[R-meta] predict function for increment of 0.05 for the moderator

[Anne-Wil Kruijt]

Dear Roger, and list,

As an utter newbie to the list and not bothered by any specific knowledge of what you’re trying to do here precisely, I do think you’ll find that it works better when you use

(preds <- predict(res, newmods= seq(-.2, .2, by = 0.05) , digits=3,addx=TRUE))

Check the outputs for c(-0.2:0.2), c(-10:10) and seq(-.2, .2, by = 0.05) (just the ‘c’ and ‘seq’ bits of code) to see why. c(lb : ub) indeed increments by 1 unit by default – the seq() allows setting different increments. 

Hope that helps!


On 16/10/2017, 18:52, "R-sig-meta-analysis on behalf of Martineau, Roger" <r-sig-meta-analysis-bounces at r-project.org on behalf of Roger.Martineau at AGR.GC.CA> wrote:

    Dear metafor user,
    
    May be someone can help me on this one.
    
    I am using centered values for a moderator ranging from -0.2 to +0.2 and getting only the prediction for -0.2.
    How to get predictions for the range [-0.2 to 0.2] for a fixed increment of 0.05 for example: -0.20, -0.15, -0.10, -0.05, -0.00, 0.05, 0.10, 0.15 and 0.20.
    
    I used the BCG example and created a new centered value for ablat after dividing by 100 (didn’t back-transform to RR purposely).
    
    It looks like I am only obtaining predictions by 1 unit increment (not on a decimal scale).
    
    Thanks in advance,
    
    Roger ☺
    
    
    
    library(metafor)
    
    ### calculate (log) risk ratios and corresponding sampling variances
    (dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg))
    
    dat <- within(dat, {
      Ablat.new <- ablat/100
      cAblat.new  <- Ablat.new - mean(Ablat.new,na.rm = TRUE)
      })
    
    ### fit mixed-effects model with absolute latitude as predictor
    (res <- rma(yi, vi, mods = ~ cAblat.new, data=dat))
    
    
    ### calculate predicted values for ablat.new
    ### N.B. omit "trans=exp" for this example
    
    
    range(dat$cAblat.new)
    
    [1] -0.2046154  0.2153846
    
    
    # if we predict for the range of values of cAblat.new
    
    (preds <- predict(res, newmods=c(-0.2:0.2), digits=3,addx=TRUE))
    
      pred    se ci.lb ci.ub  cr.lb cr.ub X.intrcpt X.ablat.new
    
     0.833 0.384 0.081 1.586 -0.094 1.761         1        -0.2
    
    
    # if we predict from 0.13 to 5.5:
    
    (preds <- predict(res, newmods=c(-10:10), digits=3,addx=TRUE))
    
          pred    se   ci.lb   ci.ub   cr.lb   cr.ub X.intrcpt X.ablat.new
    
    1   29.353 7.421  14.807  43.899  14.797  43.909         1         -10
    
    2   26.443 6.702  13.307  39.579  13.296  39.590         1          -9
    
    3   23.533 5.983  11.807  35.258  11.795  35.271         1          -8
    
    4   20.623 5.263  10.307  30.938  10.293  30.952         1          -7
    
    5   17.712 4.544   8.807  26.618   8.790  26.635         1          -6
    
    6   14.802 3.824   7.307  22.298   7.287  22.318         1          -5
    
    7   11.892 3.105   5.806  17.978   5.782  18.002         1          -4
    
    8    8.982 2.386   4.305  13.659   4.274  13.690         1          -3
    
    9    6.072 1.668   2.803   9.340   2.759   9.385         1          -2
    
    10   3.162 0.951   1.298   5.025   1.221   5.102         1          -1
    
    11   0.251 0.249  -0.237   0.740  -0.478   0.981         1           0
    
    12  -2.659 0.506  -3.650  -1.668  -3.788  -1.529         1           1
    
    13  -5.569 1.219  -7.957  -3.180  -8.018  -3.120         1           2
    
    14  -8.479 1.936 -12.274  -4.684 -12.313  -4.645         1           3
    
    15 -11.389 2.655 -16.593  -6.185 -16.621  -6.157         1           4
    
    16 -14.299 3.374 -20.913  -7.686 -20.935  -7.664         1           5
    
    17 -17.210 4.094 -25.233  -9.186 -25.251  -9.168         1           6
    
    18 -20.120 4.813 -29.553 -10.687 -29.568 -10.671         1           7
    
    19 -23.030 5.532 -33.873 -12.187 -33.886 -12.173         1           8
    
    20 -25.940 6.252 -38.193 -13.687 -38.205 -13.675         1           9
    
    21 -28.850 6.971 -42.513 -15.187 -42.524 -15.176         1          10
    
    
    
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