From 39eb74880f558f86242a998dbe3c260fd8935cc0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Zolt=C3=A1n=20BARCZA?= Date: Tue, 22 Jan 2019 15:58:37 +0100 Subject: [PATCH] Update README.org --- README.org | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.org b/README.org index 0be6728..bb717e0 100644 --- a/README.org +++ b/README.org @@ -138,7 +138,7 @@ plot(gpp4*1000,type="l") Assume that we would like to dig a bit deeper with the model and understand the effect of changing ecophysiological variables on the model results. This can easily be performed with RBBGCMuso. Execute the following command in R/RStudio: #+BEGIN_SRC R :eval no -musoQuickEffect(calibrationPar = 25, startVal = 0,endVal = 9,nSteps = 5,outVar = 3009) +musoQuickEffect(calibrationPar = 25, startVal = 0, endVal = 9, nSteps = 5, outVar = 3009) #+END_SRC This command selects the 25th line in the ecophysiological constants (EPC) file (this is base temperature), then it starts to replace the original value from 0 to 9 in 5 consecutive steps. In this example GPP is selected (variable number 3009, which is the 27th variable), so the effect of varying base temperature on GPP is calculated using 9 simulations. The result is a spectacular plot where color coding is used distinguish the parameter values.