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×î½üÓÃR´¦ÀíÊý¾Ý£¬ÓÐÒÔϼ¸¸öÎÊÌâÎʸßÊÖ: 1,You used a linear model. This assumes that your data (the residuals) are normally distributed? If not, have you tried to transform the residuals so that they are normally distributed? If residuals are not normally distributed and cannot be transformed so that they become normally distributed, have you tried a generalized linear model (GLM) with an appropriate error structure (given by the "family"-command in glm - in R: model <- glm (y ~ x), family= "gaussian / binomial / Gamma / etc." ¶ÔÓÚͳ¼ÆÊý¾ÝÖÐÓÐȱʧµÄ²¿·Ö£¬ÎÒÒ»ÂÉÓÃR´úÌæ£¬ÇëÎÊÈçºÎÅжÏÕâЩÊýÖµµÄ³öÏÖÊÇ·ñΪ"gaussian / binomial / Gamma £¿»¹ÊÇÒªÒ»¸öÒ»¸ö³¢ÊÔ£¿ gaussian / binomial / Gamma ÔõôÌåÏÖÔÚRÓï¾äÖУ¿¾ßÌåÔõô²Ù×÷£¿ 2, What y and what x did you use in "lm(formula = y ~ x)"? You can use several explanatory variables (x) in a lm or glm but only one response variable (y)? 3,With the ratio of annual species and perennial species, I suggest that you use the logarithm of this ratio: log (annuals / perennials) - this is usually done with ratio-data ¶ÔÓÚ½ÏСµÄÊýÖµ£¨0¡ª1Ö®¼ä£©£¬Ò»°ãÈ¡log£¬ÇëÎÊÊÇȡʲôΪµ×µÄº¯Êý£¿10£¬2£¬ »¹ÊÇe? лл£¡ |
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