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edited Jan 18, 2018 at 18:28

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There is little or no detail here on the substantive, scientific or practical context but the following general or specific problems seem to be biting here:

Although this can be fixed easily, your report is trivially misleading. The percentiles you are using appear to be 2.5% and 95% points. If you want to call them 0.025 and 0.95 quantiles, that's fine.

Calculating extreme percentiles requires an enormous sample size to work really well.
Regression here and elsewhere can be sensitive to granularity in the data. Your transformation is unsatisfactory as you've created outliers from your smallest values. At a guess you're using something like log$_{10}$ (response + 0.5), but that maps zeros to a value much less than the next lowest value. A transformation such as log$_{10}$ (response + 1) or cube root is likely to work better.
Whatever you're doing is not smoothing enough to make obvious sense as a solution. Does your smoothed curve make sense scientifically?
Whatever you're dealing with clearly varies most within the first year of life, so it's not obvious that a linear scale for age is appropriate.

There is little or no detail here on the substantive, scientific or practical context but the following general or specific problems seem to be biting here:

Although this can be fixed easily, your report is trivially misleading. The percentiles you are using appear to be 2.5% and 95% points. If you want to call them 0.025 and 0.95 quantiles, that's fine.

Calculating extreme percentiles requires an enormous sample size to work really well.
Regression here and elsewhere can be sensitive to granularity in the data. Your transformation is unsatisfactory as you've created outliers from your smallest values. At a guess you're using something like log$_{10}$ (response + 0.5), but that maps zeros to a value much less than the next lowest value. A transformation such as log$_{10}$ (response + 1) or cube root is likely to work better.
Whatever you're doing is not smoothing enough to make obvious sense as a solution. Does your smoothed curve make sense scientifically?
Whatever you're dealing with clearly varies most within the first year of life, so it's not obvious that a linear scale for age is appropriate.

There is little or no detail here on the substantive, scientific or practical context but the following general or specific problems seem to be biting here:

Calculating extreme percentiles requires an enormous sample size to work really well.
Regression here and elsewhere can be sensitive to granularity in the data. Your transformation is unsatisfactory as you've created outliers from your smallest values. At a guess you're using something like log$_{10}$ (response + 0.5), but that maps zeros to a value much less than the next lowest value. A transformation such as log$_{10}$ (response + 1) or cube root is likely to work better.
Whatever you're doing is not smoothing enough to make obvious sense as a solution. Does your smoothed curve make sense scientifically?
Whatever you're dealing with clearly varies most within the first year of life, so it's not obvious that a linear scale for age is appropriate.

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created Jan 18, 2018 at 15:51

Nick Cox

59.5k
8
136
212

There is little or no detail here on the substantive, scientific or practical context but the following general or specific problems seem to be biting here:

Although this can be fixed easily, your report is trivially misleading. The percentiles you are using appear to be 2.5% and 95% points. If you want to call them 0.025 and 0.95 quantiles, that's fine.
Calculating extreme percentiles requires an enormous sample size to work really well.
Regression here and elsewhere can be sensitive to granularity in the data. Your transformation is unsatisfactory as you've created outliers from your smallest values. At a guess you're using something like log$_{10}$ (response + 0.5), but that maps zeros to a value much less than the next lowest value. A transformation such as log$_{10}$ (response + 1) or cube root is likely to work better.
Whatever you're doing is not smoothing enough to make obvious sense as a solution. Does your smoothed curve make sense scientifically?
Whatever you're dealing with clearly varies most within the first year of life, so it's not obvious that a linear scale for age is appropriate.