19 * X19 + prfl20 * X20 + prfl21 * X21 + prfl22 * X22 + prfl23 * X23 + prfl24 * X24 + prfl25 * X25 + prfl26 * X26 + prfl27 * X27 + prfl28 * X28 + prfl29 * X29 + prfl30 * X30 + prfl31 * X31 + prfl32 * X32 + prfl33 * X33 + prfl34 * X34 + prfl35 * X35 + prfl36 * X36 + prfl37 * X37 + prfl38 * X38 + prfl39 * X39 + prfl40 * X40 + prfl41 * X41 + prfl42 * X42 + prfl43 * X43 + prfl44 * X44 + prfl45 * X45 + prfl46 * X46 + prfl47 * X47 + prfl48 * X48 + prfl49 * X49 + prfl50 * X50 + prfl51 * X51 + prfl52 * X52 + prfl53 * X53 + prfl54 * X54 + prfl55 * X55 + prfl56 * X56 + prfl57 * X57 + prfl58 * X58 + prfl59 * X59 + prfl60 * X60 + prfl61 * X61 + prfl62 * X62 + prfl63 * X63 + prfl64 * X64 + prfl65 * X65 + prfl66 * X66 + prfl67 * X67 + prfl68 * X68 + prfl69 * X69 + prfl70 * X70 + prfl71 * X71 + prfl72 * X72 + prfl73 * X73 + prfl74 * X74 + prfl75 * X75 + prfl76 * X76 + prfl77 * X77 + prfl78 * X78 + prfl79 * X79 + prfl80 * X80 + prfl81 * X81 + prfl82 * X82 + prfl83 * X83 + prfl84 * X84 + prfl85 * X85 + prfl86 * X86 + prfl87 * X87 + prfl88 * X88 + prfl89 * X89 + prfl90 * X90 + prfl91 * X91 + prfl92 * X92 + prfl93 * X93 + prfl94 * X94 + prfl95 * X95 + prfl96 * X96 + prfl97 * X97 + prfl98 * X98 + prfl99 * X99 + prfl100 * X100 + prfl101 * X101 + prfl102 * X102 + prfl103 * X103 + prfl104 * X104 + prfl105 * X105 + prfl106 * X106 + prfl107 * X107 + prfl108 * X108 + prfl109 * X109 + prfl110 * X110 + prfl111 * X111 + prfl112 * X112 + prfl113 * X113 + prfl114 * X114 + prfl115 * X115 + prfl116 * X116 + prfl117 * X117 + prfl118 * X118 + prfl119 * X119 + prfl120 * X120 + prfl121 * X121 + prfl122 * X122 + prfl123 * X123 + prfl124 * X124 + prfl125 * X125 + prfl126 * X126 + prfl127 * X127 + prfl128 * X128 + prfl129 * X129 + prfl130 * X130 + prfl131 * X131 + prfl132 * X132 + prfl133 * X133 + prfl134 * X134 + prfl135 * X135 + prfl136 * X136 + prfl137 * X137 + prfl138 * X138 + prfl139 * X139 + prfl140 * X140 + prfl141 * X141 + prfl142 * X142 + prfl143 * X143 + prfl144 * X144 + prfl145 * X145 + prfl146 * X146 + prfl147 * X147 + prfl148 * X148 + prfl149 * X149 + prfl150 * X150 + prfl151 * X151 + prfl152 * X152 + prfl153 * X153 + prfl154 * X154 + prfl155 * X155 + prfl156 * X156 + prfl157 * X157 + prfl158 * X158 + prfl159 * X159 + prfl160 * X160 + prfl161 * X161 + prfl162 * X162 + prfl163 * X163 + prfl164 * X164 + prfl165 * X165 + prfl166 * X166 + prfl167 * X167 + prfl168 * X168 + prfl169 * X169 + prfl170 * X170 + prfl171 * X171 + prfl172 * X172 + prfl173 * X173 + prfl174 * X174 + prfl175 * X175 + prfl176 * X176 + prfl177 * X177 + prfl178 * X178 + prfl179 * X179 + prfl180 * X180 + prfl181 * X181 + prfl182 * X182 + prfl183 * X183 + prfl184 * X184 + prfl185 * X185 + prfl186 * X186 + prfl187 * X187 + prfl188 * X188 + prfl189 * X189 + prfl190 * X190 + prfl191 * X191 + prfl192 * X192 + prfl193 * X193 + prfl194 * X194 + prfl195 * X195 + prfl196 * X196 + prfl197 * X197 + prfl198 * X198 + prfl199 * X199 + prfl200 * X200 + prfl201 * X201 + prfl202 * X202 + prfl203 * X203 + prfl204 * X204 + prfl205 * X205 + prfl206 * X206 + prfl207 * X207 + prfl208 * X208 + prfl209 * X209 + prfl210 * X210 + prfl211 * X211 + prfl212 * X212 + prfl213 * X213 + prfl214 * X214 + prfl215 * X215 + prfl216 * X216 + prfl217 * X217 + prfl218 * X218 + prfl219 * X219 + prfl220 * X220 + prfl221 * X221 + prfl222 * X222 + prfl223 * X223 + prfl224 * X224 + prfl225 * X225 + prfl226 * X226 + prfl227 * X227 + prfl228 * X228 + prfl229 * X229 + prfl230 * X230 + prfl231 * X231 + prfl232 * X232 + prfl233 * X233 + prfl234 * X234 + prfl235 * X235 + prfl236 * X236 + prfl237 * X237 + prfl238 * X238 + prfl239 * X239 + prfl240 * X240 + prfl241 * X241 + prfl242 * X242 + prfl243 * X243 + prfl244 * X244 + prfl245 * X245 + prfl246 * X246 + prfl247 * X247 + prfl248 * X248 + prfl249 * X249 + prfl250 * X250 + prfl251 * X251 + prfl252 * X252 + prfl253 * X253 + prfl254 * X254 + prfl255 * X255 + prfl256 * X256 + prfl257 * X257 + prfl258 * X258 + prfl259 * X259 + prfl260 * X260 + prfl261 * X261 + prfl262 * X262 + prfl263 * X263 + prfl264 * X264 + prfl265 * X265 + prfl266 * X266 + prfl267 * X267 + prfl268 * X268 + prfl269 * X269 + prfl270 * X270 + prfl271 * X271 + prfl272 * X272 + prfl273 * X273 + prfl274 * X274)

