Description Usage Arguments Details Value Note References Examples
View source: R/calib_predict.R
Predicts copy number (CN) from Ct (Cq) values from one or more standard curves using inverse prediction.
1 | calib_predict(calib_df, ct_df, ...)
|
calib_df |
A |
ct_df |
A |
... |
Placeholder for further arguments that might be needed by future implementations. |
The calib_df object contains data for at least one qPCR calibration curve
usually presented as Cq (Ct) value and corresponding copy number from from a series of serial dilutions.
The data.frame must contain the headers Target, Cq and SQ. The Target
column must contain unique identifiers for each calibration curve. The Cq column contains the
Cq (Ct) values and SQ contains the copy number data. Additional columns will be ignored.
The ct_df data.frame object usually contains qPCR Cq (Ct) values from sample data. The data.frame must
contain the headers calib.curve and Ct.value. The calib.curve column must contain
at least one unique calibration curve identifier corresponding to those in calib_df. Any identifiers
found in ct_df but not in calib_df will be ignored and a warning displayed. The
Ct.value column should contain the Cq (Ct) values from environmental samples. Any additional columns
in the data.frame will remain unchanged and be included in the returned object.
Non-detections in either the calib_df or ct_df data.frames should be represented as NA.
Copy number is predicted from Cq (Ct) values by fitting a linear model (lm) to data from each
calibration curve and then using the inverse.predict function from the chemCal package.
package.
A tibble object containing original data in ct_df with back transformed
copy number predictions (CN.back) and associated standard errors (CN.back.se).
Copy number values in calib_df should not be log transformed prior to using this function.
Massart, L.M, Vandenginste, B.G.M., Buydens, L.M.C., De Jong, S., Lewi, P.J., Smeyers-Verbeke, J. (1997) Handbook of Chemometrics and Qualimetrics: Part A, p. 200.
1 2 3 4 | ## Not run:
my_pred <- calib_predict(calib_df = calib_data, ct_df = field_data)
## End(Not run)
|
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