This returns values juts to prfl253

Answer 1

Apparently we have an XY problem here. I've looked at your other question and your goal seems to be to do nonlinear regression with this formula:

BH100 ~ S * ((1 - exp(-b2*TIME))/(1-exp(-b2*100)))^b3

where S is a linear combination of dummy variables (encoding one categorical variable).

First of all, you don't need to quote anything. You need to create a formula object.

Your example data:

DF <- structure(list(year = c(1987L, 1990L, 1994L, 1983L, 1986L, 1990L, 
  1998L, 2002L, 1994L, 1998L, 2002L, 2008L, 2013L, 1985L, 1989L, 
  1990L), T = c(25L, 28L, 32L, 21L, 24L, 28L, 36L, 40L, 32L, 36L, 
  40L, 46L, 51L, 23L, 27L, 28L), BH100 = c(12.6, 14.9, 18.8, 13.4, 
  16.1, 19.6, 26.7, 27.8, 17.2, 19.4, 23.5, 26.3, 28.7, 14.6, 18.5, 
  19.2), TIME = c(25L, 28L, 32L, 21L, 24L, 28L, 36L, 40L, 32L, 
  36L, 40L, 46L, 51L, 23L, 27L, 28L), dum1 = c(1L, 1L, 1L, 0L, 
  0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), dum2 = c(0L, 
  0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), 
  dum3 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 
    1L, 0L, 0L, 0L), dum4 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L)), 
  .Names = c("year", "T", "BH100", "TIME", "dum1", "dum2", "dum3", "dum4"), 
  class = "data.frame", row.names = c(NA, -16L))

Now we create a formula (I show here how to use partially linear least-squares for the problem, for this you need to work at the log scale):

fo <- as.formula(paste0("log(BH100) ~ cbind(",
                        paste(names(DF)[5:8], collapse = ", "),
                        ", log(((1 - exp(-b2*TIME))/(1-exp(-b2*100)))))"))
#log(BH100) ~ cbind(dum1, dum2, dum3, dum4, log(((1 - exp(-b2 * 
#    TIME))/(1 - exp(-b2 * 100)))))

coefs1 <- coef(fit)
#          b2    .lin.dum1    .lin.dum2    .lin.dum3    .lin.dum4        .lin5 
#1.200000e+00 2.724458e+00 3.100260e+00 3.118730e+00 2.867354e+00 4.638915e+10 

Obviously this isn't a good fit because of the tiny amount of data.

You can then use gnls to achieve a fit at the original scale. First you have to combine the dummies into a factor variable:

library(reshape2)
DF <- melt(DF, id.vars = 1:4)
DF <- DF[DF$value == 1L, -6]

And now you can fit the model:

library(nlme)
fit <- gnls(BH100 ~ a * ((1 - exp(-b2*TIME))/(1-exp(-b2*100)))^b3,
            data = DF,
            params = list(a ~ variable, b2 ~ 1, b3 ~ 1),
            start = list(b2 = coefs1["b2"], b3 = tail(coefs1, 1), 
                         a = exp(coefs1[-c(1, length(coefs1))])))
#Error in gnls(BH100 ~ a * ((1 - exp(-b2 * TIME))/(1 - exp(-b2 * 100)))^b3,  : 
#  approximate covariance matrix for parameter estimates not of full rank

Here I specified a linear model for the parameter a. Conveniently R does the dummy encoding for you. Of course, this doesn't result in a successful fit because you would need more data (and better starting values) than you provided for such a complicated model.